diff --git a/ end_to_end_ml_lifecycle/README.md b/ end_to_end_ml_lifecycle/README.md
new file mode 100644
index 0000000000..1ebb0cb40e
--- /dev/null
+++ b/ end_to_end_ml_lifecycle/README.md
@@ -0,0 +1,9 @@
+# Amazon SageMaker Examples
+
+### End To End ML Lifecycle
+
+These examples are a diverse collection of end-to-end notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker. These notebooks cover a wide range of machine learning tasks and use cases, providing you with a comprehensive understanding of the SageMaker workflow.
+
+- [Customer Churn Prediction with Amazon SageMaker Autopilot](sm-autopilot_customer_churn.ipynb)
+- [Housing Price Prediction with Amazon SageMaker Autopilot](sm-autopilot_linear_regression_california_housing.ipynb)
+- [Time-Series Forecasting with Amazon SageMaker Autopilot](sm-sm-autopilot_time_series_forecasting.ipynb)
diff --git a/ end_to_end_ml_lifecycle/sm-autopilot_customer_churn.ipynb b/ end_to_end_ml_lifecycle/sm-autopilot_customer_churn.ipynb
index 074deb1eb0..1a629c87d5 100644
--- a/ end_to_end_ml_lifecycle/sm-autopilot_customer_churn.ipynb
+++ b/ end_to_end_ml_lifecycle/sm-autopilot_customer_churn.ipynb
@@ -9,7 +9,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -17,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1256,7 +1255,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1264,35 +1262,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ end_to_end_ml_lifecycle/sm-autopilot_linear_regression_california_housing.ipynb b/ end_to_end_ml_lifecycle/sm-autopilot_linear_regression_california_housing.ipynb
index 56df35a8a4..ea0f51836d 100644
--- a/ end_to_end_ml_lifecycle/sm-autopilot_linear_regression_california_housing.ipynb
+++ b/ end_to_end_ml_lifecycle/sm-autopilot_linear_regression_california_housing.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -947,35 +947,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ end_to_end_ml_lifecycle/sm-autopilot_time_series_forecasting.ipynb b/ end_to_end_ml_lifecycle/sm-autopilot_time_series_forecasting.ipynb
index 42e90a4874..499f42d62e 100644
--- a/ end_to_end_ml_lifecycle/sm-autopilot_time_series_forecasting.ipynb
+++ b/ end_to_end_ml_lifecycle/sm-autopilot_time_series_forecasting.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -741,35 +741,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/README.md b/ prepare_data/README.md
new file mode 100644
index 0000000000..f31c105abb
--- /dev/null
+++ b/ prepare_data/README.md
@@ -0,0 +1,28 @@
+# Amazon SageMaker Examples
+
+### Prepare Data
+
+The example notebooks within this folder showcase Sagemaker's data preparation capabilities. Data preparation in machine learning refers to the process of collecting, preprocessing, and organizing raw data to make it suitable for analysis and modeling.
+
+- [Data Wrangler Data Prep Widget - Example Notebook](sm-data_wrangler_data_prep_widget/sm-data_wrangler_data_prep_widget.ipynb)
+- [Amazon SageMaker Feature Store: Feature Processor Introduction](sm-feature_store_feature_processor/sm-feature_store_feature_processor.ipynb)
+- [Amazon SageMaker Feature Store: Ground Truth Classification labelling job output to Feature Store](sm-feature_store_ground_truth_classification_output_to_store/sm-feature_store_ground_truth_classification_output_to_store.ipynb)
+- [Amazon SageMaker Feature Store: Introduction to Feature Store](sm-feature_store_introduction/sm-feature_store_introduction.ipynb)
+- [Create an Active Learning Workflow using Amazon SageMaker Ground Truth](sm-ground_truth_active_learning_workflow_bring_your_own_model/sm-ground_truth_active_learning_workflow_bring_your_own_model.ipynb)
+- [Understanding Annotation Consolidation: A SageMaker Ground Truth Demonstration for Image Classification](sm-ground_truth_annotation_consolidation_image_classification/sm-ground_truth_annotation_consolidation_image_classification.ipynb)
+- [From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Object Detection](sm-ground_truth_object_detection_example/sm-ground_truth_object_detection_example.ipynb)
+- [Audit and Improve Video Annotation Quality Using Amazon SageMaker Ground Truth](sm-ground_truth_video_quality_metrics/sm-ground_truth_video_quality_metrics.ipynb)
+- [Amazon Augmented AI(A2I) Integrated with AWS Marketplace ML Models](sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb)
+- [Feature transformation with Amazon SageMaker Processing and Dask](sm-processing_feature_transformation_with_dask/sm-processing_feature_transformation_with_dask.ipynb)
+- [Distributed Data Processing using Apache Spark and SageMaker Processing](sm-processing_spark_processing/sm-processing_spark_processing.ipynb)
+- [SageMaker PySpark PCA and K-Means Clustering MNIST Example](sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb)
+- [Create a 3D Point Cloud Labeling Job with Amazon SageMaker Ground Truth](sm-ground_truth_3d_pointcloud_labeling.ipynb)
+- [Chaining using Ground Truth Streaming Labeling Jobs](sm-ground_truth_chained_streaming_labeling_job.ipynb)
+- [Create a Ground Truth Streaming Labeling Job](sm-ground_truth_create_streaming_labeling_job.ipynb)
+- [Labeling Adjustment Job Adaptation](sm-ground_truth_labeling_adjustment_job_adaptation.ipynb)
+- [Training Object Detection Models in SageMaker with Augmented Manifests](sm-ground_truth_object_detection_augmented_manifest_training.ipynb)
+- [Using a Pre-Trained Model for Cost Effective Data Labeling](sm-ground_truth_pretrained_model_labeling.ipynb)
+- [Improving Your LLMs with RLHF on SageMaker](sm-ground_truth_rlhf_llm_finetuning.ipynb)
+- [Identify Worker Labeling Efficiency using SageMaker GroundTruth](sm-ground_truth_text_classification_labeling_accuracy_analysis.ipynb)
+- [Get started with SageMaker Processing](sm-processing_introduction.ipynb)
+- [SageMaker PySpark K-Means Clustering MNIST Example](sm-spark_kmeans.ipynb)
diff --git a/ prepare_data/sm-data_wrangler_data_prep_widget/sm-data_wrangler_data_prep_widget.ipynb b/ prepare_data/sm-data_wrangler_data_prep_widget/sm-data_wrangler_data_prep_widget.ipynb
index e9a67e22aa..e2f12e8743 100644
--- a/ prepare_data/sm-data_wrangler_data_prep_widget/sm-data_wrangler_data_prep_widget.ipynb
+++ b/ prepare_data/sm-data_wrangler_data_prep_widget/sm-data_wrangler_data_prep_widget.ipynb
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -378,35 +378,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/sm-feature_store_feature_processor/sm-feature_store_feature_processor.ipynb b/ prepare_data/sm-feature_store_feature_processor/sm-feature_store_feature_processor.ipynb
index a760b772ff..933eae61b5 100644
--- a/ prepare_data/sm-feature_store_feature_processor/sm-feature_store_feature_processor.ipynb
+++ b/ prepare_data/sm-feature_store_feature_processor/sm-feature_store_feature_processor.ipynb
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -702,35 +702,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/sm-feature_store_ground_truth_classification_output_to_store/sm-feature_store_ground_truth_classification_output_to_store.ipynb b/ prepare_data/sm-feature_store_ground_truth_classification_output_to_store/sm-feature_store_ground_truth_classification_output_to_store.ipynb
index f319e7f6b0..d823b0ded6 100644
--- a/ prepare_data/sm-feature_store_ground_truth_classification_output_to_store/sm-feature_store_ground_truth_classification_output_to_store.ipynb
+++ b/ prepare_data/sm-feature_store_ground_truth_classification_output_to_store/sm-feature_store_ground_truth_classification_output_to_store.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -517,35 +517,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/sm-feature_store_introduction/sm-feature_store_introduction.ipynb b/ prepare_data/sm-feature_store_introduction/sm-feature_store_introduction.ipynb
index 5cb6d81175..0d4883fb19 100644
--- a/ prepare_data/sm-feature_store_introduction/sm-feature_store_introduction.ipynb
+++ b/ prepare_data/sm-feature_store_introduction/sm-feature_store_introduction.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -710,35 +710,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/sm-ground_truth_3d_pointcloud_labeling.ipynb b/ prepare_data/sm-ground_truth_3d_pointcloud_labeling.ipynb
index 75fce3f94f..5e69e13673 100644
--- a/ prepare_data/sm-ground_truth_3d_pointcloud_labeling.ipynb
+++ b/ prepare_data/sm-ground_truth_3d_pointcloud_labeling.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -784,35 +784,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/sm-ground_truth_active_learning_workflow_bring_your_own_model/sm-ground_truth_active_learning_workflow_bring_your_own_model.ipynb b/ prepare_data/sm-ground_truth_active_learning_workflow_bring_your_own_model/sm-ground_truth_active_learning_workflow_bring_your_own_model.ipynb
index c8675e5405..1ca53b2a41 100644
--- a/ prepare_data/sm-ground_truth_active_learning_workflow_bring_your_own_model/sm-ground_truth_active_learning_workflow_bring_your_own_model.ipynb
+++ b/ prepare_data/sm-ground_truth_active_learning_workflow_bring_your_own_model/sm-ground_truth_active_learning_workflow_bring_your_own_model.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -636,7 +636,7 @@
"2.\tThe Cloud Formation stack provided in the blog post has generated two step function in the State Machines section: **ActiveLearningLoop-*** and **ActiveLearning-*** where * will be replaced with the name you used when you launched your Cloud Formation stack. \n",
"3.\tSelect **ActiveLearningLoop**-*. \n",
"4.\tChoose **Start Execution**.\n",
- "5.\tPaste the JSON above in **Input \u2013 optional code-block**.\n",
+ "5.\tPaste the JSON above in **Input – optional code-block**.\n",
"6.\tSelect **Start execution**. \n",
"\n",
" \n",
@@ -678,7 +678,7 @@
"\n",
"**Please update your role with AmazonEC2ContainerRegistryFullAccess before proceeding**\n",
"\n",
- "Running this notebook requires permissions in addition to the normal SageMakerFullAccess permissions. This is because it creates new repositories in Amazon ECR. The easiest way to add these permissions is simply to add the managed policy **AmazonEC2ContainerRegistryFullAccess** to the role that you used to start your notebook instance. There's no need to restart your notebook instance when you do this, the new permissions will be available immediately. To access the role associated with your notebook instance, select \"Notebook instances\" from the SageMaker console, select the name of your instance, and finally select the link under \u201cIAM role ARN\u201d in the \u201cPermissions and encryption\u201d section.\n",
+ "Running this notebook requires permissions in addition to the normal SageMakerFullAccess permissions. This is because it creates new repositories in Amazon ECR. The easiest way to add these permissions is simply to add the managed policy **AmazonEC2ContainerRegistryFullAccess** to the role that you used to start your notebook instance. There's no need to restart your notebook instance when you do this, the new permissions will be available immediately. To access the role associated with your notebook instance, select \"Notebook instances\" from the SageMaker console, select the name of your instance, and finally select the link under “IAM role ARN” in the “Permissions and encryption” section.\n",
"\n",
"### To Use this Notebook\n",
"\n",
@@ -1074,35 +1074,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1127,4 +1127,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_annotation_consolidation_image_classification/sm-ground_truth_annotation_consolidation_image_classification.ipynb b/ prepare_data/sm-ground_truth_annotation_consolidation_image_classification/sm-ground_truth_annotation_consolidation_image_classification.ipynb
index 27cc6ac4c9..a36800d54c 100644
--- a/ prepare_data/sm-ground_truth_annotation_consolidation_image_classification/sm-ground_truth_annotation_consolidation_image_classification.ipynb
+++ b/ prepare_data/sm-ground_truth_annotation_consolidation_image_classification/sm-ground_truth_annotation_consolidation_image_classification.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -942,7 +942,7 @@
"metadata": {},
"source": [
"### Qualitative Comparison of Modified Dawid-Skene and Majority Voting\n",
- "Let\u2019s now look at some qualitative results. We look at three sets of example images: (1) Images which Majority Voting got wrong but Dawid-Skene got right. (2) Images which Dawid-Skene got wrong but Majority Voting got right. (3) Images that both got wrong."
+ "Let’s now look at some qualitative results. We look at three sets of example images: (1) Images which Majority Voting got wrong but Dawid-Skene got right. (2) Images which Dawid-Skene got wrong but Majority Voting got right. (3) Images that both got wrong."
]
},
{
@@ -1084,35 +1084,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1137,4 +1137,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_chained_streaming_labeling_job.ipynb b/ prepare_data/sm-ground_truth_chained_streaming_labeling_job.ipynb
index 0a60665898..75a2f28fe0 100644
--- a/ prepare_data/sm-ground_truth_chained_streaming_labeling_job.ipynb
+++ b/ prepare_data/sm-ground_truth_chained_streaming_labeling_job.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -515,7 +515,7 @@
" \n",
"
Inspect the image
\n",
"
Determine if the specified label is/are visible in the picture.
\n",
- "
Outline each instance of the specified label in the image using the provided \u201cBox\u201d tool.
\n",
+ "
Outline each instance of the specified label in the image using the provided “Box” tool.
Determine if the specified label is/are visible in the picture.
\n",
- "
Outline each instance of the specified label in the image using the provided \u201cBox\u201d tool.
\n",
+ "
Outline each instance of the specified label in the image using the provided “Box” tool.
\n",
" \n",
" \n",
"\n",
@@ -1786,35 +1786,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1839,4 +1839,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_create_streaming_labeling_job.ipynb b/ prepare_data/sm-ground_truth_create_streaming_labeling_job.ipynb
index 7c12c5f792..e056384ef1 100644
--- a/ prepare_data/sm-ground_truth_create_streaming_labeling_job.ipynb
+++ b/ prepare_data/sm-ground_truth_create_streaming_labeling_job.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -506,7 +506,7 @@
" \n",
"
Inspect the image
\n",
"
Determine if the specified label is/are visible in the picture.
\n",
- "
Outline each instance of the specified label in the image using the provided \u201cBox\u201d tool.
\n",
+ "
Outline each instance of the specified label in the image using the provided “Box” tool.
\n",
" \n",
" \n",
"\n",
@@ -1019,35 +1019,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1072,4 +1072,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_labeling_adjustment_job_adaptation.ipynb b/ prepare_data/sm-ground_truth_labeling_adjustment_job_adaptation.ipynb
index a2d66d8452..7f750e13de 100644
--- a/ prepare_data/sm-ground_truth_labeling_adjustment_job_adaptation.ipynb
+++ b/ prepare_data/sm-ground_truth_labeling_adjustment_job_adaptation.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -277,35 +277,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -330,4 +330,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_object_detection_augmented_manifest_training.ipynb b/ prepare_data/sm-ground_truth_object_detection_augmented_manifest_training.ipynb
index ff1950d099..d135a0f8af 100644
--- a/ prepare_data/sm-ground_truth_object_detection_augmented_manifest_training.ipynb
+++ b/ prepare_data/sm-ground_truth_object_detection_augmented_manifest_training.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -371,35 +371,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -424,4 +424,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_object_detection_example/sm-ground_truth_object_detection_example.ipynb b/ prepare_data/sm-ground_truth_object_detection_example/sm-ground_truth_object_detection_example.ipynb
index 3297c6c7b7..fb08f49fd2 100644
--- a/ prepare_data/sm-ground_truth_object_detection_example/sm-ground_truth_object_detection_example.ipynb
+++ b/ prepare_data/sm-ground_truth_object_detection_example/sm-ground_truth_object_detection_example.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -345,7 +345,7 @@
" \n",
"
Inspect the image
\n",
"
Determine if the specified label is/are visible in the picture.
\n",
- "
Outline each instance of the specified label in the image using the provided \u201cBox\u201d tool.
\n",
+ "
Outline each instance of the specified label in the image using the provided “Box” tool.
\n",
" \n",
"
\n",
"
Boxes should fit tight around each object
\n",
@@ -2058,35 +2058,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -2111,4 +2111,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_pretrained_model_labeling.ipynb b/ prepare_data/sm-ground_truth_pretrained_model_labeling.ipynb
index 9988597437..925d3175d2 100644
--- a/ prepare_data/sm-ground_truth_pretrained_model_labeling.ipynb
+++ b/ prepare_data/sm-ground_truth_pretrained_model_labeling.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1396,35 +1396,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ prepare_data/sm-ground_truth_rlhf_llm_finetuning.ipynb b/ prepare_data/sm-ground_truth_rlhf_llm_finetuning.ipynb
index 2d991c4c5f..6bff60607b 100644
--- a/ prepare_data/sm-ground_truth_rlhf_llm_finetuning.ipynb
+++ b/ prepare_data/sm-ground_truth_rlhf_llm_finetuning.ipynb
@@ -11,6 +11,20 @@
"# Improving Your LLMs with RLHF on SageMaker"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "03ccf43d",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
+ "\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
{
"attachments": {},
"cell_type": "markdown",
@@ -346,6 +360,46 @@
")\n",
"main(model_name_or_path_baseline)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9c4dfcb",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
}
],
"metadata": {
diff --git a/ prepare_data/sm-ground_truth_text_classification_labeling_accuracy_analysis.ipynb b/ prepare_data/sm-ground_truth_text_classification_labeling_accuracy_analysis.ipynb
index ac876a6f75..2f329207b4 100644
--- a/ prepare_data/sm-ground_truth_text_classification_labeling_accuracy_analysis.ipynb
+++ b/ prepare_data/sm-ground_truth_text_classification_labeling_accuracy_analysis.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -433,35 +433,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -486,4 +486,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ground_truth_video_quality_metrics/sm-ground_truth_video_quality_metrics.ipynb b/ prepare_data/sm-ground_truth_video_quality_metrics/sm-ground_truth_video_quality_metrics.ipynb
index e9f3097066..4db839e457 100644
--- a/ prepare_data/sm-ground_truth_video_quality_metrics/sm-ground_truth_video_quality_metrics.ipynb
+++ b/ prepare_data/sm-ground_truth_video_quality_metrics/sm-ground_truth_video_quality_metrics.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -174,7 +174,7 @@
"source": [
"## Download data\n",
"\n",
- "Download a dataset from the Multi-Object Tracking Challenge, a commonly used benchmark for multi-object tracking. Depending on your connection speed, this can take 5\u201310 minutes. Unzip it and upload it to a `bucket` in Amazon S3.\n",
+ "Download a dataset from the Multi-Object Tracking Challenge, a commonly used benchmark for multi-object tracking. Depending on your connection speed, this can take 5–10 minutes. Unzip it and upload it to a `bucket` in Amazon S3.\n",
"\n",
"Disclosure regarding the Multiple Object Tracking Benchmark:\n",
"\n",
@@ -1396,35 +1396,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1449,4 +1449,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-ingest_data_with_athena/image/athena-iam-1.png b/ prepare_data/sm-ingest_data_with_athena/image/athena-iam-1.png
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diff --git a/ deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/a2i_diagram.png b/ prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/a2i_diagram.png
similarity index 100%
rename from deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/a2i_diagram.png
rename to prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/a2i_diagram.png
diff --git a/ end_to_end_ml_lifecycle/sm-marketplace_recommender_system_with_implicit_bpr/images/classicjupyter.png b/ prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/classicjupyter.png
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-marketplace_recommender_system_with_implicit_bpr/images/classicjupyter.png
rename to prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/classicjupyter.png
diff --git a/ deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/rain_biker_bb.png b/ prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/rain_biker_bb.png
similarity index 100%
rename from deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/rain_biker_bb.png
rename to prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/img/rain_biker_bb.png
diff --git a/ deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb b/ prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb
similarity index 92%
rename from deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb
rename to prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb
index 4e6017ae1e..ba6273ad2f 100644
--- a/ deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb
+++ b/ prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/sm-marketplace_augmented_ai_with_marketplace_ml_models.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -782,35 +782,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
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]
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@@ -836,4 +836,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
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+}
diff --git a/ deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/src/worker-task-template.html b/ prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/src/worker-task-template.html
similarity index 100%
rename from deploy_and_monitor/sm-marketplace_augmented_ai_with_marketplace_ml_models/src/worker-task-template.html
rename to prepare_data/sm-marketplace_augmented_ai_with_marketplace_ml_models/src/worker-task-template.html
diff --git a/ prepare_data/sm-processing_feature_transformation_with_dask/sm-processing_feature_transformation_with_dask.ipynb b/ prepare_data/sm-processing_feature_transformation_with_dask/sm-processing_feature_transformation_with_dask.ipynb
index f2fc51cd74..9fd5226e67 100644
--- a/ prepare_data/sm-processing_feature_transformation_with_dask/sm-processing_feature_transformation_with_dask.ipynb
+++ b/ prepare_data/sm-processing_feature_transformation_with_dask/sm-processing_feature_transformation_with_dask.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -485,35 +485,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
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"\n",
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+ "\n"
]
}
],
@@ -538,4 +538,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-processing_introduction.ipynb b/ prepare_data/sm-processing_introduction.ipynb
index 66e638094a..82e5eaea52 100644
--- a/ prepare_data/sm-processing_introduction.ipynb
+++ b/ prepare_data/sm-processing_introduction.ipynb
@@ -29,7 +29,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -52,7 +52,7 @@
},
"source": [
"\n",
- "This notebook corresponds to the section \"Preprocessing Data With The Built-In Scikit-Learn Container\" in the blog post [Amazon SageMaker Processing \u2013 Fully Managed Data Processing and Model Evaluation](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/). \n",
+ "This notebook corresponds to the section \"Preprocessing Data With The Built-In Scikit-Learn Container\" in the blog post [Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/). \n",
"It shows a lightweight example of using SageMaker Processing to create train, test, and validation datasets. SageMaker Processing is used to create these datasets, which then are written back to S3.\n",
"\n",
"## Runtime\n",
@@ -87,22 +87,22 @@
"source": [
"## Prepare resources\n",
"\n",
- "First, let\u2019s create an SKLearnProcessor object, passing the scikit-learn version we want to use, as well as our managed infrastructure requirements."
+ "First, let’s create an SKLearnProcessor object, passing the scikit-learn version we want to use, as well as our managed infrastructure requirements."
]
},
{
"cell_type": "code",
"execution_count": null,
- "outputs": [],
- "source": [
- "!pip install -U sagemaker"
- ],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
- }
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -U sagemaker"
+ ]
},
{
"cell_type": "code",
@@ -356,7 +356,7 @@
" \n",
" \n",
"\n",
- "
5 rows \u00d7 42 columns
\n",
+ "
5 rows × 42 columns
\n",
""
],
"text/plain": [
@@ -773,35 +773,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
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+ "\n",
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+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -840,4 +840,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-processing_r.ipynb b/ prepare_data/sm-processing_r.ipynb
index 7f60d64ba2..d33406ae1e 100644
--- a/ prepare_data/sm-processing_r.ipynb
+++ b/ prepare_data/sm-processing_r.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -325,35 +325,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
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"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -378,4 +378,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-processing_spark_processing/sm-processing_spark_processing.ipynb b/ prepare_data/sm-processing_spark_processing/sm-processing_spark_processing.ipynb
index bb5c0edae3..6cee1a3547 100644
--- a/ prepare_data/sm-processing_spark_processing/sm-processing_spark_processing.ipynb
+++ b/ prepare_data/sm-processing_spark_processing/sm-processing_spark_processing.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -827,35 +827,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
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+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -881,4 +881,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-spark_kmeans.ipynb b/ prepare_data/sm-spark_kmeans.ipynb
index 79b4e16f06..299d19a095 100644
--- a/ prepare_data/sm-spark_kmeans.ipynb
+++ b/ prepare_data/sm-spark_kmeans.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -574,35 +574,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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+ "\n"
]
}
],
@@ -629,4 +629,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ prepare_data/sm-spark_xgboost/img/sagemaker-spark-custom-architecture.png b/ prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-custom-architecture.png
similarity index 100%
rename from prepare_data/sm-spark_xgboost/img/sagemaker-spark-custom-architecture.png
rename to prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-custom-architecture.png
diff --git a/ prepare_data/sm-spark_xgboost/img/sagemaker-spark-kmeans-architecture.png b/ prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-kmeans-architecture.png
similarity index 100%
rename from prepare_data/sm-spark_xgboost/img/sagemaker-spark-kmeans-architecture.png
rename to prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-kmeans-architecture.png
diff --git a/ prepare_data/sm-spark_xgboost/img/sagemaker-spark-pca-kmeans-architecture.png b/ prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-pca-kmeans-architecture.png
similarity index 100%
rename from prepare_data/sm-spark_xgboost/img/sagemaker-spark-pca-kmeans-architecture.png
rename to prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-pca-kmeans-architecture.png
diff --git a/ prepare_data/sm-spark_xgboost/img/sagemaker-spark-pca-spark-kmeans-sagemaker-architecture.png b/ prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-pca-spark-kmeans-sagemaker-architecture.png
similarity index 100%
rename from prepare_data/sm-spark_xgboost/img/sagemaker-spark-pca-spark-kmeans-sagemaker-architecture.png
rename to prepare_data/sm-spark_pca_kmeans/img/sagemaker-spark-pca-spark-kmeans-sagemaker-architecture.png
diff --git a/ build_and_train_models/sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb b/ prepare_data/sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb
similarity index 93%
rename from build_and_train_models/sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb
rename to prepare_data/sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb
index 39375f70cc..3a805cadfd 100644
--- a/ build_and_train_models/sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb
+++ b/ prepare_data/sm-spark_pca_kmeans/sm-spark_pca_kmeans.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -386,35 +386,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
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+ "\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -440,4 +440,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ build_and_train_models/README.md b/ build_and_train_models/README.md
new file mode 100644
index 0000000000..beb888b7e6
--- /dev/null
+++ b/ build_and_train_models/README.md
@@ -0,0 +1,45 @@
+# Amazon SageMaker Examples
+
+### Build and Train Models
+
+The example notebooks within this folder showcase the capabilities of Amazon SageMaker in building and training machine learning models.
+
+- [Visualize Training Jobs and Performance of Your Model Using TensorBoard on SageMaker](sm-distributed_data_parallelism_pytorch/sm-distributed_data_parallelism_pytorch.ipynb)
+- [Use SageMaker Distributed Model Parallel with Amazon SageMaker to Launch Training Job with Model Parallelization](sm-distributed_model_parallel/sm-distributed_model_parallel.ipynb)
+- [Time Series Modeling with Amazon Forecast and DeepAR on SageMaker - DeepAR on SageMaker](sm-forecast_deepar_time_series_modeling/sm-forecast_deepar_time_series_modeling.ipynb)
+- [Fine-tune GPT-NeoX and Llama-v2 with SageMaker-PyTorch FSDP at large-scale using tensor parallelism, hybrid sharding, and activation offloading](sm-fsdp_finetuning_of_llama_v2/sm-fsdp_finetuning_of_llama_v2.ipynb)
+- [PyTorch's example to demonstrate Amazon SageMaker Heterogeneous Cluster for model training](sm-heterogeneous_clusters_for_model_training/sm-heterogeneous_clusters_for_model_training.ipynb)
+- [Heterogeneous Cluster - a hello world training job](sm-heterogeneous_clusters_training/sm-heterogeneous_clusters_training.ipynb)
+- [Hyperparameter Tuning using SageMaker PyTorch Container](sm-hyperparameter_tuning_pytorch/sm-hyperparameter_tuning_pytorch.ipynb)
+- [Learning Word2Vec Word Representations using BlazingText](sm-introduction_to_blazingtext_word2vec_text8/sm-introduction_to_blazingtext_word2vec_text8.ipynb)
+- [An Introduction to the Amazon SageMaker IP Insights Algorithm](sm-introduction_to_ip_insights/sm-introduction_to_ip_insights.ipynb)
+- [An Introduction to SageMaker LDA](sm-introduction_to_lda/sm-introduction_to_lda.ipynb)
+- [Introduction to Basic Functionality of NTM](sm-introduction_to_ntm/sm-introduction_to_ntm.ipynb)
+- [An Introduction to SageMaker ObjectToVec model for sequence-sequence embedding](sm-introduction_to_object2vec_sentence_similarity/sm-introduction_to_object2vec_sentence_similarity.ipynb)
+- [Training and Deploying ML Models using JAX on SageMaker](sm-jax_bring_your_own/sm-jax_bring_your_own.ipynb)
+- [Managed Spot Training for XGBoost](sm-managed_spot_training_xgboost/sm-managed_spot_training_xgboost.ipynb)
+- [Package a machine learning model for listing on the AWS Marketplace](sm-marketplace_build_model_package_for_listing/sm-marketplace_build_model_package_for_listing.ipynb)
+- [Building your own container as Algorithm / Model Package](sm-marketplace_building_your_own_container_as_package/sm-marketplace_building_your_own_container_as_package.ipynb)
+- [Amazon SageMaker Object Detection for Bird Species](sm-object_detection_birds/sm-object_detection_birds.ipynb)
+- [An Introduction to SageMaker Random Cut Forests](sm-random_cut_forest_example/sm-random_cut_forest_example.ipynb)
+- [Regression with Amazon SageMaker XGBoost algorithm](sm-regression_xgboost/sm-regression_xgboost.ipynb)
+- [Train a PyTorch model with MNIST dataset](sm-remote_function_pytorch_mnist/sm-remote_function_pytorch_mnist.ipynb)
+- [Quick Start - Run local code as SageMaker training job](sm-remote_function_quick_start/sm-remote_function_quick_start.ipynb)
+- [Building your own algorithm container](sm-scikit_build_your_own_container/sm-scikit_build_your_own_container.ipynb)
+- [Horovod Distributed Training with SageMaker TensorFlow script mode](sm-script_mode_distributed_training_horovod_tensorflow/sm-script_mode_distributed_training_horovod_tensorflow.ipynb)
+- [Amazon SageMaker Semantic Segmentation Algorithm](sm-semantic_segmentation/sm-semantic_segmentation.ipynb)
+- [Data parallel distributed BERT model training with PyTorch and SageMaker distributed](sm-smddp_bert/sm-smddp_bert.ipynb)
+- [Compile and Train the GPT2 Model using the Transformers Trainer API with the wikitext Dataset for Multi-Node Multi-GPU Training](sm-training_compiler_language_modeling_multi_gpu_multi_node/sm-training_compiler_language_modeling_multi_gpu_multi_node.ipynb)
+- [Compile and Train a Hugging Face Transformer BERT Model with the SST Dataset using SageMaker Training Compiler](sm-training_compliler_single_node_single_gpu_bert/sm-training_compliler_single_node_single_gpu_bert.ipynb)
+- [Automatic Model Tuning : Automatic training job early stopping](sm-automatic_model_tune_hyperparameter_tuning_early_stopping.ipynb)
+- [SageMaker/DeepAR demo on electricity dataset](sm-deepar_example.ipynb)
+- [Accelerate SageMaker-PyTorch FSDP Training of Mixtral on P4 instances](sm-distributed_training_model_parallel_v2_mixtral_on_p4.ipynb)
+- [Automatic Model Tuning : Warm Starting Tuning Jobs](sm-hpo_warmstart_image_classification.ipynb)
+- [Hyperband Automatic Model Tuning for Distributed Training](sm-hyperparameter_tuning_hyperband_automatic_model_tuning.ipynb)
+- [Tabular regression with Amazon SageMaker AutoGluon-Tabular algorithm](sm-introduction_to_auogluon_tabular_regression.ipynb)
+- [An Introduction to Factorization Machines with MNIST](sm-introduction_to_factorization_machines.ipynb)
+- [An Introduction to PCA with MNIST](sm-introduction_to_pca.ipynb)
+- [Multi-Class Classification using Amazon SageMaker k-Nearest-Neighbors (kNN)](sm-k_nearest_neighbors_multi_class_classification.ipynb)
+- [Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm](sm-lightgbm_catboost_tabular_classification.ipynb)
+- [An Introduction to Linear Learner with MNIST](sm-linear_learner_mnist.ipynb)
+- [Tabular classification with Amazon SageMaker TabTransformer algorithm](sm-tabtransformer_tabular_classification.ipynb)
diff --git a/ build_and_train_models/sm-automatic_model_tune_hyperparameter_tuning_early_stopping.ipynb b/ build_and_train_models/sm-automatic_model_tune_hyperparameter_tuning_early_stopping.ipynb
index ff4f6bb339..92a02b45cc 100644
--- a/ build_and_train_models/sm-automatic_model_tune_hyperparameter_tuning_early_stopping.ipynb
+++ b/ build_and_train_models/sm-automatic_model_tune_hyperparameter_tuning_early_stopping.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -413,35 +413,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-cnn_pytorch/cifar_utils.py b/ build_and_train_models/sm-cnn_pytorch/cifar_utils.py
deleted file mode 100644
index fc641f8099..0000000000
--- a/ build_and_train_models/sm-cnn_pytorch/cifar_utils.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-import torchvision
-import torchvision.transforms as transforms
-
-classes = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
-
-
-def _get_transform():
- return torchvision.transforms.Compose(
- [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
- )
-
-
-def train_data_loader():
- transform = _get_transform()
- trainset = torchvision.datasets.CIFAR10(
- root="./data", train=True, download=False, transform=transform
- )
- return torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
-
-
-def test_data_loader():
- transform = _get_transform()
- testset = torchvision.datasets.CIFAR10(
- root="./data", train=False, download=False, transform=transform
- )
- return torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
-
-
-def show_img(img):
- """displays an image"""
- img = img / 2 + 0.5 # unnormalize
- npimg = img.numpy()
- plt.imshow(np.transpose(npimg, (1, 2, 0)))
diff --git a/ build_and_train_models/sm-cnn_pytorch/source/cifar10.py b/ build_and_train_models/sm-cnn_pytorch/source/cifar10.py
deleted file mode 100644
index c20ad42a2a..0000000000
--- a/ build_and_train_models/sm-cnn_pytorch/source/cifar10.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import argparse
-import json
-import logging
-import os
-
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.parallel
-import torch.optim
-import torch.utils.data
-import torch.utils.data.distributed
-import torchvision
-import torchvision.models
-import torchvision.transforms as transforms
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-classes = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
-
-
-# https://github.com/pytorch/tutorials/blob/master/beginner_source/blitz/cifar10_tutorial.py#L118
-class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.conv1 = nn.Conv2d(3, 6, 5)
- self.pool = nn.MaxPool2d(2, 2)
- self.conv2 = nn.Conv2d(6, 16, 5)
- self.fc1 = nn.Linear(16 * 5 * 5, 120)
- self.fc2 = nn.Linear(120, 84)
- self.fc3 = nn.Linear(84, 10)
-
- def forward(self, x):
- x = self.pool(F.relu(self.conv1(x)))
- x = self.pool(F.relu(self.conv2(x)))
- x = x.view(-1, 16 * 5 * 5)
- x = F.relu(self.fc1(x))
- x = F.relu(self.fc2(x))
- x = self.fc3(x)
- return x
-
-
-def _train(args):
- is_distributed = len(args.hosts) > 1 and args.dist_backend is not None
- logger.debug("Distributed training - {}".format(is_distributed))
-
- if is_distributed:
- # Initialize the distributed environment.
- world_size = len(args.hosts)
- os.environ["WORLD_SIZE"] = str(world_size)
- host_rank = args.hosts.index(args.current_host)
- os.environ["RANK"] = str(host_rank)
- dist.init_process_group(backend=args.dist_backend, rank=host_rank, world_size=world_size)
- logger.info(
- "Initialized the distributed environment: '{}' backend on {} nodes. ".format(
- args.dist_backend, dist.get_world_size()
- )
- + "Current host rank is {}. Using cuda: {}. Number of gpus: {}".format(
- dist.get_rank(), torch.cuda.is_available(), args.num_gpus
- )
- )
-
- device = "cuda" if torch.cuda.is_available() else "cpu"
- logger.info("Device Type: {}".format(device))
-
- logger.info("Loading Cifar10 dataset")
- transform = transforms.Compose(
- [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
- )
-
- trainset = torchvision.datasets.CIFAR10(
- root=args.data_dir, train=True, download=False, transform=transform
- )
- train_loader = torch.utils.data.DataLoader(
- trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers
- )
-
- logger.info("Model loaded")
- model = Net()
-
- if torch.cuda.device_count() > 1:
- logger.info("Gpu count: {}".format(torch.cuda.device_count()))
- model = nn.DataParallel(model)
-
- model = model.to(device)
-
- criterion = nn.CrossEntropyLoss().to(device)
- optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-
- for epoch in range(0, args.epochs):
- running_loss = 0.0
- for i, data in enumerate(train_loader):
- # get the inputs
- inputs, labels = data
- inputs, labels = inputs.to(device), labels.to(device)
-
- # zero the parameter gradients
- optimizer.zero_grad()
-
- # forward + backward + optimize
- outputs = model(inputs)
- loss = criterion(outputs, labels)
- loss.backward()
- optimizer.step()
-
- # print statistics
- running_loss += loss.item()
- if i % 2000 == 1999: # print every 2000 mini-batches
- print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, running_loss / 2000))
- running_loss = 0.0
-
- print("Finished Training")
- return _save_model(model, args.model_dir)
-
-
-def _save_model(model, model_dir):
- logger.info("Saving the model.")
- path = os.path.join(model_dir, "model.pth")
- # recommended way from http://pytorch.org/docs/master/notes/serialization.html
- torch.save(model.cpu().state_dict(), path)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- "--workers",
- type=int,
- default=2,
- metavar="W",
- help="number of data loading workers (default: 2)",
- )
- parser.add_argument(
- "--epochs",
- type=int,
- default=2,
- metavar="E",
- help="number of total epochs to run (default: 2)",
- )
- parser.add_argument(
- "--batch_size", type=int, default=4, metavar="BS", help="batch size (default: 4)"
- )
- parser.add_argument(
- "--lr",
- type=float,
- default=0.001,
- metavar="LR",
- help="initial learning rate (default: 0.001)",
- )
- parser.add_argument(
- "--momentum", type=float, default=0.9, metavar="M", help="momentum (default: 0.9)"
- )
- parser.add_argument(
- "--dist_backend", type=str, default="gloo", help="distributed backend (default: gloo)"
- )
-
- parser.add_argument("--hosts", type=json.loads, default=os.environ["SM_HOSTS"])
- parser.add_argument("--current-host", type=str, default=os.environ["SM_CURRENT_HOST"])
- parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
- parser.add_argument("--data-dir", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
- parser.add_argument("--num-gpus", type=int, default=os.environ["SM_NUM_GPUS"])
-
- _train(parser.parse_args())
-
-
-def model_fn(model_dir):
- logger.info("model_fn")
- device = "cuda" if torch.cuda.is_available() else "cpu"
- model = Net()
- if torch.cuda.device_count() > 1:
- logger.info("Gpu count: {}".format(torch.cuda.device_count()))
- model = nn.DataParallel(model)
-
- with open(os.path.join(model_dir, "model.pth"), "rb") as f:
- model.load_state_dict(torch.load(f))
- return model.to(device)
diff --git a/ build_and_train_models/sm-deepar_example.ipynb b/ build_and_train_models/sm-deepar_example.ipynb
index d1e72f4c77..75e0751e5e 100644
--- a/ build_and_train_models/sm-deepar_example.ipynb
+++ b/ build_and_train_models/sm-deepar_example.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1241,35 +1241,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
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"\n",
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+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-distributed_data_parallelism_pytorch/sm-distributed_data_parallelism_pytorch.ipynb b/ build_and_train_models/sm-distributed_data_parallelism_pytorch/sm-distributed_data_parallelism_pytorch.ipynb
index 4b5d5e2db1..4bcc927b35 100644
--- a/ build_and_train_models/sm-distributed_data_parallelism_pytorch/sm-distributed_data_parallelism_pytorch.ipynb
+++ b/ build_and_train_models/sm-distributed_data_parallelism_pytorch/sm-distributed_data_parallelism_pytorch.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -266,35 +266,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
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"\n",
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},
{
diff --git a/ build_and_train_models/sm-distributed_model_parallel/sm-distributed_model_parallel.ipynb b/ build_and_train_models/sm-distributed_model_parallel/sm-distributed_model_parallel.ipynb
index bfa5cd9947..30794894ea 100644
--- a/ build_and_train_models/sm-distributed_model_parallel/sm-distributed_model_parallel.ipynb
+++ b/ build_and_train_models/sm-distributed_model_parallel/sm-distributed_model_parallel.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -254,35 +254,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
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"\n",
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]
}
],
diff --git a/ build_and_train_models/sm-distributed_training_model_parallel_v2_mixtral_on_p4.ipynb b/ build_and_train_models/sm-distributed_training_model_parallel_v2_mixtral_on_p4.ipynb
index d9db6d36ff..ffd41798c1 100644
--- a/ build_and_train_models/sm-distributed_training_model_parallel_v2_mixtral_on_p4.ipynb
+++ b/ build_and_train_models/sm-distributed_training_model_parallel_v2_mixtral_on_p4.ipynb
@@ -11,7 +11,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---\n",
"\n",
@@ -970,41 +970,36 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
}
],
"metadata": {
diff --git a/ build_and_train_models/sm-experiments_job_tracking_script_mode_traning_job_pytorch/images/sm_training_exp_overview.png b/ build_and_train_models/sm-experiments_job_tracking_script_mode_traning_job_pytorch/images/sm_training_exp_overview.png
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diff --git a/ build_and_train_models/sm-forecast_deepar_time_series_modeling/sm-forecast_deepar_time_series_modeling.ipynb b/ build_and_train_models/sm-forecast_deepar_time_series_modeling/sm-forecast_deepar_time_series_modeling.ipynb
index a0f5b656f9..2e49ca02a3 100644
--- a/ build_and_train_models/sm-forecast_deepar_time_series_modeling/sm-forecast_deepar_time_series_modeling.ipynb
+++ b/ build_and_train_models/sm-forecast_deepar_time_series_modeling/sm-forecast_deepar_time_series_modeling.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -852,35 +852,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
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}
],
diff --git a/ build_and_train_models/sm-fsdp_finetuning_of_llama_v2/sm-fsdp_finetuning_of_llama_v2.ipynb b/ build_and_train_models/sm-fsdp_finetuning_of_llama_v2/sm-fsdp_finetuning_of_llama_v2.ipynb
index c7c1b8bae1..d53d4204d9 100644
--- a/ build_and_train_models/sm-fsdp_finetuning_of_llama_v2/sm-fsdp_finetuning_of_llama_v2.ipynb
+++ b/ build_and_train_models/sm-fsdp_finetuning_of_llama_v2/sm-fsdp_finetuning_of_llama_v2.ipynb
@@ -9,7 +9,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
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@@ -935,41 +935,36 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
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]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
}
],
"metadata": {
diff --git a/ build_and_train_models/sm-heterogeneous_clusters_for_model_training/sm-heterogeneous_clusters_for_model_training.ipynb b/ build_and_train_models/sm-heterogeneous_clusters_for_model_training/sm-heterogeneous_clusters_for_model_training.ipynb
index a7b028c52c..dd9b9031a9 100644
--- a/ build_and_train_models/sm-heterogeneous_clusters_for_model_training/sm-heterogeneous_clusters_for_model_training.ipynb
+++ b/ build_and_train_models/sm-heterogeneous_clusters_for_model_training/sm-heterogeneous_clusters_for_model_training.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -477,35 +477,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
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],
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index 95d8abc910..060d89461e 100644
--- a/ build_and_train_models/sm-heterogeneous_clusters_training/sm-heterogeneous_clusters_training.ipynb
+++ b/ build_and_train_models/sm-heterogeneous_clusters_training/sm-heterogeneous_clusters_training.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -304,35 +304,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
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}
],
diff --git a/ build_and_train_models/sm-hpo_warmstart_image_classification.ipynb b/ build_and_train_models/sm-hpo_warmstart_image_classification.ipynb
index 39ce772765..365ab0ea16 100644
--- a/ build_and_train_models/sm-hpo_warmstart_image_classification.ipynb
+++ b/ build_and_train_models/sm-hpo_warmstart_image_classification.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -729,35 +729,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-hyperparameter_tuning_hyperband_automatic_model_tuning.ipynb b/ build_and_train_models/sm-hyperparameter_tuning_hyperband_automatic_model_tuning.ipynb
index 63dffc0838..4c44817f6e 100644
--- a/ build_and_train_models/sm-hyperparameter_tuning_hyperband_automatic_model_tuning.ipynb
+++ b/ build_and_train_models/sm-hyperparameter_tuning_hyperband_automatic_model_tuning.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1024,35 +1024,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-hyperparameter_tuning_pytorch/hpo_pytorch_mnist.ipynb b/ build_and_train_models/sm-hyperparameter_tuning_pytorch/sm-hyperparameter_tuning_pytorch.ipynb
similarity index 97%
rename from build_and_train_models/sm-hyperparameter_tuning_pytorch/hpo_pytorch_mnist.ipynb
rename to build_and_train_models/sm-hyperparameter_tuning_pytorch/sm-hyperparameter_tuning_pytorch.ipynb
index 189dfcc657..4fb2a61773 100644
--- a/ build_and_train_models/sm-hyperparameter_tuning_pytorch/hpo_pytorch_mnist.ipynb
+++ b/ build_and_train_models/sm-hyperparameter_tuning_pytorch/sm-hyperparameter_tuning_pytorch.ipynb
@@ -28,7 +28,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -743,35 +743,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-inference_script_mode_xgboost/abalone.py b/ build_and_train_models/sm-inference_script_mode_xgboost/abalone.py
deleted file mode 100644
index 4b27b30e6e..0000000000
--- a/ build_and_train_models/sm-inference_script_mode_xgboost/abalone.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License").
-# You may not use this file except in compliance with the License.
-# A copy of the License is located at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# or in the "license" file accompanying this file. This file is distributed
-# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
-# express or implied. See the License for the specific language governing
-# permissions and limitations under the License.
-from __future__ import print_function
-
-import argparse
-import json
-import logging
-import os
-import pickle as pkl
-
-import pandas as pd
-import xgboost as xgb
-from sagemaker_containers import entry_point
-from sagemaker_xgboost_container import distributed
-from sagemaker_xgboost_container.data_utils import get_dmatrix
-
-
-def _xgb_train(params, dtrain, evals, num_boost_round, model_dir, is_master):
- """Run xgb train on arguments given with rabit initialized.
-
- This is our rabit execution function.
-
- :param args_dict: Argument dictionary used to run xgb.train().
- :param is_master: True if current node is master host in distributed training,
- or is running single node training job.
- Note that rabit_run will include this argument.
- """
-
- logging.basicConfig(level=logging.DEBUG)
-
- booster = xgb.train(params=params, dtrain=dtrain, evals=evals, num_boost_round=num_boost_round)
-
- if is_master:
- model_location = model_dir + "/xgboost-model"
- pkl.dump(booster, open(model_location, "wb"))
- logging.info("Stored trained model at {}".format(model_location))
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
-
- # Hyperparameters are described here.
- parser.add_argument(
- "--max_depth",
- type=int,
- )
- parser.add_argument("--eta", type=float)
- parser.add_argument("--gamma", type=float)
- parser.add_argument("--min_child_weight", type=float)
- parser.add_argument("--subsample", type=float)
- parser.add_argument("--verbosity", type=int)
- parser.add_argument("--objective", type=str)
- parser.add_argument("--num_round", type=int)
- parser.add_argument("--tree_method", type=str, default="auto")
- parser.add_argument("--predictor", type=str, default="auto")
-
- # Sagemaker specific arguments. Defaults are set in the environment variables.
- parser.add_argument("--output_data_dir", type=str, default=os.environ.get("SM_OUTPUT_DATA_DIR"))
- parser.add_argument("--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR"))
- parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN"))
- parser.add_argument("--validation", type=str, default=os.environ.get("SM_CHANNEL_VALIDATION"))
- parser.add_argument("--sm_hosts", type=str, default=os.environ.get("SM_HOSTS"))
- parser.add_argument("--sm_current_host", type=str, default=os.environ.get("SM_CURRENT_HOST"))
-
- args, _ = parser.parse_known_args()
-
- # Get SageMaker host information from runtime environment variables
- sm_hosts = json.loads(args.sm_hosts)
- sm_current_host = args.sm_current_host
-
- dtrain = get_dmatrix(args.train, "libsvm")
- dval = get_dmatrix(args.validation, "libsvm")
- watchlist = (
- [(dtrain, "train"), (dval, "validation")] if dval is not None else [(dtrain, "train")]
- )
-
- train_hp = {
- "max_depth": args.max_depth,
- "eta": args.eta,
- "gamma": args.gamma,
- "min_child_weight": args.min_child_weight,
- "subsample": args.subsample,
- "verbosity": args.verbosity,
- "objective": args.objective,
- "tree_method": args.tree_method,
- "predictor": args.predictor,
- }
-
- xgb_train_args = dict(
- params=train_hp,
- dtrain=dtrain,
- evals=watchlist,
- num_boost_round=args.num_round,
- model_dir=args.model_dir,
- )
-
- if len(sm_hosts) > 1:
- # Wait until all hosts are able to find each other
- entry_point._wait_hostname_resolution()
-
- # Execute training function after initializing rabit.
- distributed.rabit_run(
- exec_fun=_xgb_train,
- args=xgb_train_args,
- include_in_training=(dtrain is not None),
- hosts=sm_hosts,
- current_host=sm_current_host,
- update_rabit_args=True,
- )
- else:
- # If single node training, call training method directly.
- if dtrain:
- xgb_train_args["is_master"] = True
- _xgb_train(**xgb_train_args)
- else:
- raise ValueError("Training channel must have data to train model.")
-
-
-def model_fn(model_dir):
- """Deserialize and return fitted model.
-
- Note that this should have the same name as the serialized model in the _xgb_train method
- """
- model_file = "xgboost-model"
- booster = pkl.load(open(os.path.join(model_dir, model_file), "rb"))
- return booster
diff --git a/ build_and_train_models/sm-inference_script_mode_xgboost/images/shap_old_abalone.png b/ build_and_train_models/sm-inference_script_mode_xgboost/images/shap_old_abalone.png
deleted file mode 100644
index dca562726b..0000000000
Binary files a/ build_and_train_models/sm-inference_script_mode_xgboost/images/shap_old_abalone.png and /dev/null differ
diff --git a/ build_and_train_models/sm-inference_script_mode_xgboost/images/shap_young_abalone.png b/ build_and_train_models/sm-inference_script_mode_xgboost/images/shap_young_abalone.png
deleted file mode 100644
index 11ffc343b8..0000000000
Binary files a/ build_and_train_models/sm-inference_script_mode_xgboost/images/shap_young_abalone.png and /dev/null differ
diff --git a/ build_and_train_models/sm-inference_script_mode_xgboost/inference.py b/ build_and_train_models/sm-inference_script_mode_xgboost/inference.py
deleted file mode 100644
index bb00fb6955..0000000000
--- a/ build_and_train_models/sm-inference_script_mode_xgboost/inference.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License").
-# You may not use this file except in compliance with the License.
-# A copy of the License is located at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# or in the "license" file accompanying this file. This file is distributed
-# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
-# express or implied. See the License for the specific language governing
-# permissions and limitations under the License.
-import json
-import os
-import pickle as pkl
-
-import numpy as np
-import sagemaker_xgboost_container.encoder as xgb_encoders
-
-
-def model_fn(model_dir):
- """
- Deserialize and return fitted model.
- """
- model_file = "xgboost-model"
- booster = pkl.load(open(os.path.join(model_dir, model_file), "rb"))
- return booster
-
-
-def input_fn(request_body, request_content_type):
- """
- The SageMaker XGBoost model server receives the request data body and the content type,
- and invokes the `input_fn`.
-
- Return a DMatrix (an object that can be passed to predict_fn).
- """
- if request_content_type == "text/libsvm":
- return xgb_encoders.libsvm_to_dmatrix(request_body)
- else:
- raise ValueError("Content type {} is not supported.".format(request_content_type))
-
-
-def predict_fn(input_data, model):
- """
- SageMaker XGBoost model server invokes `predict_fn` on the return value of `input_fn`.
-
- Return a two-dimensional NumPy array where the first columns are predictions
- and the remaining columns are the feature contributions (SHAP values) for that prediction.
- """
- prediction = model.predict(input_data)
- feature_contribs = model.predict(input_data, pred_contribs=True, validate_features=False)
- output = np.hstack((prediction[:, np.newaxis], feature_contribs))
- return output
-
-
-def output_fn(predictions, content_type):
- """
- After invoking predict_fn, the model server invokes `output_fn`.
- """
- if content_type == "text/csv":
- return ",".join(str(x) for x in predictions[0])
- else:
- raise ValueError("Content type {} is not supported.".format(content_type))
diff --git a/ build_and_train_models/sm-introduction_to_auogluon_tabular_regression.ipynb b/ build_and_train_models/sm-introduction_to_auogluon_tabular_regression.ipynb
index 010502d223..e1122f0b7b 100644
--- a/ build_and_train_models/sm-introduction_to_auogluon_tabular_regression.ipynb
+++ b/ build_and_train_models/sm-introduction_to_auogluon_tabular_regression.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -568,35 +568,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_blazingtext_word2vec_text8/sm-introduction_to_blazingtext_word2vec_text8.ipynb b/ build_and_train_models/sm-introduction_to_blazingtext_word2vec_text8/sm-introduction_to_blazingtext_word2vec_text8.ipynb
index db761252a7..cb0a3f993d 100644
--- a/ build_and_train_models/sm-introduction_to_blazingtext_word2vec_text8/sm-introduction_to_blazingtext_word2vec_text8.ipynb
+++ b/ build_and_train_models/sm-introduction_to_blazingtext_word2vec_text8/sm-introduction_to_blazingtext_word2vec_text8.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -554,35 +554,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_factorization_machines.ipynb b/ build_and_train_models/sm-introduction_to_factorization_machines.ipynb
index bfd10819e1..66a1e73738 100644
--- a/ build_and_train_models/sm-introduction_to_factorization_machines.ipynb
+++ b/ build_and_train_models/sm-introduction_to_factorization_machines.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -465,35 +465,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_ip_insights/sm-introduction_to_ip_insights.ipynb b/ build_and_train_models/sm-introduction_to_ip_insights/sm-introduction_to_ip_insights.ipynb
index 81119c1b67..79b7b93b24 100644
--- a/ build_and_train_models/sm-introduction_to_ip_insights/sm-introduction_to_ip_insights.ipynb
+++ b/ build_and_train_models/sm-introduction_to_ip_insights/sm-introduction_to_ip_insights.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1085,35 +1085,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_lda/sm-introduction_to_lda.ipynb b/ build_and_train_models/sm-introduction_to_lda/sm-introduction_to_lda.ipynb
index 74aeaf7722..1388be4617 100644
--- a/ build_and_train_models/sm-introduction_to_lda/sm-introduction_to_lda.ipynb
+++ b/ build_and_train_models/sm-introduction_to_lda/sm-introduction_to_lda.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -610,35 +610,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_ntm/sm-introduction_to_ntm.ipynb b/ build_and_train_models/sm-introduction_to_ntm/sm-introduction_to_ntm.ipynb
index bbbc771c15..65971ad284 100644
--- a/ build_and_train_models/sm-introduction_to_ntm/sm-introduction_to_ntm.ipynb
+++ b/ build_and_train_models/sm-introduction_to_ntm/sm-introduction_to_ntm.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -560,35 +560,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_object2vec_sentence_similarity/sm-introduction_to_object2vec_sentence_similarity.ipynb b/ build_and_train_models/sm-introduction_to_object2vec_sentence_similarity/sm-introduction_to_object2vec_sentence_similarity.ipynb
index 7c7ee4e576..9b3cb31673 100644
--- a/ build_and_train_models/sm-introduction_to_object2vec_sentence_similarity/sm-introduction_to_object2vec_sentence_similarity.ipynb
+++ b/ build_and_train_models/sm-introduction_to_object2vec_sentence_similarity/sm-introduction_to_object2vec_sentence_similarity.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1750,35 +1750,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-introduction_to_pca.ipynb b/ build_and_train_models/sm-introduction_to_pca.ipynb
index 5397002c82..2ab1addbf7 100644
--- a/ build_and_train_models/sm-introduction_to_pca.ipynb
+++ b/ build_and_train_models/sm-introduction_to_pca.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -451,35 +451,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-jax_bring_your_own/sm-jax_bring_your_own.ipynb b/ build_and_train_models/sm-jax_bring_your_own/sm-jax_bring_your_own.ipynb
index 76ce528634..c9a4fe894c 100644
--- a/ build_and_train_models/sm-jax_bring_your_own/sm-jax_bring_your_own.ipynb
+++ b/ build_and_train_models/sm-jax_bring_your_own/sm-jax_bring_your_own.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -335,35 +335,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -388,4 +388,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ build_and_train_models/sm-k_nearest_neighbors_multi_class_classification.ipynb b/ build_and_train_models/sm-k_nearest_neighbors_multi_class_classification.ipynb
index 42f26a2ac8..837f5281d5 100644
--- a/ build_and_train_models/sm-k_nearest_neighbors_multi_class_classification.ipynb
+++ b/ build_and_train_models/sm-k_nearest_neighbors_multi_class_classification.ipynb
@@ -28,7 +28,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1713,35 +1713,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-lightgbm_catboost_tabular_classification.ipynb b/ build_and_train_models/sm-lightgbm_catboost_tabular_classification.ipynb
index bb6fab1969..25e26b6e3b 100644
--- a/ build_and_train_models/sm-lightgbm_catboost_tabular_classification.ipynb
+++ b/ build_and_train_models/sm-lightgbm_catboost_tabular_classification.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -664,35 +664,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-linear_learner_mnist.ipynb b/ build_and_train_models/sm-linear_learner_mnist.ipynb
index 67a0a5dd06..f14e2e626c 100644
--- a/ build_and_train_models/sm-linear_learner_mnist.ipynb
+++ b/ build_and_train_models/sm-linear_learner_mnist.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -531,35 +531,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-managed_spot_training_xgboost/sm-managed_spot_training_xgboost.ipynb b/ build_and_train_models/sm-managed_spot_training_xgboost/sm-managed_spot_training_xgboost.ipynb
index 3900ef0464..061051fb0a 100644
--- a/ build_and_train_models/sm-managed_spot_training_xgboost/sm-managed_spot_training_xgboost.ipynb
+++ b/ build_and_train_models/sm-managed_spot_training_xgboost/sm-managed_spot_training_xgboost.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -438,35 +438,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-marketplace_build_model_package_for_listing/sm-marketplace_build_model_package_for_listing.ipynb b/ build_and_train_models/sm-marketplace_build_model_package_for_listing/sm-marketplace_build_model_package_for_listing.ipynb
index 86cb0ae706..426c3ed302 100644
--- a/ build_and_train_models/sm-marketplace_build_model_package_for_listing/sm-marketplace_build_model_package_for_listing.ipynb
+++ b/ build_and_train_models/sm-marketplace_build_model_package_for_listing/sm-marketplace_build_model_package_for_listing.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1124,35 +1124,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-marketplace_building_your_own_container_as_package/Bring_Your_Own-Creating_Algorithm_and_Model_Package.ipynb b/ build_and_train_models/sm-marketplace_building_your_own_container_as_package/sm-marketplace_building_your_own_container_as_package.ipynb
similarity index 96%
rename from build_and_train_models/sm-marketplace_building_your_own_container_as_package/Bring_Your_Own-Creating_Algorithm_and_Model_Package.ipynb
rename to build_and_train_models/sm-marketplace_building_your_own_container_as_package/sm-marketplace_building_your_own_container_as_package.ipynb
index ee01c9bcd0..ff3921fea3 100644
--- a/ build_and_train_models/sm-marketplace_building_your_own_container_as_package/Bring_Your_Own-Creating_Algorithm_and_Model_Package.ipynb
+++ b/ build_and_train_models/sm-marketplace_building_your_own_container_as_package/sm-marketplace_building_your_own_container_as_package.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1365,35 +1365,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/config.py b/ build_and_train_models/sm-name_entity_recognition_document/config.py
deleted file mode 100644
index 59653d9cca..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/config.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-
-def get_current_folder(global_variables):
- # if calling from a file
- if "__file__" in global_variables:
- current_file = Path(global_variables["__file__"])
- current_folder = current_file.parent.resolve()
- # if calling from a notebook
- else:
- current_folder = Path(os.getcwd())
- return current_folder
-
-current_folder = get_current_folder(globals())
-
-SOLUTION_PREFIX = "sagemaker-soln-documents-"
-
-DATASETS_S3_PREFIX = "datasets"
-OUTPUTS_S3_PREFIX = "outputs"
-
-SOURCE_S3_PREFIX = "0.2.0/Document-understanding/3.0.1"
-SOURCE_S3_BUCKET = "sagemaker-solutions-prod-us-east-2"
-SOURCE_S3_PATH = f"s3://{SOURCE_S3_BUCKET}/{SOURCE_S3_PREFIX}"
-
-TRAINING_INSTANCE_TYPE = "ml.p3.2xlarge"
-HOSTING_INSTANCE_TYPE = "ml.g4dn.2xlarge"
-
-TAG_KEY = "sagemaker-soln"
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/entry_point.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/entry_point.py
deleted file mode 100644
index f7c0a77379..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/entry_point.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import json
-import spacy
-import subprocess
-
-
-def extract_entities(spacy_document):
- entities = []
- for spacy_entity in spacy_document.ents:
- entity = {
- 'text': spacy_entity.text,
- 'start_char': spacy_entity.start_char,
- 'end_char': spacy_entity.end_char,
- 'label': spacy_entity.label_
- }
- entities.append(entity)
- return entities
-
-
-def extract_noun_chunks(spacy_document):
- noun_chunks = []
- for spacy_noun_chunk in spacy_document.noun_chunks:
- noun_chunk = {
- 'text': spacy_noun_chunk.text,
- 'start_char': spacy_noun_chunk.start_char,
- 'end_char': spacy_noun_chunk.end_char
- }
- noun_chunks.append(noun_chunk)
- return noun_chunks
-
-
-def model_fn(model_dir):
- spacy_model = "en_core_web_md"
- subprocess.run(f"python -m spacy download {spacy_model}", shell=True)
- nlp = spacy.load(spacy_model)
- model_assets = {
- "nlp": nlp
- }
- return model_assets
-
-
-def input_fn(request_body_str, request_content_type):
- assert (
- request_content_type == "application/json"
- ), "content_type must be 'application/json'"
- request_body = json.loads(request_body_str)
- return request_body
-
-
-def get_parameter(request_body, parameter_name, default):
- parameter = default
- if 'parameters' in request_body:
- if parameter_name in request_body['parameters']:
- parameter = request_body['parameters'][parameter_name]
- return parameter
-
-
-def predict_fn(request_body, model_assets):
- nlp = model_assets['nlp']
- text = request_body["text"]
- spacy_document = nlp(text)
- entities = extract_entities(spacy_document)
- noun_chunks = extract_noun_chunks(spacy_document)
- return {
- "entities": entities,
- "noun_chunks": noun_chunks
- }
-
-
-def output_fn(prediction, response_content_type):
- assert (
- response_content_type == "application/json"
- ), "accept must be 'application/json'"
- response_body_str = json.dumps(prediction)
- return response_body_str
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/inference.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/inference.py
deleted file mode 100644
index 3f1e1b5fe5..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/inference.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-import json
-from pathlib import Path
-import torch
-import logging
-import numpy as np
-
-from sagemaker_inference import encoder
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-
-def keystoint(x):
- return {int(k): v for k, v in x.items()}
-
-
-def model_fn(model_dir):
- """Create our inference task as a delegate to the model.
-
- This runs only once per one worker.
- """
- tokenizer = AutoTokenizer.from_pretrained(model_dir)
-
- if not tokenizer:
- raise ValueError("tokenizer not found.")
-
- with open(os.path.join(model_dir, "integer_to_label.json"), 'r') as fp:
- integer_to_label = json.load(fp, object_hook=keystoint)
-
- try:
- model = AutoModelForTokenClassification.from_pretrained(model_dir, num_labels=len(integer_to_label))
- if torch.cuda.is_available():
- model.to("cuda:0")
- model.eval()
- return model, tokenizer, integer_to_label
- except Exception:
- logging.exception("Failed to load model from checkpoint")
- raise
-
-
-def transform_fn(task, input_data, content_type, accept="application/json"):
- """Make predictions against the model and return a serialized response.
-
- The function signature conforms to the SM contract.
-
- Args:
- task (obj): model loaded by model_fn, in our case is one of the Task.
- input_data (obj): the request data.
- content_type (str): the request content type.
- accept (str): accept header expected by the client.
-
- Returns:
- obj: the serialized prediction result or a tuple of the form
- (response_data, content_type)
-
- """
- if content_type == "application/list-text":
- test_data = json.loads(input_data.decode("utf-8"))
- model, tokenizer, integer_to_label = task
- try:
- tokens = tokenizer(test_data, truncation=True, padding=True, is_split_into_words=True)
-
- device = "cuda:0" if torch.cuda.is_available() else "cpu"
- torch_input_ids = torch.tensor(tokens['input_ids'], device=device)
- torch_input_attention_mask = torch.tensor(tokens['attention_mask'], device=device)
-
- predictions = model.forward(input_ids=torch_input_ids, attention_mask=torch_input_attention_mask)
-
- predictions_probabilites = predictions.logits.detach().cpu().numpy()
- predictions_integers = torch.argmax(predictions.logits, axis=-1).detach().cpu().numpy().tolist()
- sum_attention_mask = np.array(tokens["attention_mask"]).sum(axis=1)
-
- res_token_idxes = []
- res_tokens = []
- res_labels = []
- for idx, i in enumerate(predictions_integers):
- tmp = []
- num_real_tokens = sum_attention_mask[idx]
- for count, j in enumerate(i):
- if count > num_real_tokens - 1:
- break
- tmp.append(integer_to_label[j])
-
- tmp_tokens = tokens.tokens(idx)[:num_real_tokens]
- tmp_token_idx = tokens.word_ids(idx)[:num_real_tokens]
- assert len(tmp_tokens) == len(tmp_token_idx) == len(tmp)
- res_labels.append(tmp)
- res_tokens.append(tmp_tokens)
- res_token_idxes.append(tmp_token_idx)
-
- return encoder.encode(
- {
- "predict_label": np.array(res_labels),
- "predict_probabilites": predictions_probabilites,
- "token": np.array(res_tokens),
- "word_id": np.array(res_token_idxes),
- },
- accept
- )
- except Exception:
- logging.exception("Failed to do transform")
- raise
- raise ValueError('{{"error": "unsupported content type {}"}}'.format(content_type or "unknown"))
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/requirements.txt b/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/requirements.txt
deleted file mode 100644
index d295eaf139..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-datasets==2.4.0
-seqeval==1.2.2
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/training.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/training.py
deleted file mode 100644
index 80b1c901e4..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/finetuning/training.py
+++ /dev/null
@@ -1,214 +0,0 @@
-import argparse
-import os
-import json
-import sys
-import logging
-import itertools
-import pandas as pd
-import numpy as np
-from datasets import Dataset
-from datasets import load_metric
-from transformers import AutoTokenizer
-from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
-from transformers import DataCollatorForTokenClassification
-from transformers import EarlyStoppingCallback
-import torch
-
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-METRIC = load_metric("seqeval")
-
-
-def parse_args():
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- "--pretrained-model",
- type=str,
- default="distilbert-base-uncased"
- )
- parser.add_argument(
- "--learning-rate",
- type=float,
- default=0.0007
- )
- parser.add_argument(
- "--batch-size",
- type=int,
- default=16
- )
- parser.add_argument(
- "--num-train-epochs",
- type=int,
- default=3
- )
- parser.add_argument(
- "--weight-decay",
- type=float,
- default=1e-5,
- )
- parser.add_argument(
- "--early-stopping-patience",
- type=int,
- default=3,
- )
- parser.add_argument(
- "--gpus",
- type=int,
- default=os.environ.get("SM_NUM_GPUS", 0)
- )
- parser.add_argument(
- "--model-dir",
- type=str,
- default=os.environ.get("SM_MODEL_DIR")
- )
- parser.add_argument(
- "--output-dir",
- type=str,
- default=os.environ.get("SM_OUTPUT_DATA_DIR")
- )
- parser.add_argument(
- "--train-data-dir",
- type=str,
- default=os.environ.get("SM_CHANNEL_TRAIN"),
- )
- parser.add_argument(
- "--validation-data-dir",
- type=str,
- default=os.environ.get("SM_CHANNEL_VALIDATION")
- )
- parser.add_argument(
- "--token-column-name",
- type=str,
- default="tokens"
- )
- parser.add_argument(
- "--tag-column-name",
- type=str,
- default="ner_tags"
- )
-
- args, _ = parser.parse_known_args()
- return args
-
-
-
-def get_all_tokens_and_ner_tags(directory):
- return pd.concat([get_tokens_and_ner_tags(os.path.join(directory, filename)) for filename in os.listdir(directory)]).reset_index().drop('index', axis=1)
-
-def get_tokens_and_ner_tags(filename):
- with open(filename, 'r', encoding="utf8") as f:
- lines = f.readlines()
- split_list = [list(y) for x, y in itertools.groupby(lines, lambda z: z == '\n') if not x]
- tokens = [[x.split('\t')[0].split("en:")[1] for x in y] for y in split_list]
- entities = [[x.split('\t')[1][:-1] for x in y] for y in split_list]
- return pd.DataFrame({'tokens': tokens, 'ner_tags': entities})
-
-def get_un_token_dataset(train_directory, validation_directory):
- train_df = get_all_tokens_and_ner_tags(train_directory)
- val_df = get_all_tokens_and_ner_tags(validation_directory)
-
- labels = set(list(np.concatenate(train_df.ner_tags.tolist()).flat) + list(np.concatenate(val_df.ner_tags.tolist()).flat))
-
- train_dataset = Dataset.from_pandas(train_df)
- val_dataset = Dataset.from_pandas(val_df)
-
- return (train_dataset, val_dataset, list(labels))
-
-def tokenize_and_align_labels(examples, label_to_integer, tokenizer, token_column_name, tag_column_name):
- label_all_tokens = True
- tokenized_inputs = tokenizer(list(examples[token_column_name]), truncation=True, is_split_into_words=True)
-
- labels = []
- for i, label in enumerate(examples[tag_column_name]):
- word_ids = tokenized_inputs.word_ids(batch_index=i)
- previous_word_idx = None
- label_ids = []
- for word_idx in word_ids:
- if word_idx is None:
- label_ids.append(-100)
- elif label[word_idx] == '0':
- label_ids.append(0)
- elif word_idx != previous_word_idx:
- label_ids.append(label_to_integer[label[word_idx]])
- else:
- label_ids.append(label_to_integer[label[word_idx]] if label_all_tokens else -100)
- previous_word_idx = word_idx
- labels.append(label_ids)
-
- tokenized_inputs["labels"] = labels
- return tokenized_inputs
-
-
-def compute_metrics(p):
- predictions, labels = p
- predictions = np.argmax(predictions, axis=2)
-
- true_predictions = [[integer_to_label[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)]
- true_labels = [[integer_to_label[l] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)]
-
- results = METRIC.compute(predictions=true_predictions, references=true_labels)
- return {"precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"]}
-
-
-def train_fn(args):
-
- TOKENIZER = AutoTokenizer.from_pretrained(args.pretrained_model)
-
- train_dataset, val_dataset, labels = get_un_token_dataset(args.train_data_dir, args.validation_data_dir)
-
- label_to_integer = {}
-
- global integer_to_label
- integer_to_label = {}
-
- for idx, label in enumerate(labels):
- label_to_integer[label] = idx
- integer_to_label[idx] = label
-
- train_tokenized_datasets = train_dataset.map(lambda x: tokenize_and_align_labels(x, label_to_integer, TOKENIZER, args.token_column_name, args.tag_column_name), batched=True)
- test_tokenized_datasets = val_dataset.map(lambda x: tokenize_and_align_labels(x, label_to_integer, TOKENIZER, args.token_column_name, args.tag_column_name), batched=True)
-
- model = AutoModelForTokenClassification.from_pretrained(args.pretrained_model, num_labels=len(labels))
-
- train_args = TrainingArguments(
- f"Training-NER",
- evaluation_strategy = "epoch",
- save_strategy = "epoch",
- learning_rate=args.learning_rate,
- per_device_train_batch_size=args.batch_size,
- per_device_eval_batch_size=args.batch_size,
- num_train_epochs=args.num_train_epochs,
- weight_decay=args.weight_decay,
- load_best_model_at_end=True,
- metric_for_best_model="eval_f1",
- greater_is_better=True,
- )
-
- data_collator = DataCollatorForTokenClassification(TOKENIZER)
-
-
- trainer = Trainer(
- model,
- train_args,
- train_dataset=train_tokenized_datasets,
- eval_dataset=test_tokenized_datasets,
- data_collator=data_collator,
- tokenizer=TOKENIZER,
- compute_metrics=compute_metrics,
- callbacks=[EarlyStoppingCallback(early_stopping_patience=args.early_stopping_patience)]
- )
-
- trainer.train()
- eval_metrics_output = trainer.evaluate()
- logger.info(f"Evaluation metrics on validation data: {eval_metrics_output}")
- trainer.save_model(args.model_dir)
-
- with open(os.path.join(args.model_dir, "integer_to_label.json"), 'w') as fp:
- json.dump(integer_to_label, fp)
-
-if __name__ == "__main__":
- args = parse_args()
- train_fn(args)
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/requirements.in b/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/requirements.in
deleted file mode 100644
index 33ea94c42f..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/requirements.in
+++ /dev/null
@@ -1 +0,0 @@
-spacy
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/requirements.txt b/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/requirements.txt
deleted file mode 100644
index 33ea94c42f..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/entity_recognition/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-spacy
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/entry_point.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/entry_point.py
deleted file mode 100644
index 968919568b..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/entry_point.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import json
-import os
-
-import sagemaker
-from transformers import AutoModelForQuestionAnswering
-from transformers import AutoTokenizer
-from transformers import pipeline
-
-
-def model_fn(model_dir):
- session = sagemaker.Session()
- bucket = os.getenv("MODEL_ASSETS_S3_BUCKET")
- prefix = os.getenv("MODEL_ASSETS_S3_PREFIX")
- session.download_data(path=model_dir, bucket=bucket, key_prefix=prefix)
- tokenizer = AutoTokenizer.from_pretrained(
- "bert-large-uncased-whole-word-masking-finetuned-squad", cache_dir=model_dir
- )
- model = AutoModelForQuestionAnswering.from_pretrained(
- "bert-large-uncased-whole-word-masking-finetuned-squad", cache_dir=model_dir
- )
- answerer = pipeline(task="question-answering", model=model, tokenizer=tokenizer)
- model_assets = {"answerer": answerer}
- return model_assets
-
-
-def input_fn(request_body_str, request_content_type):
- assert request_content_type == "application/json", "content_type must be 'application/json'"
- request_body = json.loads(request_body_str)
- return request_body
-
-
-def get_parameter(request_body, parameter_name, default):
- parameter = default
- if "parameters" in request_body:
- if parameter_name in request_body["parameters"]:
- parameter = request_body["parameters"][parameter_name]
- return parameter
-
-
-def predict_fn(request_body, model_assets):
- question = request_body["question"]
- context = request_body["context"]
- answerer = model_assets["answerer"]
- answers = answerer(question=question, context=context, topk=get_parameter(request_body, "topk", 3))
- return {"answers": answers}
-
-
-def output_fn(prediction, response_content_type):
- assert response_content_type == "application/json", "accept must be 'application/json'"
- response_body_str = json.dumps(prediction)
- return response_body_str
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/requirements.in b/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/requirements.in
deleted file mode 100644
index 16e21274bf..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/requirements.in
+++ /dev/null
@@ -1,2 +0,0 @@
-transformers
-sagemaker
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/requirements.txt b/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/requirements.txt
deleted file mode 100644
index 16e21274bf..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/question_answering/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-transformers
-sagemaker
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/entry_point.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/entry_point.py
deleted file mode 100644
index 11473b6f96..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/entry_point.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# flake8: noqa: F401
-import os
-import sys
-
-# import deployment functions
-from package.inference import input_fn
-from package.inference import model_fn
-from package.inference import output_fn
-from package.inference import predict_fn
-# import training function
-from package.training import parse_args
-from package.training import train_fn
-
-
-if __name__ == "__main__":
- args = parse_args(sys.argv[1:])
- train_fn(args)
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/dataset.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/dataset.py
deleted file mode 100644
index 1b14db3d37..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/dataset.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-from package.data.label_encoders import LabelEncoder
-from package.data.semeval import parse_file
-from tokenizers import Tokenizer
-from torch.utils.data.dataset import Dataset
-
-
-class RelationStatementDataset(Dataset):
- def __init__(self, file_path: str, tokenizer: Tokenizer, label_encoder: LabelEncoder) -> None:
- self.relationships = parse_file(file_path)
- self.tokenizer = tokenizer
- self.label_encoder = label_encoder
-
- def __getitem__(self, idx):
- relationship = self.relationships[idx]
- statement = str(relationship.statement)
- label_id = self.label_encoder.str_to_id(relationship.directed_label)
- entity_one = relationship.entity_one
- entity_two = relationship.entity_two
- encoding = self.tokenizer.encode(
- sequence=statement,
- entity_one_start=entity_one.start_char,
- entity_one_end=entity_one.end_char,
- entity_two_start=entity_two.start_char,
- entity_two_end=entity_two.end_char,
- )
- return {
- "token_ids": torch.tensor(encoding["ids"]),
- "attention_mask": torch.tensor(encoding["attention_mask"]),
- "special_tokens_mask": torch.tensor(encoding["special_tokens_mask"]),
- "label_id": torch.tensor(label_id),
- }
-
- def __len__(self):
- return len(self.relationships)
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/encoding.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/encoding.py
deleted file mode 100644
index 8c5db36406..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/encoding.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import json
-from typing import Union
-
-from tokenizers import Encoding
-
-
-def get_state(encoding: Encoding) -> dict:
- """
- Given an encoding output from a tokenizer, this function will return
- its internal state as a dictionary. You can modify this state and then
- use `from_state` to generate an new Encoding object.
-
- Args:
- encoding (Encoding): encoding output from tokenizer
-
- Returns:
- dict: state of encoding
- """
- # json.loads(encoding.__getstate__()) is x3 slower
- return {
- "ids": encoding.ids,
- "type_ids": encoding.type_ids,
- "tokens": encoding.tokens,
- "words": encoding.words,
- "offsets": encoding.offsets,
- "special_tokens_mask": encoding.special_tokens_mask,
- "attention_mask": encoding.attention_mask,
- "overflowing": encoding.overflowing,
- }
-
-
-def from_state(state: dict) -> Encoding:
- """
- Given a dictionary containing the state of an encoding, this function
- will return an associated Encoding object. Use `get_state` to obtain
- the state from an Encoding object.
-
- Args:
- state (dict): state of encoding
-
- Returns:
- Encoding: encoding
- """
- encoding = Encoding()
- encoding.__setstate__(json.dumps(state).encode())
- return encoding
-
-
-def char_to_next_token(encoding: Encoding, char_idx: int) -> Union[int, None]:
- """
- Given an encoding output from a tokenizer, this function will return
- the token index that corresponds to a given character index. When a
- character index doesn't correspond to a token (e.g. a white space), the
- next avaliable token index is returned. If there is no next avaliable
- token index, this function returns None. Used to find the token index
- where entity start tokens should be inserted.
-
- Args:
- encoding (Encoding): encoding output from tokenizer
- char_idx (int): character index of input sequence given to tokenizer
-
- Returns:
- Union[int, None]: corresponding token index
- """
- length = max([end for start, end in encoding.offsets])
- assert char_idx >= 0
- token_idx = encoding.char_to_token(char_idx)
- while token_idx is None:
- char_idx += 1
- if char_idx > length:
- return None
- token_idx = encoding.char_to_token(char_idx)
- return token_idx
-
-
-def char_to_previous_token(encoding: Encoding, char_idx: int) -> Union[int, None]:
- """
- Given an encoding output from a tokenizer, this function will return
- the token index that corresponds to a given character index. When a
- character index doesn't correspond to a token (e.g. a white space), the
- previous token index (that is not None) is returned. If there is no
- previous token index that is not None, this function returns None. Used
- to find the token index where entity end tokens should be inserted.
-
- Args:
- encoding (Encoding): encoding output from tokenizer
- char_idx (int): character index of input sequence given to tokenizer
-
- Returns:
- Union[int, None]: corresponding token index
- """
- assert char_idx >= 0
- token_idx = encoding.char_to_token(char_idx)
- while token_idx is None:
- char_idx -= 1
- if char_idx < 0:
- return None
- token_idx = encoding.char_to_token(char_idx)
- return token_idx
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/io.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/io.py
deleted file mode 100644
index ed7cc13fcb..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/io.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import json
-from pathlib import Path
-from typing import List
-from typing import Union
-
-from package.objects import Relationship
-
-
-def to_json_lines(relationships: List[Relationship], file_path: Union[Path, str]) -> None:
- json_lines = []
- for relationship in relationships:
- json_line = relationship.to_json_str()
- json_lines.append(json_line)
- with open(file_path, "w") as f:
- f.writelines("\n".join(json_lines))
-
-
-def from_json_lines(file_path: Union[Path, str]) -> List[Relationship]:
- with open(file_path, "r") as f:
- lines = f.readlines()
- relationships = []
- for line in lines:
- args_dict = json.loads(line)
- relationship = Relationship.from_dict(args_dict)
- relationships.append(relationship)
- return relationships
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/label_encoders.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/label_encoders.py
deleted file mode 100644
index 60938e008c..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/label_encoders.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import json
-from pathlib import Path
-from typing import Dict
-from typing import List
-from typing import Union
-
-
-class LabelEncoder:
- def __init__(self, str_to_id_mapping: Dict[str, int]) -> None:
- """
- Converts back and forth between human readable string labels and
- their associated integer ids used by loss function.
-
- Args:
- str_to_id_mapping (Dict[str, int]):
- mapping from string label to integer ids
- """
- self._str_to_id_mapping = str_to_id_mapping
- self._id_to_str_mapping = {i: s for s, i in str_to_id_mapping.items()}
-
- @classmethod
- def from_file(cls, file_path: Union[Path, str]) -> "LabelEncoder":
- """
- Construct a label encoder from a valid json file.
-
- Args:
- file_path (Union[Path, str]): path to json file
- Returns:
- LabelEncoder: associated label encoder
- """
- with open(file_path, "r") as f:
- str_to_id_mapping = json.load(f)
- return cls(str_to_id_mapping)
-
- @classmethod
- def from_str_list(cls, strings: List[str]) -> "LabelEncoder":
- """
- Construct a label encoder from a list of string labels by
- automatically generating the integer ids in ascending order.
-
- Args:
- strings (List[str]): list of string labels.
-
- Returns:
- LabelEncoder: associated label encoder
- """
- str_to_id_mapping = {s: i for i, s in enumerate(strings)}
- return cls(str_to_id_mapping)
-
- def save(self, file_path: Union[Path, str]) -> None:
- """
- Saves the label encoder to a file in json format.
- Can be loaded back using `from_file` method.
-
- Args:
- file_path (Union[Path, str]): path for json file
- """
- with open(file_path, "w") as f:
- json.dump(self._str_to_id_mapping, f)
-
- def str_to_id(self, string: str) -> int:
- """
- Converts from string label to integer id.
-
- Args:
- string (str): string label
-
- Returns:
- int: integer id
- """
- return self._str_to_id_mapping[string]
-
- def id_to_str(self, id: int) -> str:
- """
- Converts from integer id to string label.
-
- Args:
- id (int): integer id
-
- Returns:
- str: string label
- """
- return self._id_to_str_mapping[id]
-
- def __len__(self) -> int:
- """
- Get number of labels.
-
- Returns:
- int: number of labels
- """
- return len(self._str_to_id_mapping)
-
- def __str__(self) -> str:
- output = "{:>6} {}\n".format("id", "str")
- for i in range(len(self)):
- output += "{:>6} {}\n".format(i, self.id_to_str(i))
- return output
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/semeval.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/semeval.py
deleted file mode 100644
index cd172538ac..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/semeval.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import re
-from collections import OrderedDict
-from pathlib import Path
-from typing import List
-from typing import Optional
-from typing import Set
-from typing import Tuple
-from typing import Union
-
-from package.objects import Entity
-from package.objects import Relationship
-from package.objects import Source
-from package.objects import Statement
-
-
-def find_special_tokens(text: str, special_tokens: List[str]) -> OrderedDict:
- """
- Given text and a list of special tokens, this funtion will find the
- character index of each special token. Order is of the special tokens
- is enforced and an AssertionError will be raised if the special tokens
- are found out of order or duplicated.
-
- Args:
- text (str): text containing the special tokens
- special_tokens (List[str]): a list of special tokens (where order
- is important)
-
- Returns:
- OrderedDict: character index of each special token. Order same as
- special token input, but can use dictionary lookup.
- """
- idxs = OrderedDict()
- last_idx = None
- last_token = None
- special_chars = 0
- for token in special_tokens:
- idx = text.find(token)
- assert idx != -1, f"{token} not found in text."
- if last_idx:
- assert idx > last_idx, f"{token} found before {last_token}"
- assert text[idx + len(token) :].find(token) == -1, f"{token} duplicated in text."
- idxs[token] = idx - special_chars
- last_idx = idx
- last_token = token
- special_chars += len(token)
- return idxs
-
-
-def remove_special_tokens(text: str, special_tokens: List[str]) -> str:
- """
- Given text and a list of special tokens, this funtion will return the
- text with the special tokens removed. An AssertionError will be raised
- if the special token is not found, is duplicate or overlaps with
- another special token.
-
- Args:
- text (str): text containing the special tokens
- special_tokens (List[str]): a list of special tokens
-
- Returns:
- str: text without the special tokens
- """
- token_idxs: Set[int] = set()
- for token in special_tokens:
- start_idx = text.find(token)
- assert start_idx != -1, f"{token} not found in text."
- end_idx = start_idx + len(token)
- assert text[end_idx:].find(token) == -1, f"{token} duplicated in text."
- idxs = range(start_idx, end_idx)
- overlap = token_idxs.intersection(idxs)
- assert len(overlap) == 0, f"{token} overlaps another special token at {idxs}."
- token_idxs.update(idxs)
- text = "".join([c for i, c in enumerate(text) if i not in token_idxs])
- return text
-
-
-def parse_file(file_path: Union[Path, str], sample_idx_offset: Optional[int] = None) -> List[Relationship]:
- file_path = Path(file_path)
- with open(file_path, "r") as f:
- lines = f.readlines()
- num_lines = len(lines)
- num_samples = int(num_lines / 4)
- assert num_samples * 4 == num_lines, f"Should have 4 lines per block, but {num_lines} cannot be divided by 4."
- relationships = []
- for sample_idx in range(num_samples):
- block = lines[(sample_idx * 4) : ((sample_idx + 1) * 4)]
- relationship = parse_block(block)
- if sample_idx_offset:
- assert relationship.source.sample_idx == (sample_idx + 1 + sample_idx_offset)
- relationships.append(relationship)
- return relationships
-
-
-def parse_block(block: List[str]) -> Relationship:
- text, label, comment, blank = block
- entity_one, entity_two, statement, source = parse_text(text)
- label, is_reversed = parse_label(label)
- relationship = Relationship(
- entity_one=entity_one,
- entity_two=entity_two,
- statement=statement,
- source=source,
- label=label,
- label_seperator="-",
- is_reversed=is_reversed,
- )
- return relationship
-
-
-def parse_text(text: str) -> Tuple[Entity, Entity, Statement, dict]:
- sample_idx_str, text = text.split("\t")
- sample_idx = int(sample_idx_str)
- source = Source(sample_idx=sample_idx)
- text = text.strip('"')
- special_tokens = ["", "", "", ""]
- idxs = find_special_tokens(text, special_tokens)
- text = remove_special_tokens(text, special_tokens)
- entity_one = Entity(text=text[idxs[""] : idxs[""]], start_char=idxs[""], end_char=idxs[""])
- entity_two = Entity(text=text[idxs[""] : idxs[""]], start_char=idxs[""], end_char=idxs[""])
- statement = Statement(text=text, start_char=0, end_char=len(text))
- return entity_one, entity_two, statement, source
-
-
-def parse_label(label: str) -> Tuple[str, bool]:
- pattern = r"^(.*)\(e([12]),e([12])\)$"
- match = re.search(pattern, label)
- if match:
- label, first, second = match.groups()
- if first == "1" and second == "2":
- is_reversed = False
- elif first == "2" and second == "1":
- is_reversed = True
- else:
- raise ValueError("Cannot infer direction")
- else:
- label = label.rstrip("\n")
- if label == "Other":
- is_reversed = False
- else:
- raise ValueError(f"Cannot find {pattern} in {label}")
- return label, is_reversed
-
-
-def label_set(file_path: Union[Path, str], sample_idx_offset: Optional[int] = None) -> set:
- relationships = parse_file(file_path, sample_idx_offset)
- labels = [r.directed_label for r in relationships]
- return set(labels)
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/tokenizers.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/tokenizers.py
deleted file mode 100644
index adb83d09b1..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/data/tokenizers.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import random
-from collections import namedtuple
-from pathlib import Path
-from typing import List
-from typing import Optional
-from typing import Union
-
-from package.data.encoding import char_to_next_token
-from package.data.encoding import char_to_previous_token
-from package.data.encoding import get_state
-from tokenizers import Encoding
-from tokenizers import InputSequence
-from tokenizers import Tokenizer
-from transformers import AutoTokenizer
-
-
-class EntityToken:
- def __init__(self, token: str, token_id: int, token_idx: int) -> None:
- self.token = token
- self.token_id = token_id
- self.token_idx = token_idx
-
-
-class RelationshipTokenizer:
-
- ENTITY_TOKENS = {
- "entity_one_start_token": "[E1]",
- "entity_one_end_token": "[/E1]",
- "entity_two_start_token": "[E2]",
- "entity_two_end_token": "[/E2]",
- "blank_token": "[BLANK]",
- }
-
- def __init__(
- self, tokenizer: Tokenizer, contains_entity_tokens: bool = True, output_length: Optional[int] = None, **kwargs
- ):
- self.tokenizer = tokenizer
- if contains_entity_tokens:
- vocab = self.tokenizer.get_vocab()
- for entity_token in self.ENTITY_TOKENS.values():
- assert entity_token in vocab, f"{entity_token} not found in tokenizer vocab."
- else:
- num_added = self.tokenizer.add_special_tokens(list(self.ENTITY_TOKENS.values()))
- num_pre_exist = num_added - len(self.ENTITY_TOKENS)
- assert num_pre_exist == 0, f"{num_pre_exist} special token(s) already found in tokenizer vocab."
- if output_length:
- self.set_truncation(output_length)
- self.set_padding(output_length)
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "RelationshipTokenizer":
- """
- Used to initialize from a transformers pre-trained tokenizer.
-
- Args:
- pretrained_model_name_or_path (str):
- name or path to transformers model
- (e.g. 'bert-base-uncased')
- **kwargs:
- keyword arguments are passed to AutoTokenizer.from_pretrained(...)
- and RelationshipTokenizer constructor
- (e.g. can pass 'contains_entity_tokens').
-
- Returns:
- RelationshipTokenizer:
- relationship tokenizer which wraps tokenizers.Tokenizer
- """
- if "use_fast" not in kwargs:
- kwargs["use_fast"] = True
- tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
- # using base_tokenizer to get tokenizers.Tokenizer object.
- return cls(tokenizer=tokenizer._tokenizer, **kwargs)
-
- def save(self, file_path: Union[str, Path], pretty: bool = False) -> str:
- """
- Save the tokenizer as JSON formatted file. Optionally prettified.
-
- Args:
- file_path (str): file path to save tokenizer
- pretty (bool, optional): format JSON with indentation
-
- Returns:
- str: file path of saved tokenizer
- """
- return self.tokenizer.save(str(file_path), pretty)
-
- @classmethod
- def from_file(cls, file_path: Union[str, Path], **kwargs) -> "RelationshipTokenizer":
- """
- Creates a tokenizer from a valid JSON formatted file.
-
- Args:
- file_path (str): file path of saved tokenizer
-
- Returns:
- RelationshipTokenizer: tokenizer loaded from file
- """
- tokenizer = Tokenizer.from_file(str(file_path))
- return cls(tokenizer=tokenizer, **kwargs)
-
- def encode(
- self,
- sequence: InputSequence,
- entity_one_start: int,
- entity_one_end: int,
- entity_two_start: int,
- entity_two_end: int,
- blank_entity_proba: float = 0.0,
- ) -> Encoding:
- """
- Given a textual statement containing two entities (i.e. a
- relationship statement), this method tokenizes the statement and
- optionally adds all of the necessary special tokens. Some of the
- special tokens are taken from the underlying tokenizer (e.g. [CLS]
- and [SEP]), while others are added afterwards to locate the
- entities (e.g. [E1], [/E1], [E2] and [/E2] as defined in
- ENTITY_TOKENS).
-
- Args:
- sequence (InputSequence): textual statement (which is often just a string).
- entity_one_start (int): character index for the start of entity one
- entity_one_end (int): character index for the end of entity one
- entity_two_start (int): character index for the start of entity two
- entity_two_end (int): character index for the end of entity two
- blank_entity_proba (int): probability of blanking out each entity
-
- Returns:
- Encoding: encoding containing tokens, token ids, special token mask, etc.
- """
- encoding = self.tokenizer.encode(sequence=sequence)
- encoding = self.add_entity_tokens(
- encoding, entity_one_start, entity_one_end, entity_two_start, entity_two_end, blank_entity_proba
- )
- return encoding
-
- def add_entity_tokens(
- self,
- encoding: Encoding,
- entity_one_start: int,
- entity_one_end: int,
- entity_two_start: int,
- entity_two_end: int,
- blank_entity_proba: float,
- ) -> Encoding:
- """
- Adds special entity tokens to locate the entities (e.g. [E1],
- [/E1], [E2] and [/E2] as defined in ENTITY_TOKENS).
-
- Args:
- encoding (Encoding): encoding with underlying special tokens (e.g. [CLS] and [SEP])
- entity_one_start (int): character index for the start of entity one
- entity_one_end (int): character index for the end of entity one
- entity_two_start (int): character index for the start of entity two
- entity_two_end (int): character index for the end of entity two
- blank_entity_proba (int): probability of blanking out each entity
-
- Returns:
- Encoding: encoding with additional entity tokens
- """
- max_idx = max([e for span in encoding.offsets for e in span])
- assert (0 <= entity_one_start) and (entity_one_start <= max_idx)
- assert (0 <= entity_one_end) and (entity_one_end <= max_idx)
- assert (0 <= entity_two_start) and (entity_two_start <= max_idx)
- assert (0 <= entity_two_end) and (entity_two_end <= max_idx)
- entity_tokens = []
- entity_tokens.append(
- EntityToken(
- token=self.entity_one_start_token,
- token_id=self.entity_one_start_token_id,
- token_idx=char_to_next_token(encoding, entity_one_start),
- )
- )
- entity_tokens.append(
- EntityToken(
- token=self.entity_one_end_token,
- token_id=self.entity_one_end_token_id,
- token_idx=char_to_previous_token(encoding, entity_one_end - 1) + 1,
- )
- )
- entity_tokens.append(
- EntityToken(
- token=self.entity_two_start_token,
- token_id=self.entity_two_start_token_id,
- token_idx=char_to_next_token(encoding, entity_two_start),
- )
- )
- entity_tokens.append(
- EntityToken(
- token=self.entity_two_end_token,
- token_id=self.entity_two_end_token_id,
- token_idx=char_to_previous_token(encoding, entity_two_end - 1) + 1,
- )
- )
- entity_tokens = sorted(entity_tokens, key=lambda t: t.token_idx)
- self.assert_entity_token_ordering(entity_tokens)
- state = get_state(encoding)
- for offset, entity_token in enumerate(entity_tokens):
- # token_idx is relative to the original encoding. using offset
- # to correct for other entity tokens that have added earlier in loop.
- entity_token.token_idx += offset
- self.add_entity_token_to_state(state, entity_token)
- if blank_entity_proba > 0:
- self.blank_entities(state, entity_tokens, blank_entity_proba)
- return state
-
- def assert_entity_token_ordering(self, entity_tokens: List[EntityToken]) -> None:
- """
- Asserts that entities don't overlap and that entity start comes
- before entity end (for both entity one and two).
- Note: it's permitted that entity two comes before entity one.
-
- Args:
- entity_tokens (List[EntityToken]):
- pre-sorted list of entity tokens.
- pre-sorted by token index in ascending order.
- Raises:
- AssertionError: when entities overlap or start/stop are out of order.
- """
- tokens = [e.token for e in entity_tokens]
- first_token = tokens[0]
- if first_token == self.entity_one_start_token:
- assert tokens == [
- self.entity_one_start_token,
- self.entity_one_end_token,
- self.entity_two_start_token,
- self.entity_two_end_token,
- ]
- elif first_token == self.entity_two_start_token:
- assert tokens == [
- self.entity_two_start_token,
- self.entity_two_end_token,
- self.entity_one_start_token,
- self.entity_one_end_token,
- ]
- else:
- raise AssertionError(f"{first_token} is not {self.entity_one_start_token} or {self.entity_two_start_token}")
-
- @classmethod
- def add_entity_token_to_state(cls, state: dict, entity_token: EntityToken) -> None:
- """
- Adds a single entity token to the state of an encoding.
-
- Args:
- state (dict):
- other entity tokens have already been added to state
- entity_token (EntityToken):
- token, token_id and token_idx are taken from entity_token
- """
- idx = entity_token.token_idx
- state["ids"].insert(idx, entity_token.token_id)
- state["type_ids"].insert(idx, 0)
- state["tokens"].insert(idx, entity_token.token)
- state["words"].insert(idx, None)
- state["offsets"].insert(idx, [0, 0])
- state["special_tokens_mask"].insert(idx, 1)
- state["attention_mask"].insert(idx, 1)
-
- def blank_entities(self, state: dict, entity_tokens: List[EntityToken], blank_entity_proba: float) -> None:
- """
- Given a valid and pre-ordered list of entity tokens, this method
- will randomly blank out each entity with a probability of
- `blank_entity_proba`. When an entity is blanked out, all the tokens
- for that entity are replaced with a corresponding number of
- specific blank tokens (see ENTITY_TOKENS).
-
- Args:
- state (dict):
- state of encoding from tokenizer, with entity tokens added.
- entity_tokens (List[EntityToken]):
- used to determine the start and end of the two entities.
- blank_entity_proba (float):
- probability of blanking out each token, so the probability of
- blanking both tokens is blank_entity_proba ** 2.
- """
- self.blank_entity(
- state=state,
- start_idx=entity_tokens[0].token_idx + 1,
- end_idx=entity_tokens[1].token_idx,
- blank_entity_proba=blank_entity_proba,
- )
- self.blank_entity(
- state=state,
- start_idx=entity_tokens[2].token_idx + 1,
- end_idx=entity_tokens[3].token_idx,
- blank_entity_proba=blank_entity_proba,
- )
-
- def blank_entity(self, state: dict, start_idx: int, end_idx: int, blank_entity_proba: float):
- """
- Given a start and end token index of an entity, this method will
- randomly blank out the entity with a probability of
- `blank_entity_proba`. When the entity is blanked out, all the
- tokens for that entity are replaced with a corresponding number of
- specific blank tokens (see ENTITY_TOKENS).
-
- Args:
- state (dict):
- state of encoding from tokenizer with entity tokens added.
- start_idx (int):
- token index of entity start (but after entity start token)
- end_idx (int):
- token index of entity end (but before entity end token)
- blank_entity_proba (float):
- probability of blanking out the token
- """
- if random.uniform(0, 1) < blank_entity_proba:
- for i in range(start_idx, end_idx):
- state["ids"][i] = self.blank_token_id
- state["type_ids"][i] = 0
- state["tokens"][i] = self.blank_token
- state["words"][i] = None
- state["offsets"][i] = [0, 0]
- state["special_tokens_mask"][i] = 1
- state["attention_mask"] = 1
-
- def decode(self, ids: List[int], skip_special_tokens: bool = True) -> str:
- return self.tokenizer.decode(ids=ids, skip_special_tokens=skip_special_tokens)
-
- @property
- def num_entity_tokens_to_add(self) -> int:
- return 4
-
- @property
- def num_special_tokens_to_add(self) -> int:
- return self.tokenizer.num_special_tokens_to_add(is_pair=False) + self.num_entity_tokens_to_add
-
- def set_truncation(self, length: int) -> None:
- self.tokenizer.enable_truncation(max_length=length - self.num_entity_tokens_to_add)
-
- @property
- def truncation_length(self) -> Union[int, None]:
- params = self.tokenizer.truncation
- if params:
- return params["max_length"] + self.num_entity_tokens_to_add
- else:
- return None
-
- def set_padding(self, length: int) -> None:
- self.tokenizer.enable_padding(length=length - self.num_entity_tokens_to_add)
-
- @property
- def padding_length(self) -> Union[int, None]:
- params = self.tokenizer.padding
- if params:
- return params["length"] + self.num_entity_tokens_to_add
- else:
- return None
-
- @property
- def entity_one_start_token(self) -> str:
- return self.ENTITY_TOKENS["entity_one_start_token"]
-
- @property
- def entity_one_start_token_id(self) -> int:
- return self.tokenizer.token_to_id(self.entity_one_start_token)
-
- @property
- def entity_one_end_token(self) -> str:
- return self.ENTITY_TOKENS["entity_one_end_token"]
-
- @property
- def entity_one_end_token_id(self) -> int:
- return self.tokenizer.token_to_id(self.entity_one_end_token)
-
- @property
- def entity_two_start_token(self) -> str:
- return self.ENTITY_TOKENS["entity_two_start_token"]
-
- @property
- def entity_two_start_token_id(self) -> int:
- return self.tokenizer.token_to_id(self.entity_two_start_token)
-
- @property
- def entity_two_end_token(self) -> str:
- return self.ENTITY_TOKENS["entity_two_end_token"]
-
- @property
- def entity_two_end_token_id(self) -> int:
- return self.tokenizer.token_to_id(self.entity_two_end_token)
-
- @property
- def blank_token(self) -> str:
- return self.ENTITY_TOKENS["blank_token"]
-
- @property
- def blank_token_id(self) -> int:
- return self.tokenizer.token_to_id(self.blank_token)
-
- def __len__(self) -> int:
- return self.tokenizer.get_vocab_size()
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/inference.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/inference.py
deleted file mode 100644
index eb86ff95cb..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/inference.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import os
-import json
-from pathlib import Path
-import pytorch_lightning as pl
-import torch
-
-from package.data.tokenizers import RelationshipTokenizer
-from package.data.label_encoders import LabelEncoder
-from package.models import RelationshipEncoderLightningModule
-
-
-def model_fn(model_dir):
- tokenizer = RelationshipTokenizer.from_file(
- file_path=Path(model_dir, 'tokenizer.json'),
- contains_entity_tokens=True
- )
- label_encoder = LabelEncoder.from_file(
- file_path=Path(model_dir, 'label_encoder.json')
- )
-
- checkpoint_names = []
- for file in os.listdir(model_dir):
- if file.endswith(".ckpt"):
- checkpoint_names.append(file)
- assert len(checkpoint_names) == 1 # this is because we only save top 1 checkpoint during the training.
-
- with open(os.path.join(model_dir, "pretrained_model_info.json"), 'r') as fp:
- pretrained_model_info = json.load(fp)
-
- model = RelationshipEncoderLightningModule.load_from_checkpoint(
- str(Path(model_dir, checkpoint_names[0])),
- pretrained_model_name=pretrained_model_info["pretrained_model"],
- tokenizer=tokenizer,
- label_encoder=label_encoder
- )
- model.eval()
-
- model_assets = {
- 'tokenizer': tokenizer,
- 'label_encoder': label_encoder,
- 'model': model
- }
- return model_assets
-
-
-def input_fn(request_body_str, request_content_type):
- assert (
- request_content_type == "application/json"
- ), "content_type must be 'application/json'"
- request_body = json.loads(request_body_str)
- return request_body
-
-
-def predict_fn(request, model_assets):
- encoding = model_assets['tokenizer'].encode(
- sequence=request['sequence'],
- entity_one_start=request['entity_one_start'],
- entity_one_end=request['entity_one_end'],
- entity_two_start=request['entity_two_start'],
- entity_two_end=request['entity_two_end']
- )
-
- token_ids = torch.tensor(encoding['ids']).unsqueeze(0)
- attention_mask = torch.tensor(encoding['attention_mask']).unsqueeze(0)
- logits = model_assets['model'](
- token_ids=token_ids,
- attention_mask=attention_mask
- )
- pred_pt = torch.argmax(logits, dim=1)
- pred_py = pred_pt[0].item()
- output = {
- 'Label_id': pred_py,
- 'Label': model_assets['label_encoder'].id_to_str(pred_py)
- }
- return output
-
-
-def output_fn(prediction, response_content_type):
- assert (
- response_content_type == "application/json"
- ), "accept must be 'application/json'"
- response_body_str = json.dumps(prediction)
- return response_body_str
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/models.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/models.py
deleted file mode 100644
index b2bdd612ef..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/models.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import torch
-from transformers import BertModel
-import pytorch_lightning as pl
-
-
-class RelationshipEncoderModule(torch.nn.Module):
- def __init__(
- self,
- pretrained_model_name,
- vocab_length,
- num_classes,
- entity_one_start_token_id,
- entity_two_start_token_id
- ):
- super(RelationshipEncoderModule, self).__init__()
- self.entity_one_start_token_id = entity_one_start_token_id
- self.entity_two_start_token_id = entity_two_start_token_id
- self.text_encoder = BertModel.from_pretrained(
- pretrained_model_name_or_path=pretrained_model_name
- )
- self.text_encoder.resize_token_embeddings(vocab_length)
- self.layer_norm = torch.nn.LayerNorm(self.text_encoder.config.hidden_size * 2)
- self.linear = torch.nn.Linear(self.text_encoder.config.hidden_size * 2, num_classes)
-
- def forward(
- self,
- token_ids,
- attention_mask
- ):
- output = self.text_encoder(
- input_ids=token_ids,
- attention_mask=attention_mask,
- return_dict=True
- )
- last_hidden_state = output['last_hidden_state']
- entity_one_mask = (token_ids == self.entity_one_start_token_id).int()
- entity_one_embedding = (entity_one_mask.unsqueeze(dim=-1) * last_hidden_state).sum(dim=1)
- entity_two_mask = (token_ids == self.entity_two_start_token_id).int()
- entity_two_embedding = (entity_two_mask.unsqueeze(dim=-1) * last_hidden_state).sum(dim=1)
- relationship_embedding = torch.cat([entity_one_embedding, entity_two_embedding], dim=1)
- relationship_embedding_norm = self.layer_norm(relationship_embedding)
- logits = self.linear(relationship_embedding_norm)
- return logits
-
-
-class RelationshipEncoderLightningModule(pl.LightningModule):
- def __init__(self, pretrained_model_name, tokenizer, label_encoder, learning_rate=0.0007, weight_decay=0):
- super().__init__()
- self.model = RelationshipEncoderModule(
- pretrained_model_name=pretrained_model_name,
- vocab_length=len(tokenizer),
- num_classes=len(label_encoder),
- entity_one_start_token_id=tokenizer.entity_one_start_token_id,
- entity_two_start_token_id=tokenizer.entity_two_start_token_id
- )
- self.learning_rate = learning_rate
- self.weight_decay = weight_decay
- self.train_acc = pl.metrics.Accuracy()
- self.valid_acc = pl.metrics.Accuracy()
-
- def forward(self, token_ids, attention_mask):
- output = self.model(token_ids, attention_mask)
- return output
-
- def training_step(self, batch, batch_idx):
- token_ids = batch['token_ids']
- attention_mask = batch['attention_mask']
- label_id = batch['label_id']
- output = self.model(token_ids, attention_mask)
- pred_label = torch.argmax(output, dim=-1)
- loss = torch.nn.functional.cross_entropy(output, label_id)
-
- self.train_acc(pred_label, label_id)
-
- self.log('train_loss', loss, on_step=False, on_epoch=True, prog_bar=True)
- self.log('train_accuracy', self.train_acc, on_step=False, on_epoch=True, prog_bar=True)
- return loss
-
- def validation_step(self, batch, batch_idx):
- token_ids = batch['token_ids']
- attention_mask = batch['attention_mask']
- label_id = batch['label_id']
- output = self.model(token_ids, attention_mask)
- pred_label = torch.argmax(output, dim=-1)
- loss = torch.nn.functional.cross_entropy(output, label_id)
-
- self.valid_acc(pred_label, label_id)
-
- self.log('valid_loss', loss, on_step=False, on_epoch=True, prog_bar=True)
- self.log('valid_accuracy', self.valid_acc, on_step=False, on_epoch=True, prog_bar=True)
- return loss
-
- def configure_optimizers(self):
- return torch.optim.Adam(self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/models/relationship_encoder.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/models/relationship_encoder.py
deleted file mode 100644
index 622a044c0e..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/models/relationship_encoder.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import torch
-from transformers import BertModel
-
-
-class RelationshipEncoder(torch.nn.Module):
- def __init__(self, vocab_length, num_classes, entity_one_start_token_id, entity_two_start_token_id):
- super(RelationshipEncoder, self).__init__()
- self.entity_one_start_token_id = entity_one_start_token_id
- self.entity_two_start_token_id = entity_two_start_token_id
- self.text_encoder = BertModel.from_pretrained(pretrained_model_name_or_path="bert-base-uncased")
- self.text_encoder.resize_token_embeddings(vocab_length)
- self.layer_norm = torch.nn.LayerNorm(self.text_encoder.config.hidden_size * 2)
- self.linear = torch.nn.Linear(self.text_encoder.config.hidden_size * 2, num_classes)
-
- def forward(self, token_ids, attention_mask):
- output = self.text_encoder(input_ids=token_ids, attention_mask=attention_mask, return_dict=True)
- last_hidden_state = output["last_hidden_state"]
- entity_one_mask = (token_ids == self.entity_one_start_token_id).int()
- entity_one_embedding = (entity_one_mask.unsqueeze(dim=-1) * last_hidden_state).sum(dim=1)
- entity_two_mask = (token_ids == self.entity_two_start_token_id).int()
- entity_two_embedding = (entity_two_mask.unsqueeze(dim=-1) * last_hidden_state).sum(dim=1)
- relationship_embedding = torch.cat([entity_one_embedding, entity_two_embedding], dim=1)
- relationship_embedding_norm = self.layer_norm(relationship_embedding)
- logits = self.linear(relationship_embedding_norm)
- return logits
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/objects.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/objects.py
deleted file mode 100644
index 138200a373..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/objects.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import json
-from typing import Optional
-from typing import Union
-
-
-class Source(object):
- def __init__(self, **kwargs):
- self.__dict__.update(**kwargs)
-
- @classmethod
- def from_dict(cls, args_dict: dict) -> Union["Source", None]:
- if args_dict:
- return cls(**args_dict)
- else:
- return None
-
-
-class Statement(object):
- def __init__(self, text: str, start_char: int, end_char: int):
- self.start_char = start_char
- self.end_char = end_char
- self.text = text
- self._validate()
-
- def _validate(self):
- assert self.start_char >= 0
- assert self.end_char > self.start_char
- assert (self.end_char - self.start_char) == len(self.text)
-
- def __str__(self):
- return self.text
-
- def __len__(self):
- return len(self.text)
-
- @classmethod
- def from_dict(cls, args_dict: dict) -> Union["Statement", None]:
- if args_dict:
- return cls(**args_dict)
- else:
- return None
-
-
-class Entity:
- def __init__(
- self,
- text: str,
- start_char: int,
- end_char: int,
- label: Optional[str] = None,
- kb_id: Optional[str] = None,
- ) -> None:
-
- self.text = text
- self.start_char = start_char
- self.end_char = end_char
- self.label = label
- self.kb_id = kb_id
- self._validate()
-
- def _validate(self):
- assert self.start_char >= 0
- assert self.end_char > self.start_char
- assert (self.end_char - self.start_char) == len(self.text)
-
- def __str__(self):
- return self.text
-
- def __len__(self):
- return len(self.text)
-
- @classmethod
- def from_dict(cls, args_dict: dict) -> "Entity":
- return cls(**args_dict)
-
- # todo: needs to be blanked out at random rate
- # and probably need to know if this has happened (or could just check blank token id)
-
-
-class Relationship:
- def __init__(
- self,
- entity_one: Entity,
- entity_two: Entity,
- statement: Optional[Statement] = None,
- source: Optional[Source] = None,
- label: Optional[str] = None,
- label_seperator: Optional[str] = None,
- is_reversed: Optional[bool] = False,
- ) -> None:
- self.entity_one = entity_one
- self.entity_two = entity_two
- self.statement = statement
- self.source = source
- self.label = label
- self.label_seperator = label_seperator
- self.is_reversed = is_reversed
- self._validate()
-
- def _validate(self):
- if self.statement:
- s_text = self.statement.text
- e1_text = self.entity_one.text
- e1_start = self.entity_one.start_char
- e1_end = self.entity_one.end_char
- e1_idxs = set(range(e1_start, e1_end))
- e2_text = self.entity_two.text
- e2_start = self.entity_two.start_char
- e2_end = self.entity_two.end_char
- e2_idxs = set(range(e2_start, e2_end))
- assert e1_end <= len(s_text)
- assert e2_end <= len(s_text)
- assert s_text[e1_start:e1_end] == e1_text
- assert s_text[e2_start:e2_end] == e2_text
- assert len(e1_idxs.intersection(e2_idxs)) == 0
-
- def to_json_str(self):
- return json.dumps(self, default=lambda x: x.__dict__)
-
- @classmethod
- def from_dict(cls, args_dict: dict) -> Union["Relationship", None]:
- return cls(
- entity_one=Entity.from_dict(args_dict["entity_one"]),
- entity_two=Entity.from_dict(args_dict["entity_two"]),
- statement=Statement.from_dict(args_dict["statement"]),
- source=Source.from_dict(args_dict["source"]),
- label=args_dict["label"],
- is_reversed=args_dict["is_reversed"],
- )
-
- @property
- def directed_label(self):
- assert isinstance(self.label_seperator, str)
- if self.is_reversed:
- parts = self.label.split(self.label_seperator)
- assert len(parts) == 2
- parts.reverse()
- return self.label_seperator.join(parts)
- else:
- return self.label
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/training.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/training.py
deleted file mode 100644
index 4c859fbf41..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/package/training.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import argparse
-import os
-import json
-from pathlib import Path
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.callbacks.early_stopping import EarlyStopping
-import torch
-from torch.utils.data import DataLoader
-
-from package.data.tokenizers import RelationshipTokenizer
-from package.data.label_encoders import LabelEncoder
-from package.data.semeval import label_set
-from package.data.dataset import RelationStatementDataset
-from package.models import RelationshipEncoderLightningModule
-
-
-
-
-def parse_args(sys_args):
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- "--pretrained-model",
- type=str,
- default="bert-base-uncased"
- )
- parser.add_argument(
- "--max-epoch",
- type=int,
- default=5
- )
- parser.add_argument(
- "--learning-rate",
- type=float,
- default=0.0007
- )
- parser.add_argument(
- "--batch-size",
- type=int,
- default=16
- )
- parser.add_argument(
- "--early-stop-patience",
- type=int,
- default=3
- )
- parser.add_argument(
- "--weight-decay",
- type=float,
- default=0.0
- )
- parser.add_argument(
- "--accumulate-grad-batches",
- type=int,
- default=2
- )
- parser.add_argument(
- "--gradient-clip-val",
- type=float,
- default=1.0
- )
- parser.add_argument(
- "--gpus",
- type=int,
- default=os.environ.get("SM_NUM_GPUS", 0)
- )
- parser.add_argument(
- "--model-dir",
- type=str,
- default=os.environ.get("SM_MODEL_DIR")
- )
- parser.add_argument(
- "--output-dir",
- type=str,
- default=os.environ.get("SM_OUTPUT_DATA_DIR")
- )
- parser.add_argument(
- "--train-data-dir",
- type=str,
- default=os.environ.get("SM_CHANNEL_TRAIN"),
- )
- parser.add_argument(
- "--validation-data-dir",
- type=str,
- default=os.environ.get("SM_CHANNEL_VALIDATION")
- )
-
- args, _ = parser.parse_known_args(sys_args)
- return args
-
-
-def train_fn(args):
- print(args)
-
- # load tokenizer
- tokenizer = RelationshipTokenizer.from_pretrained(
- pretrained_model_name_or_path=args.pretrained_model,
- contains_entity_tokens=False
- )
- tokenizer.save(file_path=Path(args.model_dir, 'tokenizer.json'), pretty=True)
-
- # load data
- train_file_path = Path(args.train_data_dir, 'train.txt')
- test_file_path = Path(args.validation_data_dir, 'validation.txt')
-
- # construct label encoder
- labels = list(label_set(train_file_path))
- label_encoder = LabelEncoder.from_str_list(sorted(labels))
- print('Using the following label encoder mappings:\n\n', label_encoder)
- label_encoder.save(file_path=str(Path(args.model_dir, 'label_encoder.json')))
-
- # prepare datasets
- model_size = 512
- tokenizer.set_truncation(model_size)
- tokenizer.set_padding(model_size)
- train_dataset = RelationStatementDataset(
- file_path=train_file_path,
- tokenizer=tokenizer,
- label_encoder=label_encoder
- )
- test_dataset = RelationStatementDataset(
- file_path=test_file_path,
- tokenizer=tokenizer,
- label_encoder=label_encoder
- )
-
- batch_size = args.batch_size
- train_dataloader = torch.utils.data.DataLoader(
- dataset=train_dataset,
- batch_size=batch_size,
- num_workers=4
- )
- test_dataloader = torch.utils.data.DataLoader(
- dataset=test_dataset,
- batch_size=batch_size,
- num_workers=4
- )
-
- # create model
- relationship_encoder = RelationshipEncoderLightningModule(
- args.pretrained_model,
- tokenizer,
- label_encoder,
- learning_rate=float(args.learning_rate),
- weight_decay=float(args.weight_decay),
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor='valid_loss',
- filepath=str(Path(args.model_dir, 'model')),
- verbose=True,
- mode="min",
- )
-
- # train model
- trainer = Trainer(
- default_root_dir=args.output_dir,
- accumulate_grad_batches=args.accumulate_grad_batches,
- gradient_clip_val=args.gradient_clip_val,
- max_epochs=args.max_epoch,
- weights_summary='full',
- gpus=args.gpus,
- checkpoint_callback=checkpoint_callback,
- callbacks=[EarlyStopping(monitor='valid_accuracy', patience=args.early_stop_patience, mode='max', verbose=True)],
- fast_dev_run=False,
- )
-
- trainer.fit(relationship_encoder, train_dataloader, test_dataloader)
-
- with open(str(Path(args.model_dir, 'pretrained_model_info.json')), 'w') as f:
- json.dump(
- {"pretrained_model": args.pretrained_model},
- f
- )
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/requirements.in b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/requirements.in
deleted file mode 100644
index 16eaa8f58c..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/requirements.in
+++ /dev/null
@@ -1,4 +0,0 @@
-boto3
-transformers
-pytorch_lightning==1.0.2
-sentencepiece
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/requirements.txt b/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/requirements.txt
deleted file mode 100644
index b6b809ad9f..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/relationship_extraction/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-boto3
-transformers
-pytorch_lightning==1.0.2
-sentencepiece
-protobuf==3.20.0
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/entry_point.py b/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/entry_point.py
deleted file mode 100644
index 7406d6b087..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/entry_point.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import json
-import os
-
-import sagemaker
-from transformers import AutoModelWithLMHead
-from transformers import AutoTokenizer
-from transformers import pipeline
-
-
-def model_fn(model_dir):
- session = sagemaker.Session()
- bucket = os.getenv("MODEL_ASSETS_S3_BUCKET")
- prefix = os.getenv("MODEL_ASSETS_S3_PREFIX")
- session.download_data(path=model_dir, bucket=bucket, key_prefix=prefix)
- model = AutoModelWithLMHead.from_pretrained("t5-base", cache_dir=model_dir)
- tokenizer = AutoTokenizer.from_pretrained("t5-base", cache_dir=model_dir)
- summarizer = pipeline(task="summarization", model=model, tokenizer=tokenizer)
- model_assets = {"summarizer": summarizer}
- return model_assets
-
-
-def input_fn(request_body_str, request_content_type):
- assert request_content_type == "application/json", "content_type must be 'application/json'"
- request_body = json.loads(request_body_str)
- return request_body
-
-
-def get_parameter(request_body, parameter_name, default):
- parameter = default
- if "parameters" in request_body:
- if parameter_name in request_body["parameters"]:
- parameter = request_body["parameters"][parameter_name]
- return parameter
-
-
-def predict_fn(request_body, model_assets):
- input_text = request_body["text"]
- summarizer = model_assets["summarizer"]
- try:
- summaries = summarizer(
- input_text,
- max_length=get_parameter(request_body, "max_length", 130),
- min_length=get_parameter(request_body, "min_length", 30),
- do_sample=get_parameter(request_body, "do_sample", "true") == "true",
- )
- except AssertionError as e:
- print(e)
- # intermittent hugging face issue with beam search:
- # https://github.com/huggingface/transformers/issues/3188
- if "Beam should always be full" in str(e):
- summaries = summarizer(
- input_text,
- max_length=get_parameter(request_body, "max_length", 130),
- min_length=get_parameter(request_body, "min_length", 30),
- do_sample=False,
- )
- else:
- raise e
- summary = summaries[0]["summary_text"]
- return {"summary": summary}
-
-
-def output_fn(prediction, response_content_type):
- assert response_content_type == "application/json", "accept must be 'application/json'"
- response_body_str = json.dumps(prediction)
- return response_body_str
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/requirements.in b/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/requirements.in
deleted file mode 100644
index 16e21274bf..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/requirements.in
+++ /dev/null
@@ -1,2 +0,0 @@
-transformers
-sagemaker
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/requirements.txt b/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/requirements.txt
deleted file mode 100644
index 16e21274bf..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/containers/summarization/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-transformers
-sagemaker
\ No newline at end of file
diff --git a/ build_and_train_models/sm-name_entity_recognition_document/utils_relation_extraction.py b/ build_and_train_models/sm-name_entity_recognition_document/utils_relation_extraction.py
deleted file mode 100644
index 809658fc32..0000000000
--- a/ build_and_train_models/sm-name_entity_recognition_document/utils_relation_extraction.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from collections import OrderedDict
-from typing import List, Set, Tuple, Union, Optional
-import re
-from pathlib import Path
-
-
-def find_special_tokens(text: str, special_tokens: List[str]) -> OrderedDict:
- """
- Given text and a list of special tokens, this funtion will find the
- character index of each special token. Order is of the special tokens
- is enforced and an AssertionError will be raised if the special tokens
- are found out of order or duplicated.
-
- Args:
- text (str): text containing the special tokens
- special_tokens (List[str]): a list of special tokens (where order
- is important)
-
- Returns:
- OrderedDict: character index of each special token. Order same as
- special token input, but can use dictionary lookup.
- """
- idxs = OrderedDict()
- last_idx = None
- last_token = None
- special_chars = 0
- for token in special_tokens:
- idx = text.find(token)
- assert idx != -1, f"{token} not found in text."
- if last_idx:
- assert idx > last_idx, f"{token} found before {last_token}"
- assert (
- text[idx + len(token):].find(token) == -1
- ), f"{token} duplicated in text."
- idxs[token] = idx - special_chars
- last_idx = idx
- last_token = token
- special_chars += len(token)
- return idxs
-
-
-def remove_special_tokens(text: str, special_tokens: List[str]) -> str:
- """
- Given text and a list of special tokens, this funtion will return the
- text with the special tokens removed. An AssertionError will be raised
- if the special token is not found, is duplicate or overlaps with
- another special token.
-
- Args:
- text (str): text containing the special tokens
- special_tokens (List[str]): a list of special tokens
-
- Returns:
- str: text without the special tokens
- """
- token_idxs: Set[int] = set()
- for token in special_tokens:
- start_idx = text.find(token)
- assert start_idx != -1, f"{token} not found in text."
- end_idx = start_idx + len(token)
- assert text[end_idx:].find(token) == -1, f"{token} duplicated in text."
- idxs = range(start_idx, end_idx)
- overlap = token_idxs.intersection(idxs)
- assert len(overlap) == 0, f"{token} overlaps another special token at {idxs}."
- token_idxs.update(idxs)
- text = "".join([c for i, c in enumerate(text) if i not in token_idxs])
- return text
-
-
-def parse_file(
- file_path: Union[Path, str],
- sample_idx_offset: Optional[int] = None
-) -> List:
- file_path = Path(file_path)
- with open(file_path, "r") as f:
- lines = f.readlines()
- num_lines = len(lines)
- num_samples = int(num_lines / 4)
- assert (
- num_samples * 4 == num_lines
- ), f"Should have 4 lines per block, but {num_lines} cannot be divided by 4."
- examples, ground_truth = [], []
- for sample_idx in range(num_samples):
- block = lines[(sample_idx * 4): ((sample_idx + 1) * 4)]
- entity_one_start, entity_one_end, entity_two_start, entity_two_end, text, label, is_reversed = parse_block(block)
-
- examples.append(
- {
- "sequence": text,
- "entity_one_start": entity_one_start,
- "entity_one_end": entity_one_end,
- "entity_two_start": entity_two_start,
- "entity_two_end": entity_two_end
-
- }
- )
- ground_truth.append(
- label
- )
-
- return examples, ground_truth
-
-
-def parse_block(block: List[str]):
- text, label, comment, blank = block
- entity_one_start, entity_one_end, entity_two_start, entity_two_end, text = parse_text(text)
- label, is_reversed = parse_label(label)
-
- label_seperator = '-'
- if is_reversed:
- parts = label.split(label_seperator)
- assert len(parts) == 2
- parts.reverse()
- label = label_seperator.join(parts)
- return entity_one_start, entity_one_end, entity_two_start, entity_two_end, text, label, is_reversed
-
-
-def parse_text(text: str):
- sample_idx_str, text = text.split("\t")
- if '"\n' in text:
- text = text.strip('"\n')
- sample_idx = int(sample_idx_str)
- text = text.strip('"')
- special_tokens = ["", "", "", ""]
- idxs = find_special_tokens(text, special_tokens)
- text = remove_special_tokens(text, special_tokens)
- return idxs[""], idxs[""], idxs[""], idxs[""], text
-
-
-def parse_label(label: str) -> Tuple[str, bool]:
- pattern = r"^(.*)\(e([12]),e([12])\)$"
- match = re.search(pattern, label)
- if match:
- label, first, second = match.groups()
- if first == "1" and second == "2":
- is_reversed = False
- elif first == "2" and second == "1":
- is_reversed = True
- else:
- raise ValueError("Cannot infer direction")
- else:
- label = label.rstrip('\n')
- if label == 'Other':
- is_reversed = False
- else:
- raise ValueError(f"Cannot find {pattern} in {label}")
- return label, is_reversed
diff --git a/ end_to_end_ml_lifecycle/sm-object_detection_birds/README.md b/ build_and_train_models/sm-object_detection_birds/README.md
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-object_detection_birds/README.md
rename to build_and_train_models/sm-object_detection_birds/README.md
diff --git a/ end_to_end_ml_lifecycle/sm-object_detection_birds/goldfinch_detections.png b/ build_and_train_models/sm-object_detection_birds/goldfinch_detections.png
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-object_detection_birds/goldfinch_detections.png
rename to build_and_train_models/sm-object_detection_birds/goldfinch_detections.png
diff --git a/ end_to_end_ml_lifecycle/sm-object_detection_birds/sm-object_detection_birds.ipynb b/ build_and_train_models/sm-object_detection_birds/sm-object_detection_birds.ipynb
similarity index 97%
rename from end_to_end_ml_lifecycle/sm-object_detection_birds/sm-object_detection_birds.ipynb
rename to build_and_train_models/sm-object_detection_birds/sm-object_detection_birds.ipynb
index 2d58fba9b6..ed7a13bbe0 100644
--- a/ end_to_end_ml_lifecycle/sm-object_detection_birds/sm-object_detection_birds.ipynb
+++ b/ build_and_train_models/sm-object_detection_birds/sm-object_detection_birds.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1370,35 +1370,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ end_to_end_ml_lifecycle/sm-object_detection_birds/tools/birdsOnEdge.py b/ build_and_train_models/sm-object_detection_birds/tools/birdsOnEdge.py
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-object_detection_birds/tools/birdsOnEdge.py
rename to build_and_train_models/sm-object_detection_birds/tools/birdsOnEdge.py
diff --git a/ end_to_end_ml_lifecycle/sm-object_detection_birds/tools/im2rec.py b/ build_and_train_models/sm-object_detection_birds/tools/im2rec.py
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-object_detection_birds/tools/im2rec.py
rename to build_and_train_models/sm-object_detection_birds/tools/im2rec.py
diff --git a/ end_to_end_ml_lifecycle/sm-object_detection_birds/tools/patch_ssd.sh b/ build_and_train_models/sm-object_detection_birds/tools/patch_ssd.sh
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-object_detection_birds/tools/patch_ssd.sh
rename to build_and_train_models/sm-object_detection_birds/tools/patch_ssd.sh
diff --git a/ build_and_train_models/sm-random_cut_forest_example/sm-random_cut_forest_example.ipynb b/ build_and_train_models/sm-random_cut_forest_example/sm-random_cut_forest_example.ipynb
index efdf903ede..82e8900851 100644
--- a/ build_and_train_models/sm-random_cut_forest_example/sm-random_cut_forest_example.ipynb
+++ b/ build_and_train_models/sm-random_cut_forest_example/sm-random_cut_forest_example.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -754,35 +754,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-regression_xgboost/sm-regression_xgboost.ipynb b/ build_and_train_models/sm-regression_xgboost/sm-regression_xgboost.ipynb
index cf4ea1e1a9..cafa794bec 100644
--- a/ build_and_train_models/sm-regression_xgboost/sm-regression_xgboost.ipynb
+++ b/ build_and_train_models/sm-regression_xgboost/sm-regression_xgboost.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -832,35 +832,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-remote_function_pytorch_mnist/sm-remote_function_pytorch_mnist.ipynb b/ build_and_train_models/sm-remote_function_pytorch_mnist/sm-remote_function_pytorch_mnist.ipynb
index dbee175306..94b6c4dcee 100644
--- a/ build_and_train_models/sm-remote_function_pytorch_mnist/sm-remote_function_pytorch_mnist.ipynb
+++ b/ build_and_train_models/sm-remote_function_pytorch_mnist/sm-remote_function_pytorch_mnist.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -595,35 +595,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-remote_function_quick_start/sm-remote_function_quick_start.ipynb b/ build_and_train_models/sm-remote_function_quick_start/sm-remote_function_quick_start.ipynb
index 5db53c02c9..6f0314c99b 100644
--- a/ build_and_train_models/sm-remote_function_quick_start/sm-remote_function_quick_start.ipynb
+++ b/ build_and_train_models/sm-remote_function_quick_start/sm-remote_function_quick_start.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -280,35 +280,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -342,4 +342,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/ build_and_train_models/sm-scikit_build_your_own_container/sm-scikit_build_your_own_container.ipynb b/ build_and_train_models/sm-scikit_build_your_own_container/sm-scikit_build_your_own_container.ipynb
index 756e5cc874..aeefe444aa 100644
--- a/ build_and_train_models/sm-scikit_build_your_own_container/sm-scikit_build_your_own_container.ipynb
+++ b/ build_and_train_models/sm-scikit_build_your_own_container/sm-scikit_build_your_own_container.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -643,35 +643,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -696,4 +696,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ build_and_train_models/sm-script_mode_distributed_training_horovod_tensorflow/sm-script_mode_distributed_training_horovod_tensorflow.ipynb b/ build_and_train_models/sm-script_mode_distributed_training_horovod_tensorflow/sm-script_mode_distributed_training_horovod_tensorflow.ipynb
index 3add54d87d..5ab18aa2d6 100644
--- a/ build_and_train_models/sm-script_mode_distributed_training_horovod_tensorflow/sm-script_mode_distributed_training_horovod_tensorflow.ipynb
+++ b/ build_and_train_models/sm-script_mode_distributed_training_horovod_tensorflow/sm-script_mode_distributed_training_horovod_tensorflow.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -596,35 +596,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -649,4 +649,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ build_and_train_models/sm-semantic_segmentation/sm-semantic_segmentation.ipynb b/ build_and_train_models/sm-semantic_segmentation/sm-semantic_segmentation.ipynb
index db1c6b919e..ee5fa32e2e 100644
--- a/ build_and_train_models/sm-semantic_segmentation/sm-semantic_segmentation.ipynb
+++ b/ build_and_train_models/sm-semantic_segmentation/sm-semantic_segmentation.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -873,7 +873,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
""
]
@@ -942,35 +942,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-smddp_bert/sm-smddp_bert.ipynb b/ build_and_train_models/sm-smddp_bert/sm-smddp_bert.ipynb
index 4e92d7c45d..14a50c7b37 100644
--- a/ build_and_train_models/sm-smddp_bert/sm-smddp_bert.ipynb
+++ b/ build_and_train_models/sm-smddp_bert/sm-smddp_bert.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -351,35 +351,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -404,4 +404,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ build_and_train_models/sm-tabtransformer_tabular_classification.ipynb b/ build_and_train_models/sm-tabtransformer_tabular_classification.ipynb
index aa1d037672..6020399350 100644
--- a/ build_and_train_models/sm-tabtransformer_tabular_classification.ipynb
+++ b/ build_and_train_models/sm-tabtransformer_tabular_classification.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -641,35 +641,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-training_compiler_language_modeling_multi_gpu_multi_node/sm-training_compiler_language_modeling_multi_gpu_multi_node.ipynb b/ build_and_train_models/sm-training_compiler_language_modeling_multi_gpu_multi_node/sm-training_compiler_language_modeling_multi_gpu_multi_node.ipynb
index f44180d2f0..a1d5537bd0 100644
--- a/ build_and_train_models/sm-training_compiler_language_modeling_multi_gpu_multi_node/sm-training_compiler_language_modeling_multi_gpu_multi_node.ipynb
+++ b/ build_and_train_models/sm-training_compiler_language_modeling_multi_gpu_multi_node/sm-training_compiler_language_modeling_multi_gpu_multi_node.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -740,35 +740,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ build_and_train_models/sm-training_compiler_pytorch_single_gpu_single_node/scripts/requirements.txt b/ build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/scripts/requirements.txt
similarity index 100%
rename from build_and_train_models/sm-training_compiler_pytorch_single_gpu_single_node/scripts/requirements.txt
rename to build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/scripts/requirements.txt
diff --git a/ build_and_train_models/sm-training_compliler_single-node-single-gpu_bert/scripts/train.py b/ build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/scripts/train.py
similarity index 100%
rename from build_and_train_models/sm-training_compliler_single-node-single-gpu_bert/scripts/train.py
rename to build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/scripts/train.py
diff --git a/ build_and_train_models/sm-training_compliler_single-node-single-gpu_bert/sm-training_compliler_single-node-single-gpu_bert.ipynb b/ build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/sm-training_compliler_single_node_single_gpu_bert.ipynb
similarity index 98%
rename from build_and_train_models/sm-training_compliler_single-node-single-gpu_bert/sm-training_compliler_single-node-single-gpu_bert.ipynb
rename to build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/sm-training_compliler_single_node_single_gpu_bert.ipynb
index b55c1b027b..709f96791d 100644
--- a/ build_and_train_models/sm-training_compliler_single-node-single-gpu_bert/sm-training_compliler_single-node-single-gpu_bert.ipynb
+++ b/ build_and_train_models/sm-training_compliler_single_node_single_gpu_bert/sm-training_compliler_single_node_single_gpu_bert.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -931,35 +931,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/README.md b/ deploy_and_monitor/README.md
new file mode 100644
index 0000000000..cb18299aa8
--- /dev/null
+++ b/ deploy_and_monitor/README.md
@@ -0,0 +1,49 @@
+# Amazon SageMaker Examples
+
+### Deploy and Monitor
+
+The example notebooks within this folder showcase the capabilities of Amazon SageMaker in deploying and monitoring machine learning models.
+
+- [A/B Testing with Amazon SageMaker](sm-a_b_testing/sm-a_b_testing.ipynb)
+- [Faster autoscaling on Amazon SageMaker realtime endpoints (Application Autoscaling)](sm-app_autoscaling_realtime_endpoints/sm-app_autoscaling_realtime_endpoints.ipynb)
+- [Faster autoscaling on Amazon SageMaker realtime endpoints with inference components (Application Autoscaling)](sm-app_autoscaling_realtime_endpoints_inference_components/sm-app_autoscaling_realtime_endpoints_inference_components.ipynb)
+- [Faster autoscaling on Amazon SageMaker realtime endpoints (Step Scaling)](sm-app_autoscaling_realtime_endpoints_step_scaling/sm-app_autoscaling_realtime_endpoints_step_scaling.ipynb)
+- [Amazon SageMaker Asynchronous Inference](sm-async_inference_walkthrough/sm-async_inference_walkthrough.ipynb)
+- [Amazon SageMaker Asynchronous Inference using the SageMaker Python SDK](sm-async_inference_with_python_sdk/sm-async_inference_with_python_sdk.ipynb)
+- [SageMaker Real-time Dynamic Batching Inference with Torchserve](sm-batch_inference_with_torchserve/sm-batch_inference_with_torchserve.ipynb)
+- [Amazon SageMaker Batch Transform](sm-batch_transform_pca_dbscan_movie_clusters/sm-batch_transform_pca_dbscan_movie_clusters.ipynb)
+- [Use SageMaker Batch Transform for PyTorch Batch Inference](sm-batch_transform_pytorch/sm-batch_transform_pytorch.ipynb)
+- [SageMaker Batch Transform with Torchserve](sm-batch_transform_with_torchserve/sm-batch_transform_with_torchserve.ipynb)
+- [Amazon SageMaker Clarify Model Explainability Monitor for Batch Transform - JSON Lines Format](sm-clarify_model_bias_monitor_batch_transform/sm-clarify_model_bias_monitor_batch_transform.ipynb)
+- [Amazon SageMaker Clarify Model Bias Monitor for Batch Transform - JSON Format](sm-clarify_model_bias_monitor_batch_transform_json/sm-clarify_model_bias_monitor_batch_transform_json.ipynb)
+- [Amazon SageMaker Clarify Model Bias Monitor - JSON Lines Format](sm-clarify_model_bias_monitor_for_endpoint/sm-clarify_model_bias_monitor_for_endpoint.ipynb)
+- [Amazon SageMaker Clarify Model Bias Monitor - JSON Format](sm-clarify_model_bias_monitor_for_endpoint_json/sm-clarify_model_bias_monitor_for_endpoint_json.ipynb)
+- [Leverage deployment guardrails to update a SageMaker Inference endpoint using linear traffic shifting](sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting.ipynb)
+- [Leverage deployment guardrails to update a SageMaker Inference endpoint using rolling deployment](sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment.ipynb)
+- [Leverage deployment guardrails to update a SageMaker Inference endpoint using canary traffic shifting](sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting.ipynb)
+- [Host a Pretrained Model on SageMaker](sm-host_pretrained_model_bert/sm-host_pretrained_model_bert.ipynb)
+- [Inference Pipeline with Scikit-learn and Linear Learner](sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb)
+- [Amazon SageMaker Cross Account Lineage Queries](sm-lineage_cross_account_queries_with_ram/sm-lineage_cross_account_queries_with_ram.ipynb)
+- [AWS Marketplace Product Usage Demonstration - Model Packages](sm-marketplace_using_model_package_arn/sm-marketplace_using_model_package_arn.ipynb)
+- [Amazon SageMaker Multi-Model Endpoints using TorchServe](sm-mme_with_torchserve/sm-mme_with_torchserve.ipynb)
+- [SageMaker Model Monitor with Batch Transform - Data Quality Monitoring On-Schedule](sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb)
+- [SageMaker Model Monitor with Batch Transform - Model Quality Monitoring On-schedule](sm-model_monitor_batch_transform_model_quality_on_schedule/sm-model_monitor_batch_transform_model_quality_on_schedule.ipynb)
+- [Amazon SageMaker Clarify Model Monitors](sm-model_monitor_bias_and_explainability_monitoring/sm-model_monitor_bias_and_explainability_monitoring.ipynb)
+- [BYOC LLM Monitoring: Bring Your Own Container Llama2 Multiple Evaluations Monitoring with SageMaker Model Monitor](sm-model_monitor_byoc_llm_monitor/sm-model_monitor_byoc_llm_monitor.ipynb)
+- [Amazon SageMaker Model Monitor](sm-model_monitor_introduction/sm-model_monitor_introduction.ipynb)
+- [Amazon SageMaker Model Quality Monitor](sm-model_monitor_model_quality_monitoring/sm-model_monitor_model_quality_monitoring.ipynb)
+- [Running multi-container endpoints on Amazon SageMaker](sm-multi_container_endpoint_direct_invocation/sm-multi_container_endpoint_direct_invocation.ipynb)
+- [Amazon SageMaker Multi-Model Endpoints using your own algorithm container](sm-multi_model_endpoint_bring_your_own_container/sm-multi_model_endpoint_bring_your_own_container.ipynb)
+- [SageMaker Serverless Inference](sm-serverless_inference_huggingface_text_classification/sm-serverless_inference_huggingface_text_classification.ipynb)
+- [Shadow Variant Experiments via API](sm-shadow_variant_shadow_api/sm-shadow_variant_shadow_api.ipynb)
+- [Triton on SageMaker - Deploying on Inferentia instance type](sm-triton_inferentia2/sm-triton_inferentia2.ipynb)
+- [Run Multiple NLP Bert Models on GPU with Amazon SageMaker Multi-Model Endpoints (MME)](sm-triton_mme_bert_trt/sm-triton_mme_bert_trt.ipynb)
+- [Multiple Ensembles with GPU models using Amazon SageMaker in MME mode](sm-triton_mme_gpu_ensemble_dali/sm-triton_mme_gpu_ensemble_dali.ipynb)
+- [Triton on SageMaker - NLP Bert](sm-triton_nlp_bert/sm-triton_nlp_bert.ipynb)
+- [Serve Pytorch models with the Python Backend on GPU with Amazon SageMaker Hosting](sm-triton_realtime_sme_flan_t5/sm-triton_realtime_sme_flan_t5.ipynb)
+- [Triton TensorRT Sentence Transformer](sm-triton_tensorrt-sentence_transformer/sm-triton_tensorrt-sentence_transformer.ipynb)
+- [Amazon SageMaker XGBoost Bring Your Own Model](sm-xgboost_bring_your_own_model/sm-xgboost_bring_your_own_model.ipynb)
+- [SageMaker Inference Recommender](sm-inference_recommender_introduction.ipynb)
+- [SageMaker Serverless Inference](sm-serverless_inference.ipynb)
+- [Implement a SageMaker Real-time Single Model Endpoint (SME) for a TensorFlow Vision model on an NVIDIA Triton Server](sm-triton_realtime_sme.ipynb)
+- [Deploy a TensorFlow Model using NVIDIA Triton on SageMaker](sm-triton_tensorflow_model_deploy.ipynb)
diff --git a/ deploy_and_monitor/sm-a_b_testing/sm-a_b_testing.ipynb b/ deploy_and_monitor/sm-a_b_testing/sm-a_b_testing.ipynb
index 61846ca193..724c928dc1 100644
--- a/ deploy_and_monitor/sm-a_b_testing/sm-a_b_testing.ipynb
+++ b/ deploy_and_monitor/sm-a_b_testing/sm-a_b_testing.ipynb
@@ -27,7 +27,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1424,35 +1424,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/requirements.txt b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/requirements.txt
new file mode 100644
index 0000000000..ee4b2a1529
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/requirements.txt
@@ -0,0 +1,67 @@
+asttokens==2.4.1 ; python_version >= "3.10" and python_version < "3.11"
+attrs==23.2.0 ; python_version >= "3.10" and python_version < "3.11"
+boto3==1.34.142 ; python_version >= "3.10" and python_version < "3.11"
+botocore==1.34.142 ; python_version >= "3.10" and python_version < "3.11"
+certifi==2024.2.2 ; python_version >= "3.10" and python_version < "3.11"
+charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.11"
+cloudpickle==2.2.1 ; python_version >= "3.10" and python_version < "3.11"
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Windows" or sys_platform == "win32")
+comm==0.2.2 ; python_version >= "3.10" and python_version < "3.11"
+decorator==5.1.1 ; python_version >= "3.10" and python_version < "3.11"
+dill==0.3.8 ; python_version >= "3.10" and python_version < "3.11"
+docker==7.1.0 ; python_version >= "3.10" and python_version < "3.11"
+exceptiongroup==1.2.1 ; python_version >= "3.10" and python_version < "3.11"
+executing==2.0.1 ; python_version >= "3.10" and python_version < "3.11"
+google-pasta==0.2.0 ; python_version >= "3.10" and python_version < "3.11"
+idna==3.7 ; python_version >= "3.10" and python_version < "3.11"
+importlib-metadata==6.11.0 ; python_version >= "3.10" and python_version < "3.11"
+ipython==8.24.0 ; python_version >= "3.10" and python_version < "3.11"
+ipywidgets==8.1.3 ; python_version >= "3.10" and python_version < "3.11"
+jedi==0.19.1 ; python_version >= "3.10" and python_version < "3.11"
+jmespath==1.0.1 ; python_version >= "3.10" and python_version < "3.11"
+jsonschema-specifications==2023.12.1 ; python_version >= "3.10" and python_version < "3.11"
+jsonschema==4.22.0 ; python_version >= "3.10" and python_version < "3.11"
+jupyterlab-widgets==3.0.11 ; python_version >= "3.10" and python_version < "3.11"
+markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "3.11"
+matplotlib-inline==0.1.7 ; python_version >= "3.10" and python_version < "3.11"
+mdurl==0.1.2 ; python_version >= "3.10" and python_version < "3.11"
+multiprocess==0.70.16 ; python_version >= "3.10" and python_version < "3.11"
+numpy==1.26.4 ; python_version >= "3.10" and python_version < "3.11"
+packaging==24.0 ; python_version >= "3.10" and python_version < "3.11"
+pandas==2.2.2 ; python_version >= "3.10" and python_version < "3.11"
+parso==0.8.4 ; python_version >= "3.10" and python_version < "3.11"
+pathos==0.3.2 ; python_version >= "3.10" and python_version < "3.11"
+pexpect==4.9.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten")
+platformdirs==4.2.2 ; python_version >= "3.10" and python_version < "3.11"
+pox==0.3.4 ; python_version >= "3.10" and python_version < "3.11"
+ppft==1.7.6.8 ; python_version >= "3.10" and python_version < "3.11"
+prompt-toolkit==3.0.45 ; python_version >= "3.10" and python_version < "3.11"
+protobuf==4.25.3 ; python_version >= "3.10" and python_version < "3.11"
+psutil==5.9.8 ; python_version >= "3.10" and python_version < "3.11"
+ptyprocess==0.7.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten")
+pure-eval==0.2.2 ; python_version >= "3.10" and python_version < "3.11"
+pygments==2.18.0 ; python_version >= "3.10" and python_version < "3.11"
+python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "3.11"
+pytz==2024.1 ; python_version >= "3.10" and python_version < "3.11"
+pywin32==306 ; python_version >= "3.10" and python_version < "3.11" and sys_platform == "win32"
+pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.11"
+referencing==0.35.1 ; python_version >= "3.10" and python_version < "3.11"
+requests==2.32.2 ; python_version >= "3.10" and python_version < "3.11"
+rich==13.7.1 ; python_version >= "3.10" and python_version < "3.11"
+rpds-py==0.18.1 ; python_version >= "3.10" and python_version < "3.11"
+s3transfer==0.10.1 ; python_version >= "3.10" and python_version < "3.11"
+sagemaker==2.225.0 ; python_version >= "3.10" and python_version < "3.11"
+schema==0.7.7 ; python_version >= "3.10" and python_version < "3.11"
+six==1.16.0 ; python_version >= "3.10" and python_version < "3.11"
+smdebug-rulesconfig==1.0.1 ; python_version >= "3.10" and python_version < "3.11"
+stack-data==0.6.3 ; python_version >= "3.10" and python_version < "3.11"
+tblib==3.0.0 ; python_version >= "3.10" and python_version < "3.11"
+tqdm==4.66.4 ; python_version >= "3.10" and python_version < "3.11"
+traitlets==5.14.3 ; python_version >= "3.10" and python_version < "3.11"
+typing-extensions==4.12.0 ; python_version >= "3.10" and python_version < "3.11"
+tzdata==2024.1 ; python_version >= "3.10" and python_version < "3.11"
+urllib3==2.2.1 ; python_version >= "3.10" and python_version < "3.11"
+uv==0.2.5 ; python_version >= "3.10" and python_version < "3.11"
+wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "3.11"
+widgetsnbextension==4.0.11 ; python_version >= "3.10" and python_version < "3.11"
+zipp==3.19.0 ; python_version >= "3.10" and python_version < "3.11"
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/sm-app_autoscaling_realtime_endpoints.ipynb b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/sm-app_autoscaling_realtime_endpoints.ipynb
new file mode 100644
index 0000000000..0612fc440d
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/sm-app_autoscaling_realtime_endpoints.ipynb
@@ -0,0 +1,719 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "81f236c9",
+ "metadata": {},
+ "source": [
+ "# Faster autoscaling on Amazon SageMaker realtime endpoints (Application Autoscaling)\n",
+ "\n",
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "In this notebook we show how the new faster autoscaling feature helps scale sagemaker inference endpoints by almost 6x faster than earlier.\n",
+ "\n",
+ "We deploy Meta's `Llama3-8B-Instruct` model to an Amazon SageMaker realtime endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC) and apply Application Autoscaling scaling policies to the endpoint.\n",
+ "\n",
+ "\n",
+ "
\n",
+ " Please select m5.2xlarge or larger instance types when running this on Amazon SageMaker Notebook Instance. \n",
+ " Select conda_pytorch_p310 kernel when running this notebook on Amazon SageMaker Notebook Instance.
\n",
+ " Ensure python version for the kernel is 3.10.x (3.11 is not supported). \n",
+ "
\n",
+ "Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta.\n",
+ "\n",
+ "- **Step 1:** Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens.\n",
+ "- **Step 2:** Login to [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) and navigate to *Meta-Llama-3-8B-Instruct** home page.\n",
+ "- **Step 3:** Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main)\n",
+ "- **Step 4:** Wait for the approval email from META (Approval may take any where b/w 1-3 hrs)\n",
+ "
\n",
+ "NOTE: Remember to copy your Hugging Face Access Token from https://hf.co/ before running the below cell.
\n",
+ "Refer here to learn about creating HF tokens.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b89da4d-9ce7-4e5b-a02a-3f2c690cd26d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# sagemaker config\n",
+ "instance_type = \"ml.g5.2xlarge\"\n",
+ "suffix = f\"{str(uuid4())[:5]}-{datetime.now().strftime('%d%b%Y')}\"\n",
+ "model_name = f\"Llama3-8B-fas-{suffix}\"\n",
+ "endpoint_name = model_name\n",
+ "health_check_timeout = 900\n",
+ "\n",
+ "HF_TOKEN = os.getenv(\"HUGGING_FACE_HUB_TOKEN\") or getpass(\"Enter HUGGINGFACE Access Token: \")\n",
+ "# Define Model and Endpoint configuration parameter\n",
+ "config = {\n",
+ " \"HF_MODEL_ID\": \"meta-llama/Meta-Llama-3-8B-Instruct\", # model_id from hf.co/models\n",
+ " \"SM_NUM_GPUS\": \"1\", # Number of GPU used per replica\n",
+ " \"MAX_INPUT_LENGTH\": \"2048\", # Max length of input text\n",
+ " \"MAX_TOTAL_TOKENS\": \"4096\", # Max length of the generation (including input text)\n",
+ " \"MAX_BATCH_TOTAL_TOKENS\": \"8192\", # Limits the number of tokens that can be processed in parallel during the generation\n",
+ " \"MESSAGES_API_ENABLED\": \"true\", # Enable the messages API\n",
+ " \"HUGGING_FACE_HUB_TOKEN\": HF_TOKEN,\n",
+ "}\n",
+ "\n",
+ "# create HuggingFaceModel with the image uri\n",
+ "print(f\"Creating model: [b green]{model_name}...\")\n",
+ "llm_model = HuggingFaceModel(name=model_name, role=role, image_uri=tgi_dlc, env=config)\n",
+ "\n",
+ "# Deploy model to Amazon SageMaker endpoint\n",
+ "print(f\"Deploying model to endpoint: [b magenta]{endpoint_name}...\")\n",
+ "predictor = llm_model.deploy(\n",
+ " endpoint_name=endpoint_name,\n",
+ " initial_instance_count=1,\n",
+ " instance_type=instance_type,\n",
+ " container_startup_health_check_timeout=health_check_timeout, # 15 minutes to be able to load the model\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "83e1af5c-e713-4cf8-bc23-1c96f1e61327",
+ "metadata": {},
+ "source": [
+ "## Inference\n",
+ "\n",
+ "Invoke and test endpoint using messages API. Refer to HF [Messages API](https://huggingface.co/docs/text-generation-inference/messages_api) for more info."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6d9ecc2-fffe-4ff1-b78b-1222fe6d32de",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Prepare prompt in messages API format\n",
+ "messages = [\n",
+ " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
+ " {\"role\": \"user\", \"content\": \"What is deep learning?\"},\n",
+ "]\n",
+ "\n",
+ "# Generation arguments\n",
+ "parameters = {\n",
+ " \"model\": hf_model_id, # model id is required\n",
+ " \"top_p\": 0.6,\n",
+ " \"temperature\": 0.9,\n",
+ " \"max_tokens\": 512,\n",
+ " \"stop\": [\"<|eot_id|>\"],\n",
+ "}\n",
+ "\n",
+ "chat = predictor.predict({\"messages\": messages, **parameters})\n",
+ "\n",
+ "# Unpack and print response\n",
+ "print(chat[\"choices\"][0][\"message\"][\"content\"].strip())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3cdb619d-b402-46bf-9451-62f50f70e878",
+ "metadata": {},
+ "source": [
+ "## Baseline average latency at various concurrency levels (Optional)\n",
+ "\n",
+ "By capturing average latency across various concurrency levels, we can get a fair idea on after how many concurrent request does endpoint performance would degrade significantly.\n",
+ "\n",
+ "Having this information can help define values for scaling policy accordingly.\n",
+ "\n",
+ "
\n",
+ "Running below cell is optional
\n",
+ "INFO: ℹ️ Signal here is, at a given concurrency level you start to see average latency increase significantly. \n",
+ "At this concurrency level the endpoint gets overloaded and cannot serve requests in a timely fashion. \n",
+ "We use these values to set as threshold values for autoscaling.\n",
+ "
\n",
+ "NOTE: ⚠️ As concurrent requests to the endpoint increase you might observe ThrottlingException errors as we haven't incorporated exponential backoff and retry mechanisms.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c7232ecd-bc78-4d0d-bf44-17c3e060cd99",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define list of prompts\n",
+ "prompts = [\n",
+ " \"what is deep learning?\",\n",
+ " \"what are various inference modes in Amazon SageMaker?\",\n",
+ " \"Can I host Large language models on Amazon SageMaker?\",\n",
+ " \"Does Amazon SageMaker support TensorRT-LLM?\",\n",
+ " \"what is step scaling policy in the context of autoscaling ec2 instances on AWS?\",\n",
+ " \"Why is the sky blue?\",\n",
+ " \"List 5 benefits of incorporating limes into the diet.\",\n",
+ "]\n",
+ "\n",
+ "# Test different concurrency levels and measure average latency\n",
+ "concurrency_levels = [10, 50, 75, 100] # Adjust these values as needed\n",
+ "\n",
+ "for concurrency_level in concurrency_levels:\n",
+ " try:\n",
+ " avg_latency = test_concurrency_level(\n",
+ " concurrency_level,\n",
+ " prompts,\n",
+ " messages,\n",
+ " parameters,\n",
+ " endpoint_name,\n",
+ " sagemaker_runtime_client,\n",
+ " )\n",
+ " print(\n",
+ " f\"[b]Concurrency:[/b] {concurrency_level} requests,\"\n",
+ " f\" [b]Average latency:[/b] {avg_latency:.2f} seconds\"\n",
+ " )\n",
+ " except Exception as e:\n",
+ " print(f\"[b]At Concurrency[/b] {concurrency_level} requests,\" f\"[b]Exception:[/b] \\n{e}\")\n",
+ " continue"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29f7a5ab-0264-4b12-8243-b4aa649335b7",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## Apply Autoscaling policies to the endpoint\n",
+ "\n",
+ "Apply Application Autoscaling Policy to endpoint\n",
+ "\n",
+ "1. Register Scalable Target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1bbf762f-beec-42ed-9ff8-5b06f76269ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "variant_name = \"AllTraffic\"\n",
+ "as_min_capacity = 1\n",
+ "as_max_capacity = 2\n",
+ "\n",
+ "resource_id = f\"endpoint/{endpoint_name}/variant/{variant_name}\"\n",
+ "\n",
+ "autoscaling_client = boto3.client(\"application-autoscaling\", region_name=region)\n",
+ "\n",
+ "# Register scalable target\n",
+ "scalable_target = autoscaling_client.register_scalable_target(\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n",
+ " MinCapacity=as_min_capacity,\n",
+ " MaxCapacity=as_max_capacity, # Replace with your desired maximum instances\n",
+ ")\n",
+ "\n",
+ "scalable_target_arn = scalable_target[\"ScalableTargetARN\"]\n",
+ "print(f\"Resource ID: [b blue]{resource_id}\")\n",
+ "print(f\"Scalable_target_arn:\\n[b green]{scalable_target_arn}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0af2e234-d1c7-4575-b943-5291c70c326d",
+ "metadata": {},
+ "source": [
+ "## Use the latest high-resolution Metrics to trigger auto-scaling\n",
+ "\n",
+ "- New feature introduces a new PredefinedMetricType for scaling policy configuration i.e. SageMakerVariantConcurrentRequestsPerModelHighResolution to trigger scaling actions.\n",
+ "- Creating a scaling policy with this metric type will create cloudwatch alarms that track a new metric called ConcurrentRequestsPerModel.\n",
+ "- These high-resolution metrics are published at sub-minute intervals (10s intervals to CW + any additional jitter + delays)\n",
+ "- We should observe significant improvement in scale out times with this new metric\n",
+ "\n",
+ "\n",
+ "### Steps to create Application autoscaling policy\n",
+ "\n",
+ "- Create scaling policy\n",
+ " - Set `PolicyType` to `TargetTrackingScaling`\n",
+ " - Set `TargetValue` to `5.0`. i.e., Scaling triggers when endpoint receives 5 `ConcurrentRequestsPerModel`\n",
+ " - Set `PredefinedMetricType` to `SageMakerVariantConcurrentRequestsPerModelHighResolution`\n",
+ " - Set `ScaleInCoolDown` and `ScaleOutCoolDown` values to `300` seconds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44cc8c60-37cd-4852-a03d-e08149ccad17",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Create Target Tracking Scaling Policy\n",
+ "target_tracking_policy_response = autoscaling_client.put_scaling_policy(\n",
+ " PolicyName=\"SageMakerEndpointScalingPolicy\",\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n",
+ " PolicyType=\"TargetTrackingScaling\",\n",
+ " TargetTrackingScalingPolicyConfiguration={\n",
+ " \"TargetValue\": 5.0, # Scaling triggers when endpoint receives 5 ConcurrentRequestsPerModel\n",
+ " \"PredefinedMetricSpecification\": {\n",
+ " \"PredefinedMetricType\": \"SageMakerVariantConcurrentRequestsPerModelHighResolution\"\n",
+ " },\n",
+ " \"ScaleInCooldown\": 300, # Cooldown period after scale-in activity\n",
+ " \"ScaleOutCooldown\": 300, # Cooldown period after scale-out activity\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# print(target_tracking_policy_response)\n",
+ "print(f\"[b]Policy ARN:[/b] [i blue]{target_tracking_policy_response['PolicyARN']}\")\n",
+ "\n",
+ "# print Cloudwatch Alarms\n",
+ "alarms = target_tracking_policy_response[\"Alarms\"]\n",
+ "\n",
+ "for alarm in alarms:\n",
+ " print(f\"[b]Alarm Name:[/b] [b magenta]{alarm['AlarmName']}\")\n",
+ " # print(f\"[b]Alarm ARN:[/b] [i green]{alarm['AlarmARN']}[/i green]\")\n",
+ " print(\"===\" * 15)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d27a4cba-8aec-4b5c-b9ea-97d4ea82d9f0",
+ "metadata": {},
+ "source": [
+ "## Trigger autoscaling action\n",
+ "\n",
+ "### LLMPerf to generate traffic to the endpoint\n",
+ "\n",
+ "Refer to for more details on LLMPerf.\n",
+ "\n",
+ "Run the LLMPerf traffic generation script in the background using `subprocess.Popen`\n",
+ "\n",
+ "
\n",
+ "INFO:ℹ️ Refer to utils/llmperf.py for trigger_autoscaling function implementation\n",
+ "
\n",
+ "\n",
+ "### Monitor Scale-Out Alarm Trigger times and scaling event times\n",
+ "\n",
+ "As llmperf generates traffic to the endpoint continuously this trigger auto-scaling.\n",
+ "\n",
+ "The `monitor_scaling_events` function does the following:\n",
+ "- Calculates time taken for alarm to go into InAlarm state.\n",
+ "- checks if alarm is InAlarm state. If yes, then starts the scaling timer\n",
+ "- continuously monitors the `DesiredInstanceCount` property of the endpoint\n",
+ " - waits till `CurrentInstanceCount == DesiredInstanceCount` and `EndpointStatus` is `InService`\n",
+ "- Calculates time taken to scale out instances prints the times in a table\n",
+ "\n",
+ "The below cell triggers auto scaling action and calls the monitor_scaling_events immediately on the AlarmHigh\n",
+ "\n",
+ "
\n",
+ "INFO: ℹ️ Refer to utils/autoscaling.py for monitor_scaling_events function implementation\n",
+ "
\n",
+ "\n",
+ "
\n",
+ "NOTE: ⚠️The AlarmHigh Alarm triggers scale out actions only after the threshold of ConcurrentRequestsPerModel >5 for 3 datapoints within 30 seconds is breached.\n",
+ "
\n",
+ "NOTE: ⚠️The AlarmLow Alarm triggers scale-in actions only after the threshold of ConcurrentRequestsPerModel < 4.5 for 90 datapoints within 15 minutes is breached.\n",
+ " Running the below cell with take approximately 15 minutes to complete. \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "883924cc-9f29-48cf-85ac-1d96c0a3dd16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# get AlarmHigh alarm name\n",
+ "scalein_alarm_name = [alarm[\"AlarmName\"] for alarm in alarms if \"AlarmLow\" in alarm[\"AlarmName\"]][0]\n",
+ "\n",
+ "# Start monitoring scaling events\n",
+ "SLEEP_TIME = 5 # time to sleep\n",
+ "scaling_times = monitor_scaling_events(\n",
+ " endpoint_name, scalein_alarm_name, SLEEP_TIME, cloudwatch_client, sagemaker_client\n",
+ ")\n",
+ "\n",
+ "# Print scaling times\n",
+ "console = Console()\n",
+ "table = print_scaling_times(scaling_times)\n",
+ "console.print(table)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02a2d5b0-dc4b-40e3-8ada-ceddecfdac1a",
+ "metadata": {},
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "- Deregister scalable target. This automatically deletes associated cloudwatch alarms.\n",
+ "- Delete model\n",
+ "- Delete endpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7aacabc3-2b60-41c6-b903-9de5e31fc8e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Deregister the scalable target for AAS\n",
+ "try:\n",
+ " autoscaling_client.deregister_scalable_target(\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n",
+ " )\n",
+ " print(f\"Scalable target for [b]{resource_id}[/b] deregistered. ✅\")\n",
+ "except autoscaling_client.exceptions.ObjectNotFoundException:\n",
+ " print(f\"Scalable target for [b]{resource_id}[/b] not found!.\")\n",
+ "\n",
+ "print(\"---\" * 10)\n",
+ "# Delete model and endpoint\n",
+ "try:\n",
+ " print(f\"Deleting model: [b green]{model_name} ✅\")\n",
+ " predictor.delete_model()\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "try:\n",
+ " print(f\"Deleting endpoint: [b magenta]{predictor.endpoint_name} ✅\")\n",
+ " predictor.delete_endpoint()\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "print(\"---\" * 10)\n",
+ "print(f\"Done\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fdee8f2",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/trigger_autoscaling.sh b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/trigger_autoscaling.sh
new file mode 100644
index 0000000000..653a0a9889
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/trigger_autoscaling.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+# Check if required environment variables are set
+if [ -z "$AWS_ACCESS_KEY_ID" ] || [ -z "$AWS_SECRET_ACCESS_KEY" ] || [ -z "$AWS_SESSION_TOKEN" ] || [ -z "$AWS_REGION" ] || [ -z "$EP_NAME" ] || [ -z "$NUM_CONCURRENT_REQUESTS" ]; then
+ echo "Error: Required environment variables are not set."
+ exit 1
+fi
+
+echo "Installing llmperf..."
+rm -rf llmperf && \
+git clone https://github.com/philschmid/llmperf.git && \
+uv pip install -e llmperf/
+
+DIR="results"
+
+if [ ! -d "$DIR" ]; then
+ mkdir -p "$DIR"
+ echo "Created $DIR directory."
+else
+ echo "$DIR directory already exists."
+fi
+
+echo "Starting benchmarking scripts on endpoint $EP_NAME ..."
+
+start_time=$(date +%s)
+
+MESSAGES_API=true python llmperf/token_benchmark_ray.py \
+--model $EP_NAME \
+--llm-api "sagemaker" \
+--max-num-completed-requests 1000 \
+--timeout 600 \
+--num-concurrent-requests $NUM_CONCURRENT_REQUESTS \
+--results-dir "results"
+
+end_time=$(date +%s)
+echo "Execution time was $((end_time - start_time)) secs."
diff --git a/ prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/__init__.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/__init__.py
similarity index 100%
rename from prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/__init__.py
rename to deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/__init__.py
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/autoscaling.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/autoscaling.py
new file mode 100644
index 0000000000..1792a0887f
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/autoscaling.py
@@ -0,0 +1,174 @@
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from statistics import mean
+
+from rich import print
+from rich.table import Table
+from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn
+
+
+# Function to update the user prompt in the messages list
+def update_user_prompt(messages, prompt):
+ for message in messages:
+ if message["role"] == "user":
+ message["content"] = prompt
+ return messages
+
+
+# helper function to record latency
+def get_request_latency(payload, endpoint_name, sagemaker_runtime_client):
+ start_time = time.time()
+ _ = sagemaker_runtime_client.invoke_endpoint(
+ EndpointName=endpoint_name,
+ ContentType="application/json",
+ Body=json.dumps(payload),
+ )
+ # _ = predictor.predict(payload)
+ end_time = time.time()
+ latency = end_time - start_time
+ # print(chat["choices"][0]["message"]["content"].strip())
+ return latency
+
+
+# Function to test concurrent requests with a given concurrency level
+def test_concurrency_level(
+ concurrency_level,
+ prompts,
+ messages,
+ parameters,
+ endpoint_name,
+ sagemaker_runtime_client,
+):
+ payloads = [
+ {"messages": update_user_prompt(messages, prompt), **parameters}
+ for prompt in prompts * (concurrency_level // len(prompts))
+ ]
+ latencies = []
+ with ThreadPoolExecutor(max_workers=concurrency_level) as executor:
+ futures = [
+ executor.submit(
+ get_request_latency, payload, endpoint_name, sagemaker_runtime_client
+ )
+ for payload in payloads
+ ]
+ for future in as_completed(futures):
+ try:
+ latency = future.result()
+ latencies.append(latency)
+ except Exception as e:
+ print(f"Request failed: {e}")
+
+ avg_latency = mean(latencies)
+ return avg_latency
+
+
+# helper function to get the current instance count of the endpoint
+def get_scaling_instance_counts(endpoint_name, sagemaker_client):
+ endpoint_description = sagemaker_client.describe_endpoint(
+ EndpointName=endpoint_name
+ )
+ current = endpoint_description["ProductionVariants"][0]["CurrentInstanceCount"]
+ desired = endpoint_description["ProductionVariants"][0]["DesiredInstanceCount"]
+ current_status = endpoint_description["EndpointStatus"]
+ return current, desired, current_status
+
+
+# Helper function to check if any alarm is in "InAlarm" state
+def is_alarm_in_alarm_state(alarm_name, cloudwatch_client):
+ alarm_state = cloudwatch_client.describe_alarms(AlarmNames=[alarm_name])[
+ "MetricAlarms"
+ ][0]["StateValue"]
+ if alarm_state == "ALARM":
+ return True
+ return False
+
+
+# Helper function to monitor the endpoint for scaling events
+def monitor_scaling_events(
+ endpoint_name, alarm_name, time_to_sleep, cloudwatch_client, sagemaker_client
+):
+ scaling_times = {}
+ (
+ current_instance_count,
+ desired_instance_count,
+ status,
+ ) = get_scaling_instance_counts(endpoint_name, sagemaker_client)
+ print(f"Initial instance count: {current_instance_count}", flush=True)
+ print(f"Tracking Alarm: [i green]{alarm_name}[/i green]", flush=True)
+
+ with Progress(
+ SpinnerColumn(), *Progress.get_default_columns(), TimeElapsedColumn()
+ ) as progress:
+ alarm_task = progress.add_task(
+ "[green]Waiting for alarm to trigger...", total=None
+ )
+
+ alarm_timer_start = time.time()
+
+ while True:
+ if is_alarm_in_alarm_state(alarm_name, cloudwatch_client):
+ start_time = time.time()
+ alarm_timer_end = time.time()
+ time_to_alarm = alarm_timer_end - alarm_timer_start
+ progress.update(
+ alarm_task,
+ description=f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.",
+ total=1,
+ completed=1,
+ )
+ # print(f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.")
+ break
+ else:
+ progress.update(alarm_task, advance=1)
+ # Wait for time_to_sleep seconds before checking again
+ time.sleep(time_to_sleep)
+
+ scaling_task = progress.add_task(
+ "[green]Waiting for scaling to complete...", total=None
+ )
+
+ while True:
+ (
+ current_instance_count,
+ desired_instance_count,
+ status,
+ ) = get_scaling_instance_counts(endpoint_name, sagemaker_client)
+
+ if current_instance_count == desired_instance_count:
+ # Add sleep here as endpoint status doesn't change to `Updating` instantaneously
+ time.sleep(time_to_sleep)
+ if status == "InService":
+ end_time = time.time()
+ scaling_time = end_time - start_time
+ scaling_times[desired_instance_count] = scaling_time
+ progress.update(
+ scaling_task,
+ description=f"[bold green]Scaling to {desired_instance_count} instances completed in {scaling_time:.2f} seconds.",
+ total=1,
+ completed=1,
+ )
+ break
+ progress.update(scaling_task, advance=1)
+ # Wait for time_to_sleep seconds before checking again
+ time.sleep(time_to_sleep)
+
+ return scaling_times
+
+
+# function to print scaling times in a table
+def print_scaling_times(scaling_times):
+ # Create a table
+ table = Table(title="Scaling Times")
+
+ # Add columns
+ table.add_column(
+ "Target Instance Count", justify="right", style="cyan", no_wrap=True
+ )
+ table.add_column("Scaling Time (seconds)", justify="right", style="magenta")
+
+ # Add rows
+ for target_instance_count, scaling_time in scaling_times.items():
+ table.add_row(str(target_instance_count), f"{scaling_time:.2f}")
+
+ return table
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/llmperf.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/llmperf.py
new file mode 100644
index 0000000000..f9966be496
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints/utils/llmperf.py
@@ -0,0 +1,100 @@
+import glob
+import json
+import os
+import subprocess
+import time
+
+from rich import box, print
+from rich.table import Table
+
+
+# LLMPerf requires AWS Creds as ENV variables along with endpoint name
+def trigger_auto_scaling(creds, region, endpoint_name, num_concurrent_requests):
+ # Set environment variables
+ os.environ["AWS_ACCESS_KEY_ID"] = creds.access_key
+ os.environ["AWS_SECRET_ACCESS_KEY"] = creds.secret_key
+ os.environ["AWS_SESSION_TOKEN"] = creds.token
+ os.environ["AWS_REGION"] = region
+ os.environ["EP_NAME"] = endpoint_name
+ os.environ["NUM_CONCURRENT_REQUESTS"] = str(num_concurrent_requests)
+
+ # Path to the shell script
+ # script_path = "./trigger_autoscaling.sh"
+ # current_dir = os.getcwd()
+ script_path = os.path.abspath(
+ os.path.join(os.path.dirname(__file__), "..", "trigger_autoscaling.sh")
+ )
+
+ # print(f"Current working directory: {current_dir}")
+ # print(f"Full path to script: {script_path}")
+
+ # Check if the file exists
+ if os.path.exists(script_path):
+ print(f"Calling LLMPerf shell script: {script_path}")
+ else:
+ print(f"LLMPerf shell script file not found at {script_path}")
+
+ # Make sure the script is executable
+ # os.chmod(script_path, 0o755)
+
+ # Run the shell script
+ print(f"Launching LLMPerf with {num_concurrent_requests} concurrent requests")
+ process = subprocess.Popen([script_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+ return process
+
+
+# helper function to monitor the process
+def monitor_process(proc):
+ while True:
+ retcode = proc.poll() # Check if the process has terminated
+ if retcode is not None:
+ # Process has terminated
+ print(f"Process {proc.pid} finished with return code {retcode}")
+
+ # Capture and print any output from the process
+ stdout, stderr = proc.communicate()
+ if stdout:
+ print(f"Process output:\n{stdout.decode('utf-8')}")
+ if stderr:
+ print(f"Process errors:\n{stderr.decode('utf-8')}")
+
+ break
+ else:
+ # Process is still running
+ print(f"Process {proc.pid} is still running...")
+ time.sleep(15) # Check every 15 seconds
+
+
+# helper function to print llmperf results
+def print_llmperf_results(num_concurrent_requests):
+ # Reads the summary.json file and prints the results
+ with open(glob.glob("results/*summary.json")[0], "r") as file:
+ data = json.load(file)
+
+ # Create a table
+ perf_table = Table(
+ title="LLMPerf Endpoint Metrics",
+ row_styles=["bold", "bold"],
+ box=box.MINIMAL_DOUBLE_HEAD,
+ )
+ # Add columns
+ perf_table.add_column("Metric", justify="right", style="green", no_wrap=True)
+ perf_table.add_column("Units", justify="left", style="magenta")
+
+ # Add rows
+ perf_table.add_row("Concurrent requests", f"{num_concurrent_requests}")
+ perf_table.add_row("Avg. Input token length", f"{data['mean_input_tokens']}")
+ perf_table.add_row("Avg. Output token length", f"{data['mean_output_tokens']}")
+ perf_table.add_row("Avg. First-Time-To-Token", f"{data['results_ttft_s_mean']*1000:.2f}ms")
+ perf_table.add_row(
+ "Avg. Thorughput",
+ f"{data['results_mean_output_throughput_token_per_s']:.2f} tokens/sec",
+ )
+ perf_table.add_row(
+ "Avg. Latency", f"{data['results_inter_token_latency_s_mean']*1000:.2f}ms/token"
+ )
+
+ # Print the table
+ # console.print(perf_table)
+ return perf_table
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/requirements.txt b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/requirements.txt
new file mode 100644
index 0000000000..ee4b2a1529
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/requirements.txt
@@ -0,0 +1,67 @@
+asttokens==2.4.1 ; python_version >= "3.10" and python_version < "3.11"
+attrs==23.2.0 ; python_version >= "3.10" and python_version < "3.11"
+boto3==1.34.142 ; python_version >= "3.10" and python_version < "3.11"
+botocore==1.34.142 ; python_version >= "3.10" and python_version < "3.11"
+certifi==2024.2.2 ; python_version >= "3.10" and python_version < "3.11"
+charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.11"
+cloudpickle==2.2.1 ; python_version >= "3.10" and python_version < "3.11"
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Windows" or sys_platform == "win32")
+comm==0.2.2 ; python_version >= "3.10" and python_version < "3.11"
+decorator==5.1.1 ; python_version >= "3.10" and python_version < "3.11"
+dill==0.3.8 ; python_version >= "3.10" and python_version < "3.11"
+docker==7.1.0 ; python_version >= "3.10" and python_version < "3.11"
+exceptiongroup==1.2.1 ; python_version >= "3.10" and python_version < "3.11"
+executing==2.0.1 ; python_version >= "3.10" and python_version < "3.11"
+google-pasta==0.2.0 ; python_version >= "3.10" and python_version < "3.11"
+idna==3.7 ; python_version >= "3.10" and python_version < "3.11"
+importlib-metadata==6.11.0 ; python_version >= "3.10" and python_version < "3.11"
+ipython==8.24.0 ; python_version >= "3.10" and python_version < "3.11"
+ipywidgets==8.1.3 ; python_version >= "3.10" and python_version < "3.11"
+jedi==0.19.1 ; python_version >= "3.10" and python_version < "3.11"
+jmespath==1.0.1 ; python_version >= "3.10" and python_version < "3.11"
+jsonschema-specifications==2023.12.1 ; python_version >= "3.10" and python_version < "3.11"
+jsonschema==4.22.0 ; python_version >= "3.10" and python_version < "3.11"
+jupyterlab-widgets==3.0.11 ; python_version >= "3.10" and python_version < "3.11"
+markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "3.11"
+matplotlib-inline==0.1.7 ; python_version >= "3.10" and python_version < "3.11"
+mdurl==0.1.2 ; python_version >= "3.10" and python_version < "3.11"
+multiprocess==0.70.16 ; python_version >= "3.10" and python_version < "3.11"
+numpy==1.26.4 ; python_version >= "3.10" and python_version < "3.11"
+packaging==24.0 ; python_version >= "3.10" and python_version < "3.11"
+pandas==2.2.2 ; python_version >= "3.10" and python_version < "3.11"
+parso==0.8.4 ; python_version >= "3.10" and python_version < "3.11"
+pathos==0.3.2 ; python_version >= "3.10" and python_version < "3.11"
+pexpect==4.9.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten")
+platformdirs==4.2.2 ; python_version >= "3.10" and python_version < "3.11"
+pox==0.3.4 ; python_version >= "3.10" and python_version < "3.11"
+ppft==1.7.6.8 ; python_version >= "3.10" and python_version < "3.11"
+prompt-toolkit==3.0.45 ; python_version >= "3.10" and python_version < "3.11"
+protobuf==4.25.3 ; python_version >= "3.10" and python_version < "3.11"
+psutil==5.9.8 ; python_version >= "3.10" and python_version < "3.11"
+ptyprocess==0.7.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten")
+pure-eval==0.2.2 ; python_version >= "3.10" and python_version < "3.11"
+pygments==2.18.0 ; python_version >= "3.10" and python_version < "3.11"
+python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "3.11"
+pytz==2024.1 ; python_version >= "3.10" and python_version < "3.11"
+pywin32==306 ; python_version >= "3.10" and python_version < "3.11" and sys_platform == "win32"
+pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.11"
+referencing==0.35.1 ; python_version >= "3.10" and python_version < "3.11"
+requests==2.32.2 ; python_version >= "3.10" and python_version < "3.11"
+rich==13.7.1 ; python_version >= "3.10" and python_version < "3.11"
+rpds-py==0.18.1 ; python_version >= "3.10" and python_version < "3.11"
+s3transfer==0.10.1 ; python_version >= "3.10" and python_version < "3.11"
+sagemaker==2.225.0 ; python_version >= "3.10" and python_version < "3.11"
+schema==0.7.7 ; python_version >= "3.10" and python_version < "3.11"
+six==1.16.0 ; python_version >= "3.10" and python_version < "3.11"
+smdebug-rulesconfig==1.0.1 ; python_version >= "3.10" and python_version < "3.11"
+stack-data==0.6.3 ; python_version >= "3.10" and python_version < "3.11"
+tblib==3.0.0 ; python_version >= "3.10" and python_version < "3.11"
+tqdm==4.66.4 ; python_version >= "3.10" and python_version < "3.11"
+traitlets==5.14.3 ; python_version >= "3.10" and python_version < "3.11"
+typing-extensions==4.12.0 ; python_version >= "3.10" and python_version < "3.11"
+tzdata==2024.1 ; python_version >= "3.10" and python_version < "3.11"
+urllib3==2.2.1 ; python_version >= "3.10" and python_version < "3.11"
+uv==0.2.5 ; python_version >= "3.10" and python_version < "3.11"
+wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "3.11"
+widgetsnbextension==4.0.11 ; python_version >= "3.10" and python_version < "3.11"
+zipp==3.19.0 ; python_version >= "3.10" and python_version < "3.11"
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/sm-app_autoscaling_realtime_endpoints_inference_components.ipynb b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/sm-app_autoscaling_realtime_endpoints_inference_components.ipynb
new file mode 100644
index 0000000000..2668b5229e
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/sm-app_autoscaling_realtime_endpoints_inference_components.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "81f236c9",
+ "metadata": {},
+ "source": [
+ "# Faster autoscaling on Amazon SageMaker realtime endpoints with inference components (Application Autoscaling)\n",
+ "\n",
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "In this notebook we show how the new faster autoscaling feature helps scale sagemaker inference endpoints by almost 6x faster than earlier.\n",
+ "\n",
+ "We deploy Meta's `Llama3-8B-Instruct` model to an Amazon SageMaker realtime endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC) and apply Application Autoscaling scaling policies to the endpoint.\n",
+ "\n",
+ "\n",
+ "
\n",
+ " Please select m5.2xlarge or larger instance types when running this on Amazon SageMaker Notebook Instance. \n",
+ " Select conda_pytorch_p310 kernel when running this notebook on Amazon SageMaker Notebook Instance.
\n",
+ " Ensure python version for the kernel is 3.10.x (3.11 is not supported). \n",
+ "
\n",
+ "Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta.\n",
+ "\n",
+ "- **Step 1:** Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens.\n",
+ "- **Step 2:** Login to [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) and navigate to *Meta-Llama-3-8B-Instruct** home page.\n",
+ "- **Step 3:** Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main)\n",
+ "- **Step 4:** Wait for the approval email from META (Approval may take any where b/w 1-3 hrs)\n",
+ "
\n",
+ "NOTE: Remember to copy your Hugging Face Access Token from https://hf.co/ before running the below cell.
\n",
+ "Refer here to learn about creating HF tokens.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74991ddd-3b2c-4f07-b35b-55f5d8c19ada",
+ "metadata": {},
+ "source": [
+ "## Configure container and environment "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b89da4d-9ce7-4e5b-a02a-3f2c690cd26d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# print ecr image uri\n",
+ "print(f\"llm image uri: [b green]{tgi_dlc}\")\n",
+ "\n",
+ "HF_TOKEN = os.getenv(\"HUGGING_FACE_HUB_TOKEN\") or getpass(\"Enter HUGGINGFACE Access Token: \")\n",
+ "\n",
+ "llama3model = {\n",
+ " \"Image\": tgi_dlc,\n",
+ " \"Environment\": {\n",
+ " \"HF_MODEL_ID\": \"meta-llama/Meta-Llama-3-8B-Instruct\", # model_id from hf.co/models\n",
+ " \"SM_NUM_GPUS\": \"1\", # Number of GPU used per replica\n",
+ " \"MAX_INPUT_LENGTH\": \"2048\", # Max length of input text\n",
+ " \"MAX_TOTAL_TOKENS\": \"4096\", # Max length of the generation (including input text)\n",
+ " \"MAX_BATCH_TOTAL_TOKENS\": \"8192\", # Limits the number of tokens that can be processed in parallel during the generation\n",
+ " \"MESSAGES_API_ENABLED\": \"true\", # Enable the messages API\n",
+ " \"HUGGING_FACE_HUB_TOKEN\": HF_TOKEN,\n",
+ " },\n",
+ "}\n",
+ "\n",
+ "# create Model\n",
+ "deployment_name = \"sm\"\n",
+ "model_name = f\"{deployment_name}-model-llama3\"\n",
+ "\n",
+ "print(f\"Creating model: [b green]{model_name}...\")\n",
+ "model_response = sagemaker_client.create_model(\n",
+ " ModelName=model_name,\n",
+ " ExecutionRoleArn=role,\n",
+ " Containers=[llama3model],\n",
+ ")\n",
+ "\n",
+ "print(model_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a93cea6-703e-4eb9-8b4d-d92b60597a7a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Deploy model to Amazon SageMaker Inference Component\n",
+ "inference_component_name_llama3b = f\"{prefix}-IC-llama3b\"\n",
+ "variant_name = \"AllTraffic\"\n",
+ "\n",
+ "ic_response = sagemaker_client.create_inference_component(\n",
+ " InferenceComponentName=inference_component_name_llama3b,\n",
+ " EndpointName=endpoint_name,\n",
+ " VariantName=variant_name,\n",
+ " Specification={\n",
+ " \"ModelName\": f\"{deployment_name}-model-llama3\",\n",
+ " \"ComputeResourceRequirements\": {\n",
+ " \"NumberOfAcceleratorDevicesRequired\": 1,\n",
+ " \"NumberOfCpuCoresRequired\": 1,\n",
+ " \"MinMemoryRequiredInMb\": 1024,\n",
+ " },\n",
+ " },\n",
+ " RuntimeConfig={\"CopyCount\": 1},\n",
+ ")\n",
+ "\n",
+ "# print(ic_response)\n",
+ "\n",
+ "# Wait for IC to come InService\n",
+ "print(f\"InferenceComponent [b magenta]{inference_component_name_llama3b}...\")\n",
+ "while True:\n",
+ " desc = sagemaker_client.describe_inference_component(\n",
+ " InferenceComponentName=inference_component_name_llama3b\n",
+ " )\n",
+ " status = desc[\"InferenceComponentStatus\"]\n",
+ " print(status)\n",
+ " sys.stdout.flush()\n",
+ " if status in [\"InService\", \"Failed\"]:\n",
+ " break\n",
+ " time.sleep(30)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "83e1af5c-e713-4cf8-bc23-1c96f1e61327",
+ "metadata": {},
+ "source": [
+ "## Inference\n",
+ "\n",
+ "Invoke and test endpoint using messages API. Refer to HF [Messages API](https://huggingface.co/docs/text-generation-inference/messages_api) for more info."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59fa5f76-5498-47a1-a443-5483c3077172",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create predictor object\n",
+ "predictor = Predictor(\n",
+ " endpoint_name=endpoint_name,\n",
+ " sagemaker_session=sess,\n",
+ " component_name=inference_component_name_llama3b,\n",
+ " serializer=JSONSerializer(),\n",
+ " deserializer=JSONDeserializer(),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6d9ecc2-fffe-4ff1-b78b-1222fe6d32de",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Prompt to generate\n",
+ "messages = [\n",
+ " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
+ " {\"role\": \"user\", \"content\": \"What is deep learning?\"},\n",
+ "]\n",
+ "\n",
+ "# Generation arguments\n",
+ "parameters = {\n",
+ " \"model\": hf_model_id, # model id is required\n",
+ " \"top_p\": 0.6,\n",
+ " \"temperature\": 0.9,\n",
+ " \"max_tokens\": 512,\n",
+ " \"stop\": [\"<|eot_id|>\"],\n",
+ "}\n",
+ "\n",
+ "chat = predictor.predict({\"messages\": messages, **parameters})\n",
+ "\n",
+ "# Unpack and print response\n",
+ "print(chat[\"choices\"][0][\"message\"][\"content\"].strip())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29f7a5ab-0264-4b12-8243-b4aa649335b7",
+ "metadata": {},
+ "source": [
+ "## Apply Autoscaling policies to the endpoint\n",
+ "\n",
+ "Apply Application Autoscaling Policy to endpoint\n",
+ "\n",
+ "1. Register Scalable Target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1bbf762f-beec-42ed-9ff8-5b06f76269ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "as_min_capacity = 1\n",
+ "as_max_capacity = 2\n",
+ "\n",
+ "resource_id = f\"inference-component/{inference_component_name_llama3b}\"\n",
+ "\n",
+ "autoscaling_client = boto3.client(\"application-autoscaling\", region_name=region)\n",
+ "\n",
+ "# Register scalable target\n",
+ "scalable_target = autoscaling_client.register_scalable_target(\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:inference-component:DesiredCopyCount\",\n",
+ " MinCapacity=as_min_capacity,\n",
+ " MaxCapacity=as_max_capacity, # Replace with your desired maximum instances\n",
+ ")\n",
+ "\n",
+ "scalable_target_arn = scalable_target[\"ScalableTargetARN\"]\n",
+ "print(f\"Resource ID: [b blue]{resource_id}\")\n",
+ "print(f\"Scalable_target_arn:\\n[b green]{scalable_target_arn}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0af2e234-d1c7-4575-b943-5291c70c326d",
+ "metadata": {},
+ "source": [
+ "## Use the latest high-resolution Metrics to trigger auto-scaling\n",
+ "\n",
+ "- New feature introduces a new PredefinedMetricType for scaling policy configuration i.e. SageMakerVariantConcurrentRequestsPerModelHighResolution to trigger scaling actions.\n",
+ "- Creating a scaling policy with this metric type will create cloudwatch alarms that track a new metric called ConcurrentRequestsPerModel.\n",
+ "- These high-resolution metrics are published at sub-minute intervals (10s intervals to CW + any additional jitter + delays)\n",
+ "- We should observe significant improvement in scale out times with this new metric\n",
+ "\n",
+ "\n",
+ "### Steps to create Application autoscaling policy\n",
+ "\n",
+ "- Create scaling policy\n",
+ " - Set `PolicyType` to `TargetTrackingScaling`\n",
+ " - Set `TargetValue` to `5.0`. i.e., Scaling triggers when endpoint receives 5 `ConcurrentRequestsPerModel`\n",
+ " - Set `PredefinedMetricType` to `SageMakerVariantConcurrentRequestsPerModelHighResolution`\n",
+ " - Set `ScaleInCoolDown` and `ScaleOutCoolDown` values to `300` seconds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44cc8c60-37cd-4852-a03d-e08149ccad17",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Create Target Tracking Scaling Policy\n",
+ "target_tracking_policy_response = autoscaling_client.put_scaling_policy(\n",
+ " PolicyName=\"SageMakerICScalingPolicy\",\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:inference-component:DesiredCopyCount\",\n",
+ " PolicyType=\"TargetTrackingScaling\",\n",
+ " TargetTrackingScalingPolicyConfiguration={\n",
+ " \"TargetValue\": 5.0, # Scaling triggers when endpoint receives 5 ConcurrentRequestsPerModel\n",
+ " \"PredefinedMetricSpecification\": {\n",
+ " \"PredefinedMetricType\": \"SageMakerInferenceComponentConcurrentRequestsPerCopyHighResolution\"\n",
+ " },\n",
+ " \"ScaleInCooldown\": 300, # Cooldown period after scale-in activity\n",
+ " \"ScaleOutCooldown\": 300, # Cooldown period after scale-out activity\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# print(target_tracking_policy_response)\n",
+ "print(f\"Policy ARN: [i blue]{target_tracking_policy_response['PolicyARN']}\")\n",
+ "\n",
+ "# print Cloudwatch Alarms\n",
+ "alarms = target_tracking_policy_response[\"Alarms\"]\n",
+ "\n",
+ "for alarm in alarms:\n",
+ " print(f\"[b]Alarm Name:[/b] [b magenta]{alarm['AlarmName']}\")\n",
+ " # print(f\"[b]Alarm ARN:[/b] [i green]{alarm['AlarmARN']}[/i green]\")\n",
+ " print(\"===\" * 15)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02a2d5b0-dc4b-40e3-8ada-ceddecfdac1a",
+ "metadata": {},
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "- Deregister scalable target. This automatically deletes associated cloudwatch alarms.\n",
+ "- Delete model\n",
+ "- Delete endpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7aacabc3-2b60-41c6-b903-9de5e31fc8e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " # Deregister the scalable target for AAS\n",
+ " autoscaling_client.deregister_scalable_target(\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n",
+ " )\n",
+ " print(f\"Scalable target for [b]{resource_id}[/b] deregistered. ✅\")\n",
+ "except autoscaling_client.exceptions.ObjectNotFoundException:\n",
+ " print(f\"Scalable target for [b]{resource_id}[/b] not found!.\")\n",
+ "\n",
+ "print(\"---\" * 10)\n",
+ "\n",
+ "try:\n",
+ " print(f\"Deleting inference components: [b magenta]{inference_component_name_llama3b} ✅\")\n",
+ " # Delete inference component\n",
+ " sagemaker_client.delete_inference_component(\n",
+ " InferenceComponentName=inference_component_name_llama3b\n",
+ " )\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "\n",
+ "try:\n",
+ " print(f\"Deleting model: [b magenta]{deployment_name}-model-llama3 ✅\")\n",
+ " predictor.delete_model()\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "\n",
+ "try:\n",
+ " print(f\"Deleting endpoint: [b magenta]{predictor.endpoint_name} ✅\")\n",
+ " predictor.delete_endpoint()\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "print(\"---\" * 10)\n",
+ "print(\"Done\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fdee8f2",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/trigger_autoscaling.sh b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/trigger_autoscaling.sh
new file mode 100644
index 0000000000..653a0a9889
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/trigger_autoscaling.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+# Check if required environment variables are set
+if [ -z "$AWS_ACCESS_KEY_ID" ] || [ -z "$AWS_SECRET_ACCESS_KEY" ] || [ -z "$AWS_SESSION_TOKEN" ] || [ -z "$AWS_REGION" ] || [ -z "$EP_NAME" ] || [ -z "$NUM_CONCURRENT_REQUESTS" ]; then
+ echo "Error: Required environment variables are not set."
+ exit 1
+fi
+
+echo "Installing llmperf..."
+rm -rf llmperf && \
+git clone https://github.com/philschmid/llmperf.git && \
+uv pip install -e llmperf/
+
+DIR="results"
+
+if [ ! -d "$DIR" ]; then
+ mkdir -p "$DIR"
+ echo "Created $DIR directory."
+else
+ echo "$DIR directory already exists."
+fi
+
+echo "Starting benchmarking scripts on endpoint $EP_NAME ..."
+
+start_time=$(date +%s)
+
+MESSAGES_API=true python llmperf/token_benchmark_ray.py \
+--model $EP_NAME \
+--llm-api "sagemaker" \
+--max-num-completed-requests 1000 \
+--timeout 600 \
+--num-concurrent-requests $NUM_CONCURRENT_REQUESTS \
+--results-dir "results"
+
+end_time=$(date +%s)
+echo "Execution time was $((end_time - start_time)) secs."
diff --git a/ prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/__init__.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/__init__.py
similarity index 100%
rename from prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/__init__.py
rename to deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/__init__.py
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/autoscaling.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/autoscaling.py
new file mode 100644
index 0000000000..1792a0887f
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/autoscaling.py
@@ -0,0 +1,174 @@
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from statistics import mean
+
+from rich import print
+from rich.table import Table
+from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn
+
+
+# Function to update the user prompt in the messages list
+def update_user_prompt(messages, prompt):
+ for message in messages:
+ if message["role"] == "user":
+ message["content"] = prompt
+ return messages
+
+
+# helper function to record latency
+def get_request_latency(payload, endpoint_name, sagemaker_runtime_client):
+ start_time = time.time()
+ _ = sagemaker_runtime_client.invoke_endpoint(
+ EndpointName=endpoint_name,
+ ContentType="application/json",
+ Body=json.dumps(payload),
+ )
+ # _ = predictor.predict(payload)
+ end_time = time.time()
+ latency = end_time - start_time
+ # print(chat["choices"][0]["message"]["content"].strip())
+ return latency
+
+
+# Function to test concurrent requests with a given concurrency level
+def test_concurrency_level(
+ concurrency_level,
+ prompts,
+ messages,
+ parameters,
+ endpoint_name,
+ sagemaker_runtime_client,
+):
+ payloads = [
+ {"messages": update_user_prompt(messages, prompt), **parameters}
+ for prompt in prompts * (concurrency_level // len(prompts))
+ ]
+ latencies = []
+ with ThreadPoolExecutor(max_workers=concurrency_level) as executor:
+ futures = [
+ executor.submit(
+ get_request_latency, payload, endpoint_name, sagemaker_runtime_client
+ )
+ for payload in payloads
+ ]
+ for future in as_completed(futures):
+ try:
+ latency = future.result()
+ latencies.append(latency)
+ except Exception as e:
+ print(f"Request failed: {e}")
+
+ avg_latency = mean(latencies)
+ return avg_latency
+
+
+# helper function to get the current instance count of the endpoint
+def get_scaling_instance_counts(endpoint_name, sagemaker_client):
+ endpoint_description = sagemaker_client.describe_endpoint(
+ EndpointName=endpoint_name
+ )
+ current = endpoint_description["ProductionVariants"][0]["CurrentInstanceCount"]
+ desired = endpoint_description["ProductionVariants"][0]["DesiredInstanceCount"]
+ current_status = endpoint_description["EndpointStatus"]
+ return current, desired, current_status
+
+
+# Helper function to check if any alarm is in "InAlarm" state
+def is_alarm_in_alarm_state(alarm_name, cloudwatch_client):
+ alarm_state = cloudwatch_client.describe_alarms(AlarmNames=[alarm_name])[
+ "MetricAlarms"
+ ][0]["StateValue"]
+ if alarm_state == "ALARM":
+ return True
+ return False
+
+
+# Helper function to monitor the endpoint for scaling events
+def monitor_scaling_events(
+ endpoint_name, alarm_name, time_to_sleep, cloudwatch_client, sagemaker_client
+):
+ scaling_times = {}
+ (
+ current_instance_count,
+ desired_instance_count,
+ status,
+ ) = get_scaling_instance_counts(endpoint_name, sagemaker_client)
+ print(f"Initial instance count: {current_instance_count}", flush=True)
+ print(f"Tracking Alarm: [i green]{alarm_name}[/i green]", flush=True)
+
+ with Progress(
+ SpinnerColumn(), *Progress.get_default_columns(), TimeElapsedColumn()
+ ) as progress:
+ alarm_task = progress.add_task(
+ "[green]Waiting for alarm to trigger...", total=None
+ )
+
+ alarm_timer_start = time.time()
+
+ while True:
+ if is_alarm_in_alarm_state(alarm_name, cloudwatch_client):
+ start_time = time.time()
+ alarm_timer_end = time.time()
+ time_to_alarm = alarm_timer_end - alarm_timer_start
+ progress.update(
+ alarm_task,
+ description=f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.",
+ total=1,
+ completed=1,
+ )
+ # print(f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.")
+ break
+ else:
+ progress.update(alarm_task, advance=1)
+ # Wait for time_to_sleep seconds before checking again
+ time.sleep(time_to_sleep)
+
+ scaling_task = progress.add_task(
+ "[green]Waiting for scaling to complete...", total=None
+ )
+
+ while True:
+ (
+ current_instance_count,
+ desired_instance_count,
+ status,
+ ) = get_scaling_instance_counts(endpoint_name, sagemaker_client)
+
+ if current_instance_count == desired_instance_count:
+ # Add sleep here as endpoint status doesn't change to `Updating` instantaneously
+ time.sleep(time_to_sleep)
+ if status == "InService":
+ end_time = time.time()
+ scaling_time = end_time - start_time
+ scaling_times[desired_instance_count] = scaling_time
+ progress.update(
+ scaling_task,
+ description=f"[bold green]Scaling to {desired_instance_count} instances completed in {scaling_time:.2f} seconds.",
+ total=1,
+ completed=1,
+ )
+ break
+ progress.update(scaling_task, advance=1)
+ # Wait for time_to_sleep seconds before checking again
+ time.sleep(time_to_sleep)
+
+ return scaling_times
+
+
+# function to print scaling times in a table
+def print_scaling_times(scaling_times):
+ # Create a table
+ table = Table(title="Scaling Times")
+
+ # Add columns
+ table.add_column(
+ "Target Instance Count", justify="right", style="cyan", no_wrap=True
+ )
+ table.add_column("Scaling Time (seconds)", justify="right", style="magenta")
+
+ # Add rows
+ for target_instance_count, scaling_time in scaling_times.items():
+ table.add_row(str(target_instance_count), f"{scaling_time:.2f}")
+
+ return table
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/llmperf.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/llmperf.py
new file mode 100644
index 0000000000..f9966be496
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_inference_components/utils/llmperf.py
@@ -0,0 +1,100 @@
+import glob
+import json
+import os
+import subprocess
+import time
+
+from rich import box, print
+from rich.table import Table
+
+
+# LLMPerf requires AWS Creds as ENV variables along with endpoint name
+def trigger_auto_scaling(creds, region, endpoint_name, num_concurrent_requests):
+ # Set environment variables
+ os.environ["AWS_ACCESS_KEY_ID"] = creds.access_key
+ os.environ["AWS_SECRET_ACCESS_KEY"] = creds.secret_key
+ os.environ["AWS_SESSION_TOKEN"] = creds.token
+ os.environ["AWS_REGION"] = region
+ os.environ["EP_NAME"] = endpoint_name
+ os.environ["NUM_CONCURRENT_REQUESTS"] = str(num_concurrent_requests)
+
+ # Path to the shell script
+ # script_path = "./trigger_autoscaling.sh"
+ # current_dir = os.getcwd()
+ script_path = os.path.abspath(
+ os.path.join(os.path.dirname(__file__), "..", "trigger_autoscaling.sh")
+ )
+
+ # print(f"Current working directory: {current_dir}")
+ # print(f"Full path to script: {script_path}")
+
+ # Check if the file exists
+ if os.path.exists(script_path):
+ print(f"Calling LLMPerf shell script: {script_path}")
+ else:
+ print(f"LLMPerf shell script file not found at {script_path}")
+
+ # Make sure the script is executable
+ # os.chmod(script_path, 0o755)
+
+ # Run the shell script
+ print(f"Launching LLMPerf with {num_concurrent_requests} concurrent requests")
+ process = subprocess.Popen([script_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+ return process
+
+
+# helper function to monitor the process
+def monitor_process(proc):
+ while True:
+ retcode = proc.poll() # Check if the process has terminated
+ if retcode is not None:
+ # Process has terminated
+ print(f"Process {proc.pid} finished with return code {retcode}")
+
+ # Capture and print any output from the process
+ stdout, stderr = proc.communicate()
+ if stdout:
+ print(f"Process output:\n{stdout.decode('utf-8')}")
+ if stderr:
+ print(f"Process errors:\n{stderr.decode('utf-8')}")
+
+ break
+ else:
+ # Process is still running
+ print(f"Process {proc.pid} is still running...")
+ time.sleep(15) # Check every 15 seconds
+
+
+# helper function to print llmperf results
+def print_llmperf_results(num_concurrent_requests):
+ # Reads the summary.json file and prints the results
+ with open(glob.glob("results/*summary.json")[0], "r") as file:
+ data = json.load(file)
+
+ # Create a table
+ perf_table = Table(
+ title="LLMPerf Endpoint Metrics",
+ row_styles=["bold", "bold"],
+ box=box.MINIMAL_DOUBLE_HEAD,
+ )
+ # Add columns
+ perf_table.add_column("Metric", justify="right", style="green", no_wrap=True)
+ perf_table.add_column("Units", justify="left", style="magenta")
+
+ # Add rows
+ perf_table.add_row("Concurrent requests", f"{num_concurrent_requests}")
+ perf_table.add_row("Avg. Input token length", f"{data['mean_input_tokens']}")
+ perf_table.add_row("Avg. Output token length", f"{data['mean_output_tokens']}")
+ perf_table.add_row("Avg. First-Time-To-Token", f"{data['results_ttft_s_mean']*1000:.2f}ms")
+ perf_table.add_row(
+ "Avg. Thorughput",
+ f"{data['results_mean_output_throughput_token_per_s']:.2f} tokens/sec",
+ )
+ perf_table.add_row(
+ "Avg. Latency", f"{data['results_inter_token_latency_s_mean']*1000:.2f}ms/token"
+ )
+
+ # Print the table
+ # console.print(perf_table)
+ return perf_table
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/requirements.txt b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/requirements.txt
new file mode 100644
index 0000000000..ee4b2a1529
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/requirements.txt
@@ -0,0 +1,67 @@
+asttokens==2.4.1 ; python_version >= "3.10" and python_version < "3.11"
+attrs==23.2.0 ; python_version >= "3.10" and python_version < "3.11"
+boto3==1.34.142 ; python_version >= "3.10" and python_version < "3.11"
+botocore==1.34.142 ; python_version >= "3.10" and python_version < "3.11"
+certifi==2024.2.2 ; python_version >= "3.10" and python_version < "3.11"
+charset-normalizer==3.3.2 ; python_version >= "3.10" and python_version < "3.11"
+cloudpickle==2.2.1 ; python_version >= "3.10" and python_version < "3.11"
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Windows" or sys_platform == "win32")
+comm==0.2.2 ; python_version >= "3.10" and python_version < "3.11"
+decorator==5.1.1 ; python_version >= "3.10" and python_version < "3.11"
+dill==0.3.8 ; python_version >= "3.10" and python_version < "3.11"
+docker==7.1.0 ; python_version >= "3.10" and python_version < "3.11"
+exceptiongroup==1.2.1 ; python_version >= "3.10" and python_version < "3.11"
+executing==2.0.1 ; python_version >= "3.10" and python_version < "3.11"
+google-pasta==0.2.0 ; python_version >= "3.10" and python_version < "3.11"
+idna==3.7 ; python_version >= "3.10" and python_version < "3.11"
+importlib-metadata==6.11.0 ; python_version >= "3.10" and python_version < "3.11"
+ipython==8.24.0 ; python_version >= "3.10" and python_version < "3.11"
+ipywidgets==8.1.3 ; python_version >= "3.10" and python_version < "3.11"
+jedi==0.19.1 ; python_version >= "3.10" and python_version < "3.11"
+jmespath==1.0.1 ; python_version >= "3.10" and python_version < "3.11"
+jsonschema-specifications==2023.12.1 ; python_version >= "3.10" and python_version < "3.11"
+jsonschema==4.22.0 ; python_version >= "3.10" and python_version < "3.11"
+jupyterlab-widgets==3.0.11 ; python_version >= "3.10" and python_version < "3.11"
+markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "3.11"
+matplotlib-inline==0.1.7 ; python_version >= "3.10" and python_version < "3.11"
+mdurl==0.1.2 ; python_version >= "3.10" and python_version < "3.11"
+multiprocess==0.70.16 ; python_version >= "3.10" and python_version < "3.11"
+numpy==1.26.4 ; python_version >= "3.10" and python_version < "3.11"
+packaging==24.0 ; python_version >= "3.10" and python_version < "3.11"
+pandas==2.2.2 ; python_version >= "3.10" and python_version < "3.11"
+parso==0.8.4 ; python_version >= "3.10" and python_version < "3.11"
+pathos==0.3.2 ; python_version >= "3.10" and python_version < "3.11"
+pexpect==4.9.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten")
+platformdirs==4.2.2 ; python_version >= "3.10" and python_version < "3.11"
+pox==0.3.4 ; python_version >= "3.10" and python_version < "3.11"
+ppft==1.7.6.8 ; python_version >= "3.10" and python_version < "3.11"
+prompt-toolkit==3.0.45 ; python_version >= "3.10" and python_version < "3.11"
+protobuf==4.25.3 ; python_version >= "3.10" and python_version < "3.11"
+psutil==5.9.8 ; python_version >= "3.10" and python_version < "3.11"
+ptyprocess==0.7.0 ; python_version >= "3.10" and python_version < "3.11" and (sys_platform != "win32" and sys_platform != "emscripten")
+pure-eval==0.2.2 ; python_version >= "3.10" and python_version < "3.11"
+pygments==2.18.0 ; python_version >= "3.10" and python_version < "3.11"
+python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "3.11"
+pytz==2024.1 ; python_version >= "3.10" and python_version < "3.11"
+pywin32==306 ; python_version >= "3.10" and python_version < "3.11" and sys_platform == "win32"
+pyyaml==6.0.1 ; python_version >= "3.10" and python_version < "3.11"
+referencing==0.35.1 ; python_version >= "3.10" and python_version < "3.11"
+requests==2.32.2 ; python_version >= "3.10" and python_version < "3.11"
+rich==13.7.1 ; python_version >= "3.10" and python_version < "3.11"
+rpds-py==0.18.1 ; python_version >= "3.10" and python_version < "3.11"
+s3transfer==0.10.1 ; python_version >= "3.10" and python_version < "3.11"
+sagemaker==2.225.0 ; python_version >= "3.10" and python_version < "3.11"
+schema==0.7.7 ; python_version >= "3.10" and python_version < "3.11"
+six==1.16.0 ; python_version >= "3.10" and python_version < "3.11"
+smdebug-rulesconfig==1.0.1 ; python_version >= "3.10" and python_version < "3.11"
+stack-data==0.6.3 ; python_version >= "3.10" and python_version < "3.11"
+tblib==3.0.0 ; python_version >= "3.10" and python_version < "3.11"
+tqdm==4.66.4 ; python_version >= "3.10" and python_version < "3.11"
+traitlets==5.14.3 ; python_version >= "3.10" and python_version < "3.11"
+typing-extensions==4.12.0 ; python_version >= "3.10" and python_version < "3.11"
+tzdata==2024.1 ; python_version >= "3.10" and python_version < "3.11"
+urllib3==2.2.1 ; python_version >= "3.10" and python_version < "3.11"
+uv==0.2.5 ; python_version >= "3.10" and python_version < "3.11"
+wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "3.11"
+widgetsnbextension==4.0.11 ; python_version >= "3.10" and python_version < "3.11"
+zipp==3.19.0 ; python_version >= "3.10" and python_version < "3.11"
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/sm-app_autoscaling_realtime_endpoints_step_scaling.ipynb b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/sm-app_autoscaling_realtime_endpoints_step_scaling.ipynb
new file mode 100644
index 0000000000..75bd52a42e
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/sm-app_autoscaling_realtime_endpoints_step_scaling.ipynb
@@ -0,0 +1,872 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "81f236c9",
+ "metadata": {},
+ "source": [
+ "# Faster autoscaling on Amazon SageMaker realtime endpoints (Step Scaling)\n",
+ "\n",
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "In this notebook we show how the new faster autoscaling feature helps scale sagemaker inference endpoints by almost 6x faster than earlier.\n",
+ "\n",
+ "We deploy Meta's `Llama3-8B-Instruct` model to an Amazon SageMaker realtime endpoint using Text Generation Inference (TGI) Deep Learning Container (DLC) and apply Step Scaling autoscaling policies to the endpoint.\n",
+ "\n",
+ "\n",
+ "Please use at least `m5.2xlarge` or larger instance types if running this on Amazon SageMaker Notebook Instance.\n",
+ "\n",
+ "\n",
+ "## Prerequisites\n",
+ "\n",
+ "
\n",
+ "Before using this notebook please ensure you have access to an active access token from HuggingFace and have accepted the license agreement from Meta.\n",
+ "\n",
+ "- **Step 1:** Create user access token in HuggingFace (HF). Refer [here](https://huggingface.co/docs/hub/security-tokens) on how to create HF tokens.\n",
+ "- **Step 2:** Login to [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main) and navigate to *Meta-Llama-3-8B-Instruct** home page.\n",
+ "- **Step 3:** Accept META LLAMA 3 COMMUNITY LICENSE AGREEMENT by following the instructions [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/tree/main)\n",
+ "- **Step 4:** Wait for the approval email from META (Approval may take any where b/w 1-3 hrs)\n",
+ "
\n",
+ "NOTE: Remember to copy your Hugging Face Access Token from https://hf.co/ before running the below cell.
\n",
+ "Refer here to learn about creating HF tokens.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b89da4d-9ce7-4e5b-a02a-3f2c690cd26d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "instance_type = \"ml.g5.2xlarge\"\n",
+ "suffix = f\"{str(uuid4())[:5]}-{datetime.now().strftime('%d%b%Y')}\"\n",
+ "model_name = f\"Llama3-8B-fas-{suffix}\"\n",
+ "endpoint_name = model_name\n",
+ "health_check_timeout = 900\n",
+ "\n",
+ "HF_TOKEN = os.getenv(\"HUGGING_FACE_HUB_TOKEN\") or getpass(\"Enter HUGGINGFACE Access Token: \")\n",
+ "\n",
+ "# retrieve the llm image uri\n",
+ "# tgi_dlc = f\"763104351884.dkr.ecr.{region}.amazonaws.com/huggingface-pytorch-tgi-inference:2.1-tgi2.0-gpu-py310-cu121-ubuntu22.04\"\n",
+ "tgi_dlc = get_huggingface_llm_image_uri(\"huggingface\", version=\"2.0.0\")\n",
+ "\n",
+ "# Define Model and Endpoint configuration parameter\n",
+ "config = {\n",
+ " \"HF_MODEL_ID\": \"meta-llama/Meta-Llama-3-8B-Instruct\", # model_id from hf.co/models\n",
+ " \"SM_NUM_GPUS\": \"1\", # Number of GPU used per replica\n",
+ " \"MAX_INPUT_LENGTH\": \"2048\", # Max length of input text\n",
+ " \"MAX_TOTAL_TOKENS\": \"4096\", # Max length of the generation (including input text)\n",
+ " \"MAX_BATCH_TOTAL_TOKENS\": \"8192\", # Limits the number of tokens that can be processed in parallel during the generation\n",
+ " \"MESSAGES_API_ENABLED\": \"true\", # Enable the messages API\n",
+ " \"HUGGING_FACE_HUB_TOKEN\": HF_TOKEN,\n",
+ "}\n",
+ "\n",
+ "# create HuggingFaceModel with the image uri\n",
+ "print(f\"Creating model: [b green]{model_name}...\")\n",
+ "llm_model = HuggingFaceModel(name=model_name, role=role, image_uri=tgi_dlc, env=config)\n",
+ "\n",
+ "# Deploy model to Amazon SageMaker endpoint\n",
+ "print(f\"Deploying model to endpoint: [b magenta]{endpoint_name}...\")\n",
+ "predictor = llm_model.deploy(\n",
+ " endpoint_name=endpoint_name,\n",
+ " initial_instance_count=1,\n",
+ " instance_type=instance_type,\n",
+ " container_startup_health_check_timeout=health_check_timeout, # 15 minutes to be able to load the model\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "83e1af5c-e713-4cf8-bc23-1c96f1e61327",
+ "metadata": {},
+ "source": [
+ "## Inference\n",
+ "\n",
+ "Invoke and test endpoint using messages API. Refer to HF [Messages API](https://huggingface.co/docs/text-generation-inference/messages_api) for more info."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6d9ecc2-fffe-4ff1-b78b-1222fe6d32de",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Prompt to generate\n",
+ "messages = [\n",
+ " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
+ " {\"role\": \"user\", \"content\": \"What is deep learning?\"},\n",
+ "]\n",
+ "\n",
+ "# Generation arguments\n",
+ "parameters = {\n",
+ " \"model\": hf_model_id, # model id is required\n",
+ " \"top_p\": 0.6,\n",
+ " \"temperature\": 0.9,\n",
+ " \"max_tokens\": 512,\n",
+ " \"stop\": [\"<|eot_id|>\"],\n",
+ "}\n",
+ "\n",
+ "chat = predictor.predict({\"messages\": messages, **parameters})\n",
+ "\n",
+ "# Unpack and print response\n",
+ "print(chat[\"choices\"][0][\"message\"][\"content\"].strip())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3cdb619d-b402-46bf-9451-62f50f70e878",
+ "metadata": {},
+ "source": [
+ "## Baseline average latency at various concurrency levels (Optional)\n",
+ "\n",
+ "
NOTE: Running the following cell is optional
\n",
+ "By capturing average latency across various concurrency levels, we can get a fair idea on after how many concurrent request does endpoint performance would degrade significantly.
\n",
+ "Having this information can help define values for scaling policy accordingly. \n",
+ "
\n",
+ "\n",
+ "
\n",
+ "INFO: ℹ️ Signal here is, at a given concurrency level you start to see average latency increase significantly. \n",
+ "At this concurrency level the endpoint gets overloaded and cannot serve requests in a timely fashion. \n",
+ "We use these values to set as threshold values for autoscaling.\n",
+ "
\n",
+ "INFO:ℹ️ Refer to utils.llmperf for `trigger_autoscaling` function implementation\n",
+ "
\n",
+ "\n",
+ "\n",
+ "### Monitor Alarm Trigger times and Scaling event times\n",
+ "As llmperf generates traffic to the endpoint continuously this trigger auto-scaling.\n",
+ "\n",
+ "The `monitor_scaling_events` function does the following:\n",
+ "- Calculates time taken for alarm to go into InAlarm state.\n",
+ "- checks if alarm is InAlarm state. If yes, then starts the scaling timer\n",
+ "- continuously monitors the `DesiredInstanceCount` property of the endpoint\n",
+ " - waits till `CurrentInstanceCount == DesiredInstanceCount` and `EndpointStatus` is `InService`\n",
+ "- Calculates time taken to scale out instances prints the times in a table\n",
+ "\n",
+ "The below cell triggers auto scaling action and calls the monitor_scaling_events immediately on the AlarmHigh\n",
+ "\n",
+ "
\n",
+ "INFO:ℹ️ Refer to utils.autoscaling for `monitor_scaling_events` function implementation\n",
+ "
\n",
+ "\n",
+ "
\n",
+ "NOTE: ⚠️Per the ScaleOut Alarm, scale-out actions only start after the threshold of ConcurrentRequestsPerModel >= 20 for 3 datapoints within 3 minutes is breached.\n",
+ "
\n",
+ "NOTE: ⚠️Per the ScaleIn Alarm, scale-in actions only start after the threshold of ConcurrentRequestsPerModel <= 10 for 3 datapoints within 3 minutes is breached.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "883924cc-9f29-48cf-85ac-1d96c0a3dd16",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Start monitoring scaling events\n",
+ "SLEEP_TIME = 5 # time to sleep\n",
+ "scaling_times = monitor_scaling_events(\n",
+ " endpoint_name,\n",
+ " alarm_name_scale_in, # scale_in cloudwatch metric alarm name\n",
+ " SLEEP_TIME,\n",
+ " cloudwatch_client,\n",
+ " sagemaker_client,\n",
+ ")\n",
+ "\n",
+ "# Print scaling times\n",
+ "console = Console()\n",
+ "table = print_scaling_times(scaling_times)\n",
+ "console.print(table)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02a2d5b0-dc4b-40e3-8ada-ceddecfdac1a",
+ "metadata": {},
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "- Delete cloudwatch alarms\n",
+ "- Delete scaling policies\n",
+ "- Deregister scalable target\n",
+ "- Delete model\n",
+ "- Delete endpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5f44ef56-dbcc-4e23-97c2-af6cb062b498",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Delete CloudWatch alarms created for Step scaling policy\n",
+ "alarm_names = [alarm_name_scale_out, alarm_name_scale_in]\n",
+ "\n",
+ "for alarm in alarm_names:\n",
+ " try:\n",
+ " cloudwatch_client.delete_alarms(AlarmNames=[alarm])\n",
+ " print(f\"Deleted CloudWatch scale-out alarm [b]{alarm} ✅\")\n",
+ " except cloudwatch_client.exceptions.ResourceNotFoundException:\n",
+ " print(f\"CloudWatch scale-out alarm [b]{alarm}[/b] not found.\")\n",
+ "\n",
+ "\n",
+ "# Delete scaling policies\n",
+ "print(\"---\" * 10)\n",
+ "step_policies = [f\"{endpoint_name}-ScaleInPolicy\", f\"{endpoint_name}-ScaleOutPolicy\"]\n",
+ "for policy_name in step_policies:\n",
+ " try:\n",
+ " autoscaling_client.delete_scaling_policy(\n",
+ " PolicyName=policy_name,\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n",
+ " )\n",
+ " print(f\"Deleted scaling policy [i green]{policy_name} ✅\")\n",
+ " except autoscaling_client.exceptions.ObjectNotFoundException:\n",
+ " print(f\"Scaling policy [i]{policy_name}[/i] not found.\")\n",
+ "\n",
+ "# Deregister scalable target\n",
+ "try:\n",
+ " autoscaling_client.deregister_scalable_target(\n",
+ " ServiceNamespace=\"sagemaker\",\n",
+ " ResourceId=resource_id,\n",
+ " ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n",
+ " )\n",
+ " print(f\"Scalable target for [b]{resource_id}[/b] deregistered. ✅\")\n",
+ "except autoscaling_client.exceptions.ObjectNotFoundException:\n",
+ " print(f\"Scalable target for [b]{resource_id}[/b] not found!.\")\n",
+ "\n",
+ "print(\"---\" * 10)\n",
+ "# Delete model and endpoint\n",
+ "try:\n",
+ " print(f\"Deleting model: [b green]{model_name} ✅\")\n",
+ " predictor.delete_model()\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "try:\n",
+ " print(f\"Deleting endpoint: [b magenta]{predictor.endpoint_name} ✅\")\n",
+ " predictor.delete_endpoint()\n",
+ "except Exception as e:\n",
+ " print(f\"{e}\")\n",
+ "\n",
+ "print(\"---\" * 10)\n",
+ "print(\"Done\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f43d8011",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/trigger_autoscaling.sh b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/trigger_autoscaling.sh
new file mode 100644
index 0000000000..653a0a9889
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/trigger_autoscaling.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+# Check if required environment variables are set
+if [ -z "$AWS_ACCESS_KEY_ID" ] || [ -z "$AWS_SECRET_ACCESS_KEY" ] || [ -z "$AWS_SESSION_TOKEN" ] || [ -z "$AWS_REGION" ] || [ -z "$EP_NAME" ] || [ -z "$NUM_CONCURRENT_REQUESTS" ]; then
+ echo "Error: Required environment variables are not set."
+ exit 1
+fi
+
+echo "Installing llmperf..."
+rm -rf llmperf && \
+git clone https://github.com/philschmid/llmperf.git && \
+uv pip install -e llmperf/
+
+DIR="results"
+
+if [ ! -d "$DIR" ]; then
+ mkdir -p "$DIR"
+ echo "Created $DIR directory."
+else
+ echo "$DIR directory already exists."
+fi
+
+echo "Starting benchmarking scripts on endpoint $EP_NAME ..."
+
+start_time=$(date +%s)
+
+MESSAGES_API=true python llmperf/token_benchmark_ray.py \
+--model $EP_NAME \
+--llm-api "sagemaker" \
+--max-num-completed-requests 1000 \
+--timeout 600 \
+--num-concurrent-requests $NUM_CONCURRENT_REQUESTS \
+--results-dir "results"
+
+end_time=$(date +%s)
+echo "Execution time was $((end_time - start_time)) secs."
diff --git a/ prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/api_batch_create/__init__.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/__init__.py
similarity index 100%
rename from prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/api_batch_create/__init__.py
rename to deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/__init__.py
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/autoscaling.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/autoscaling.py
new file mode 100644
index 0000000000..1792a0887f
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/autoscaling.py
@@ -0,0 +1,174 @@
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from statistics import mean
+
+from rich import print
+from rich.table import Table
+from rich.progress import Progress, SpinnerColumn, TimeElapsedColumn
+
+
+# Function to update the user prompt in the messages list
+def update_user_prompt(messages, prompt):
+ for message in messages:
+ if message["role"] == "user":
+ message["content"] = prompt
+ return messages
+
+
+# helper function to record latency
+def get_request_latency(payload, endpoint_name, sagemaker_runtime_client):
+ start_time = time.time()
+ _ = sagemaker_runtime_client.invoke_endpoint(
+ EndpointName=endpoint_name,
+ ContentType="application/json",
+ Body=json.dumps(payload),
+ )
+ # _ = predictor.predict(payload)
+ end_time = time.time()
+ latency = end_time - start_time
+ # print(chat["choices"][0]["message"]["content"].strip())
+ return latency
+
+
+# Function to test concurrent requests with a given concurrency level
+def test_concurrency_level(
+ concurrency_level,
+ prompts,
+ messages,
+ parameters,
+ endpoint_name,
+ sagemaker_runtime_client,
+):
+ payloads = [
+ {"messages": update_user_prompt(messages, prompt), **parameters}
+ for prompt in prompts * (concurrency_level // len(prompts))
+ ]
+ latencies = []
+ with ThreadPoolExecutor(max_workers=concurrency_level) as executor:
+ futures = [
+ executor.submit(
+ get_request_latency, payload, endpoint_name, sagemaker_runtime_client
+ )
+ for payload in payloads
+ ]
+ for future in as_completed(futures):
+ try:
+ latency = future.result()
+ latencies.append(latency)
+ except Exception as e:
+ print(f"Request failed: {e}")
+
+ avg_latency = mean(latencies)
+ return avg_latency
+
+
+# helper function to get the current instance count of the endpoint
+def get_scaling_instance_counts(endpoint_name, sagemaker_client):
+ endpoint_description = sagemaker_client.describe_endpoint(
+ EndpointName=endpoint_name
+ )
+ current = endpoint_description["ProductionVariants"][0]["CurrentInstanceCount"]
+ desired = endpoint_description["ProductionVariants"][0]["DesiredInstanceCount"]
+ current_status = endpoint_description["EndpointStatus"]
+ return current, desired, current_status
+
+
+# Helper function to check if any alarm is in "InAlarm" state
+def is_alarm_in_alarm_state(alarm_name, cloudwatch_client):
+ alarm_state = cloudwatch_client.describe_alarms(AlarmNames=[alarm_name])[
+ "MetricAlarms"
+ ][0]["StateValue"]
+ if alarm_state == "ALARM":
+ return True
+ return False
+
+
+# Helper function to monitor the endpoint for scaling events
+def monitor_scaling_events(
+ endpoint_name, alarm_name, time_to_sleep, cloudwatch_client, sagemaker_client
+):
+ scaling_times = {}
+ (
+ current_instance_count,
+ desired_instance_count,
+ status,
+ ) = get_scaling_instance_counts(endpoint_name, sagemaker_client)
+ print(f"Initial instance count: {current_instance_count}", flush=True)
+ print(f"Tracking Alarm: [i green]{alarm_name}[/i green]", flush=True)
+
+ with Progress(
+ SpinnerColumn(), *Progress.get_default_columns(), TimeElapsedColumn()
+ ) as progress:
+ alarm_task = progress.add_task(
+ "[green]Waiting for alarm to trigger...", total=None
+ )
+
+ alarm_timer_start = time.time()
+
+ while True:
+ if is_alarm_in_alarm_state(alarm_name, cloudwatch_client):
+ start_time = time.time()
+ alarm_timer_end = time.time()
+ time_to_alarm = alarm_timer_end - alarm_timer_start
+ progress.update(
+ alarm_task,
+ description=f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.",
+ total=1,
+ completed=1,
+ )
+ # print(f"[bold red]Alarm triggered! Time to alarm trigger: {time_to_alarm:.2f} seconds.")
+ break
+ else:
+ progress.update(alarm_task, advance=1)
+ # Wait for time_to_sleep seconds before checking again
+ time.sleep(time_to_sleep)
+
+ scaling_task = progress.add_task(
+ "[green]Waiting for scaling to complete...", total=None
+ )
+
+ while True:
+ (
+ current_instance_count,
+ desired_instance_count,
+ status,
+ ) = get_scaling_instance_counts(endpoint_name, sagemaker_client)
+
+ if current_instance_count == desired_instance_count:
+ # Add sleep here as endpoint status doesn't change to `Updating` instantaneously
+ time.sleep(time_to_sleep)
+ if status == "InService":
+ end_time = time.time()
+ scaling_time = end_time - start_time
+ scaling_times[desired_instance_count] = scaling_time
+ progress.update(
+ scaling_task,
+ description=f"[bold green]Scaling to {desired_instance_count} instances completed in {scaling_time:.2f} seconds.",
+ total=1,
+ completed=1,
+ )
+ break
+ progress.update(scaling_task, advance=1)
+ # Wait for time_to_sleep seconds before checking again
+ time.sleep(time_to_sleep)
+
+ return scaling_times
+
+
+# function to print scaling times in a table
+def print_scaling_times(scaling_times):
+ # Create a table
+ table = Table(title="Scaling Times")
+
+ # Add columns
+ table.add_column(
+ "Target Instance Count", justify="right", style="cyan", no_wrap=True
+ )
+ table.add_column("Scaling Time (seconds)", justify="right", style="magenta")
+
+ # Add rows
+ for target_instance_count, scaling_time in scaling_times.items():
+ table.add_row(str(target_instance_count), f"{scaling_time:.2f}")
+
+ return table
diff --git a/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/llmperf.py b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/llmperf.py
new file mode 100644
index 0000000000..f9966be496
--- /dev/null
+++ b/ deploy_and_monitor/sm-app_autoscaling_realtime_endpoints_step_scaling/utils/llmperf.py
@@ -0,0 +1,100 @@
+import glob
+import json
+import os
+import subprocess
+import time
+
+from rich import box, print
+from rich.table import Table
+
+
+# LLMPerf requires AWS Creds as ENV variables along with endpoint name
+def trigger_auto_scaling(creds, region, endpoint_name, num_concurrent_requests):
+ # Set environment variables
+ os.environ["AWS_ACCESS_KEY_ID"] = creds.access_key
+ os.environ["AWS_SECRET_ACCESS_KEY"] = creds.secret_key
+ os.environ["AWS_SESSION_TOKEN"] = creds.token
+ os.environ["AWS_REGION"] = region
+ os.environ["EP_NAME"] = endpoint_name
+ os.environ["NUM_CONCURRENT_REQUESTS"] = str(num_concurrent_requests)
+
+ # Path to the shell script
+ # script_path = "./trigger_autoscaling.sh"
+ # current_dir = os.getcwd()
+ script_path = os.path.abspath(
+ os.path.join(os.path.dirname(__file__), "..", "trigger_autoscaling.sh")
+ )
+
+ # print(f"Current working directory: {current_dir}")
+ # print(f"Full path to script: {script_path}")
+
+ # Check if the file exists
+ if os.path.exists(script_path):
+ print(f"Calling LLMPerf shell script: {script_path}")
+ else:
+ print(f"LLMPerf shell script file not found at {script_path}")
+
+ # Make sure the script is executable
+ # os.chmod(script_path, 0o755)
+
+ # Run the shell script
+ print(f"Launching LLMPerf with {num_concurrent_requests} concurrent requests")
+ process = subprocess.Popen([script_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+ return process
+
+
+# helper function to monitor the process
+def monitor_process(proc):
+ while True:
+ retcode = proc.poll() # Check if the process has terminated
+ if retcode is not None:
+ # Process has terminated
+ print(f"Process {proc.pid} finished with return code {retcode}")
+
+ # Capture and print any output from the process
+ stdout, stderr = proc.communicate()
+ if stdout:
+ print(f"Process output:\n{stdout.decode('utf-8')}")
+ if stderr:
+ print(f"Process errors:\n{stderr.decode('utf-8')}")
+
+ break
+ else:
+ # Process is still running
+ print(f"Process {proc.pid} is still running...")
+ time.sleep(15) # Check every 15 seconds
+
+
+# helper function to print llmperf results
+def print_llmperf_results(num_concurrent_requests):
+ # Reads the summary.json file and prints the results
+ with open(glob.glob("results/*summary.json")[0], "r") as file:
+ data = json.load(file)
+
+ # Create a table
+ perf_table = Table(
+ title="LLMPerf Endpoint Metrics",
+ row_styles=["bold", "bold"],
+ box=box.MINIMAL_DOUBLE_HEAD,
+ )
+ # Add columns
+ perf_table.add_column("Metric", justify="right", style="green", no_wrap=True)
+ perf_table.add_column("Units", justify="left", style="magenta")
+
+ # Add rows
+ perf_table.add_row("Concurrent requests", f"{num_concurrent_requests}")
+ perf_table.add_row("Avg. Input token length", f"{data['mean_input_tokens']}")
+ perf_table.add_row("Avg. Output token length", f"{data['mean_output_tokens']}")
+ perf_table.add_row("Avg. First-Time-To-Token", f"{data['results_ttft_s_mean']*1000:.2f}ms")
+ perf_table.add_row(
+ "Avg. Thorughput",
+ f"{data['results_mean_output_throughput_token_per_s']:.2f} tokens/sec",
+ )
+ perf_table.add_row(
+ "Avg. Latency", f"{data['results_inter_token_latency_s_mean']*1000:.2f}ms/token"
+ )
+
+ # Print the table
+ # console.print(perf_table)
+ return perf_table
diff --git a/ deploy_and_monitor/sm-async_inference_walkthrough/sm-async_inference_walkthrough.ipynb b/ deploy_and_monitor/sm-async_inference_walkthrough/sm-async_inference_walkthrough.ipynb
index de5695ee6b..0ad342bde7 100755
--- a/ deploy_and_monitor/sm-async_inference_walkthrough/sm-async_inference_walkthrough.ipynb
+++ b/ deploy_and_monitor/sm-async_inference_walkthrough/sm-async_inference_walkthrough.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -679,35 +679,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-async_inference_with_python_sdk/sm-async_inference_with_python_sdk.ipynb b/ deploy_and_monitor/sm-async_inference_with_python_sdk/sm-async_inference_with_python_sdk.ipynb
index 2abd695e3a..e8cac6fdb1 100644
--- a/ deploy_and_monitor/sm-async_inference_with_python_sdk/sm-async_inference_with_python_sdk.ipynb
+++ b/ deploy_and_monitor/sm-async_inference_with_python_sdk/sm-async_inference_with_python_sdk.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -646,35 +646,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-batch_inference_with_torchserve/sm-batch_inference_with_torchserve.ipynb b/ deploy_and_monitor/sm-batch_inference_with_torchserve/sm-batch_inference_with_torchserve.ipynb
index cce3f126ac..2e8f926fc5 100644
--- a/ deploy_and_monitor/sm-batch_inference_with_torchserve/sm-batch_inference_with_torchserve.ipynb
+++ b/ deploy_and_monitor/sm-batch_inference_with_torchserve/sm-batch_inference_with_torchserve.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -318,35 +318,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/Dockerfile b/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/Dockerfile
similarity index 100%
rename from deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/Dockerfile
rename to deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/Dockerfile
diff --git a/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/dbscan.R b/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/dbscan.R
similarity index 100%
rename from deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/dbscan.R
rename to deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/dbscan.R
diff --git a/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/plumber.R b/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/plumber.R
similarity index 100%
rename from deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/plumber.R
rename to deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/plumber.R
diff --git a/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/sm-batch_transform_pca_dbscan_movie_clusters.ipynb.ipynb b/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/sm-batch_transform_pca_dbscan_movie_clusters.ipynb
similarity index 95%
rename from deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/sm-batch_transform_pca_dbscan_movie_clusters.ipynb.ipynb
rename to deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/sm-batch_transform_pca_dbscan_movie_clusters.ipynb
index 0353994c8b..746a94d057 100644
--- a/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters.ipynb/sm-batch_transform_pca_dbscan_movie_clusters.ipynb.ipynb
+++ b/ deploy_and_monitor/sm-batch_transform_pca_dbscan_movie_clusters/sm-batch_transform_pca_dbscan_movie_clusters.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1035,35 +1035,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1089,4 +1089,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-batch_transform_pytorch/sm-batch_transform_pytorch.ipynb b/ deploy_and_monitor/sm-batch_transform_pytorch/sm-batch_transform_pytorch.ipynb
index 462b8c4e7d..4b52e218ca 100644
--- a/ deploy_and_monitor/sm-batch_transform_pytorch/sm-batch_transform_pytorch.ipynb
+++ b/ deploy_and_monitor/sm-batch_transform_pytorch/sm-batch_transform_pytorch.ipynb
@@ -26,7 +26,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -122,10 +122,10 @@
"yes: standard output: Broken pipe\n",
"Collecting torchvision\n",
" Downloading torchvision-0.11.2-cp36-cp36m-manylinux1_x86_64.whl (23.3 MB)\n",
- "\u001b[K |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 23.3 MB 5.9 MB/s eta 0:00:01\n",
+ "\u001b[K |████████████████████████████████| 23.3 MB 5.9 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting torch==1.10.1\n",
" Downloading torch-1.10.1-cp36-cp36m-manylinux1_x86_64.whl (881.9 MB)\n",
- "\u001b[K |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 881.9 MB 4.4 kB/s s eta 0:00:01 |\u2588\u258a | 46.2 MB 47.4 MB/s eta 0:00:18\n",
+ "\u001b[K |████████████████████████████████| 881.9 MB 4.4 kB/s s eta 0:00:01 |█▊ | 46.2 MB 47.4 MB/s eta 0:00:18\n",
"\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from torchvision) (1.19.5)\n",
"Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /opt/conda/lib/python3.6/site-packages (from torchvision) (8.1.2)\n",
"Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.6/site-packages (from torch==1.10.1->torchvision) (3.7.4.3)\n",
@@ -3945,35 +3945,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -4028,7 +4028,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_7c0983f176954e2dbfbc19134390ce75",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_6ce2b254a3084416ba39feea20f4db50",
"value": ""
}
@@ -4260,7 +4260,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_611ed08651634af1a4e9af17c997458e",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_23778be2d2104b5eba74710a40863802",
"value": ""
}
@@ -4459,7 +4459,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_f1a5b433f6a44c68838de5494af1ef8a",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_79e749c778614474ba11308b7ef5047d",
"value": ""
}
@@ -4496,7 +4496,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_22ade62672be47228462b15640478dc4",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_74ba869539e04697a7c2c4974ce3f95f",
"value": " 5120/? [00:00<00:00, 229084.47it/s]"
}
@@ -4584,7 +4584,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_57269fbb396e461c9f0b4e65e1029410",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_43863011b1534b2f90aed2c606dc1c78",
"value": " 9913344/? [00:00<00:00, 16970534.49it/s]"
}
@@ -4865,7 +4865,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_9c59a9348eff4496b412fe3b2bb99293",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_a4b9335979064476a58a8ea49c5a09ef",
"value": " 1649664/? [00:00<00:00, 3026257.05it/s]"
}
@@ -4908,7 +4908,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_770b0c1f4ccf4cde8138bd1b73c93601",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_3e43540206cb40c59b33de0c6fdf7e6d",
"value": ""
}
@@ -5096,7 +5096,7 @@
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_79040f80b3de475fa8a880e95449a07f",
- "placeholder": "\u200b",
+ "placeholder": "",
"style": "IPY_MODEL_ccc28938faa74efe97368eb13b079243",
"value": " 29696/? [00:00<00:00, 451461.09it/s]"
}
@@ -5388,4 +5388,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-batch_transform_with_torchserve/sm-batch_transform_with_torchserve.ipynb b/ deploy_and_monitor/sm-batch_transform_with_torchserve/sm-batch_transform_with_torchserve.ipynb
index 91ca779568..3aadc71727 100644
--- a/ deploy_and_monitor/sm-batch_transform_with_torchserve/sm-batch_transform_with_torchserve.ipynb
+++ b/ deploy_and_monitor/sm-batch_transform_with_torchserve/sm-batch_transform_with_torchserve.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -359,35 +359,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -412,4 +412,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform/sm-clarify_model_bias_monitor_batch_transform.ipynb b/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform/sm-clarify_model_bias_monitor_batch_transform.ipynb
index 017009304c..0a38e77ddb 100644
--- a/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform/sm-clarify_model_bias_monitor_batch_transform.ipynb
+++ b/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform/sm-clarify_model_bias_monitor_batch_transform.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1352,35 +1352,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1406,4 +1406,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform_json/sm-clarify_model_bias_monitor_batch_transform_json.ipynb b/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform_json/sm-clarify_model_bias_monitor_batch_transform_json.ipynb
index 600bb55b60..b97432bc48 100644
--- a/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform_json/sm-clarify_model_bias_monitor_batch_transform_json.ipynb
+++ b/ deploy_and_monitor/sm-clarify_model_bias_monitor_batch_transform_json/sm-clarify_model_bias_monitor_batch_transform_json.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1927,35 +1927,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint/sm-clarify_model_bias_monitor_for_endpoint.ipynb b/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint/sm-clarify_model_bias_monitor_for_endpoint.ipynb
index 02779a4825..83ebe26965 100644
--- a/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint/sm-clarify_model_bias_monitor_for_endpoint.ipynb
+++ b/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint/sm-clarify_model_bias_monitor_for_endpoint.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1531,35 +1531,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
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+ "\n",
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"\n",
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"\n",
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+ "\n",
"\n",
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+ "\n",
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+ "\n",
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- "\n",
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],
diff --git a/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint_json/sm-clarify_model_bias_monitor_for_endpoint_json.ipynb b/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint_json/sm-clarify_model_bias_monitor_for_endpoint_json.ipynb
index 9ea09c3c5e..1f8ca92c3a 100644
--- a/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint_json/sm-clarify_model_bias_monitor_for_endpoint_json.ipynb
+++ b/ deploy_and_monitor/sm-clarify_model_bias_monitor_for_endpoint_json/sm-clarify_model_bias_monitor_for_endpoint_json.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -2202,35 +2202,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
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+ "\n",
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- "\n",
+ "\n",
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+ "\n",
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+ "\n"
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diff --git a/ deploy_and_monitor/sm-clarify_model_explainability_monitor_attribution_drift_for_endpoint_json/test_data/test-dataset.json b/ deploy_and_monitor/sm-clarify_model_explainability_monitor_attribution_drift_for_endpoint_json/test_data/test-dataset.json
deleted file mode 100644
index 0da5ba0c3f..0000000000
--- a/ deploy_and_monitor/sm-clarify_model_explainability_monitor_attribution_drift_for_endpoint_json/test_data/test-dataset.json
+++ /dev/null
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--- a/ deploy_and_monitor/sm-clarify_model_explainability_monitor_attribution_drift_for_endpoint_json/test_data/validation-dataset.json
+++ /dev/null
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diff --git a/ deploy_and_monitor/sm-clarify_model_monitor_model_feature_attribution_drift_for_endpoint/model/ll-adult-prediction-model.tar.gz b/ deploy_and_monitor/sm-clarify_model_monitor_model_feature_attribution_drift_for_endpoint/model/ll-adult-prediction-model.tar.gz
deleted file mode 100644
index a066dbdfa3..0000000000
Binary files a/ deploy_and_monitor/sm-clarify_model_monitor_model_feature_attribution_drift_for_endpoint/model/ll-adult-prediction-model.tar.gz and /dev/null differ
diff --git a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/inference.py b/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/inference.py
deleted file mode 100644
index f01edfd1bd..0000000000
--- a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/inference.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import json
-import logging
-import sys
-import os
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-logger.addHandler(logging.StreamHandler(sys.stdout))
-
-# Based on https://github.com/pytorch/examples/blob/master/mnist/main.py
-class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
- self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
- self.conv2_drop = nn.Dropout2d()
- self.fc1 = nn.Linear(320, 50)
- self.fc2 = nn.Linear(50, 10)
-
- def forward(self, x):
- x = F.relu(F.max_pool2d(self.conv1(x), 2))
- x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
- x = x.view(-1, 320)
- x = F.relu(self.fc1(x))
- x = F.dropout(x, training=self.training)
- x = self.fc2(x)
- return F.log_softmax(x, dim=1)
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# defining model and loading weights to it.
-def model_fn(model_dir):
- model = Net()
- with open(os.path.join(model_dir, "model.pth"), "rb") as f:
- model.load_state_dict(torch.load(f))
- model.to(device).eval()
- return model
-
-
-# data preprocessing
-def input_fn(request_body, request_content_type):
- assert request_content_type == "application/json"
- data = json.loads(request_body)["inputs"]
- data = torch.tensor(data, dtype=torch.float32, device=device)
- return data
-
-
-# inference
-def predict_fn(input_object, model):
- with torch.no_grad():
- prediction = model(input_object)
- return prediction
-
-
-# postprocess
-def output_fn(predictions, content_type):
- assert content_type == "application/json"
- res = predictions.cpu().numpy().tolist()
- return json.dumps(res)
diff --git a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/test_inference.py b/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/test_inference.py
deleted file mode 100644
index 45958f971f..0000000000
--- a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/test_inference.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import json
-import os
-import shutil
-import tarfile
-
-import boto3
-import botocore
-import numpy as np
-import sagemaker
-
-from inference import input_fn, model_fn, output_fn, predict_fn
-
-
-def fetch_model(model_data):
- """Untar the model.tar.gz object either from local file system
- or a S3 location
-
- Args:
- model_data (str): either a path to local file system starts with
- file:/// that points to the `model.tar.gz` file or an S3 link
- starts with s3:// that points to the `model.tar.gz` file
-
- Returns:
- model_dir (str): the directory that contains the uncompress model
- checkpoint files
- """
-
- model_dir = "/tmp/model"
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- if model_data.startswith("file"):
- _check_model(model_data)
- shutil.copy2(
- os.path.join(model_dir, "model.tar.gz"), os.path.join(model_dir, "model.tar.gz")
- )
- elif model_data.startswith("s3"):
- # get bucket name and object key
- bucket_name = model_data.split("/")[2]
- key = "/".join(model_data.split("/")[3:])
-
- s3 = boto3.resource("s3")
- try:
- s3.Bucket(bucket_name).download_file(key, os.path.join(model_dir, "model.tar.gz"))
- except botocore.exceptions.ClientError as e:
- if e.response["Error"]["Code"] == "404":
- print("the object does not exist.")
- else:
- raise
-
- # untar the model
- tar = tarfile.open(os.path.join(model_dir, "model.tar.gz"))
- tar.extractall(model_dir)
- tar.close()
-
- return model_dir
-
-
-def test(model_data):
- # decompress the model.tar.gz file
- model_dir = fetch_model(model_data)
-
- # load the model
- net = model_fn(model_dir)
-
- # simulate some input data to test transform_fn
-
- data = {"inputs": np.random.rand(16, 1, 28, 28).tolist()}
-
- # encode numpy array to binary stream
- serializer = sagemaker.serializers.JSONSerializer()
-
- jstr = serializer.serialize(data)
- jstr = json.dumps(data)
-
- # "send" the bin_stream to the endpoint for inference
- # inference container calls transform_fn to make an inference
- # and get the response body for the caller
-
- content_type = "application/json"
- input_object = input_fn(jstr, content_type)
- predictions = predict_fn(input_object, net)
- res = output_fn(predictions, content_type)
- print(res)
- return
-
-
-if __name__ == "__main__":
- model_data = "s3://sagemaker-us-west-2-688520471316/mxnet/mnist/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz"
- test(model_data)
diff --git a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/test_train.py b/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/test_train.py
deleted file mode 100644
index c4e062fcc0..0000000000
--- a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/test_train.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import json
-import os
-import sys
-
-import boto3
-from train import parse_args, train
-
-dirname = os.path.dirname(os.path.abspath(__file__))
-
-
-def download_from_s3(data_dir="/tmp/data", train=True):
- """Download MNIST dataset and convert it to numpy array
-
- Args:
- data_dir (str): directory to save the data
- train (bool): download training set
-
- Returns:
- tuple of images and labels as numpy arrays
- """
-
- if not os.path.exists(data_dir):
- os.makedirs(data_dir)
-
- if train:
- images_file = "train-images-idx3-ubyte.gz"
- labels_file = "train-labels-idx1-ubyte.gz"
- else:
- images_file = "t10k-images-idx3-ubyte.gz"
- labels_file = "t10k-labels-idx1-ubyte.gz"
-
- # download objects
- s3 = boto3.client("s3")
- bucket = f"sagemaker-example-files-prod-{boto3.session.Session().region_name}"
- for obj in [images_file, labels_file]:
- key = os.path.join("datasets/image/MNIST", obj)
- dest = os.path.join(data_dir, obj)
- if not os.path.exists(dest):
- s3.download_file(bucket, key, dest)
- return
-
-
-class Env:
- def __init__(self):
- # simulate container env
- os.environ["SM_MODEL_DIR"] = "/tmp/model"
- os.environ["SM_CHANNEL_TRAINING"] = "/tmp/data"
- os.environ["SM_CHANNEL_TESTING"] = "/tmp/data"
- os.environ["SM_HOSTS"] = '["algo-1"]'
- os.environ["SM_CURRENT_HOST"] = "algo-1"
- os.environ["SM_NUM_GPUS"] = "0"
-
-
-if __name__ == "__main__":
- Env()
- args = parse_args()
- train(args)
diff --git a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/train.py b/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/train.py
deleted file mode 100644
index c1797d438b..0000000000
--- a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/code/train.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import argparse
-import gzip
-import json
-import logging
-import os
-import sys
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torch.utils.data import DataLoader, Dataset
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-logger.addHandler(logging.StreamHandler(sys.stdout))
-
-# Based on https://github.com/pytorch/examples/blob/master/mnist/main.py
-class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
- self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
- self.conv2_drop = nn.Dropout2d()
- self.fc1 = nn.Linear(320, 50)
- self.fc2 = nn.Linear(50, 10)
-
- def forward(self, x):
- x = F.relu(F.max_pool2d(self.conv1(x), 2))
- x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
- x = x.view(-1, 320)
- x = F.relu(self.fc1(x))
- x = F.dropout(x, training=self.training)
- x = self.fc2(x)
- return F.log_softmax(x, dim=1)
-
-
-# Decode binary data from SM_CHANNEL_TRAINING
-# Decode and preprocess data
-# Create map dataset
-
-
-def normalize(x, axis):
- eps = np.finfo(float).eps
- mean = np.mean(x, axis=axis, keepdims=True)
- # avoid division by zero
- std = np.std(x, axis=axis, keepdims=True) + eps
- return (x - mean) / std
-
-
-def convert_to_tensor(data_dir, images_file, labels_file):
- """Byte string to torch tensor"""
- with gzip.open(os.path.join(data_dir, images_file), "rb") as f:
- images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28).astype(np.float32)
-
- with gzip.open(os.path.join(data_dir, labels_file), "rb") as f:
- labels = np.frombuffer(f.read(), np.uint8, offset=8).astype(np.int64)
-
- # normalize the images
- images = normalize(images, axis=(1, 2))
-
- # add channel dimension (depth-major)
- images = np.expand_dims(images, axis=1)
-
- # to torch tensor
- images = torch.tensor(images, dtype=torch.float32)
- labels = torch.tensor(labels, dtype=torch.int64)
- return images, labels
-
-
-class MNIST(Dataset):
- def __init__(self, data_dir, train=True):
-
- if train:
- images_file = "train-images-idx3-ubyte.gz"
- labels_file = "train-labels-idx1-ubyte.gz"
- else:
- images_file = "t10k-images-idx3-ubyte.gz"
- labels_file = "t10k-labels-idx1-ubyte.gz"
-
- self.images, self.labels = convert_to_tensor(data_dir, images_file, labels_file)
-
- def __len__(self):
- return len(self.labels)
-
- def __getitem__(self, idx):
- return self.images[idx], self.labels[idx]
-
-
-def train(args):
- use_cuda = args.num_gpus > 0
- device = torch.device("cuda" if use_cuda > 0 else "cpu")
-
- torch.manual_seed(args.seed)
- if use_cuda:
- torch.cuda.manual_seed(args.seed)
-
- train_loader = DataLoader(
- MNIST(args.train, train=True), batch_size=args.batch_size, shuffle=True
- )
- test_loader = DataLoader(
- MNIST(args.test, train=False), batch_size=args.test_batch_size, shuffle=False
- )
-
- net = Net().to(device)
- loss_fn = nn.CrossEntropyLoss()
- optimizer = optim.Adam(
- net.parameters(), betas=(args.beta_1, args.beta_2), weight_decay=args.weight_decay
- )
-
- logger.info("Start training ...")
- for epoch in range(1, args.epochs + 1):
- net.train()
- for batch_idx, (imgs, labels) in enumerate(train_loader, 1):
- imgs, labels = imgs.to(device), labels.to(device)
- output = net(imgs)
- loss = loss_fn(output, labels)
-
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
- if batch_idx % args.log_interval == 0:
- print(
- "Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}".format(
- epoch,
- batch_idx * len(imgs),
- len(train_loader.sampler),
- 100.0 * batch_idx / len(train_loader),
- loss.item(),
- )
- )
-
- # test the model
- test(net, test_loader, device)
-
- # save model checkpoint
- save_model(net, args.model_dir)
- return
-
-
-def test(model, test_loader, device):
- model.eval()
- test_loss = 0
- correct = 0
- with torch.no_grad():
- for imgs, labels in test_loader:
- imgs, labels = imgs.to(device), labels.to(device)
- output = model(imgs)
- test_loss += F.cross_entropy(output, labels, reduction="sum").item()
-
- pred = output.max(1, keepdim=True)[1]
- correct += pred.eq(labels.view_as(pred)).sum().item()
-
- test_loss /= len(test_loader.dataset)
- logger.info(
- "Test set: Average loss: {:.4f}, Accuracy: {}/{}, {})\n".format(
- test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
- )
- )
- return
-
-
-def save_model(model, model_dir):
- logger.info("Saving the model")
- path = os.path.join(model_dir, "model.pth")
- torch.save(model.cpu().state_dict(), path)
- return
-
-
-def parse_args():
- parser = argparse.ArgumentParser()
-
- # Data and model checkpoints directories
- parser.add_argument(
- "--batch-size",
- type=int,
- default=64,
- metavar="N",
- help="input batch size for training (default: 64)",
- )
- parser.add_argument(
- "--test-batch-size",
- type=int,
- default=1000,
- metavar="N",
- help="input batch size for testing (default: 1000)",
- )
- parser.add_argument(
- "--epochs", type=int, default=1, metavar="N", help="number of epochs to train (default: 1)"
- )
- parser.add_argument(
- "--learning-rate",
- type=float,
- default=0.001,
- metavar="LR",
- help="learning rate (default: 0.01)",
- )
- parser.add_argument(
- "--beta_1", type=float, default=0.9, metavar="BETA1", help="beta1 (default: 0.9)"
- )
- parser.add_argument(
- "--beta_2", type=float, default=0.999, metavar="BETA2", help="beta2 (default: 0.999)"
- )
- parser.add_argument(
- "--weight-decay",
- type=float,
- default=1e-4,
- metavar="WD",
- help="L2 weight decay (default: 1e-4)",
- )
- parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
- parser.add_argument(
- "--log-interval",
- type=int,
- default=100,
- metavar="N",
- help="how many batches to wait before logging training status",
- )
- parser.add_argument(
- "--backend",
- type=str,
- default=None,
- help="backend for distributed training (tcp, gloo on cpu and gloo, nccl on gpu)",
- )
-
- # Container environment
- parser.add_argument("--hosts", type=list, default=json.loads(os.environ["SM_HOSTS"]))
- parser.add_argument("--current-host", type=str, default=os.environ["SM_CURRENT_HOST"])
- parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
- parser.add_argument("--train", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
- parser.add_argument("--test", type=str, default=os.environ["SM_CHANNEL_TESTING"])
- parser.add_argument("--num-gpus", type=int, default=os.environ["SM_NUM_GPUS"])
-
- return parser.parse_args()
-
-
-if __name__ == "__main__":
- args = parse_args()
- train(args)
diff --git a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/utils/mnist.py b/ deploy_and_monitor/sm-deploy_trained_pytorch_model/utils/mnist.py
deleted file mode 100644
index d0899d6a5e..0000000000
--- a/ deploy_and_monitor/sm-deploy_trained_pytorch_model/utils/mnist.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import gzip
-import json
-import os
-from urllib import request
-
-import boto3
-import numpy as np
-
-dirname = os.path.dirname(os.path.abspath(__file__))
-
-
-def mnist_to_numpy(data_dir="/tmp/data", train=True):
- """Download MNIST dataset and convert it to numpy array
-
- Args:
- data_dir (str): directory to save the data
- train (bool): download training set
-
- Returns:
- tuple of images and labels as numpy arrays
- """
-
- if not os.path.exists(data_dir):
- os.makedirs(data_dir)
-
- if train:
- images_file = "train-images-idx3-ubyte.gz"
- labels_file = "train-labels-idx1-ubyte.gz"
- else:
- images_file = "t10k-images-idx3-ubyte.gz"
- labels_file = "t10k-labels-idx1-ubyte.gz"
-
- # download objects
- s3 = boto3.client("s3")
- bucket = f"sagemaker-example-files-prod-{boto3.session.Session().region_name}"
- for obj in [images_file, labels_file]:
- key = os.path.join("datasets/image/MNIST", obj)
- dest = os.path.join(data_dir, obj)
- if not os.path.exists(dest):
- s3.download_file(bucket, key, dest)
-
- return _convert_to_numpy(data_dir, images_file, labels_file)
-
-
-def _convert_to_numpy(data_dir, images_file, labels_file):
- """Byte string to numpy arrays"""
- with gzip.open(os.path.join(data_dir, images_file), "rb") as f:
- images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
-
- with gzip.open(os.path.join(data_dir, labels_file), "rb") as f:
- labels = np.frombuffer(f.read(), np.uint8, offset=8)
-
- return (images, labels)
-
-
-def normalize(x, axis):
- eps = np.finfo(float).eps
-
- mean = np.mean(x, axis=axis, keepdims=True)
- # avoid division by zero
- std = np.std(x, axis=axis, keepdims=True) + eps
- return (x - mean) / std
-
-
-def adjust_to_framework(x, framework="pytorch"):
- """Adjust a ``numpy.ndarray`` to be used as input for specified framework
-
- Args:
- x (numpy.ndarray): Batch of images to be adjusted
- to follow the convention in pytorch / tensorflow / mxnet
-
- framework (str): Framework to use. Takes value in
- ``pytorch``, ``tensorflow`` or ``mxnet``
- Return:
- numpy.ndarray following the convention of tensors in the given
- framework
- """
-
- if x.ndim == 3:
- # input is gray-scale
- x = np.expand_dims(x, 1)
-
- if framework in ["pytorch", "mxnet"]:
- # depth-major
- return x
- elif framework == "tensorlfow":
- # depth-minor
- return np.transpose(x, (0, 2, 3, 1))
- elif framework == "mxnet":
- return x
- else:
- raise ValueError(
- "framework must be one of " + "[pytorch, tensorflow, mxnet], got {}".format(framework)
- )
-
-
-if __name__ == "__main__":
- X, Y = mnist_to_numpy()
- X, Y = X.astype(np.float32), Y.astype(np.int8)
diff --git a/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting.ipynb b/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting.ipynb
index bd1f1ada93..e16ed461b4 100644
--- a/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting.ipynb
+++ b/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_linear_traffic_shifting.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -986,35 +986,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment.ipynb b/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment.ipynb
index d4c9b4bd36..ca4d30e797 100644
--- a/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment.ipynb
+++ b/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment/sm-deployment_guardrails_update_inference_endpoint_with_rolling_deployment.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -955,35 +955,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting.ipynb b/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting.ipynb
index c20fd071f1..41611478f0 100644
--- a/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting.ipynb
+++ b/ deploy_and_monitor/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting/sm-deployment_guardrails_update_inference_endpoint_with_with_canary_traffic_shifting.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -954,35 +954,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-experiments_local_experiment_tracking/images/charts_overview.png b/ deploy_and_monitor/sm-experiments_local_experiment_tracking/images/charts_overview.png
deleted file mode 100644
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diff --git a/ deploy_and_monitor/sm-host_pretrained_model_bert/sm-host_pretrained_model_bert.ipynb b/ deploy_and_monitor/sm-host_pretrained_model_bert/sm-host_pretrained_model_bert.ipynb
index b9944ffe61..bd73b20061 100644
--- a/ deploy_and_monitor/sm-host_pretrained_model_bert/sm-host_pretrained_model_bert.ipynb
+++ b/ deploy_and_monitor/sm-host_pretrained_model_bert/sm-host_pretrained_model_bert.ipynb
@@ -26,7 +26,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
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@@ -583,35 +583,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
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]
}
],
diff --git a/ ml_ops/sm-inference_pipeline_with_scikit_linear_learner/sklearn_abalone_featurizer.py b/ deploy_and_monitor/sm-inference_pipeline_with_scikit_linear_learner/sklearn_abalone_featurizer.py
similarity index 100%
rename from ml_ops/sm-inference_pipeline_with_scikit_linear_learner/sklearn_abalone_featurizer.py
rename to deploy_and_monitor/sm-inference_pipeline_with_scikit_linear_learner/sklearn_abalone_featurizer.py
diff --git a/ ml_ops/sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb b/ deploy_and_monitor/sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb
similarity index 92%
rename from ml_ops/sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb
rename to deploy_and_monitor/sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb
index 37e4d7111d..5f04446585 100644
--- a/ ml_ops/sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb
+++ b/ deploy_and_monitor/sm-inference_pipeline_with_scikit_linear_learner/sm-inference_pipeline_with_scikit_linear_learner.ipynb
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
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- "\n",
+ "\n",
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@@ -704,35 +704,35 @@
"\n",
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]
}
],
diff --git a/ deploy_and_monitor/sm-inference_recommender_introduction.ipynb b/ deploy_and_monitor/sm-inference_recommender_introduction.ipynb
index 033190c214..7ded6e9ed7 100644
--- a/ deploy_and_monitor/sm-inference_recommender_introduction.ipynb
+++ b/ deploy_and_monitor/sm-inference_recommender_introduction.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
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@@ -951,35 +951,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
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]
}
],
diff --git a/ deploy_and_monitor/sm-lineage_cross_account_queries_with_ram/sm-lineage_cross_account_queries_with_ram.ipynb b/ deploy_and_monitor/sm-lineage_cross_account_queries_with_ram/sm-lineage_cross_account_queries_with_ram.ipynb
index cbac9536c8..d1575f3f89 100644
--- a/ deploy_and_monitor/sm-lineage_cross_account_queries_with_ram/sm-lineage_cross_account_queries_with_ram.ipynb
+++ b/ deploy_and_monitor/sm-lineage_cross_account_queries_with_ram/sm-lineage_cross_account_queries_with_ram.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
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@@ -208,35 +208,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
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]
}
],
@@ -264,4 +264,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-marketplace_using_model_package_arn/sm-marketplace_using_model_package_arn.ipynb b/ deploy_and_monitor/sm-marketplace_using_model_package_arn/sm-marketplace_using_model_package_arn.ipynb
index 922553882f..e18bd8c816 100644
--- a/ deploy_and_monitor/sm-marketplace_using_model_package_arn/sm-marketplace_using_model_package_arn.ipynb
+++ b/ deploy_and_monitor/sm-marketplace_using_model_package_arn/sm-marketplace_using_model_package_arn.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -290,35 +290,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
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+ "\n",
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+ "\n"
]
}
],
@@ -343,4 +343,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-mme_with_torchserve/sm-mme_with_torchserve.ipynb b/ deploy_and_monitor/sm-mme_with_torchserve/sm-mme_with_torchserve.ipynb
index 129fc61207..436ca27540 100644
--- a/ deploy_and_monitor/sm-mme_with_torchserve/sm-mme_with_torchserve.ipynb
+++ b/ deploy_and_monitor/sm-mme_with_torchserve/sm-mme_with_torchserve.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
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- "\n",
+ "\n",
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"---"
]
@@ -855,35 +855,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
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+ "\n",
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- ""
+ ""
]
}
],
diff --git a/ deploy_and_monitor/sm-clarify_model_explainability_monitor_batch_transform/model/xgb-churn-prediction-model.tar.gz b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/model/xgb-churn-prediction-model.tar.gz
similarity index 100%
rename from deploy_and_monitor/sm-clarify_model_explainability_monitor_batch_transform/model/xgb-churn-prediction-model.tar.gz
rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/model/xgb-churn-prediction-model.tar.gz
diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb
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rename from ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb
rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb
index 41bd278f97..bc3709a1c5 100644
--- a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb
+++ b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/sm-model_monitor_batch_transform_data_quality_on_schedule.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
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}
],
diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-gt-col.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-gt-col.csv
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rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-gt-col.csv
diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/data/test-dataset-input-cols.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-input-cols.csv
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rename from end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/data/test-dataset-input-cols.csv
rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-input-cols.csv
diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-with-header.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset-with-header.csv
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diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test-dataset.csv
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diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test_sample.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test_sample.csv
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rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/test_sample.csv
diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/data/training-dataset-with-header.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-dataset-with-header.csv
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diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-dataset-with-inf-and-header.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-dataset-with-inf-and-header.csv
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/data/training-dataset-without-header.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-dataset-without-header.csv
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diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-inputs-with-header.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-inputs-with-header.csv
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rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/training-inputs-with-header.csv
diff --git a/ deploy_and_monitor/sm-clarify_model_explainability_monitor_batch_transform/test_data/upload-test-file.txt b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/upload-test-file.txt
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rename from deploy_and_monitor/sm-clarify_model_explainability_monitor_batch_transform/test_data/upload-test-file.txt
rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/upload-test-file.txt
diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/data/validation.csv b/ deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/validation.csv
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/data/validation.csv
rename to deploy_and_monitor/sm-model_monitor_batch_transform_data_quality_on_schedule/test_data/validation.csv
diff --git a/ deploy_and_monitor/sm-model_monitor_batch_transform_model_quality_on_schedule/sm-model_monitor_batch_transform_model_quality_on_schedule.ipynb b/ deploy_and_monitor/sm-model_monitor_batch_transform_model_quality_on_schedule/sm-model_monitor_batch_transform_model_quality_on_schedule.ipynb
index cab1755d94..5ef20683e8 100644
--- a/ deploy_and_monitor/sm-model_monitor_batch_transform_model_quality_on_schedule/sm-model_monitor_batch_transform_model_quality_on_schedule.ipynb
+++ b/ deploy_and_monitor/sm-model_monitor_batch_transform_model_quality_on_schedule/sm-model_monitor_batch_transform_model_quality_on_schedule.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
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"\n",
"---"
]
@@ -864,35 +864,35 @@
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}
],
diff --git a/ deploy_and_monitor/sm-model_monitor_bias_and_explainability_monitoring/sm-model_monitor_bias_and_explainability_monitoring.ipynb b/ deploy_and_monitor/sm-model_monitor_bias_and_explainability_monitoring/sm-model_monitor_bias_and_explainability_monitoring.ipynb
index 9925dfba2c..15d8254c8b 100644
--- a/ deploy_and_monitor/sm-model_monitor_bias_and_explainability_monitoring/sm-model_monitor_bias_and_explainability_monitoring.ipynb
+++ b/ deploy_and_monitor/sm-model_monitor_bias_and_explainability_monitoring/sm-model_monitor_bias_and_explainability_monitoring.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1259,35 +1259,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/Dockerfile b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/Dockerfile
index 937e02d59c..b6a100119e 100644
--- a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/Dockerfile
+++ b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/Dockerfile
@@ -1,4 +1,19 @@
-FROM --platform=linux/amd64 python:3.10-slim-buster as build
+FROM --platform=linux/amd64 ubuntu:22.04 as build
+
+# Install required packages
+RUN apt-get update && apt-get install -y \
+ python3.10 \
+ python3.10-dev \
+ python3-pip \
+ build-essential \
+ libssl-dev \
+ libffi-dev \
+ git \
+ && rm -rf /var/lib/apt/lists/*
+
+# Set the default Python version to 3.10
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
+RUN update-alternatives --config python3
# Copy requirements.txt and install dependencies
COPY requirements.txt /opt/program/requirements.txt
@@ -14,4 +29,4 @@ ENV DOCKER_CONTAINER=1 EVAL_RESULTS_PATH=/opt/ml/processing/output/
RUN chmod +x /opt/program/main.py
# Set entrypoint to main.py
-ENTRYPOINT ["python3", "/opt/program/main.py"]
+ENTRYPOINT ["python3", "/opt/program/main.py"]
\ No newline at end of file
diff --git a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/requirements.txt b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/requirements.txt
index 085fbd1862..f167474dcc 100644
--- a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/requirements.txt
+++ b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/requirements.txt
@@ -1,3 +1,8 @@
python-dotenv==1.0.1
pytest==8.2.2
+pytest-cov==5.0.0
fmeval==1.0.3
+langkit==0.0.32
+langchain==0.2.6
+langchain-community==0.2.6
+gpt4all==2.7.0
\ No newline at end of file
diff --git a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/sm-model_monitor_byoc_llm_monitor.ipynb b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/sm-model_monitor_byoc_llm_monitor.ipynb
index bb31e58189..fa4e907611 100644
--- a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/sm-model_monitor_byoc_llm_monitor.ipynb
+++ b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/sm-model_monitor_byoc_llm_monitor.ipynb
@@ -5,7 +5,7 @@
"id": "8af3794b",
"metadata": {},
"source": [
- "# BYOC LLM Monitoring: Bring Your Own Container Llama2 Monitoring with SageMaker Model Monitor"
+ "# BYOC LLM Monitoring: Bring Your Own Container Llama2 Multiple Evaluations Monitoring with SageMaker Model Monitor"
]
},
{
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -28,7 +28,7 @@
"metadata": {},
"source": [
"---\n",
- "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy and monitor a JumpStart Llama 2 fine-tuned model for Toxicity levels. The container associated with this notebook employs the [FMEval open-source library](https://github.com/aws/fmeval) for LLM evaluation.\n",
+ "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy and monitor a JumpStart Llama 2 fine-tuned model for Toxicity, Answer Relevance and Accuracy, and Readability. The container associated with this notebook employs [FMEval](https://github.com/aws/fmeval) for LLM Toxicity evaluation, [LangChain](https://python.langchain.com/v0.1/docs/guides/productionization/evaluation/) for Answer Relevance and Accuracy, and [WhyLabs LangKit](https://whylabs.ai/langkit) for Readability.\n",
"\n",
"To perform inference on these models, you need to pass custom_attributes='accept_eula=true' as part of header. This means you have read and accept the end-user-license-agreement (EULA) of the model. EULA can be found in model card description or from https://ai.meta.com/resources/models-and-libraries/llama-downloads/. By default, this notebook sets custom_attributes='accept_eula=false', so all inference requests will fail until you explicitly change this custom attribute.\n",
"\n",
@@ -54,7 +54,7 @@
"# Prerequisites\n",
"- **IF RUNNING LOCALLY (not SageMaker Studio/Classic)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. For information on how to complete this, see this [documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html)\n",
"\n",
- "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: Please ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup."
+ "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. Please also ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup."
]
},
{
@@ -89,7 +89,7 @@
},
{
"cell_type": "markdown",
- "id": "20ea8b91",
+ "id": "9eeebb0b",
"metadata": {},
"source": [
"## Retreive your SageMaker Session and Configure Execution Role"
@@ -107,33 +107,19 @@
"\n",
"sess = sagemaker.Session()\n",
"# sagemaker session bucket -> used for uploading data, models and logs\n",
- "# sagemaker will automatically create this bucket if it does not exist\n",
+ "# sagemaker will automatically create this bucket if it not exists\n",
"sagemaker_session_bucket = None\n",
"if sagemaker_session_bucket is None and sess is not None:\n",
" sagemaker_session_bucket = sess.default_bucket()\n",
"\n",
- "iam = boto3.client(\"iam\")\n",
"# Here, we create a role for SageMaker. The role ARN must be specified when calling the predict() method. If this fails, you can manually specify the role ARN in the except block.\n",
"try:\n",
" role = sagemaker.get_execution_role()\n",
- " role_name = role.split(\"/\")[-1]\n",
- "\n",
- " # Attach the required policies to the role\n",
- " iam.attach_role_policy(\n",
- " RoleName=role_name,\n",
- " PolicyArn=\"arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess\",\n",
- " )\n",
- " iam.attach_role_policy(\n",
- " RoleName=role_name,\n",
- " PolicyArn=\"arn:aws:iam::aws:policy/CloudWatchFullAccess\",\n",
- " )\n",
- "\n",
- " role = sagemaker.get_execution_role()\n",
"except ValueError:\n",
- " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess','AmazonEC2ContainerRegistryFullAccess', and 'CloudWatchFullAccess' roles. See the linked documentation for help.\n",
+ " iam = boto3.client(\"iam\")\n",
+ " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess' role. See the linked documentation for help.\n",
" role = iam.get_role(RoleName=\"\")[\"Role\"][\"Arn\"]\n",
"\n",
- "\n",
"sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
"\n",
"print(f\"sagemaker role arn: {role}\")\n",
@@ -208,7 +194,7 @@
"source": [
"from sagemaker.model_monitor import DataCaptureConfig\n",
"\n",
- "s3_root_dir = \"byoc-monitor-llm\"\n",
+ "s3_root_dir = \"byoc-multiple-eval-monitor-llm\"\n",
"\n",
"s3_capture_upload_path = f\"s3://{bucket}/{s3_root_dir}/datacapture\"\n",
"\n",
@@ -232,8 +218,7 @@
"id": "d033889e",
"metadata": {},
"source": [
- "### Deploy JumpStart Model\n",
- "Note: This will take roughly 10 mins"
+ "### Note: This next cell will take ~10 minutes"
]
},
{
@@ -302,9 +287,9 @@
"id": "c2fbb9af",
"metadata": {},
"source": [
- "### Single invocation\n",
+ "### Example of a single invocation\n",
"\n",
- "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`, otherwise an error will be raised."
+ "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`"
]
},
{
@@ -344,7 +329,7 @@
"id": "04c200cf",
"metadata": {},
"source": [
- "The following cell will send 10 queries to the endpoint. Feel free to adjust the number of queries to whatever amount you feel is enough captured data.\n",
+ "The following cell will send questions to the endpoint until stopped. Feel free to stop the cell whenever you feel you have captured enough data.\n",
"\n",
"**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`"
]
@@ -385,7 +370,7 @@
"id": "862ab1d3",
"metadata": {},
"source": [
- "# Build and Push the Image to ECR"
+ "# Build and Push the Container to ECR"
]
},
{
@@ -395,7 +380,7 @@
"metadata": {},
"outputs": [],
"source": [
- "ecr_repo_name = \"byoc-llm\"\n",
+ "ecr_repo_name = \"byoc-llm-multiple-eval\"\n",
"aws_region = sess.boto_region_name\n",
"aws_account_id = sess.account_id()"
]
@@ -406,7 +391,7 @@
"metadata": {},
"source": [
"#### **IMPORTANT:** If running locally (not on SageMaker Studio), delete ' --network sagemaker'\n",
- "Build the image. This will take some time."
+ "Build the image. This will take ~5 mins."
]
},
{
@@ -452,7 +437,7 @@
"id": "50cc4260",
"metadata": {},
"source": [
- "Push the image to ECR. This will take some time, as we are pushing a ~9GB image. Ensure that your AWS credentials are fresh."
+ "Push the image to ECR. This will take some time, as the image is ~9GB. Ensure that your AWS credentials are fresh."
]
},
{
@@ -494,12 +479,17 @@
"bucket = sess.default_bucket()\n",
"\n",
"monitor = ModelMonitor(\n",
- " base_job_name=\"byoc-llm-monitor\",\n",
+ " base_job_name=\"byoc-llm-multiple-eval-monitor\",\n",
" role=role,\n",
" image_uri=image_uri,\n",
" instance_count=1,\n",
- " instance_type=\"ml.m5.2xlarge\",\n",
- " env={\"bucket\": bucket},\n",
+ " instance_type=\"ml.c5.9xlarge\",\n",
+ " env={\n",
+ " \"bucket\": bucket,\n",
+ " \"TOXICITY\": \"Enabled\",\n",
+ " \"READABILITY\": \"Enabled\",\n",
+ " \"RELEVANCE_AND_ACCURACY\": \"Enabled\",\n",
+ " }, # Change one to DISABLED if metrics not desired.\n",
")"
]
},
@@ -508,7 +498,7 @@
"id": "fb40b933",
"metadata": {},
"source": [
- "**Note**: The following cell sets a **one-time** monitoring schedule for demonstration purposes. A one-time monitoring schedule will execute immediately. If you would like to set an hourly schedule, swap out the commented line. It is important to know that hourly schedules will only begin at the start of the next full hour, so you will not see immediate results."
+ "**Note**: The following cell sets a **one-time** monitoring schedule for demonstration purposes. A one-time monioring schedule will execute immediately. If you would like to set an hourly schedule, swap out the commented line. It is important to know that hourly schedules will only begin at the start of the next full hour, so you will not see immediate results."
]
},
{
@@ -564,8 +554,17 @@
"\n",
"try:\n",
" execution_output = monitor.list_executions()[-1].output\n",
- " s3_path_to_report = f\"{execution_output.destination}/toxicity_custom_dataset.jsonl\"\n",
- " print(s3.S3Downloader.read_file(s3_path_to_report))\n",
+ " s3_path_to_toxicity_report = f\"{execution_output.destination}/toxicity_custom_dataset.jsonl\"\n",
+ " s3_path_to_readability_report = f\"{execution_output.destination}/readability_eval_results.jsonl\"\n",
+ " s3_path_to_relevance_and_accuracy_report = (\n",
+ " f\"{execution_output.destination}/relevance_and_accuracy_eval_results.jsonl\"\n",
+ " )\n",
+ " print(\"Toxicity report: \\n\")\n",
+ " print(s3.S3Downloader.read_file(s3_path_to_toxicity_report), \"\\n\")\n",
+ " print(\"Readability report: \\n\")\n",
+ " print(s3.S3Downloader.read_file(s3_path_to_readability_report), \"\\n\")\n",
+ " print(\"Relevance and Accuracy report: \\n\")\n",
+ " print(s3.S3Downloader.read_file(s3_path_to_relevance_and_accuracy_report))\n",
"except:\n",
" print(\"Report not found. Please wait and try again.\")"
]
@@ -576,7 +575,7 @@
"metadata": {},
"source": [
"### View Cloudwatch Dashboard Graph\n",
- "The following cell will generate a CloudWatch Dashboard for viewing the evaluation results from the monitoring schedule you ran. For more information on dashboard formatting, see [here](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/CloudWatch-Dashboard-Body-Structure.html#Dashboard-Body-Overall-Structure)"
+ "The following cell will generate a CloudWatch Dashboard for the monitoring schedule you created. For more information on dashboard formatting, see [here](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/CloudWatch-Dashboard-Body-Structure.html#Dashboard-Body-Overall-Structure)"
]
},
{
@@ -597,7 +596,8 @@
" {\"Name\": \"MonitoringSchedule\", \"Value\": monitoring_schedule_name},\n",
" ],\n",
")\n",
- "metric_names = [metric[\"MetricName\"] for metric in metric_list[\"Metrics\"]]"
+ "metric_names = [metric[\"MetricName\"] for metric in metric_list[\"Metrics\"]]\n",
+ "print(metric_names)"
]
},
{
@@ -629,7 +629,7 @@
" ]\n",
" )\n",
"\n",
- "widget_title = \"LLM Evaluation Graph\"\n",
+ "widget_title = \"LLM Multiple Evaluations Graph\"\n",
"\n",
"dash_data = json.dumps(\n",
" {\n",
@@ -659,14 +659,14 @@
" \"width\": 11,\n",
" \"height\": 11,\n",
" \"properties\": {\n",
- " \"markdown\": \"# LLM Evaluation Descriptions\\n## Toxicity\\nToxicity is measured in 7 different categories:\\n- `toxicity`\\n- `severe_toxicity`\\n- `obscene`\\n- `threat`\\n- `insult`\\n- `identity_attack`\\n- `sexual_explicit`\\n\\nEach score is a number between 0 and 1, with 1 denoting extreme toxicity. To obtain the toxicity scores, the FMEval library uses the open-source [Detoxify](https://github.com/unitaryai/detoxify) model to grade each LLM output.\"\n",
+ " \"markdown\": \"# LLM Evaluation Descriptions\\n## Toxicity\\nToxicity is measured in 7 different categories:\\n- `toxicity`\\n- `severe_toxicity`\\n- `obscene`\\n- `threat`\\n- `insult`\\n- `identity_attack`\\n- `sexual_explicit`\\n\\nEach score is a number between 0 and 1, with 1 denoting extreme toxicity. To obtain the toxicity scores, the FMEval library uses the open-source [Detoxify](https://github.com/unitaryai/detoxify) model to grade each LLM output.\\n \\n\\n\\n## Readability\\nReadability is measured in 11 different categories. These measurements are created and aggregating by the WhyLabs LangKit `textstat` module. For information on scoring for each metric, read their documentation [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).\\n\\n## Relevance and Accuracy\\nRelevance and accuracy is graded on a single score from 1-10. The prompt and response from the monitored LLM are provided to an evaluator LLM with intructions as follows:\\n\\n> Please act as an impartial judge and evaluate the quality of the response provided by an AI assistant to the user question displayed below. For this evaluation, you should primarily consider the following criteria:\\n> - helpfulness: Is the submission helpful, insightful, and appropriate?\\n> - relevance: Is the submission referring to a real quote from the text?\\n> - correctness: Is the submission correct, accurate, and factual?\\n> - depth: Does the submission demonstrate depth of thought?\\n\\n> Begin your evaluation by providing a short explanation. Be as objective as possible. After providing your explanation, you must rate the response on a scale of 1 to 10 by strictly following this format: '[[rating]]', for example: 'Rating: [[5]]'.\",\n",
" },\n",
" },\n",
" ],\n",
" }\n",
")\n",
"\n",
- "dashboard_name = \"byoc-llm-monitoring\"\n",
+ "dashboard_name = \"byoc-llm-multiple-monitoring\"\n",
"cwClient.put_dashboard(DashboardName=dashboard_name, DashboardBody=dash_data)"
]
},
@@ -759,35 +759,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/cloudwatch_logger.py b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/cloudwatch_logger.py
index 0e120c97ee..a38ba7b020 100644
--- a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/cloudwatch_logger.py
+++ b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/cloudwatch_logger.py
@@ -64,7 +64,7 @@ def log(self, eval_results: Dict, destination: str):
logger.info(f"Logged metrics: {json.dumps(metric_data)}")
logger.info(f"Logged to {destination}")
except PermissionError as e:
-
+ logger.warning(f"Unable to write to {destination}")
print(f"Error: {e}")
print(f"Evaluation results logged to: {destination}")
diff --git a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/evaluator.py b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/evaluator.py
index e3f06a28cd..0ae7564325 100644
--- a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/evaluator.py
+++ b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/components/evaluator.py
@@ -1,8 +1,17 @@
from typing import Set, Optional
import logging
-import json
+from langkit import light_metrics, extract
from fmeval.eval_algorithms.toxicity import Toxicity, ToxicityConfig, DataConfig
from fmeval.exceptions import EvalAlgorithmClientError
+from langchain_community.llms.gpt4all import GPT4All
+from gpt4all import GPT4All as fileDownloader
+from langchain.evaluation.scoring import ScoreStringEvalChain
+import json
+from json import JSONDecodeError
+from typing import Any, Callable, Optional, Sequence, Tuple
+import re
+import os
+import random
# Model Input/Output specify which fields FMEVal looks in our dataset.
# Reference https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-foundation-model-evaluate-auto-lib-custom.html
@@ -15,6 +24,45 @@
TOXICITY_EVALUATOR_MODEL = "detoxify"
DEFAULT_EVALUATIONS = {'toxicity', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat', 'sexual_explicit'}
+DEFAULT_REPORT_PATH = './tests/output'
+READABILITY_REPORT_FILENAME = 'readability_eval_results.jsonl'
+RELEVANCE_AND_ACCURACY_REPORT_FILENAME = 'relevance_and_accuracy_eval_results.jsonl'
+REPORT_PATH = os.getenv("EVAL_RESULTS_PATH") if "EVAL_RESULTS_PATH" in os.environ else DEFAULT_REPORT_PATH
+
+# These are all of the readability evaluations we can run.
+READABILITY_EVALUATIONS = {
+ "flesch_reading_ease",
+ "automated_readability_index",
+ "aggregate_reading_level",
+ "syllable_count",
+ "lexicon_count",
+ "sentence_count",
+ "character_count",
+ "letter_count",
+ "polysyllable_count",
+ "monosyllable_count",
+ "difficult_words",
+ }
+
+# These are all of the toxicity evaluations we can run.
+TOXICITY_EVALUATIONS = {
+ "toxicity",
+ "severe_toxicity",
+ "obscene",
+ "identity_attack",
+ "insult",
+ "threat",
+ "sexual_explicit"
+ }
+
+RELEVANCE_AND_ACCURACY_EVALUATIONS = {
+ "relevance_and_accuracy_score"
+}
+
+ANSWER_RELEVANCY_MODEL = "Meta-Llama-3-8B-Instruct.Q4_0.gguf"
+
+DEFAULT_EVALUATIONS = {"TOXICITY", "READABILITY", "RELEVANCE_AND_ACCURACY"}
+
logger = logging.getLogger(__name__)
class Evaluator:
@@ -24,7 +72,7 @@ class Evaluator:
the /opt/ml/processing/output/data.jsonl file and uses the FMEval open-source library to
execute the specified evaluation tasks.
"""
- def __init__(self, eval_config: Optional[Set[str]] = None):
+ def __init__(self, eval_config: Optional[Set[str]] = DEFAULT_EVALUATIONS):
"""
Constructor
:param eval_config: A Set of evaluation tasks to run. If not provided, all evaluation tasks will be run.
@@ -47,7 +95,37 @@ def evaluate(self, dataset_uri: str):
:raises: ValueError if the dataset_uri is not a valid string.
:return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary.
"""
+
+
+ if not isinstance(dataset_uri, str):
+ raise ValueError("dataset_uri must be a valid string")
+
+ if not isinstance(REPORT_PATH, str):
+ raise ValueError("report_path must be a valid string")
+
+ toxicity_results = {}
+ readability_results = {}
+ relevance_and_accuracy_results = {}
+ if "TOXICITY" in self.eval_config:
+ toxicity_results = self._evaluate_toxicity(dataset_uri)
+
+ if "READABILITY" in self.eval_config:
+ readability_results = self._evaluate_readability(dataset_uri)
+
+ if "RELEVANCE_AND_ACCURACY" in self.eval_config:
+ relevance_and_accuracy_results = self._evaluate_relevance_and_accuracy(dataset_uri)
+
+ return {**toxicity_results, **readability_results, **relevance_and_accuracy_results}
+
+
+ def _evaluate_toxicity(self, dataset_uri: str):
+ """
+ Evaluates the data for Toxicity using the FMEval library.
+ :param dataset_uri: The path to the dataset file.
+ :raises: ValueError if the dataset_uri is not a valid string.
+ :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary.
+ """
if not isinstance(dataset_uri, str):
raise ValueError("dataset_uri must be a valid string")
@@ -59,11 +137,6 @@ def evaluate(self, dataset_uri: str):
model_output_location=MODEL_OUTPUT_LOCATION,
)
- if not self.eval_config:
- configured_evals = DEFAULT_EVALUATIONS
- else:
- configured_evals = set(self.eval_config)
-
eval_algo = Toxicity(ToxicityConfig(model_type=TOXICITY_EVALUATOR_MODEL))
try:
@@ -75,10 +148,179 @@ def evaluate(self, dataset_uri: str):
eval_results = {}
for eval_score in eval_output[0].dataset_scores:
- if eval_score.name in configured_evals:
- eval_results[eval_score.name] = eval_score.value
+ eval_results[eval_score.name] = eval_score.value
logger.info(f"Evaluation Results: {eval_results}")
return eval_results
-
\ No newline at end of file
+
+
+ def _evaluate_readability(self, dataset_uri: str):
+ """
+ Evaluates the data for readability using the WhyLabs Langkit Library.
+
+ :param dataset_uri: The path to the dataset file.
+ :raises: ValueError if the dataset_uri is not a valid string.
+ :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary.
+ """
+
+ text_schema = light_metrics.init()
+
+ line_count = 0
+ try:
+ with open(dataset_uri, 'r') as file:
+ lines = file.readlines()
+ except:
+ logger.error("Could not read file.")
+ return {}
+
+ if len(lines) == 0:
+ logger.info("No data to evaluate")
+ return {}
+
+ results = []
+ totals = {field: 0 for field in READABILITY_EVALUATIONS}
+
+ if len(lines) <= 100:
+ sample_lines = lines
+ else:
+ sample_lines = random.sample(lines, 100)
+
+ for line in sample_lines:
+ try:
+ data = json.loads(line)
+ line_count += 1
+
+ readability_evals = clean_readability_dict(extract({"prompt": data['answer']}, schema=text_schema))
+ result_dict = {
+ "prompt": data["content"],
+ "response": data["answer"],
+ **readability_evals,
+ }
+ results.append(result_dict)
+ for key, value in result_dict.items():
+ if key in totals:
+ totals[key] += value
+ except (KeyError, JSONDecodeError) as e:
+ logger.error(f"Data malformed. {e}")
+ return {}
+
+ report_filepath = os.path.join(REPORT_PATH, READABILITY_REPORT_FILENAME)
+
+ logger.info(f"Writing readability evaluation results to {report_filepath}")
+ write_eval_result_file(report_filepath, results)
+
+ return {key: value / (line_count if line_count > 0 else 1) for key, value in totals.items()}
+
+ def _evaluate_relevance_and_accuracy(self, dataset_uri: str):
+ """
+ Evaluates the data for relevance and accuracy using the FMEval library.
+
+ :param dataset_uri: The path to the dataset file.
+ :raises: ValueError if the dataset_uri is not a valid string.
+ :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary.
+ """
+
+ if not isinstance(dataset_uri, str):
+ raise ValueError("dataset_uri must be a valid string")
+
+
+ fileDownloader.retrieve_model(ANSWER_RELEVANCY_MODEL) # downloads / loads a 4.66GB LLM
+ model = GPT4All(model=ANSWER_RELEVANCY_MODEL, verbose=False, n_batch=128, n_threads=36 if 'DOCKER_CONTAINER' in os.environ else None)
+ evaluator_model = ScoreStringEvalChain.from_llm(
+ llm=model, verbose=False
+ )
+
+ line_count = 0
+ try:
+ with open(dataset_uri, 'r') as file:
+ lines = file.readlines()
+ except:
+ logger.error("Could not read file.")
+ return {}
+
+ if not lines:
+ logger.info("No data to evaluate")
+ return {}
+
+ # Initialize our list of individualy response scores and summed total scores (for later averaging)
+ results = []
+ totals = {field: 0 for field in RELEVANCE_AND_ACCURACY_EVALUATIONS}
+ # Randomly sample 10 prompt and responses for evaluation
+ if len(lines) <= 10:
+ sample_lines = lines
+ else:
+ sample_lines = random.sample(lines, 10)
+
+ logger.info("Starting evaluation")
+ for line in sample_lines:
+ try:
+ data = json.loads(line)
+ line_count += 1
+ logger.info(f"Evaluating line: {line_count}")
+
+ accuracy_relevance_eval_result = evaluator_model.evaluate_strings(
+ prediction=data["answer"],
+ input=data["content"],
+ )
+
+ result_dict = {
+ "prompt": data["content"],
+ "response": data["answer"],
+ "relevance_and_accuracy_analysis": accuracy_relevance_eval_result["reasoning"],
+ "relevance_and_accuracy_score": accuracy_relevance_eval_result["score"],
+ }
+ # Add all scores for this response to result list and sum total scores
+ results.append(result_dict)
+ for key, value in result_dict.items():
+ if key in totals:
+ totals[key] += value
+ except ValueError as e:
+ logger.warning(f"Error evaluating line, continuing: {e}")
+ continue
+ except (KeyError, JSONDecodeError) as e:
+ logger.error(f"Data malformed {e}")
+ return {}
+
+ report_filepath = os.path.join(REPORT_PATH, RELEVANCE_AND_ACCURACY_REPORT_FILENAME)
+ write_eval_result_file(report_filepath, results)
+
+ # Returns average scores
+ return {key: value / (line_count if line_count > 0 else 1) for key, value in totals.items()}
+
+
+def clean_readability_dict(evals):
+ """
+ Cleans the readability dictionary by removing the 'prompt' and 'has_patterns' keys. Also, removes 'prompt.' prefix from fields which is
+ the default behavior of the LangKit extract function.
+ :param evals: The dictionary to clean.
+ :return: The cleaned dictionary.
+ """
+ evals.pop('prompt')
+
+ # Remove 'prompt.' from every key
+ new_evals = {}
+ for key, value in evals.items():
+ new_key = key.replace('prompt.', '')
+ new_evals[new_key] = value
+
+ try:
+ new_evals.pop('has_patterns')
+ except:
+ logger.info("No patterns found")
+
+ return new_evals
+
+def write_eval_result_file(report_filepath, results):
+ """
+ Writes the evaluation results to a file in the specified directory.
+ :param formatted_data_dir: The directory to write the file to.
+ :param report_path: The directory to write the file to
+ :param results: The evaluation results to write.
+ :return: None
+ """
+ formatted_data_dir = os.path.dirname(report_filepath)
+ os.makedirs(formatted_data_dir, exist_ok=True)
+ with open(report_filepath, 'w') as output_file:
+ for result_dict in results:
+ output_file.write(json.dumps(result_dict) + '\n')
\ No newline at end of file
diff --git a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/main.py b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/main.py
index 758932e787..24737bab9c 100644
--- a/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/main.py
+++ b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/main.py
@@ -1,9 +1,13 @@
import logging
import sys
import site
+import json
+import os
from components.data_loader import DataLoader
from components.evaluator import Evaluator
from components.cloudwatch_logger import CloudWatchLogger
+from langkit import textstat
+from whylogs.experimental.core.udf_schema import udf_schema
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
@@ -18,21 +22,48 @@
# See https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html
CLOUDWATCH_METRICS_DESTINATION = '/opt/ml/output/metrics/cloudwatch/cloudwatch_metrics.jsonl'
-# These are all of the evaluations we can run.
-EVALUATIONS = {
- "toxicity",
- "severe_toxicity",
- "obscene",
- "identity_attack",
- "insult",
- "threat",
- "sexual_explicit"
- }
+PROCESSING_JOB_CONFIG_FILE = '/opt/ml/config/processingjobconfig.json'
+
+DEFAULT_EVAL_LIST = {"TOXICITY", "READABILITY", "RELEVANCE_AND_ACCURACY"}
+
+def get_evaluations():
+ """
+ Retrieves the specified evaluations from the processing job config file.
+ If we are in a docker container, we are running a monitoring job, and the config file has
+ the endpoint name and monitoring schedule name.
+
+ For information about processingjobcongfig.json file, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-contract-inputs.html
+
+ :returns A tuple containing the endpoint name and monitoring schedule name.
+ """
+
+ if 'DOCKER_CONTAINER' in os.environ:
+ try:
+ with open(PROCESSING_JOB_CONFIG_FILE, 'r') as config:
+ params = json.load(config)
+ logger.info("Reading Env params")
+ eval_list = set()
+
+ if params["Environment"]["TOXICITY"] == "Enabled":
+ eval_list.add("TOXICITY")
+ if params["Environment"]["READABILITY"] == "Enabled":
+ eval_list.add("READABILITY")
+ if params["Environment"]["RELEVANCE_AND_ACCURACY"] == "Enabled":
+ eval_list.add("RELEVANCE_AND_ACCURACY")
+
+ return eval_list
+ except KeyError as e:
+ logger.error(f"Environment does not have any evaluations enables.")
+ raise e
+ else:
+ return DEFAULT_EVAL_LIST
if __name__ == "__main__":
+
try:
+ evaluations = get_evaluations()
data_loader = DataLoader()
- evaluator = Evaluator(EVALUATIONS)
+ evaluator = Evaluator(eval_config=evaluations)
cloudwatch_logger = CloudWatchLogger()
data_loader.execute_etl(INPUT_DATA_SOURCE, CLEANED_DATA_DESTINATION)
diff --git a/ prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/api_batch_metadata_post/__init__.py b/ deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/utils/__init__.py
similarity index 100%
rename from prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/api_batch_metadata_post/__init__.py
rename to deploy_and_monitor/sm-model_monitor_byoc_llm_monitor/src/utils/__init__.py
diff --git a/ deploy_and_monitor/sm-model_monitor_introduction/sm-model_monitor_introduction.ipynb b/ deploy_and_monitor/sm-model_monitor_introduction/sm-model_monitor_introduction.ipynb
index 69e93e2572..8f2d4ca07f 100644
--- a/ deploy_and_monitor/sm-model_monitor_introduction/sm-model_monitor_introduction.ipynb
+++ b/ deploy_and_monitor/sm-model_monitor_introduction/sm-model_monitor_introduction.ipynb
@@ -26,7 +26,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -47,11 +47,11 @@
"source": [
"This notebook shows how to:\n",
"\n",
- "\u2022 Host a machine learning model in Amazon SageMaker and capture inference requests, results, and metadata \n",
+ "• Host a machine learning model in Amazon SageMaker and capture inference requests, results, and metadata \n",
"\n",
- "\u2022 Analyze a training dataset to generate baseline constraints\n",
+ "• Analyze a training dataset to generate baseline constraints\n",
"\n",
- "\u2022 Monitor a live endpoint for violations against constraints\n",
+ "• Monitor a live endpoint for violations against constraints\n",
"\n",
"## Background\n",
"\n",
@@ -2365,35 +2365,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -2434,4 +2434,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-model_monitor_model_quality_monitoring/sm-model_monitor_model_quality_monitoring.ipynb b/ deploy_and_monitor/sm-model_monitor_model_quality_monitoring/sm-model_monitor_model_quality_monitoring.ipynb
index a79c0809a7..683a342cf8 100644
--- a/ deploy_and_monitor/sm-model_monitor_model_quality_monitoring/sm-model_monitor_model_quality_monitoring.ipynb
+++ b/ deploy_and_monitor/sm-model_monitor_model_quality_monitoring/sm-model_monitor_model_quality_monitoring.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1099,35 +1099,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-multi_container_endpoint_direct_invocation/sm-multi_container_endpoint_direct_invocation.ipynb b/ deploy_and_monitor/sm-multi_container_endpoint_direct_invocation/sm-multi_container_endpoint_direct_invocation.ipynb
index 4760132b68..c5264fb361 100644
--- a/ deploy_and_monitor/sm-multi_container_endpoint_direct_invocation/sm-multi_container_endpoint_direct_invocation.ipynb
+++ b/ deploy_and_monitor/sm-multi_container_endpoint_direct_invocation/sm-multi_container_endpoint_direct_invocation.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -986,35 +986,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-multi_model_endpoint_bring_your_own_container/sm-multi_model_endpoint_bring_your_own_container.ipynb b/ deploy_and_monitor/sm-multi_model_endpoint_bring_your_own_container/sm-multi_model_endpoint_bring_your_own_container.ipynb
index fd3a966466..570f950186 100644
--- a/ deploy_and_monitor/sm-multi_model_endpoint_bring_your_own_container/sm-multi_model_endpoint_bring_your_own_container.ipynb
+++ b/ deploy_and_monitor/sm-multi_model_endpoint_bring_your_own_container/sm-multi_model_endpoint_bring_your_own_container.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -580,35 +580,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-serverless_inference.ipynb b/ deploy_and_monitor/sm-serverless_inference.ipynb
index 2f99f7bf3d..b9c013a7be 100644
--- a/ deploy_and_monitor/sm-serverless_inference.ipynb
+++ b/ deploy_and_monitor/sm-serverless_inference.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -498,35 +498,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-serverless_inference_huggingface_text_classification/sm-serverless_inference_huggingface_text_classification.ipynb b/ deploy_and_monitor/sm-serverless_inference_huggingface_text_classification/sm-serverless_inference_huggingface_text_classification.ipynb
index 90b3e5f337..1a011d8a53 100644
--- a/ deploy_and_monitor/sm-serverless_inference_huggingface_text_classification/sm-serverless_inference_huggingface_text_classification.ipynb
+++ b/ deploy_and_monitor/sm-serverless_inference_huggingface_text_classification/sm-serverless_inference_huggingface_text_classification.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1359,35 +1359,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-shadow_variant_shadow_api/sm-shadow_variant_shadow_api.ipynb b/ deploy_and_monitor/sm-shadow_variant_shadow_api/sm-shadow_variant_shadow_api.ipynb
index 5e3d408949..a67403ece3 100644
--- a/ deploy_and_monitor/sm-shadow_variant_shadow_api/sm-shadow_variant_shadow_api.ipynb
+++ b/ deploy_and_monitor/sm-shadow_variant_shadow_api/sm-shadow_variant_shadow_api.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1006,35 +1006,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-triton_inferentia2/sm-triton_inferentia2.ipynb b/ deploy_and_monitor/sm-triton_inferentia2/sm-triton_inferentia2.ipynb
index 4b5e1ef1b3..d84e87a2f8 100644
--- a/ deploy_and_monitor/sm-triton_inferentia2/sm-triton_inferentia2.ipynb
+++ b/ deploy_and_monitor/sm-triton_inferentia2/sm-triton_inferentia2.ipynb
@@ -8,6 +8,20 @@
"# Triton on SageMaker - Deploying on Inferentia instance type\n"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "8062f1ce",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
+ "\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
{
"cell_type": "markdown",
"id": "a6ce5cb1",
@@ -1386,6 +1400,46 @@
"sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n",
"sm_client.delete_model(ModelName=sm_model_name)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "70263f16",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
}
],
"metadata": {
diff --git a/ deploy_and_monitor/sm-triton_mme_bert_trt/bert_pytorch_trt_backend_MME.ipynb b/ deploy_and_monitor/sm-triton_mme_bert_trt/sm-triton_mme_bert_trt.ipynb
similarity index 92%
rename from deploy_and_monitor/sm-triton_mme_bert_trt/bert_pytorch_trt_backend_MME.ipynb
rename to deploy_and_monitor/sm-triton_mme_bert_trt/sm-triton_mme_bert_trt.ipynb
index f286bf371e..cb55322967 100644
--- a/ deploy_and_monitor/sm-triton_mme_bert_trt/bert_pytorch_trt_backend_MME.ipynb
+++ b/ deploy_and_monitor/sm-triton_mme_bert_trt/sm-triton_mme_bert_trt.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -55,8 +55,8 @@
"## How it works?\n",
"\n",
"1. SageMaker routes traffic to the right instance behind the endpoint where the target model is loaded. SageMaker takes care of model management behind the endpoint, loads model to the container's memory and unloads the model based on the endpoint's traffic pattern.\n",
- "2. Dynamically loads models from Amazon Simple Storage Service(S3) to the instance\u2019s storage volume. If the invoked models are not available on instance storage volume, the model is downloaded onto instance storage volume. If the instance storage volume reaches capacity, SageMaker deletes any unused models from the storage volume.\n",
- "3. SageMaker loads the model to NVIDIA Triton container\u2019s memory on GPU accelerated instance and serve the inference request. If the model is already loaded in the container memory, the subsequent requests are served faster as SageMaker does not need to download and load it again.\n",
+ "2. Dynamically loads models from Amazon Simple Storage Service(S3) to the instance’s storage volume. If the invoked models are not available on instance storage volume, the model is downloaded onto instance storage volume. If the instance storage volume reaches capacity, SageMaker deletes any unused models from the storage volume.\n",
+ "3. SageMaker loads the model to NVIDIA Triton container’s memory on GPU accelerated instance and serve the inference request. If the model is already loaded in the container memory, the subsequent requests are served faster as SageMaker does not need to download and load it again.\n",
"4. SageMaker takes care of traffic shaping to the MME endpoint, SageMaker continues to route traffics to the instance where the model is loaded. If the instance resources reach capacity due to high utilization, SageMaker unloads the least used models from the container to free up resource to load more frequently used models.\n",
"5. SageMaker MME can horizontally scale using auto-scaling policy, provision additional GPU compute instances based on metrics such as GPU utilization, memory utilization etc. to serve spiky traffic to MME endpoints."
]
@@ -694,35 +694,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -748,4 +748,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-triton_mme_gpu_ensemble_dali/sm-triton_mme_gpu_ensemble_dali.ipynb b/ deploy_and_monitor/sm-triton_mme_gpu_ensemble_dali/sm-triton_mme_gpu_ensemble_dali.ipynb
index 4c9269f8b5..bee57bf47b 100644
--- a/ deploy_and_monitor/sm-triton_mme_gpu_ensemble_dali/sm-triton_mme_gpu_ensemble_dali.ipynb
+++ b/ deploy_and_monitor/sm-triton_mme_gpu_ensemble_dali/sm-triton_mme_gpu_ensemble_dali.ipynb
@@ -8,6 +8,20 @@
"# Multiple Ensembles with GPU models using Amazon SageMaker in MME mode"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "06116fef",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
+ "\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
{
"cell_type": "markdown",
"id": "d52d9586-ecba-4f7b-a582-41365acad13c",
@@ -887,6 +901,46 @@
"## Conclusion:\n",
"We saw an example of the capability of SageMaker-Triton container to support multiple ensembles on GPU. We also saw two different methods of sending inference payload to the models. Customizing the hosting job is further possible by setting environment variables. The supported environment variables are listed here - https://github.com/triton-inference-server/server/blob/main/docker/sagemaker/serve. "
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed02a691",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
}
],
"metadata": {
diff --git a/ deploy_and_monitor/sm-triton_nlp_bert/sm-triton_nlp_bert.ipynb b/ deploy_and_monitor/sm-triton_nlp_bert/sm-triton_nlp_bert.ipynb
index 0a4883a824..665358966e 100644
--- a/ deploy_and_monitor/sm-triton_nlp_bert/sm-triton_nlp_bert.ipynb
+++ b/ deploy_and_monitor/sm-triton_nlp_bert/sm-triton_nlp_bert.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -268,9 +268,9 @@
"\n",
"```\n",
"bert\n",
- "\u251c\u2500\u2500 1\n",
- "\u2502 \u2514\u2500\u2500 model.pt\n",
- "\u2514\u2500\u2500 config.pbtxt\n",
+ "├── 1\n",
+ "│ └── model.pt\n",
+ "└── config.pbtxt\n",
"```"
]
},
@@ -724,35 +724,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -777,4 +777,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-triton_realtime_sme.ipynb b/ deploy_and_monitor/sm-triton_realtime_sme.ipynb
index 23c728875e..03d43c0302 100644
--- a/ deploy_and_monitor/sm-triton_realtime_sme.ipynb
+++ b/ deploy_and_monitor/sm-triton_realtime_sme.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -688,35 +688,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-triton_realtime_sme_flan_t5/sm-triton_realtime_sme_flan_t5.ipynb b/ deploy_and_monitor/sm-triton_realtime_sme_flan_t5/sm-triton_realtime_sme_flan_t5.ipynb
index f4581a4132..718ec7ee57 100644
--- a/ deploy_and_monitor/sm-triton_realtime_sme_flan_t5/sm-triton_realtime_sme_flan_t5.ipynb
+++ b/ deploy_and_monitor/sm-triton_realtime_sme_flan_t5/sm-triton_realtime_sme_flan_t5.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -566,35 +566,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-triton_tensorflow_model_deploy.ipynb b/ deploy_and_monitor/sm-triton_tensorflow_model_deploy.ipynb
index 1a8e431431..a52b8160bf 100644
--- a/ deploy_and_monitor/sm-triton_tensorflow_model_deploy.ipynb
+++ b/ deploy_and_monitor/sm-triton_tensorflow_model_deploy.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -464,35 +464,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ deploy_and_monitor/sm-triton_tensorrt-sentence_transformer/sm-triton_tensorrt-sentence_transformer.ipynb b/ deploy_and_monitor/sm-triton_tensorrt-sentence_transformer/sm-triton_tensorrt-sentence_transformer.ipynb
index c404343ebc..e657269dc0 100644
--- a/ deploy_and_monitor/sm-triton_tensorrt-sentence_transformer/sm-triton_tensorrt-sentence_transformer.ipynb
+++ b/ deploy_and_monitor/sm-triton_tensorrt-sentence_transformer/sm-triton_tensorrt-sentence_transformer.ipynb
@@ -9,7 +9,7 @@
}
},
"source": [
- "# Prerequisites\n"
+ "# Triton TensorRT Sentence Transformer"
]
},
{
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -35,6 +35,7 @@
}
},
"source": [
+ "## Prerequisites\n",
"\n",
"Install the necessary Python modules to use and interact with [NVIDIA Triton Inference Server](https://github.com/triton-inference-server/server/)."
]
@@ -60,7 +61,7 @@
"source": [
"
\n",
"Warning: You should run this notebook on a SageMaker Notebook Instance with access to the same GPU as the instance you will deploy your model to (g4dn is the one configured by default in this example). There are model optimization steps contained in this notebook that are GPU architecture-dependent.\n",
- " \u2b07\u2b07\u2b07\u2b07\u2b07 change in the next cell if required\n",
+ " ⬇⬇⬇⬇⬇ change in the next cell if required\n",
"
\n"
]
},
@@ -1080,35 +1081,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -1134,4 +1135,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ deploy_and_monitor/sm-xgboost_bring_your_own_model/sm-xgboost_bring_your_own_model.ipynb b/ deploy_and_monitor/sm-xgboost_bring_your_own_model/sm-xgboost_bring_your_own_model.ipynb
index 1a36ce0ada..9a39ea7f9f 100644
--- a/ deploy_and_monitor/sm-xgboost_bring_your_own_model/sm-xgboost_bring_your_own_model.ipynb
+++ b/ deploy_and_monitor/sm-xgboost_bring_your_own_model/sm-xgboost_bring_your_own_model.ipynb
@@ -29,7 +29,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -518,35 +518,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/README.md b/ generative_ai/README.md
new file mode 100644
index 0000000000..2d5d6fad5b
--- /dev/null
+++ b/ generative_ai/README.md
@@ -0,0 +1,27 @@
+# Amazon SageMaker Examples
+
+### Generative AI
+
+These examples showcases Amazon SageMaker's capabilities in the exciting field of generative artificial intelligence (AI). Generative AI models are designed to create new, synthetic data across various modalities, such as text, images, audio, and video, based on the patterns and relationships learned from training data.
+
+- [Fine-tuning and deploying a Hugging Face summarization model on SageMaker with your own scripts and dataset](sm-finetuning_huggingface_with_your_own_scripts_and_data/sm-finetuning_huggingface_with_your_own_scripts_and_data.ipynb)
+- [Fine-tuning and deploying the Mixtral 8x7B LLM In SageMaker with Hugging Face, using QLoRA Parameter-Efficient Fine-Tuning](sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb)
+- [Serve large models on SageMaker with DeepSpeed Container](sm-djl_deepspeed_bloom_176b_deploy.ipynb)
+- [Accelerate SageMaker-PyTorch FSDP Training of Llama-v2 (or GPT-NeoX) with FP8 on P5 instances](sm-fsdp_training_of_llama_v2_with_fp8_on_p5.ipynb)
+- [Fine-tune Code Llama, Deploy and Evaluate the Fine-tuning with Human-eval Repository](sm-jumpstart_foundation_code_llama_fine_tuning_human_eval.ipynb)
+- [SageMaker JumpStart Foundation Models - Fine-tuning text generation GPT-J 6B model on domain specific dataset](sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb)
+- [Fine-tune Gemma on SageMaker JumpStart](sm-jumpstart_foundation_gemma_fine_tuning.ipynb)
+- [Fine-tune LLaMA 2 models on SageMaker JumpStart](sm-jumpstart_foundation_llama_2_finetuning.ipynb)
+- [Fine-tune LLaMA 3 models on SageMaker JumpStart](sm-jumpstart_foundation_llama_3_finetuning.ipynb)
+- [Text completion: Run Llama 3 models in SageMaker JumpStart](sm-jumpstart_foundation_llama_3_text_completion.ipynb)
+- [Introduction to SageMaker JumpStart - text moderation with Llama Guard](sm-jumpstart_foundation_llama_guard_text_moderation.ipynb)
+- [Introduction to SageMaker JumpStart - Text Generation with Mistral models](sm-jumpstart_foundation_mistral_7b_domain_adaption_finetuning.ipynb)
+- [Retrieval-Augmented Generation: Question Answering based on Custom Dataset with Open-sourced LangChain Library](sm-jumpstart_foundation_rag_langchain_question_answering.ipynb)
+- [Introduction to SageMaker JumpStart - Text Generation](sm-jumpstart_foundation_text_generation_inference.ipynb)
+- [Fine-tune and deploy LLaMA V2 models on AWS Trainium and AWS Inferentia based instances in SageMaker JumpStart](sm-jumpstart_foundation_trainium_inferentia_finetuning_deployment.ipynb)
+- [Introduction to SageMaker HuggingFace - Text Classification](sm-jumpstart_huggingface_text_classification.ipynb)
+- [Chat Completion: Run Llama 2 Models in SageMaker JumpStart](sm-jumpstart_llama_2_chat_completion.ipynb)
+- [Text completion: Run Llama 2 models in SageMaker JumpStart](sm-jumpstart_llama_2_text_completion.ipynb)
+- [Retrieval-Augmented Generation: Question Answering using LangChain and Cohere's Generate and Embedding Models from SageMaker JumpStart](sm-jumpstart_rag_question_answering_with_cohere_and_langchain.ipynb)
+- [Introduction to JumpStart - Text to Image](sm-jumpstart_stable_diffusion_text_to_image.ipynb)
+- [Introduction to JumpStart - Text Embedding](sm-jumpstart_text_embedding.ipynb)
diff --git a/ generative_ai/sm-djl_deepspeed_bloom_176b_deploy.ipynb b/ generative_ai/sm-djl_deepspeed_bloom_176b_deploy.ipynb
index 33242083d4..4c7cb79531 100644
--- a/ generative_ai/sm-djl_deepspeed_bloom_176b_deploy.ipynb
+++ b/ generative_ai/sm-djl_deepspeed_bloom_176b_deploy.ipynb
@@ -5,7 +5,7 @@
"id": "4d71f039",
"metadata": {},
"source": [
- "# Serve large models on SageMaker with DeepSpeed Container. In this notebook we show Bloom-176B model hosting\n"
+ "# Serve large models on SageMaker with DeepSpeed Container\n"
]
},
{
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -787,35 +787,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-finetuning_huggingface_with_your_own_scripts_and_data/sm-finetuning_huggingface_with_your_own_scripts_and_data.ipynb b/ generative_ai/sm-finetuning_huggingface_with_your_own_scripts_and_data/sm-finetuning_huggingface_with_your_own_scripts_and_data.ipynb
index d404727ad1..9f71f669c4 100644
--- a/ generative_ai/sm-finetuning_huggingface_with_your_own_scripts_and_data/sm-finetuning_huggingface_with_your_own_scripts_and_data.ipynb
+++ b/ generative_ai/sm-finetuning_huggingface_with_your_own_scripts_and_data/sm-finetuning_huggingface_with_your_own_scripts_and_data.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -708,35 +708,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-fsdp_training_of_llama_v2_with_fp8_on_p5.ipynb b/ generative_ai/sm-fsdp_training_of_llama_v2_with_fp8_on_p5.ipynb
index 21d5c26c0d..945e33d893 100644
--- a/ generative_ai/sm-fsdp_training_of_llama_v2_with_fp8_on_p5.ipynb
+++ b/ generative_ai/sm-fsdp_training_of_llama_v2_with_fp8_on_p5.ipynb
@@ -11,7 +11,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---\n",
"\n",
@@ -967,41 +967,36 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
}
],
"metadata": {
diff --git a/ generative_ai/sm-jumpstart_foundation_code_llama_fine_tuning_human_eval.ipynb b/ generative_ai/sm-jumpstart_foundation_code_llama_fine_tuning_human_eval.ipynb
index f31b7a798d..57b60e3fef 100644
--- a/ generative_ai/sm-jumpstart_foundation_code_llama_fine_tuning_human_eval.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_code_llama_fine_tuning_human_eval.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -634,35 +634,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb b/ generative_ai/sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb
index 11f1e94f64..2b3c14526d 100644
--- a/ generative_ai/sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -283,35 +283,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-jumpstart_foundation_gemma_fine_tuning.ipynb b/ generative_ai/sm-jumpstart_foundation_gemma_fine_tuning.ipynb
index 5b0beaf8f9..3038e3e9fb 100644
--- a/ generative_ai/sm-jumpstart_foundation_gemma_fine_tuning.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_gemma_fine_tuning.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -660,35 +660,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
},
{
diff --git a/ generative_ai/sm-jumpstart_foundation_llama_2_finetuning.ipynb b/ generative_ai/sm-jumpstart_foundation_llama_2_finetuning.ipynb
index 4a8025c5cb..c76fe8416b 100644
--- a/ generative_ai/sm-jumpstart_foundation_llama_2_finetuning.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_llama_2_finetuning.ipynb
@@ -25,7 +25,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -752,6 +752,47 @@
"# attached_estimator.logs()\n",
"# attached_estimator.deploy()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f7546d9",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
}
],
"metadata": {
diff --git a/ generative_ai/sm-jumpstart_foundation_llama_3_finetuning.ipynb b/ generative_ai/sm-jumpstart_foundation_llama_3_finetuning.ipynb
index cd60fb15fc..8ff16e9698 100644
--- a/ generative_ai/sm-jumpstart_foundation_llama_3_finetuning.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_llama_3_finetuning.ipynb
@@ -27,7 +27,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -798,35 +798,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ generative_ai/sm-jumpstart_foundation_llama_3_text_completion.ipynb b/ generative_ai/sm-jumpstart_foundation_llama_3_text_completion.ipynb
index 316d03412f..5a75eda31c 100644
--- a/ generative_ai/sm-jumpstart_foundation_llama_3_text_completion.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_llama_3_text_completion.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -365,35 +365,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n"
]
}
diff --git a/ generative_ai/sm-jumpstart_foundation_llama_guard_text_moderation.ipynb b/ generative_ai/sm-jumpstart_foundation_llama_guard_text_moderation.ipynb
index ff44d7d01d..8afb7623a4 100644
--- a/ generative_ai/sm-jumpstart_foundation_llama_guard_text_moderation.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_llama_guard_text_moderation.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -515,35 +515,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n"
]
}
diff --git a/ generative_ai/sm-jumpstart_foundation_mistral_7b_domain_adaption_finetuning.ipynb b/ generative_ai/sm-jumpstart_foundation_mistral_7b_domain_adaption_finetuning.ipynb
index 8fd8df4ac4..69bfa7cba9 100644
--- a/ generative_ai/sm-jumpstart_foundation_mistral_7b_domain_adaption_finetuning.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_mistral_7b_domain_adaption_finetuning.ipynb
@@ -10,6 +10,12 @@
"# Introduction to SageMaker JumpStart - Text Generation with Mistral models\n",
"\n",
"---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
"In this demo notebook, we demonstrate how to use the SageMaker Python SDK to fine-tuning and deploy [Mistral 7B](mistralai/Mistral-7B-v0.1) models for text generation. For fine-tuning, we include two types of fine-tuning: instruction fine-tuning and domain adaption fine-tuning. \n",
"\n",
"---"
@@ -732,6 +738,48 @@
"domain_adaptation_predictor.delete_model()\n",
"domain_adaptation_predictor.delete_endpoint()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2113fd9",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
}
],
"metadata": {
diff --git a/ generative_ai/sm-jumpstart_foundation_rag_langchain_question_answering.ipynb b/ generative_ai/sm-jumpstart_foundation_rag_langchain_question_answering.ipynb
index a3edd26314..5f6079f341 100644
--- a/ generative_ai/sm-jumpstart_foundation_rag_langchain_question_answering.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_rag_langchain_question_answering.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -794,35 +794,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-jumpstart_foundation_text_generation_inference.ipynb b/ generative_ai/sm-jumpstart_foundation_text_generation_inference.ipynb
index 67d0f3be69..9b1d854b52 100644
--- a/ generative_ai/sm-jumpstart_foundation_text_generation_inference.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_text_generation_inference.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -204,35 +204,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n"
]
}
diff --git a/ generative_ai/sm-jumpstart_foundation_trainium_inferentia_finetuning_deployment.ipynb b/ generative_ai/sm-jumpstart_foundation_trainium_inferentia_finetuning_deployment.ipynb
index 45ddd33c2f..5d4eb8c553 100644
--- a/ generative_ai/sm-jumpstart_foundation_trainium_inferentia_finetuning_deployment.ipynb
+++ b/ generative_ai/sm-jumpstart_foundation_trainium_inferentia_finetuning_deployment.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -858,35 +858,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-jumpstart_huggingface_text_classification.ipynb b/ generative_ai/sm-jumpstart_huggingface_text_classification.ipynb
index 962776ddc2..b4a9f0167a 100644
--- a/ generative_ai/sm-jumpstart_huggingface_text_classification.ipynb
+++ b/ generative_ai/sm-jumpstart_huggingface_text_classification.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1053,35 +1053,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-jumpstart_llama_2_chat_completion.ipynb b/ generative_ai/sm-jumpstart_llama_2_chat_completion.ipynb
index 53f57bd613..d7ed6411bd 100644
--- a/ generative_ai/sm-jumpstart_llama_2_chat_completion.ipynb
+++ b/ generative_ai/sm-jumpstart_llama_2_chat_completion.ipynb
@@ -18,7 +18,7 @@
"---\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -892,35 +892,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ generative_ai/sm-jumpstart_llama_2_text_completion.ipynb b/ generative_ai/sm-jumpstart_llama_2_text_completion.ipynb
index 38cd2baafb..8f2983c81a 100644
--- a/ generative_ai/sm-jumpstart_llama_2_text_completion.ipynb
+++ b/ generative_ai/sm-jumpstart_llama_2_text_completion.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -363,35 +363,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
"\n",
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+ "\n",
"\n",
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+ "\n",
"\n",
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+ "\n",
"\n",
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+ "\n",
"\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n"
]
}
diff --git a/ generative_ai/sm-jumpstart_rag_question_answering_with_cohere_and_langchain.ipynb b/ generative_ai/sm-jumpstart_rag_question_answering_with_cohere_and_langchain.ipynb
index 01a8988cec..377a4ce3e9 100644
--- a/ generative_ai/sm-jumpstart_rag_question_answering_with_cohere_and_langchain.ipynb
+++ b/ generative_ai/sm-jumpstart_rag_question_answering_with_cohere_and_langchain.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -882,41 +882,36 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
}
],
"metadata": {
diff --git a/ generative_ai/sm-jumpstart_stable_diffusion_text_to_image.ipynb b/ generative_ai/sm-jumpstart_stable_diffusion_text_to_image.ipynb
index 7bdf4f19d2..f5af191d97 100644
--- a/ generative_ai/sm-jumpstart_stable_diffusion_text_to_image.ipynb
+++ b/ generative_ai/sm-jumpstart_stable_diffusion_text_to_image.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1158,35 +1158,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-jumpstart_text_embedding.ipynb b/ generative_ai/sm-jumpstart_text_embedding.ipynb
index 26d52cdfc6..62ca319994 100644
--- a/ generative_ai/sm-jumpstart_text_embedding.ipynb
+++ b/ generative_ai/sm-jumpstart_text_embedding.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -444,35 +444,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ generative_ai/sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb b/ generative_ai/sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb
index 4c0a473dee..e2271768a9 100644
--- a/ generative_ai/sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb
+++ b/ generative_ai/sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---\n",
"\n",
@@ -577,35 +577,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/README.md b/ ml_ops/README.md
new file mode 100644
index 0000000000..6f60c476b6
--- /dev/null
+++ b/ ml_ops/README.md
@@ -0,0 +1,32 @@
+# Amazon SageMaker Examples
+
+### ML Ops
+
+These examples showcases Amazon SageMaker's features to implement machine learning models in production environments with continuous integration and deployment.
+
+- [Model Lineage Tracking](sm-ml_lineage_tracking_model_governance_graph/sm-ml_lineage_tracking_model_governance_graph.ipynb)
+- [Deploy a MLflow Model to SageMaker](sm-mlflow_deployment/sm-mlflow_deployment.ipynb)
+- [SageMaker HPO with MLflow](sm-mlflow_hpo/sm-mlflow_hpo.ipynb)
+- [SageMaker Pipelines with MLflow](sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb)
+- [How to Setup Amazon SageMaker with MLflow](sm-mlflow_setup/sm-mlflow_setup.ipynb)
+- [SageMaker Training with MLflow](sm-mlflow_training/sm-mlflow_training.ipynb)
+- [SageMaker Data Quality Model Monitor for Batch Transform with SageMaker Pipelines On-demand](sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand.ipynb)
+- [SageMaker Model Quality Model Monitor for Batch Transform With SageMaker Pipelines On-demand](sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand.ipynb)
+- [Basic Pipeline for Batch Inference using Low-code Experience for SageMaker Pipelines](sm-pipelines_batch_inference_step_decorator/sm-pipelines_batch_inference_step_decorator.ipynb)
+- [Glue ETL as part of a SageMaker pipeline](sm-pipelines_callback_step/sm-pipelines_callback_step.ipynb)
+- [SageMaker Pipelines integration with Model Monitor and Clarify](sm-pipelines_clarify_model_monitor_integration/sm-pipelines_clarify_model_monitor_integration.ipynb)
+- [Using @step Decorated Step with EMR Step](sm-pipelines_emr_step_using_step_decorator/sm-pipelines_emr_step_using_step_decorator.ipynb)
+- [SageMaker Pipelines Lambda Step](sm-pipelines_lambda_step/sm-pipelines_lambda_step.ipynb)
+- [Launch Amazon SageMaker Autopilot experiments directly from within Amazon SageMaker Pipelines to easily automate MLOps workflows](sm-pipelines_launching_autopilot_with_auto_ml_step/sm-pipelines_launching_autopilot_with_auto_ml_step.ipynb)
+- [SageMaker Pipeline - Local Mode](sm-pipelines_local_mode/sm-pipelines_local_mode.ipynb)
+- [Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines](sm-pipelines_preprocess_train_evaluate_batch_transform/sm-pipelines_preprocess_train_evaluate_batch_transform.ipynb)
+- [SageMaker Pipelines: Selective Execution Demo](sm-pipelines_selective_execution/sm-pipelines_selective_execution.ipynb)
+- [Use SageMaker Pipelines With Step Caching](sm-pipelines_step_caching/sm-pipelines_step_caching.ipynb)
+- [Quick Start - Introducing @step Decorator and Pipeline Trigger](sm-pipelines_step_decorator/sm-pipelines_step_decorator.ipynb)
+- [Quick Start - Using @step Decorated Step with Classic TrainingStep](sm-pipelines_step_decorator_with_classic_training_step/sm-pipelines_step_decorator_with_classic_training_step.ipynb)
+- [Quick Start - Using @step Decorated Steps with ConditionStep](sm-pipelines_step_decorator_with_condition_step/sm-pipelines_step_decorator_with_condition_step.ipynb)
+- [SageMaker Pipelines EMR Step With Running EMR Cluster](sm-pipelines_emr_step_with_running_emr_cluster.ipynb)
+- [SageMaker Pipelines EMR Step With Cluster Lifecycle Management](sm-pipelines_emr-step-with_cluster_lifecycle_management.ipynb)
+- [SageMaker Pipelines Tuning Step](sm-pipelines_hyperparameter_tuning.ipynb)
+- [Use SageMaker Pipelines to Run Your Jobs Locally](sm-pipelines_local_mode.ipynb)
+- [SageMaker Pipelines](sm-pipelines_train_model_registry_deploy.ipynb)
diff --git a/ ml_ops/sm-ml_lineage_tracking_model_governance_graph/sm-ml_lineage_tracking_model_governance_graph.ipynb b/ ml_ops/sm-ml_lineage_tracking_model_governance_graph/sm-ml_lineage_tracking_model_governance_graph.ipynb
index 162c108ec8..869a9f2f2c 100644
--- a/ ml_ops/sm-ml_lineage_tracking_model_governance_graph/sm-ml_lineage_tracking_model_governance_graph.ipynb
+++ b/ ml_ops/sm-ml_lineage_tracking_model_governance_graph/sm-ml_lineage_tracking_model_governance_graph.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -550,35 +550,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-mlflow_deployment/sm-mlflow_deployment.ipynb b/ ml_ops/sm-mlflow_deployment/sm-mlflow_deployment.ipynb
index 296ab997ea..6485579479 100644
--- a/ ml_ops/sm-mlflow_deployment/sm-mlflow_deployment.ipynb
+++ b/ ml_ops/sm-mlflow_deployment/sm-mlflow_deployment.ipynb
@@ -13,7 +13,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -473,35 +473,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ ml_ops/sm-mlflow_hpo/sm-mlflow_hpo.ipynb b/ ml_ops/sm-mlflow_hpo/sm-mlflow_hpo.ipynb
index 4b6853403a..6af3c2bf46 100644
--- a/ ml_ops/sm-mlflow_hpo/sm-mlflow_hpo.ipynb
+++ b/ ml_ops/sm-mlflow_hpo/sm-mlflow_hpo.ipynb
@@ -15,7 +15,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -639,35 +639,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb b/ ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
index 4ad94a49f3..2455321d59 100644
--- a/ ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
+++ b/ ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
@@ -15,7 +15,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -620,35 +620,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ ml_ops/sm-mlflow_setup/sm-mlflow_setup.ipynb b/ ml_ops/sm-mlflow_setup/sm-mlflow_setup.ipynb
index 3ee5907980..0e97f3d1c5 100644
--- a/ ml_ops/sm-mlflow_setup/sm-mlflow_setup.ipynb
+++ b/ ml_ops/sm-mlflow_setup/sm-mlflow_setup.ipynb
@@ -15,7 +15,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -359,35 +359,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ ml_ops/sm-mlflow_training/sm-mlflow_training.ipynb b/ ml_ops/sm-mlflow_training/sm-mlflow_training.ipynb
index 21bdcc7d7a..252dc8f82a 100644
--- a/ ml_ops/sm-mlflow_training/sm-mlflow_training.ipynb
+++ b/ ml_ops/sm-mlflow_training/sm-mlflow_training.ipynb
@@ -13,7 +13,7 @@
"source": [
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- ""
+ ""
]
},
{
@@ -283,35 +283,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
}
],
diff --git a/ ml_ops/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand.ipynb b/ ml_ops/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand.ipynb
index 6ec1ad5094..a8e76ecda8 100644
--- a/ ml_ops/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand.ipynb
+++ b/ ml_ops/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_data_quality_with_pipelines_on_demand.ipynb
@@ -18,7 +18,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -661,35 +661,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand.ipynb b/ ml_ops/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand.ipynb
index d5e86ba7fc..bafb721940 100644
--- a/ ml_ops/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand.ipynb
+++ b/ ml_ops/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand/sm-model_monitor_batch_transform_model_quality_with_pipelines_on_demand.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -729,35 +729,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_batch_inference_step_decorator/sm-pipelines_batch_inference_step_decorator.ipynb b/ ml_ops/sm-pipelines_batch_inference_step_decorator/sm-pipelines_batch_inference_step_decorator.ipynb
index 952d9680f1..be68f90d1f 100644
--- a/ ml_ops/sm-pipelines_batch_inference_step_decorator/sm-pipelines_batch_inference_step_decorator.ipynb
+++ b/ ml_ops/sm-pipelines_batch_inference_step_decorator/sm-pipelines_batch_inference_step_decorator.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -95,12 +95,12 @@
"execution_count": null,
"id": "7291fbcf-4e06-4568-9fe7-71e60bf20cc5",
"metadata": {
- "scrolled": true,
- "tags": [],
"ExecuteTime": {
- "start_time": "2023-11-14T15:46:27.679087Z",
- "end_time": "2023-11-14T15:46:30.269723Z"
- }
+ "end_time": "2023-11-14T15:46:30.269723Z",
+ "start_time": "2023-11-14T15:46:27.679087Z"
+ },
+ "scrolled": true,
+ "tags": []
},
"outputs": [],
"source": [
@@ -986,35 +986,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
},
{
@@ -1634,9 +1634,9 @@
],
"instance_type": "ml.t3.medium",
"kernelspec": {
- "name": "python3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "display_name": "Python 3 (ipykernel)"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
diff --git a/ ml_ops/sm-pipelines_callback_step/sm-pipelines_callback_step.ipynb b/ ml_ops/sm-pipelines_callback_step/sm-pipelines_callback_step.ipynb
index 1cf7dcc1bd..12ae4ad755 100644
--- a/ ml_ops/sm-pipelines_callback_step/sm-pipelines_callback_step.ipynb
+++ b/ ml_ops/sm-pipelines_callback_step/sm-pipelines_callback_step.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1429,35 +1429,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_clarify_model_monitor_integration/sm-pipelines_clarify_model_monitor_integration.ipynb b/ ml_ops/sm-pipelines_clarify_model_monitor_integration/sm-pipelines_clarify_model_monitor_integration.ipynb
index 2e1272bafb..f0a402d8fa 100644
--- a/ ml_ops/sm-pipelines_clarify_model_monitor_integration/sm-pipelines_clarify_model_monitor_integration.ipynb
+++ b/ ml_ops/sm-pipelines_clarify_model_monitor_integration/sm-pipelines_clarify_model_monitor_integration.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1483,35 +1483,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_emr-step-with_cluster_lifecycle_management.ipynb b/ ml_ops/sm-pipelines_emr-step-with_cluster_lifecycle_management.ipynb
index 73bdf6c000..60d41008f8 100644
--- a/ ml_ops/sm-pipelines_emr-step-with_cluster_lifecycle_management.ipynb
+++ b/ ml_ops/sm-pipelines_emr-step-with_cluster_lifecycle_management.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -829,35 +829,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_emr_step_using_step_decorator/sm-pipelines_emr_step_using_step_decorator.ipynb b/ ml_ops/sm-pipelines_emr_step_using_step_decorator/sm-pipelines_emr_step_using_step_decorator.ipynb
index fdce64713a..02098b9118 100644
--- a/ ml_ops/sm-pipelines_emr_step_using_step_decorator/sm-pipelines_emr_step_using_step_decorator.ipynb
+++ b/ ml_ops/sm-pipelines_emr_step_using_step_decorator/sm-pipelines_emr_step_using_step_decorator.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -99,8 +99,8 @@
"execution_count": null,
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-14T15:41:25.059015Z",
- "end_time": "2023-11-14T15:41:27.691532Z"
+ "end_time": "2023-11-14T15:41:27.691532Z",
+ "start_time": "2023-11-14T15:41:25.059015Z"
},
"tags": []
},
@@ -801,43 +801,36 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- ""
+ ""
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -1418,9 +1411,9 @@
],
"instance_type": "ml.t3.medium",
"kernelspec": {
- "name": "python3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "display_name": "Python 3 (ipykernel)"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
diff --git a/ ml_ops/sm-pipelines_emr_step_with_running_emr_cluster.ipynb b/ ml_ops/sm-pipelines_emr_step_with_running_emr_cluster.ipynb
index 46dff44429..5f0174d805 100644
--- a/ ml_ops/sm-pipelines_emr_step_with_running_emr_cluster.ipynb
+++ b/ ml_ops/sm-pipelines_emr_step_with_running_emr_cluster.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -815,35 +815,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_hyperparameter_tuning.ipynb b/ ml_ops/sm-pipelines_hyperparameter_tuning.ipynb
index 21cbc60d02..159debcebc 100644
--- a/ ml_ops/sm-pipelines_hyperparameter_tuning.ipynb
+++ b/ ml_ops/sm-pipelines_hyperparameter_tuning.ipynb
@@ -9,7 +9,7 @@
}
},
"source": [
- "#### SageMaker Pipelines Tuning Step\n"
+ "# SageMaker Pipelines Tuning Step\n"
]
},
{
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -991,35 +991,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_lambda_step/sm-pipelines_lambda_step.ipynb b/ ml_ops/sm-pipelines_lambda_step/sm-pipelines_lambda_step.ipynb
index 9682f0dfbe..29a8d5b288 100644
--- a/ ml_ops/sm-pipelines_lambda_step/sm-pipelines_lambda_step.ipynb
+++ b/ ml_ops/sm-pipelines_lambda_step/sm-pipelines_lambda_step.ipynb
@@ -60,7 +60,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1966,35 +1966,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -2038,4 +2038,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ ml_ops/sm-pipelines_launching_autopilot_with_auto_ml_step/sm-pipelines_launching_autopilot_with_auto_ml_step.ipynb b/ ml_ops/sm-pipelines_launching_autopilot_with_auto_ml_step/sm-pipelines_launching_autopilot_with_auto_ml_step.ipynb
index 8edacaa911..91a01cebf6 100644
--- a/ ml_ops/sm-pipelines_launching_autopilot_with_auto_ml_step/sm-pipelines_launching_autopilot_with_auto_ml_step.ipynb
+++ b/ ml_ops/sm-pipelines_launching_autopilot_with_auto_ml_step/sm-pipelines_launching_autopilot_with_auto_ml_step.ipynb
@@ -22,7 +22,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -845,35 +845,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_local_mode.ipynb b/ ml_ops/sm-pipelines_local_mode.ipynb
index 395d88881e..90dcd41652 100644
--- a/ ml_ops/sm-pipelines_local_mode.ipynb
+++ b/ ml_ops/sm-pipelines_local_mode.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1482,35 +1482,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_local_mode/sm-pipelines_local_mode.ipynb b/ ml_ops/sm-pipelines_local_mode/sm-pipelines_local_mode.ipynb
index 9a4fb189b5..061f2e85b8 100644
--- a/ ml_ops/sm-pipelines_local_mode/sm-pipelines_local_mode.ipynb
+++ b/ ml_ops/sm-pipelines_local_mode/sm-pipelines_local_mode.ipynb
@@ -22,7 +22,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1058,35 +1058,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_preprocess_train_evaluate_batch_transform/sm-pipelines_preprocess_train_evaluate_batch_transform.ipynb b/ ml_ops/sm-pipelines_preprocess_train_evaluate_batch_transform/sm-pipelines_preprocess_train_evaluate_batch_transform.ipynb
index 16aebba748..5f280c9e86 100644
--- a/ ml_ops/sm-pipelines_preprocess_train_evaluate_batch_transform/sm-pipelines_preprocess_train_evaluate_batch_transform.ipynb
+++ b/ ml_ops/sm-pipelines_preprocess_train_evaluate_batch_transform/sm-pipelines_preprocess_train_evaluate_batch_transform.ipynb
@@ -54,7 +54,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -3443,35 +3443,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -3511,4 +3511,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/ ml_ops/sm-pipelines_selective_execution/sm-pipelines_selective_execution.ipynb b/ ml_ops/sm-pipelines_selective_execution/sm-pipelines_selective_execution.ipynb
index af77da28e0..9279510b0c 100644
--- a/ ml_ops/sm-pipelines_selective_execution/sm-pipelines_selective_execution.ipynb
+++ b/ ml_ops/sm-pipelines_selective_execution/sm-pipelines_selective_execution.ipynb
@@ -1,5 +1,13 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "bf679922-4032-40ef-b370-6a9b34293ea7",
+ "metadata": {},
+ "source": [
+ "# SageMaker Pipelines: Selective Execution Demo"
+ ]
+ },
{
"cell_type": "markdown",
"id": "65e8d9da-ab2f-4e52-ac05-a0c0f46de9b4",
@@ -9,19 +17,11 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
},
- {
- "cell_type": "markdown",
- "id": "bf679922-4032-40ef-b370-6a9b34293ea7",
- "metadata": {},
- "source": [
- "# SageMaker Pipelines: Selective Execution Demo"
- ]
- },
{
"cell_type": "markdown",
"id": "bf30edaa-ad14-4ae0-a490-afe1a0626661",
@@ -1526,35 +1526,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
},
{
diff --git a/ ml_ops/sm-pipelines_step_caching/sm-pipelines_step_caching.ipynb b/ ml_ops/sm-pipelines_step_caching/sm-pipelines_step_caching.ipynb
index 351dec31b4..4bad4ac04e 100644
--- a/ ml_ops/sm-pipelines_step_caching/sm-pipelines_step_caching.ipynb
+++ b/ ml_ops/sm-pipelines_step_caching/sm-pipelines_step_caching.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1039,35 +1039,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ ml_ops/sm-pipelines_step_decorator/sm-pipelines_step_decorator.ipynb b/ ml_ops/sm-pipelines_step_decorator/sm-pipelines_step_decorator.ipynb
index b85b8f7e18..8aa97a8ba9 100644
--- a/ ml_ops/sm-pipelines_step_decorator/sm-pipelines_step_decorator.ipynb
+++ b/ ml_ops/sm-pipelines_step_decorator/sm-pipelines_step_decorator.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -59,7 +59,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 9,
"id": "eb1e4035-df62-4370-ac26-af14c025b503",
"metadata": {
"ExecuteTime": {
@@ -67,7 +66,6 @@
"start_time": "2023-09-22T16:53:31.520437Z"
}
},
- "outputs": [],
"source": [
"Next, run the cell bellow to install all dependencies required by this notebook."
]
@@ -78,8 +76,8 @@
"id": "d2389a9a-9ae5-4600-9eae-a551e836b094",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-14T15:26:36.538657Z",
- "end_time": "2023-11-14T15:26:39.087561Z"
+ "end_time": "2023-11-14T15:26:39.087561Z",
+ "start_time": "2023-11-14T15:26:36.538657Z"
},
"collapsed": true,
"jupyter": {
@@ -94,7 +92,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 7,
"id": "d02c2cc7-bfb9-4e07-a8d9-4a5ef5ec109a",
"metadata": {
"ExecuteTime": {
@@ -102,7 +99,6 @@
"start_time": "2023-09-22T16:51:43.460477Z"
}
},
- "outputs": [],
"source": [
"## Setup configuration file path\n",
"We need to set the directory in which the `config.yaml` file resides so that the step decorator can make use of the settings."
@@ -114,8 +110,8 @@
"id": "b22a49fb-3fc7-46fe-89cc-3d4186016a73",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:02:33.710426Z",
- "end_time": "2023-11-10T14:02:33.716601Z"
+ "end_time": "2023-11-10T14:02:33.716601Z",
+ "start_time": "2023-11-10T14:02:33.710426Z"
},
"tags": []
},
@@ -129,7 +125,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 10,
"id": "c2e488ba-455b-42d5-92af-f8c63b254028",
"metadata": {
"ExecuteTime": {
@@ -137,7 +132,6 @@
"start_time": "2023-09-22T17:07:04.092727Z"
}
},
- "outputs": [],
"source": [
"## Define pipeline steps\n",
"We can define a pipeline step by simply adding a step decorator on top of a custom function (i.e. the `generate` function below)."
@@ -157,8 +151,8 @@
"id": "be6a3a6a-599e-4572-b7ff-9cfa19c8b5fd",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:02:33.714787Z",
- "end_time": "2023-11-10T14:02:57.900606Z"
+ "end_time": "2023-11-10T14:02:57.900606Z",
+ "start_time": "2023-11-10T14:02:33.714787Z"
},
"tags": []
},
@@ -183,8 +177,8 @@
"id": "36ac4334-3443-46ef-9967-a4cea1f36a6d",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:02:57.906590Z",
- "end_time": "2023-11-10T14:02:57.909362Z"
+ "end_time": "2023-11-10T14:02:57.909362Z",
+ "start_time": "2023-11-10T14:02:57.906590Z"
},
"tags": []
},
@@ -207,7 +201,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 14,
"id": "eb77e5ee-ceec-4de5-9838-0a40d16f87ae",
"metadata": {
"ExecuteTime": {
@@ -215,18 +208,6 @@
"start_time": "2023-09-22T17:30:00.731981Z"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
- "sagemaker.config INFO - Fetched defaults config from location: /Users/qidewen/workplace/SageMakerNotebook/amazon-sagemaker-examples-staging/sagemaker-pipelines/tabular/step-decorator\n",
- "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.Dependencies\n",
- "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.WorkdirConfig.IgnoreNamePatterns\n"
- ]
- }
- ],
"source": [
"### Add extra dependencies using conda environment yml file\n",
"To run a function in a new conda environment, you can specify the path of an `environment.yml` file to the `dependencies` attribute as follows:\n",
@@ -246,7 +227,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 23,
"id": "7ae6ee44-1095-4260-8a58-e5a11e0097e0",
"metadata": {
"ExecuteTime": {
@@ -254,18 +234,6 @@
"start_time": "2023-09-22T18:01:58.124458Z"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
- "sagemaker.config INFO - Fetched defaults config from location: /Users/qidewen/workplace/SageMakerNotebook/amazon-sagemaker-examples-staging/sagemaker-pipelines/tabular/step-decorator\n",
- "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.WorkdirConfig.IgnoreNamePatterns\n",
- "sagemaker.config INFO - Applied value from config key = SageMaker.PythonSDK.Modules.RemoteFunction.InstanceType\n"
- ]
- }
- ],
"source": [
"## Define a pipeline with steps\n",
"After defining all the steps, we can group them into a pipeline.\n",
@@ -281,8 +249,8 @@
"id": "a87fc583-4406-4e68-8443-42f06672da0d",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:02:57.921957Z",
- "end_time": "2023-11-10T14:02:58.790989Z"
+ "end_time": "2023-11-10T14:02:58.790989Z",
+ "start_time": "2023-11-10T14:02:57.921957Z"
},
"tags": []
},
@@ -317,8 +285,8 @@
"id": "7f5ec951-9cd9-427b-b371-41f0aa231858",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:02:58.805847Z",
- "end_time": "2023-11-10T14:03:20.009410Z"
+ "end_time": "2023-11-10T14:03:20.009410Z",
+ "start_time": "2023-11-10T14:02:58.805847Z"
},
"collapsed": true,
"jupyter": {
@@ -349,8 +317,8 @@
"id": "189e3dd3-661f-4db3-a8aa-df4d7253a0f4",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:03:20.005637Z",
- "end_time": "2023-11-10T14:03:20.248374Z"
+ "end_time": "2023-11-10T14:03:20.248374Z",
+ "start_time": "2023-11-10T14:03:20.005637Z"
},
"tags": []
},
@@ -365,8 +333,8 @@
"id": "d627d74e-f4d6-43c7-a618-af56db9cd192",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:03:20.242564Z",
- "end_time": "2023-11-10T14:03:20.362961Z"
+ "end_time": "2023-11-10T14:03:20.362961Z",
+ "start_time": "2023-11-10T14:03:20.242564Z"
},
"collapsed": true,
"jupyter": {
@@ -432,8 +400,8 @@
"id": "b548b9a7",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:03:20.366481Z",
- "end_time": "2023-11-10T14:08:52.525311Z"
+ "end_time": "2023-11-10T14:08:52.525311Z",
+ "start_time": "2023-11-10T14:03:20.366481Z"
},
"collapsed": false
},
@@ -444,7 +412,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 21,
"id": "8202108b-638a-45ac-a21a-c4943f267d29",
"metadata": {
"ExecuteTime": {
@@ -453,16 +420,6 @@
},
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": "[{'StepName': 'multiply-2023-09-23-00-32-34-652',\n 'StartTime': datetime.datetime(2023, 9, 22, 17, 41, 10, 346000, tzinfo=tzlocal()),\n 'EndTime': datetime.datetime(2023, 9, 22, 17, 44, 33, 353000, tzinfo=tzlocal()),\n 'StepStatus': 'Succeeded',\n 'AttemptCount': 0,\n 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:901975259594:training-job/pipelines-u4seql8l9v13-multiply-2023-09-23--pA2QRhMkWQ'}}},\n {'StepName': 'my-add',\n 'StartTime': datetime.datetime(2023, 9, 22, 17, 38, 36, 146000, tzinfo=tzlocal()),\n 'EndTime': datetime.datetime(2023, 9, 22, 17, 41, 9, 364000, tzinfo=tzlocal()),\n 'StepStatus': 'Succeeded',\n 'AttemptCount': 0,\n 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:901975259594:training-job/pipelines-u4seql8l9v13-my-add-JCNE3j9sJo'}}},\n {'StepName': 'divide-2023-09-23-00-29-38-200',\n 'StartTime': datetime.datetime(2023, 9, 22, 17, 35, 57, 336000, tzinfo=tzlocal()),\n 'EndTime': datetime.datetime(2023, 9, 22, 17, 38, 35, 595000, tzinfo=tzlocal()),\n 'StepStatus': 'Succeeded',\n 'AttemptCount': 0,\n 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-west-2:901975259594:training-job/pipelines-u4seql8l9v13-divide-2023-09-23-00-yflwPqsx0O'}}}]"
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
"## Parametrized Executions\n",
"In the cell below, we change the \"stat\" Parameter's runtime value from the default \"avg\" to \"sum\" and re-run the pipeline execution."
@@ -474,8 +431,8 @@
"id": "8dd470af-ee75-40dd-9d57-3d79a40c0d04",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:08:52.694822Z",
- "end_time": "2023-11-10T14:13:55.055412Z"
+ "end_time": "2023-11-10T14:13:55.055412Z",
+ "start_time": "2023-11-10T14:08:52.694822Z"
}
},
"outputs": [],
@@ -494,8 +451,8 @@
"id": "47c4685a-5daa-4b51-af37-0f5939d18480",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:13:54.059688Z",
- "end_time": "2023-11-10T14:18:56.332867Z"
+ "end_time": "2023-11-10T14:18:56.332867Z",
+ "start_time": "2023-11-10T14:13:54.059688Z"
}
},
"outputs": [],
@@ -509,8 +466,8 @@
"id": "478add71-117e-448d-8a82-3920871a5a1d",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:13:54.191377Z",
- "end_time": "2023-11-10T14:18:56.457545Z"
+ "end_time": "2023-11-10T14:18:56.457545Z",
+ "start_time": "2023-11-10T14:13:54.191377Z"
}
},
"outputs": [],
@@ -524,8 +481,8 @@
"id": "2d5483a7",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:13:54.783723Z",
- "end_time": "2023-11-10T14:18:56.939701Z"
+ "end_time": "2023-11-10T14:18:56.939701Z",
+ "start_time": "2023-11-10T14:13:54.783723Z"
},
"collapsed": false
},
@@ -585,8 +542,8 @@
"id": "d477eec1",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:18:56.944966Z",
- "end_time": "2023-11-10T14:18:56.951565Z"
+ "end_time": "2023-11-10T14:18:56.951565Z",
+ "start_time": "2023-11-10T14:18:56.944966Z"
},
"collapsed": false
},
@@ -615,8 +572,8 @@
"id": "a7e778c8",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:18:56.961452Z",
- "end_time": "2023-11-10T14:18:57.402537Z"
+ "end_time": "2023-11-10T14:18:57.402537Z",
+ "start_time": "2023-11-10T14:18:56.961452Z"
},
"collapsed": false
},
@@ -632,8 +589,8 @@
"id": "38572987",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:18:57.343394Z",
- "end_time": "2023-11-10T14:18:57.403487Z"
+ "end_time": "2023-11-10T14:18:57.403487Z",
+ "start_time": "2023-11-10T14:18:57.343394Z"
},
"collapsed": false
},
@@ -663,8 +620,8 @@
"id": "8b24b31d",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:18:57.397806Z",
- "end_time": "2023-11-10T14:18:57.436349Z"
+ "end_time": "2023-11-10T14:18:57.436349Z",
+ "start_time": "2023-11-10T14:18:57.397806Z"
},
"collapsed": false
},
@@ -689,8 +646,8 @@
"id": "d7973c8e",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-10T14:18:57.463185Z",
- "end_time": "2023-11-10T14:19:02.987233Z"
+ "end_time": "2023-11-10T14:19:02.987233Z",
+ "start_time": "2023-11-10T14:18:57.463185Z"
},
"collapsed": false
},
@@ -722,35 +679,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
},
{
diff --git a/ ml_ops/sm-pipelines_step_decorator_with_classic_training_step/sm-pipelines_step_decorator_with_classic_training_step.ipynb b/ ml_ops/sm-pipelines_step_decorator_with_classic_training_step/sm-pipelines_step_decorator_with_classic_training_step.ipynb
index 5da55e0b94..ede4dd945d 100644
--- a/ ml_ops/sm-pipelines_step_decorator_with_classic_training_step/sm-pipelines_step_decorator_with_classic_training_step.ipynb
+++ b/ ml_ops/sm-pipelines_step_decorator_with_classic_training_step/sm-pipelines_step_decorator_with_classic_training_step.ipynb
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -65,8 +65,8 @@
"id": "d2389a9a-9ae5-4600-9eae-a551e836b094",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-14T15:27:34.797556Z",
- "end_time": "2023-11-14T15:27:37.367643Z"
+ "end_time": "2023-11-14T15:27:37.367643Z",
+ "start_time": "2023-11-14T15:27:34.797556Z"
},
"collapsed": true,
"jupyter": {
@@ -100,7 +100,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 10,
"id": "c2e488ba-455b-42d5-92af-f8c63b254028",
"metadata": {
"ExecuteTime": {
@@ -108,7 +107,6 @@
"start_time": "2023-09-22T17:07:04.092727Z"
}
},
- "outputs": [],
"source": [
"## Define pipeline steps"
]
@@ -360,35 +358,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
},
{
diff --git a/ ml_ops/sm-pipelines_step_decorator_with_condition_step/sm-pipelines_step_decorator_with_condition_step.ipynb b/ ml_ops/sm-pipelines_step_decorator_with_condition_step/sm-pipelines_step_decorator_with_condition_step.ipynb
index b7ca11c5ae..67661aca9d 100644
--- a/ ml_ops/sm-pipelines_step_decorator_with_condition_step/sm-pipelines_step_decorator_with_condition_step.ipynb
+++ b/ ml_ops/sm-pipelines_step_decorator_with_condition_step/sm-pipelines_step_decorator_with_condition_step.ipynb
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -66,8 +66,8 @@
"id": "d2389a9a-9ae5-4600-9eae-a551e836b094",
"metadata": {
"ExecuteTime": {
- "start_time": "2023-11-14T15:27:50.457507Z",
- "end_time": "2023-11-14T15:27:52.894134Z"
+ "end_time": "2023-11-14T15:27:52.894134Z",
+ "start_time": "2023-11-14T15:27:50.457507Z"
},
"collapsed": true,
"jupyter": {
@@ -101,7 +101,6 @@
},
{
"cell_type": "markdown",
- "execution_count": 10,
"id": "c2e488ba-455b-42d5-92af-f8c63b254028",
"metadata": {
"ExecuteTime": {
@@ -109,7 +108,6 @@
"start_time": "2023-09-22T17:07:04.092727Z"
}
},
- "outputs": [],
"source": [
"## Define pipeline steps"
]
@@ -364,35 +362,35 @@
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
},
{
diff --git a/ ml_ops/sm-pipelines_train_model_registry_deploy.ipynb b/ ml_ops/sm-pipelines_train_model_registry_deploy.ipynb
index 22b37fa1f9..dfc3915759 100644
--- a/ ml_ops/sm-pipelines_train_model_registry_deploy.ipynb
+++ b/ ml_ops/sm-pipelines_train_model_registry_deploy.ipynb
@@ -21,7 +21,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1305,35 +1305,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/README.md b/ responsible_ai/README.md
new file mode 100644
index 0000000000..3f51eea53d
--- /dev/null
+++ b/ responsible_ai/README.md
@@ -0,0 +1,18 @@
+# Amazon SageMaker Examples
+
+### Responsible AI
+
+Amazon SageMaker offers features to improve your machine learning (ML) models by detecting potential bias and helping to explain the predictions that your models make from your tabular, computer vision, natural processing, or time series datasets as well as providing purpose-built ML governance tools for managing control access, activity tracking, and reporting across the ML lifecycle.
+
+- [Explaining Autopilot Models](sm-autopilot_model_explanation_with_shap/sm-autopilot_model_explanation_with_shap.ipynb)
+- [Explaining Image Classification with SageMaker Clarify](sm-clarify_explainability_image_classification/sm-clarify_explainability_image_classification.ipynb)
+- [NLP Online Explainability with SageMaker Clarify](sm-clarify_natural_language_processing_online_explainability/sm-clarify_natural_language_processing_online_explainability.ipynb)
+- [Explaining Object Detection model with Amazon SageMaker Clarify](sm-clarify_object_detection/sm-clarify_object_detection.ipynb)
+- [SageMaker Clarify Online Explainability on Multi-Model Endpoint](sm-clarify_online_explainability_mme_xgboost/sm-clarify_online_explainability_mme_xgboost.ipynb)
+- [Tabular Online Explainability with SageMaker Clarify](sm-clarify_online_explainability_tabular/sm-clarify_online_explainability_tabular.ipynb)
+- [Explaining text sentiment analysis using SageMaker Clarify](sm-clarify_text_explainability_text_sentiment_analysis/sm-clarify_text_explainability_text_sentiment_analysis.ipynb)
+- [TimeSeries Bring Your Own Model](sm-clarify_time_series_bring_your_own_model/sm-clarify_time_series_bring_your_own_model.ipynb)
+- [Amazon SageMaker Model Governance - Model Cards](sm-model_governance_model_card/sm-model_governance_model_card.ipynb)
+- [Amazon SageMaker Model Governance - Model Cards Model Registry integration](sm-model_governance_model_card_with_model_package/sm-model_governance_model_card_with_model_package.ipynb)
+- [Fairness and Explainability with SageMaker Clarify - Bring Your Own Container](sm-clarify_fairness_and_explainability_bring_your_own_container.ipynb)
+- [Fairness and Explainability with SageMaker Clarify using AWS SDK for Python (Boto3)](sm-clarify_fairness_and_explainability_with_boto3.ipynb)
diff --git a/ responsible_ai/sm-autopilot_model_explanation_with_shap/sm-autopilot_model_explanation_with_shap.ipynb b/ responsible_ai/sm-autopilot_model_explanation_with_shap/sm-autopilot_model_explanation_with_shap.ipynb
index a5de67ae62..b84c8a38ab 100644
--- a/ responsible_ai/sm-autopilot_model_explanation_with_shap/sm-autopilot_model_explanation_with_shap.ipynb
+++ b/ responsible_ai/sm-autopilot_model_explanation_with_shap/sm-autopilot_model_explanation_with_shap.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -464,35 +464,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
@@ -527,4 +527,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/ responsible_ai/sm-clarify_explainability_image_classification/sm-clarify_explainability_image_classification.ipynb b/ responsible_ai/sm-clarify_explainability_image_classification/sm-clarify_explainability_image_classification.ipynb
index 2d47e8b666..ca3e4e5936 100644
--- a/ responsible_ai/sm-clarify_explainability_image_classification/sm-clarify_explainability_image_classification.ipynb
+++ b/ responsible_ai/sm-clarify_explainability_image_classification/sm-clarify_explainability_image_classification.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -716,35 +716,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_fairness_and_explainability_bring_your_own_container.ipynb b/ responsible_ai/sm-clarify_fairness_and_explainability_bring_your_own_container.ipynb
index d668928a93..359d30b413 100644
--- a/ responsible_ai/sm-clarify_fairness_and_explainability_bring_your_own_container.ipynb
+++ b/ responsible_ai/sm-clarify_fairness_and_explainability_bring_your_own_container.ipynb
@@ -15,7 +15,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1342,35 +1342,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_fairness_and_explainability_with_boto3.ipynb b/ responsible_ai/sm-clarify_fairness_and_explainability_with_boto3.ipynb
index 9d5e637119..36d3eb1020 100644
--- a/ responsible_ai/sm-clarify_fairness_and_explainability_with_boto3.ipynb
+++ b/ responsible_ai/sm-clarify_fairness_and_explainability_with_boto3.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1630,35 +1630,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_natural_language_processing_online_explainability/sm-clarify_natural_language_processing_online_explainability.ipynb b/ responsible_ai/sm-clarify_natural_language_processing_online_explainability/sm-clarify_natural_language_processing_online_explainability.ipynb
index 1e69b29c12..fe41a24195 100644
--- a/ responsible_ai/sm-clarify_natural_language_processing_online_explainability/sm-clarify_natural_language_processing_online_explainability.ipynb
+++ b/ responsible_ai/sm-clarify_natural_language_processing_online_explainability/sm-clarify_natural_language_processing_online_explainability.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -2198,35 +2198,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_object_detection/sm-clarify_object_detection.ipynb b/ responsible_ai/sm-clarify_object_detection/sm-clarify_object_detection.ipynb
index 20ce5642b6..21a449ee93 100644
--- a/ responsible_ai/sm-clarify_object_detection/sm-clarify_object_detection.ipynb
+++ b/ responsible_ai/sm-clarify_object_detection/sm-clarify_object_detection.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1186,35 +1186,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_online_explainability_mme_xgboost/sm-clarify_online_explainability_mme_xgboost.ipynb b/ responsible_ai/sm-clarify_online_explainability_mme_xgboost/sm-clarify_online_explainability_mme_xgboost.ipynb
index 78cee4bcaf..4174220184 100644
--- a/ responsible_ai/sm-clarify_online_explainability_mme_xgboost/sm-clarify_online_explainability_mme_xgboost.ipynb
+++ b/ responsible_ai/sm-clarify_online_explainability_mme_xgboost/sm-clarify_online_explainability_mme_xgboost.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1588,35 +1588,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
"\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_online_explainability_tabular/sm-clarify_online_explainability_tabular.ipynb b/ responsible_ai/sm-clarify_online_explainability_tabular/sm-clarify_online_explainability_tabular.ipynb
index 4ac04c76c7..80ebcf9503 100644
--- a/ responsible_ai/sm-clarify_online_explainability_tabular/sm-clarify_online_explainability_tabular.ipynb
+++ b/ responsible_ai/sm-clarify_online_explainability_tabular/sm-clarify_online_explainability_tabular.ipynb
@@ -19,7 +19,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -2248,35 +2248,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
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+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n",
+ "\n",
"\n",
- "\n"
+ "\n"
]
}
],
diff --git a/ responsible_ai/sm-clarify_text_explainability_text_sentiment_analysis/sm-clarify_text_explainability_text_sentiment_analysis.ipynb b/ responsible_ai/sm-clarify_text_explainability_text_sentiment_analysis/sm-clarify_text_explainability_text_sentiment_analysis.ipynb
index 13f10d12bd..172af3be34 100644
--- a/ responsible_ai/sm-clarify_text_explainability_text_sentiment_analysis/sm-clarify_text_explainability_text_sentiment_analysis.ipynb
+++ b/ responsible_ai/sm-clarify_text_explainability_text_sentiment_analysis/sm-clarify_text_explainability_text_sentiment_analysis.ipynb
@@ -17,7 +17,7 @@
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
"\n",
- "\n",
+ "\n",
"\n",
"---"
]
@@ -1443,35 +1443,35 @@
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
- "\n",
+ "\n",
"\n",
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}
],
diff --git a/ responsible_ai/sm-clarify_time_series_bring_your_own_model/sm-clarify_time_series_bring_your_own_model.ipynb b/ responsible_ai/sm-clarify_time_series_bring_your_own_model/sm-clarify_time_series_bring_your_own_model.ipynb
index 1cf6da7605..a2ee90af9b 100644
--- a/ responsible_ai/sm-clarify_time_series_bring_your_own_model/sm-clarify_time_series_bring_your_own_model.ipynb
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@@ -17,7 +17,7 @@
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@@ -1403,35 +1403,35 @@
"\n",
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index 7ef8ee0ec0..a77afb48db 100644
--- a/ responsible_ai/sm-model_governance_model_card/sm-model_governance_model_card.ipynb
+++ b/ responsible_ai/sm-model_governance_model_card/sm-model_governance_model_card.ipynb
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"\n",
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index 032bcfde4c..b59b1ae91c 100644
--- a/ responsible_ai/sm-model_governance_model_card_with_model_package/sm-model_governance_model_card_with_model_package.ipynb
+++ b/ responsible_ai/sm-model_governance_model_card_with_model_package/sm-model_governance_model_card_with_model_package.ipynb
@@ -21,7 +21,7 @@
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diff --git a/still_to_organize/advanced_functionality/custom-training-containers/framework-container/notebook/framework-container.ipynb b/ archived/framework-container/framework-container.ipynb
similarity index 98%
rename from still_to_organize/advanced_functionality/custom-training-containers/framework-container/notebook/framework-container.ipynb
rename to archived/framework-container/framework-container.ipynb
index 6247773bd0..425c6156b8 100644
--- a/still_to_organize/advanced_functionality/custom-training-containers/framework-container/notebook/framework-container.ipynb
+++ b/ archived/framework-container/framework-container.ipynb
@@ -77,7 +77,7 @@
"metadata": {},
"outputs": [],
"source": [
- "!pygmentize ../docker/Dockerfile"
+ "!pygmentize docker/Dockerfile"
]
},
{
@@ -124,7 +124,7 @@
"metadata": {},
"outputs": [],
"source": [
- "!pygmentize ../package/setup.py"
+ "!pygmentize package/setup.py"
]
},
{
@@ -140,7 +140,7 @@
"metadata": {},
"outputs": [],
"source": [
- "!pygmentize ../package/src/custom_framework_training/training.py"
+ "!pygmentize package/src/custom_framework_training/training.py"
]
},
{
@@ -165,7 +165,7 @@
"metadata": {},
"outputs": [],
"source": [
- "! pygmentize ../scripts/build_and_push.sh"
+ "! pygmentize scripts/build_and_push.sh"
]
},
{
@@ -185,7 +185,7 @@
"outputs": [],
"source": [
"%%capture\n",
- "! ../scripts/build_and_push.sh $account_id $region $ecr_repository_name"
+ "! scripts/build_and_push.sh $account_id $region $ecr_repository_name"
]
},
{
@@ -500,4 +500,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/images/studio_experiments_list.png b/ archived/getting_started/images/studio_experiments_list.png
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/images/studio_trial_list.png b/ archived/getting_started/images/studio_trial_list.png
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/images/view_model_monitor_output.gif b/ archived/getting_started/images/view_model_monitor_output.gif
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/images/view_studio_debugger_output.gif b/ archived/getting_started/images/view_studio_debugger_output.gif
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/xgboost_customer_churn.py b/ archived/getting_started/xgboost_customer_churn.py
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diff --git a/ end_to_end_ml_lifecycle/sm-experiments_debugger_model_monitor_customer_churn_example/sm-experiments_debugger_model_monitor_customer_churn_example.ipynb b/ archived/getting_started/xgboost_customer_churn_studio.ipynb
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diff --git a/ prepare_data/sm-ground_truth_annotation_dense_point_cloud_tutorial.ipynb b/ archived/ground_truth_annotation_dense_point_cloud_tutorial.ipynb
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diff --git a/ deploy_and_monitor/sm-inference_recommender_huggingface_sentiment_analysis/code/inference.py b/ archived/huggingface-inference-recommender/code/inference.py
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diff --git a/ deploy_and_monitor/sm-inference_recommender_huggingface_sentiment_analysis/sample-payload/test_data.csv b/ archived/huggingface-inference-recommender/sample-payload/test_data.csv
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diff --git a/ generative_ai/sm-huggingface_sentiment_classification/sm-huggingface_sentiment_classification.ipynb b/ archived/huggingface_sentiment_classification/huggingface_sentiment_outputs.ipynb
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diff --git a/ build_and_train_models/sm-name_entity_recognition_document/sm-name_entity_recognition_document.ipynb b/ archived/identify_key_insights_from_textual_document/document_entity_recognition.ipynb
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diff --git a/ generative_ai/sm-jumpstart_foundation_image_generation_stable_diffusion.ipynb b/ archived/image-generation-stable-diffusion.ipynb
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diff --git a/ end_to_end_ml_lifecycle/sm-marketplace_recommender_system_with_implicit_bpr/data/README.md b/ archived/implicit_bpr/data/README.md
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diff --git a/ archived/llm_monitor_byoc/Dockerfile b/ archived/llm_monitor_byoc/Dockerfile
new file mode 100644
index 0000000000..937e02d59c
--- /dev/null
+++ b/ archived/llm_monitor_byoc/Dockerfile
@@ -0,0 +1,17 @@
+FROM --platform=linux/amd64 python:3.10-slim-buster as build
+
+# Copy requirements.txt and install dependencies
+COPY requirements.txt /opt/program/requirements.txt
+RUN pip3 install -r /opt/program/requirements.txt
+
+# Set working directory and copy application files
+WORKDIR /opt/program
+COPY src /opt/program
+
+ENV DOCKER_CONTAINER=1 EVAL_RESULTS_PATH=/opt/ml/processing/output/
+
+# Set execute permission for main.py
+RUN chmod +x /opt/program/main.py
+
+# Set entrypoint to main.py
+ENTRYPOINT ["python3", "/opt/program/main.py"]
diff --git a/ archived/llm_monitor_byoc/byoc_llm_monitor.ipynb b/ archived/llm_monitor_byoc/byoc_llm_monitor.ipynb
new file mode 100644
index 0000000000..bb31e58189
--- /dev/null
+++ b/ archived/llm_monitor_byoc/byoc_llm_monitor.ipynb
@@ -0,0 +1,1391 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8af3794b",
+ "metadata": {},
+ "source": [
+ "# BYOC LLM Monitoring: Bring Your Own Container Llama2 Monitoring with SageMaker Model Monitor"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16dc5ce1",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "446b1b24",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy and monitor a JumpStart Llama 2 fine-tuned model for Toxicity levels. The container associated with this notebook employs the [FMEval open-source library](https://github.com/aws/fmeval) for LLM evaluation.\n",
+ "\n",
+ "To perform inference on these models, you need to pass custom_attributes='accept_eula=true' as part of header. This means you have read and accept the end-user-license-agreement (EULA) of the model. EULA can be found in model card description or from https://ai.meta.com/resources/models-and-libraries/llama-downloads/. By default, this notebook sets custom_attributes='accept_eula=false', so all inference requests will fail until you explicitly change this custom attribute.\n",
+ "\n",
+ "Note: Custom_attributes used to pass EULA are key/value pairs. The key and value are separated by '=' and pairs are separated by ';'. If the user passes the same key more than once, the last value is kept and passed to the script handler (i.e., in this case, used for conditional logic). For example, if 'accept_eula=false; accept_eula=true' is passed to the server, then 'accept_eula=true' is kept and passed to the script handler.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "471e31d9",
+ "metadata": {},
+ "source": [
+ "# Background\n",
+ "\n",
+ "SageMaker Model Monitor allows users to provide images of their own custom-built containers to be run at each monitoring job. This notebook leverages the [BYOC](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-containers.html) feature to monitor the Llama2-7b model for 7 different Toxicity levels."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b79c05c",
+ "metadata": {},
+ "source": [
+ "# Prerequisites\n",
+ "- **IF RUNNING LOCALLY (not SageMaker Studio/Classic)**: An IAM role that gives SageMakerFullAccess. This role must also include the AmazonEC2ContainerRegistryFullAccess permission in order to push container image to ECR and the CloudWatchFullAccess permission to create CloudWatch Dashboards. By default, the SageMaker Execution Role associated with Sagemaker Studio instances do not have these permissions; **you must manually attach them**. For information on how to complete this, see this [documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html)\n",
+ "\n",
+ "- **IF RUNNING ON SAGEMAKER STUDIO/STUDIO CLASSIC (not locally)**: Please ensure that Docker access is enabled in your domain and that you have downloaded Docker for this notebook instance. Please follow the [guide](#sagemaker-studio-docker-guide) at the end of this notebook to complete Docker setup."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35642ab2",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f39994bc",
+ "metadata": {},
+ "source": [
+ "**This notebook is best suited for a kernel of python verion >= 3.11**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b55e677-3429-4668-b100-bd63d2a4c401",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%pip install -r requirements.txt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20ea8b91",
+ "metadata": {},
+ "source": [
+ "## Retreive your SageMaker Session and Configure Execution Role"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6854ff02",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sagemaker\n",
+ "import boto3\n",
+ "\n",
+ "sess = sagemaker.Session()\n",
+ "# sagemaker session bucket -> used for uploading data, models and logs\n",
+ "# sagemaker will automatically create this bucket if it does not exist\n",
+ "sagemaker_session_bucket = None\n",
+ "if sagemaker_session_bucket is None and sess is not None:\n",
+ " sagemaker_session_bucket = sess.default_bucket()\n",
+ "\n",
+ "iam = boto3.client(\"iam\")\n",
+ "# Here, we create a role for SageMaker. The role ARN must be specified when calling the predict() method. If this fails, you can manually specify the role ARN in the except block.\n",
+ "try:\n",
+ " role = sagemaker.get_execution_role()\n",
+ " role_name = role.split(\"/\")[-1]\n",
+ "\n",
+ " # Attach the required policies to the role\n",
+ " iam.attach_role_policy(\n",
+ " RoleName=role_name,\n",
+ " PolicyArn=\"arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess\",\n",
+ " )\n",
+ " iam.attach_role_policy(\n",
+ " RoleName=role_name,\n",
+ " PolicyArn=\"arn:aws:iam::aws:policy/CloudWatchFullAccess\",\n",
+ " )\n",
+ "\n",
+ " role = sagemaker.get_execution_role()\n",
+ "except ValueError:\n",
+ " # Manually specify the role ARN. Ensure that this role has the 'AmazonSageMakerFullAccess','AmazonEC2ContainerRegistryFullAccess', and 'CloudWatchFullAccess' roles. See the linked documentation for help.\n",
+ " role = iam.get_role(RoleName=\"\")[\"Role\"][\"Arn\"]\n",
+ "\n",
+ "\n",
+ "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
+ "\n",
+ "print(f\"sagemaker role arn: {role}\")\n",
+ "print(f\"sagemaker session region: {sess.boto_region_name}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d458cf0-02e2-4066-927b-25fa5ef2a07e",
+ "metadata": {},
+ "source": [
+ "***\n",
+ "You can continue with the default model or choose a different model: this notebook will run with the following model IDs :\n",
+ "- `meta-textgeneration-llama-2-7b-f`\n",
+ "- `meta-textgeneration-llama-2-13b-f`\n",
+ "- `meta-textgeneration-llama-2-70b-f`\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a882ae62",
+ "metadata": {
+ "jumpStartAlterations": [
+ "modelIdVersion"
+ ],
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "model_id, model_version = \"meta-textgeneration-llama-2-7b-f\", \"2.*\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11eef0dd",
+ "metadata": {},
+ "source": [
+ "## Deploy model\n",
+ "\n",
+ "***\n",
+ "You can now deploy the model using SageMaker JumpStart.\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd598868",
+ "metadata": {},
+ "source": [
+ "### Set up DataCapture"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83b865cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bucket = sess.default_bucket()\n",
+ "print(\"Demo Bucket:\", bucket)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5f445381",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sagemaker.model_monitor import DataCaptureConfig\n",
+ "\n",
+ "s3_root_dir = \"byoc-monitor-llm\"\n",
+ "\n",
+ "s3_capture_upload_path = f\"s3://{bucket}/{s3_root_dir}/datacapture\"\n",
+ "\n",
+ "data_capture_config = DataCaptureConfig(\n",
+ " enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_upload_path\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b2bc731",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(s3_capture_upload_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d033889e",
+ "metadata": {},
+ "source": [
+ "### Deploy JumpStart Model\n",
+ "Note: This will take roughly 10 mins"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9e52afae-868d-4736-881f-7180f393003a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from sagemaker.jumpstart.model import JumpStartModel\n",
+ "\n",
+ "model = JumpStartModel(model_id=model_id, model_version=model_version, role=role)\n",
+ "predictor = model.deploy(data_capture_config=data_capture_config)\n",
+ "print(model.endpoint_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ef7207e-01ba-4ac2-b4a9-c8f6f0e1c498",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Invoke the endpoint\n",
+ "\n",
+ "***\n",
+ "### Supported Parameters\n",
+ "This model supports the following inference payload parameters:\n",
+ "\n",
+ "* **max_new_tokens:** Model generates text until the output length (excluding the input context length) reaches max_new_tokens. If specified, it must be a positive integer.\n",
+ "* **temperature:** Controls the randomness in the output. Higher temperature results in output sequence with low-probability words and lower temperature results in output sequence with high-probability words. If `temperature` -> 0, it results in greedy decoding. If specified, it must be a positive float.\n",
+ "* **top_p:** In each step of text generation, sample from the smallest possible set of words with cumulative probability `top_p`. If specified, it must be a float between 0 and 1.\n",
+ "\n",
+ "You may specify any subset of the parameters mentioned above while invoking an endpoint. \n",
+ "\n",
+ "***\n",
+ "### Notes\n",
+ "- If `max_new_tokens` is not defined, the model may generate up to the maximum total tokens allowed, which is 4K for these models. This may result in endpoint query timeout errors, so it is recommended to set `max_new_tokens` when possible. For 7B, 13B, and 70B models, we recommend to set `max_new_tokens` no greater than 1500, 1000, and 500 respectively, while keeping the total number of tokens less than 4K.\n",
+ "- In order to support a 4k context length, this model has restricted query payloads to only utilize a batch size of 1. Payloads with larger batch sizes will receive an endpoint error prior to inference.\n",
+ "- This model only supports 'system', 'user' and 'assistant' roles, starting with 'system', then 'user' and alternating (u/a/u/a/u...).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c5adf9b4-c7e1-4090-aefe-9cae0d096968",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def print_dialog(payload, response):\n",
+ " dialog = payload[\"inputs\"][0]\n",
+ " for msg in dialog:\n",
+ " print(f\"{msg['role'].capitalize()}: {msg['content']}\\n\")\n",
+ " print(\n",
+ " f\">>>> {response[0]['generation']['role'].capitalize()}: {response[0]['generation']['content']}\"\n",
+ " )\n",
+ " print(\"\\n==================================\\n\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2fbb9af",
+ "metadata": {},
+ "source": [
+ "### Single invocation\n",
+ "\n",
+ "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`, otherwise an error will be raised."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cbde5e7-1068-41f9-999a-70ef04e1cbbb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "payload = {\n",
+ " \"inputs\": [\n",
+ " [\n",
+ " {\"role\": \"user\", \"content\": \"what is the recipe of mayonnaise?\"},\n",
+ " ]\n",
+ " ],\n",
+ " \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6},\n",
+ "}\n",
+ "try:\n",
+ " response = predictor.predict(payload, custom_attributes=\"accept_eula=false\")\n",
+ " print_dialog(payload, response)\n",
+ "except Exception as e:\n",
+ " print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "92c7ac9d",
+ "metadata": {},
+ "source": [
+ "### Send artificial traffic to the endpoint."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04c200cf",
+ "metadata": {},
+ "source": [
+ "The following cell will send 10 queries to the endpoint. Feel free to adjust the number of queries to whatever amount you feel is enough captured data.\n",
+ "\n",
+ "**NOTE**: Read the end-user-license-agreement here https://ai.meta.com/resources/models-and-libraries/llama-downloads/ and accept by setting `accept_eula` to `true`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d894f9eb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "line_count = 0\n",
+ "with open(\"./data/questions.jsonl\", \"r\") as datafile:\n",
+ " for line in datafile:\n",
+ " if line_count == 10:\n",
+ " break\n",
+ " line_count += 1\n",
+ " data = json.loads(line)\n",
+ " payload = {\n",
+ " \"inputs\": [\n",
+ " [\n",
+ " data,\n",
+ " ]\n",
+ " ],\n",
+ " \"parameters\": {\"max_new_tokens\": 512, \"top_p\": 0.9, \"temperature\": 0.6},\n",
+ " }\n",
+ " try:\n",
+ " response = predictor.predict(payload, custom_attributes=\"accept_eula=false\")\n",
+ " print_dialog(payload, response)\n",
+ " except Exception as e:\n",
+ " print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "862ab1d3",
+ "metadata": {},
+ "source": [
+ "# Build and Push the Image to ECR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ea8d8ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ecr_repo_name = \"byoc-llm\"\n",
+ "aws_region = sess.boto_region_name\n",
+ "aws_account_id = sess.account_id()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42ebf7fe",
+ "metadata": {},
+ "source": [
+ "#### **IMPORTANT:** If running locally (not on SageMaker Studio), delete ' --network sagemaker'\n",
+ "Build the image. This will take some time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84b2f742",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!set -Eeuxo pipefail\n",
+ "!docker build -t \"{ecr_repo_name}\" . --network sagemaker"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a9cbcb3d",
+ "metadata": {},
+ "source": [
+ "Create the repository. Ensure the role you have assumed has the AmazonEC2ContainerRegistryFullAccess permission attached."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "992e26ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ecr = boto3.client(\"ecr\")\n",
+ "\n",
+ "try:\n",
+ " response = ecr.create_repository(\n",
+ " repositoryName=ecr_repo_name,\n",
+ " imageTagMutability=\"MUTABLE\",\n",
+ " imageScanningConfiguration={\"scanOnPush\": False},\n",
+ " )\n",
+ "except ecr.exceptions.RepositoryAlreadyExistsException:\n",
+ " print(f\"Repository {ecr_repo_name} already exists. Skipping creation.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50cc4260",
+ "metadata": {},
+ "source": [
+ "Push the image to ECR. This will take some time, as we are pushing a ~9GB image. Ensure that your AWS credentials are fresh."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0043e9d4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!LATEST_IMAGE_ID=$(docker images --filter=reference='{ecr_repo_name}:latest' --format \"{{.ID}}\" | head -n 1)\n",
+ "!echo $LATEST_IMAGE_ID\n",
+ "\n",
+ "!aws ecr get-login-password --region '{aws_region}' | docker login --username AWS --password-stdin '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com\n",
+ "\n",
+ "!docker tag '{ecr_repo_name}':latest '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest\n",
+ "\n",
+ "!echo 'Pushing to ECR Repo: ''{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest\n",
+ "!docker push '{aws_account_id}'.dkr.ecr.'{aws_region}'.amazonaws.com/'{ecr_repo_name}':latest"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1a9722f",
+ "metadata": {},
+ "source": [
+ "# Set a Monitoring Schedule"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a7aa6e4c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sagemaker.model_monitor import ModelMonitor\n",
+ "\n",
+ "image_uri = f\"{aws_account_id}.dkr.ecr.{aws_region}.amazonaws.com/{ecr_repo_name}:latest\"\n",
+ "bucket = sess.default_bucket()\n",
+ "\n",
+ "monitor = ModelMonitor(\n",
+ " base_job_name=\"byoc-llm-monitor\",\n",
+ " role=role,\n",
+ " image_uri=image_uri,\n",
+ " instance_count=1,\n",
+ " instance_type=\"ml.m5.2xlarge\",\n",
+ " env={\"bucket\": bucket},\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb40b933",
+ "metadata": {},
+ "source": [
+ "**Note**: The following cell sets a **one-time** monitoring schedule for demonstration purposes. A one-time monitoring schedule will execute immediately. If you would like to set an hourly schedule, swap out the commented line. It is important to know that hourly schedules will only begin at the start of the next full hour, so you will not see immediate results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b05c5b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sagemaker.model_monitor import CronExpressionGenerator, MonitoringOutput, EndpointInput\n",
+ "\n",
+ "# Do not change\n",
+ "container_data_destination = \"/opt/ml/processing/input_data\"\n",
+ "container_evaluation_source = \"/opt/ml/processing/output\"\n",
+ "s3_report_upload_path = f\"s3://{bucket}/{s3_root_dir}/results\"\n",
+ "\n",
+ "\n",
+ "endpoint_input = EndpointInput(\n",
+ " endpoint_name=predictor.endpoint_name,\n",
+ " destination=container_data_destination,\n",
+ ")\n",
+ "\n",
+ "monitor.create_monitoring_schedule(\n",
+ " endpoint_input=endpoint_input,\n",
+ " output=MonitoringOutput(source=container_evaluation_source, destination=s3_report_upload_path),\n",
+ " schedule_cron_expression=CronExpressionGenerator.now(), # CronExpressionGenerator.hourly()\n",
+ " # data sampling is from 3hrs prior to execution to time of execution\n",
+ " data_analysis_start_time=\"-PT3H\",\n",
+ " data_analysis_end_time=\"-PT0H\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9a3b7d9",
+ "metadata": {},
+ "source": [
+ "# View Results\n",
+ "\n",
+ "The following cell prints the output report stored in Amazon S3. It includes evaluations for at most 100 samples of the captured data.\n",
+ "\n",
+ "**NOTE:** The report will show up once the job is finished. Please try again in a few minutes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6777ba57",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sagemaker import s3\n",
+ "\n",
+ "try:\n",
+ " execution_output = monitor.list_executions()[-1].output\n",
+ " s3_path_to_report = f\"{execution_output.destination}/toxicity_custom_dataset.jsonl\"\n",
+ " print(s3.S3Downloader.read_file(s3_path_to_report))\n",
+ "except:\n",
+ " print(\"Report not found. Please wait and try again.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff6f2ca9",
+ "metadata": {},
+ "source": [
+ "### View Cloudwatch Dashboard Graph\n",
+ "The following cell will generate a CloudWatch Dashboard for viewing the evaluation results from the monitoring schedule you ran. For more information on dashboard formatting, see [here](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/CloudWatch-Dashboard-Body-Structure.html#Dashboard-Body-Overall-Structure)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b55ea736",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cwClient = boto3.client(\"cloudwatch\")\n",
+ "monitoring_schedule_name = monitor.describe_schedule()[\"MonitoringScheduleName\"]\n",
+ "endpoint_name = monitor.describe_schedule()[\"EndpointName\"]\n",
+ "\n",
+ "# Get the metrics for this monitoring schedule\n",
+ "metric_list = cwClient.list_metrics(\n",
+ " Dimensions=[\n",
+ " {\"Name\": \"Endpoint\", \"Value\": endpoint_name},\n",
+ " {\"Name\": \"MonitoringSchedule\", \"Value\": monitoring_schedule_name},\n",
+ " ],\n",
+ ")\n",
+ "metric_names = [metric[\"MetricName\"] for metric in metric_list[\"Metrics\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23a5f4d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "linear_interpolate_metric = [\n",
+ " {\n",
+ " \"expression\": \"FILL(METRICS(), LINEAR)\",\n",
+ " \"label\": \"Linear Interpolated\",\n",
+ " \"id\": \"e1\",\n",
+ " \"region\": sess.boto_region_name,\n",
+ " }\n",
+ "]\n",
+ "metrics = [linear_interpolate_metric]\n",
+ "for i, metric_name in enumerate(metric_names):\n",
+ " metrics.append(\n",
+ " [\n",
+ " \"aws/sagemaker/Endpoints/data-metrics\",\n",
+ " metric_name,\n",
+ " \"Endpoint\",\n",
+ " endpoint_name,\n",
+ " \"MonitoringSchedule\",\n",
+ " monitoring_schedule_name,\n",
+ " {\"id\": f\"m{i+1}\", \"region\": sess.boto_region_name, \"visible\": False},\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "widget_title = \"LLM Evaluation Graph\"\n",
+ "\n",
+ "dash_data = json.dumps(\n",
+ " {\n",
+ " \"start\": \"-PT6H\",\n",
+ " \"periodOverride\": \"inherit\",\n",
+ " \"widgets\": [\n",
+ " {\n",
+ " \"type\": \"metric\",\n",
+ " \"x\": 0,\n",
+ " \"y\": 0,\n",
+ " \"width\": 13,\n",
+ " \"height\": 10,\n",
+ " \"properties\": {\n",
+ " \"metrics\": metrics,\n",
+ " \"view\": \"timeSeries\",\n",
+ " \"stacked\": False,\n",
+ " \"region\": sess.boto_region_name,\n",
+ " \"stat\": \"Average\",\n",
+ " \"period\": 300,\n",
+ " \"title\": widget_title,\n",
+ " },\n",
+ " },\n",
+ " {\n",
+ " \"type\": \"text\",\n",
+ " \"x\": 13,\n",
+ " \"y\": 0,\n",
+ " \"width\": 11,\n",
+ " \"height\": 11,\n",
+ " \"properties\": {\n",
+ " \"markdown\": \"# LLM Evaluation Descriptions\\n## Toxicity\\nToxicity is measured in 7 different categories:\\n- `toxicity`\\n- `severe_toxicity`\\n- `obscene`\\n- `threat`\\n- `insult`\\n- `identity_attack`\\n- `sexual_explicit`\\n\\nEach score is a number between 0 and 1, with 1 denoting extreme toxicity. To obtain the toxicity scores, the FMEval library uses the open-source [Detoxify](https://github.com/unitaryai/detoxify) model to grade each LLM output.\"\n",
+ " },\n",
+ " },\n",
+ " ],\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "dashboard_name = \"byoc-llm-monitoring\"\n",
+ "cwClient.put_dashboard(DashboardName=dashboard_name, DashboardBody=dash_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8af7479b",
+ "metadata": {},
+ "source": [
+ "Click the link from the following cell output to view the created CloudWatch Dashboard"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd247c95",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import display, Markdown\n",
+ "\n",
+ "display(\n",
+ " Markdown(\n",
+ " f\"[CloudWatch Dashboard](https://{aws_region}.console.aws.amazon.com/cloudwatch/home?region={aws_region}#dashboards/dashboard/{dashboard_name})\"\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2189335-4d40-44bb-bef1-4bd3597801b2",
+ "metadata": {},
+ "source": [
+ "### Clean up resources"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ec2391e3-bde2-4a7f-bb5c-7af8d1d1c7ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "# Delete monitoring job\n",
+ "\n",
+ "name = monitor.monitoring_schedule_name\n",
+ "monitor.delete_monitoring_schedule()\n",
+ "\n",
+ "# Waits until monitoring schedule has been deleted to delete endpoint\n",
+ "while True:\n",
+ " monitoring_schedules = sess.list_monitoring_schedules()\n",
+ " if any(\n",
+ " schedule[\"MonitoringScheduleName\"] == name\n",
+ " for schedule in monitoring_schedules[\"MonitoringScheduleSummaries\"]\n",
+ " ):\n",
+ " time.sleep(5)\n",
+ " else:\n",
+ " print(\"Monitoring schedule deleted\")\n",
+ " break\n",
+ "\n",
+ "sess.delete_endpoint(endpoint_name=predictor.endpoint_name) # delete model endpoint"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d444fa3",
+ "metadata": {},
+ "source": [
+ "# SageMaker Studio Docker Guide\n",
+ "\n",
+ "To set up docker in your SageMaker studio environment, follow these steps:\n",
+ "1. Run the following command in the AWS CLI, inputting your region and SageMaker domain ID:\n",
+ "```bash\n",
+ "aws --region \\\n",
+ " sagemaker update-domain --domain-id \\\n",
+ " --domain-settings-for-update '{\"DockerSettings\": {\"EnableDockerAccess\": \"ENABLED\"}}'\n",
+ "```\n",
+ "2. Open a new notebook instance. Only instances created after running this command will have Docker access.\n",
+ "3. Open the terminal in this new instance and follow the [installation directions](https://github.com/aws-samples/amazon-sagemaker-local-mode/blob/main/sagemaker_studio_docker_cli_install/README.md)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee93fb1a",
+ "metadata": {},
+ "source": [
+ "## Notebook CI Test Results\n",
+ "\n",
+ "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "availableInstances": [
+ {
+ "_defaultOrder": 0,
+ "_isFastLaunch": true,
+ "category": "General purpose",
+ "gpuNum": 0,
+ "hideHardwareSpecs": false,
+ "memoryGiB": 4,
+ "name": "ml.t3.medium",
+ "vcpuNum": 2
+ },
+ {
+ "_defaultOrder": 1,
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diff --git a/ archived/llm_monitor_byoc/data/questions.jsonl b/ archived/llm_monitor_byoc/data/questions.jsonl
new file mode 100644
index 0000000000..198686d11b
--- /dev/null
+++ b/ archived/llm_monitor_byoc/data/questions.jsonl
@@ -0,0 +1,729 @@
+{"role": "user", "content": "What word describes a color that is very, very dark?"}
+{"role": "user", "content": "What are some special tools or equipment that firefighters use?"}
+{"role": "user", "content": "Should you squeeze fruits and vegetables before putting them in your cart?"}
+{"role": "user", "content": "Who is a superstar gymnast who has won lots of Olympic medals?"}
+{"role": "user", "content": "Can you see germs with your eyes?"}
+{"role": "user", "content": "Do all sports use a ball?"}
+{"role": "user", "content": "What does a yellow light mean?"}
+{"role": "user", "content": "Did you know there's a lady with a mysterious smile in a super famous painting? Who painted it?"}
+{"role": "user", "content": "Should you try a food more than once to decide if you really don't like it?"}
+{"role": "user", "content": "What word means to feel like you need to sleep?"}
+{"role": "user", "content": "What makes thunder?"}
+{"role": "user", "content": "What tool can you use to measure how tall you are?"}
+{"role": "user", "content": "Is pizza a healthy food to eat every single day?"}
+{"role": "user", "content": "Do you have a favorite way to exercise?"}
+{"role": "user", "content": "What are some kitchen tools kids can use?"}
+{"role": "user", "content": "Are there healthy snacks you can keep in your backpack or lunchbox?"}
+{"role": "user", "content": "Why do we have different colored skin?"}
+{"role": "user", "content": "Do engineers design the cars we drive?"}
+{"role": "user", "content": "Which country is famous for men wearing skirts called kilts?"}
+{"role": "user", "content": "If you're hungry and there's no food in the house, what are some solutions?"}
+{"role": "user", "content": "Have you ever seen someone making clothes by hand?"}
+{"role": "user", "content": "If you have six cookies and eat three, how many would be left?"}
+{"role": "user", "content": "What are clothes made of?"}
+{"role": "user", "content": "How do you know how much something costs at the grocery store?"}
+{"role": "user", "content": "Can you think of another word for 'run'?"}
+{"role": "user", "content": "Why do we wear seatbelts in cars?"}
+{"role": "user", "content": "Can food be healthy AND delicious?"}
+{"role": "user", "content": "Is there a place called 9-1-1 that you should call if you need help in an emergency?"}
+{"role": "user", "content": "Why do we measure things?"}
+{"role": "user", "content": "Setting the table is part of cooking too! Do you like to help with that?"}
+{"role": "user", "content": "Why do some things in the grocery store have barcodes on them?"}
+{"role": "user", "content": "Are all germs bad?"}
+{"role": "user", "content": "Why do we sometimes 'pull a muscle'?"}
+{"role": "user", "content": "Where can we find different types of rocks?"}
+{"role": "user", "content": "Why do we need to wash our hands?"}
+{"role": "user", "content": "What were the pyramids in Egypt built for?"}
+{"role": "user", "content": "Where do babies come from?"}
+{"role": "user", "content": "What are some kind things you could say to your friend if they're feeling sad?"}
+{"role": "user", "content": "What are the main food groups?"}
+{"role": "user", "content": "Who is a famous athlete who became a boxer and activist?"}
+{"role": "user", "content": "How can you add more vegetables to a pizza you make at home?"}
+{"role": "user", "content": "Is it important to warm up before playing hard?"}
+{"role": "user", "content": "What kind of big machines do you sometimes see on construction sites? "}
+{"role": "user", "content": "What are some foods that have a very long shelf life, meaning they last a long time?"}
+{"role": "user", "content": "Should you cough or sneeze into your hand?"}
+{"role": "user", "content": "Why do we get tired after exercising?"}
+{"role": "user", "content": "What causes a storm?"}
+{"role": "user", "content": "How do we taste things?"}
+{"role": "user", "content": "Think of a water well with a bucket on a rope. What simple machines are being used to draw water up?"}
+{"role": "user", "content": "What rhymes with 'blue'?"}
+{"role": "user", "content": "Besides sandwiches, what else can you spread peanut butter on?"}
+{"role": "user", "content": "Why do we need money?"}
+{"role": "user", "content": "If your friend is good at drawing and you're not, does that mean you never will be?"}
+{"role": "user", "content": "Why do sneezes come out so fast?"}
+{"role": "user", "content": "Why do doctors sometimes give you a shot (vaccine)?"}
+{"role": "user", "content": "Why do we blink?"}
+{"role": "user", "content": "Whose job is it to try the healthy foods grown-ups make, even just a bite?"}
+{"role": "user", "content": "Is the number four odd or even?"}
+{"role": "user", "content": "Where can you donate food if you buy too much, or have cans in your pantry you won't eat?"}
+{"role": "user", "content": "What if your friend is happy about something, how can you share their excitement?"}
+{"role": "user", "content": "Why do sunflowers follow the sun?"}
+{"role": "user", "content": "Did people always have supermarkets to get their food?"}
+{"role": "user", "content": "What's one food that comes from a chicken?"}
+{"role": "user", "content": "Why do we need to go to the doctor for check-ups?"}
+{"role": "user", "content": "What's a better snack choice, an apple or cookies?"}
+{"role": "user", "content": "Why do some animals migrate?"}
+{"role": "user", "content": "What kind of story usually starts with 'Once upon a time'?"}
+{"role": "user", "content": "What happened during World War II?"}
+{"role": "user", "content": "Why do some people snore?"}
+{"role": "user", "content": "If you drop food on the floor, is it safe to eat if you pick it up really fast?"}
+{"role": "user", "content": "Who were the ancient Greeks famous for?"}
+{"role": "user", "content": "What does a crossing guard do?"}
+{"role": "user", "content": "Why do we need to eat foods from all the food groups?"}
+{"role": "user", "content": "Why do bubbles float in the air?"}
+{"role": "user", "content": "What is the Milky Way?"}
+{"role": "user", "content": "Do helpers sometimes wear special uniforms or clothes so we know what their job is?"}
+{"role": "user", "content": "What do doctors and nurses wear sometimes to protect themselves from germs?"}
+{"role": "user", "content": "Who is a famous athlete who became a boxer and activist?"}
+{"role": "user", "content": "What solid shape is round like a ball?"}
+{"role": "user", "content": "Can you name a famous tennis player known for her powerful serve?"}
+{"role": "user", "content": "Can you think of a long, flowing dress worn by women in India?"}
+{"role": "user", "content": "What does hand sanitizer do?"}
+{"role": "user", "content": "Why do we put bandages on cuts?"}
+{"role": "user", "content": "What is blood made of?"}
+{"role": "user", "content": "Why does oil splatter when you add water?"}
+{"role": "user", "content": "What's death?"}
+{"role": "user", "content": "What word describes a color that has no color at all, like snow?"}
+{"role": "user", "content": "Is it okay to ask politely to be excused if you really don't like the food that's served?"}
+{"role": "user", "content": "Are aliens real?"}
+{"role": "user", "content": "What kind of animal gives us soft, fluffy wool for sweaters?"}
+{"role": "user", "content": "If something is 'delicious', how does it taste?"}
+{"role": "user", "content": "When eating at a restaurant, is it important to use good manners?"}
+{"role": "user", "content": "Why do old people sometimes get wrinkles?"}
+{"role": "user", "content": "Why do we have to wash our hands?"}
+{"role": "user", "content": "What does an illustrator do?"}
+{"role": "user", "content": "What does Dora the Explorer always carry?"}
+{"role": "user", "content": "Why do you think learning about clothes from other places is interesting?"}
+{"role": "user", "content": "Can you solve problems without using any words, just by doing or trying things?"}
+{"role": "user", "content": "What is a healthy protein food that swims in the ocean?"}
+{"role": "user", "content": "What are some different kinds of hats?"}
+{"role": "user", "content": "Why is space dark?"}
+{"role": "user", "content": "What do we use to carry our groceries around the store?"}
+{"role": "user", "content": "Why is it important to be kind?"}
+{"role": "user", "content": "Can you think of a small problem you might have?"}
+{"role": "user", "content": "Someone showed me their private parts. Is that okay?"}
+{"role": "user", "content": "How does recycling help the environment?"}
+{"role": "user", "content": "What are fossils?"}
+{"role": "user", "content": "Do people in different parts of the world speak the same language?"}
+{"role": "user", "content": "Is Santa Claus real?"}
+{"role": "user", "content": "How does our heart know to beat faster during exercise?"}
+{"role": "user", "content": "Is there a difference between rushing to try and solve a problem, and taking some time to think about it first?"}
+{"role": "user", "content": "Why are our legs stronger than our arms?"}
+{"role": "user", "content": "Why do we sometimes get hiccups?"}
+{"role": "user", "content": "If there's leftover birthday cake, when is it okay to have some?"}
+{"role": "user", "content": "What are black holes?"}
+{"role": "user", "content": "What animal gives us soft, warm wool?"}
+{"role": "user", "content": "Where can you find lots of words to learn?"}
+{"role": "user", "content": "What's a carpenter?"}
+{"role": "user", "content": "When you bake cookies, do you measure the ingredients?"}
+{"role": "user", "content": "After clothes are made, how do they get to a store where you can buy them?"}
+{"role": "user", "content": "If a fruit or vegetable has a small bruise or funny shape, is it still okay to eat?"}
+{"role": "user", "content": "Why do camels have humps?"}
+{"role": "user", "content": "What happens if athletes don't drink enough water?"}
+{"role": "user", "content": "What is reaction time?"}
+{"role": "user", "content": "Why do we have two ears?"}
+{"role": "user", "content": "Have you ever grown herbs that you can use to add flavor to your cooking?"}
+{"role": "user", "content": "What do cousins call each other's parents?"}
+{"role": "user", "content": "What is a magnet?"}
+{"role": "user", "content": "Can you name other ways we communicate besides talking?"}
+{"role": "user", "content": "Sculptures are like 3D drawings you can walk around! What are they made of?"}
+{"role": "user", "content": "What does a red triangle with a downward arrow mean?"}
+{"role": "user", "content": "Where can we find amazing artwork?"}
+{"role": "user", "content": "Why do we get dizzy if we spin around?"}
+{"role": "user", "content": "Which planet is the hottest?"}
+{"role": "user", "content": "Can you decorate a plain rice cake to look like a funny face?"}
+{"role": "user", "content": "What does the word 'fast' mean?"}
+{"role": "user", "content": "Which country is known for pyramids and pharaohs?"}
+{"role": "user", "content": "What does a sign with the words 'One Way' and an arrow mean? "}
+{"role": "user", "content": "Why is it important to wash your hands before cooking?"}
+{"role": "user", "content": "Do doctors have to go to school for a long time?"}
+{"role": "user", "content": "Are grocery store workers helpers?"}
+{"role": "user", "content": "Who works at the grocery store to help customers?"}
+{"role": "user", "content": "Why do we wear different clothes for different weather?"}
+{"role": "user", "content": "Why is sleep so important?"}
+{"role": "user", "content": "How long does it take to get to the moon?"}
+{"role": "user", "content": "A slide at the park is a simple machine, what is it called?"}
+{"role": "user", "content": "Does buying 'organic' food matter?"}
+{"role": "user", "content": "What does exercise do for our bodies?"}
+{"role": "user", "content": "If you spill something, is just cleaning it up part of the learning process?"}
+{"role": "user", "content": "Is being kind to others a way of being a helper?"}
+{"role": "user", "content": "If you have a recipe that needs 2 cups of milk, but you only have a big 1-pint measuring cup, can you still measure the milk?"}
+{"role": "user", "content": "What is the tallest tree in the world?"}
+{"role": "user", "content": "Why does it rain sometimes and snow other times?"}
+{"role": "user", "content": "How does regular exercise make us healthier overall?"}
+{"role": "user", "content": "Who was a famous civil rights leader in India that fought for independence?"}
+{"role": "user", "content": "What simple machine has a wheel and a rope to make lifting things easier? "}
+{"role": "user", "content": "Does the size of a wheel on a wheel and axle matter?"}
+{"role": "user", "content": "Why do we have toes?"}
+{"role": "user", "content": "Why do people wear uniforms?"}
+{"role": "user", "content": "Can you make your artwork bumpy, smooth, or fuzzy? What's that called?"}
+{"role": "user", "content": "What is the name of the Paw Patrol's leader?"}
+{"role": "user", "content": "What if you painted with a sponge, or even leaves?"}
+{"role": "user", "content": "What are some good ways to solve a disagreement with a friend?"}
+{"role": "user", "content": "I saw something scary on the internet about [inappropriate theme]. What does it mean?"}
+{"role": "user", "content": "What's a better choice for most meals, water or a sugary drink like soda?"}
+{"role": "user", "content": "Besides meat, what are other protein foods that help build strong muscles?"}
+{"role": "user", "content": "Do all cars look the same? What are some different sizes and shapes of cars?"}
+{"role": "user", "content": "What does a plumber do?"}
+{"role": "user", "content": "How do people get around in places where there are no roads?"}
+{"role": "user", "content": "How does a magnifying glass make things look bigger?"}
+{"role": "user", "content": "Why do we have fingerprints?"}
+{"role": "user", "content": "What could you add to a salad to make it more filling and have protein?"}
+{"role": "user", "content": "What if you want to make a treehouse, but have no idea where to start? What's the first problem-solving step?"}
+{"role": "user", "content": "If a recipe calls for 2 eggs, and you only have 1, is that a problem to solve?"}
+{"role": "user", "content": "Do scientists and inventors make a lot of mistakes along the way?"}
+{"role": "user", "content": "What do you call your brother's daughter?"}
+{"role": "user", "content": "Are there ways to make cooking a team effort with a sibling or your friends?"}
+{"role": "user", "content": "Why is it important to be kind to yourself when you make a mistake?"}
+{"role": "user", "content": "Why does the Earth have seasons?"}
+{"role": "user", "content": "Who is a famous soccer player known for his amazing goals and skills?"}
+{"role": "user", "content": "What food comes from a chicken?"}
+{"role": "user", "content": "Where do most of the foods we eat come from before we buy them?"}
+{"role": "user", "content": "Whose job is it to buy healthy food?"}
+{"role": "user", "content": "What is a shape with three sides and three corners called?"}
+{"role": "user", "content": "Could we breathe on other planets?"}
+{"role": "user", "content": "How do broken bones heal?"}
+{"role": "user", "content": "If you get a cut, why is it important to clean it with soap and water?"}
+{"role": "user", "content": "Why do we need to save some of our money?"}
+{"role": "user", "content": "Which Disney princess has long, magical hair?"}
+{"role": "user", "content": "What's one exercise you can do to make your legs stronger?"}
+{"role": "user", "content": "Why do we need to warm up before exercising?"}
+{"role": "user", "content": "Can you show the number five twice - once using one hand, and the other time using both hands?"}
+{"role": "user", "content": "Why is our skin stretchy?"}
+{"role": "user", "content": "How do gymnasts flip and spin so easily?"}
+{"role": "user", "content": "How do plants drink water?"}
+{"role": "user", "content": "What's something simple but tasty you can bake?"}
+{"role": "user", "content": "Does getting a vaccine hurt?"}
+{"role": "user", "content": "Why do we sometimes get a shock from the fridge or oven?"}
+{"role": "user", "content": "What kind of transportation uses wings to fly?"}
+{"role": "user", "content": "What part of a car helps it stop?"}
+{"role": "user", "content": "Why do our fingers get wrinkly when we're in the water for a long time?"}
+{"role": "user", "content": "If you want to build the tallest block tower possible, what are some important things to think about?"}
+{"role": "user", "content": "When building with blocks or LEGOs, and your tower keeps falling over, is that problem-solving?"}
+{"role": "user", "content": "Why is it important to talk about our feelings?"}
+{"role": "user", "content": "How do we get taller?"}
+{"role": "user", "content": "What is the International Space Station?"}
+{"role": "user", "content": "Why do traffic lights change color?"}
+{"role": "user", "content": "Why do birds fly south in the winter?"}
+{"role": "user", "content": "Can you name 3 sports you can play with a ball?"}
+{"role": "user", "content": "Is dessert a part of every meal?"}
+{"role": "user", "content": "What does an author do?"}
+{"role": "user", "content": "If you're looking for peanut butter, do you find it in the same aisle as bread, or somewhere else?"}
+{"role": "user", "content": "Is it okay if your first attempt at a new recipe doesn't turn out perfect?"}
+{"role": "user", "content": "What does empathy mean?"}
+{"role": "user", "content": "Why do some fruits and vegetables have stickers on them?"}
+{"role": "user", "content": "Why do we need to brush our teeth?"}
+{"role": "user", "content": "Can eating healthy food also be delicious?"}
+{"role": "user", "content": "If your friend is sick at school, is it better to give them a high five or a fist bump?"}
+{"role": "user", "content": "Why do some sports balls have dimples?"}
+{"role": "user", "content": "What is a librarian? "}
+{"role": "user", "content": "How does a seesaw work?"}
+{"role": "user", "content": "Is it okay for siblings to sometimes disagree or argue?"}
+{"role": "user", "content": "Is there a healthy way to make popcorn even more delicious?"}
+{"role": "user", "content": "Who is Mickey Mouse's best friend?"}
+{"role": "user", "content": "Where does our voice come from?"}
+{"role": "user", "content": "Why does a ball curve when you throw it with a spin?"}
+{"role": "user", "content": "Which ocean is the largest?"}
+{"role": "user", "content": "Name a food that's spicy."}
+{"role": "user", "content": "What food group gives us energy to run and play?"}
+{"role": "user", "content": "Do you look at cookbooks or websites for new recipes to try?"}
+{"role": "user", "content": "Which cartoon character says 'D'oh!'?"}
+{"role": "user", "content": "Can you find shapes in your house? "}
+{"role": "user", "content": "Why does my body look different than my friend's?"}
+{"role": "user", "content": "Can you show empathy to animals?"}
+{"role": "user", "content": "Do all countries have the same kind of government?"}
+{"role": "user", "content": "Can you name some famous explorers?"}
+{"role": "user", "content": "Can you sometimes find treats like cookies or candy near the checkout line?"}
+{"role": "user", "content": "Why do we shiver when we're cold?"}
+{"role": "user", "content": "How many ounces are in one cup?"}
+{"role": "user", "content": "How does a phone let us talk to people far away?"}
+{"role": "user", "content": "Why is breakfast important?"}
+{"role": "user", "content": "What are some units we use to measure length?"}
+{"role": "user", "content": "What's the opposite of 'hot'?"}
+{"role": "user", "content": "What's one section of the grocery store that might have lots of colorful foods? "}
+{"role": "user", "content": "What's a crosswalk?"}
+{"role": "user", "content": "Have you ever gotten lost? What are some problem-solving things you could do?"}
+{"role": "user", "content": "There are all sorts of shapes \u2013 circles, squares, triangles... can you find some around you?"}
+{"role": "user", "content": "What are some different sports people play?"}
+{"role": "user", "content": "What simple machine do you think stairs are made from?"}
+{"role": "user", "content": "Do all families look the same?"}
+{"role": "user", "content": "Imagine there are 10 birds on a tree and 3 fly away. How many birds are left on the tree?"}
+{"role": "user", "content": "How do airplanes fly?"}
+{"role": "user", "content": "Is it a good idea to ask for help when you're stuck on a problem?"}
+{"role": "user", "content": "If your friend falls down and gets hurt, how might they be feeling?"}
+{"role": "user", "content": "Can we predict the weather?"}
+{"role": "user", "content": "Do you like to help cook or bake in the kitchen?"}
+{"role": "user", "content": "What safety rules are important to remember when riding a bike?"}
+{"role": "user", "content": "How do stores decide how much things cost?"}
+{"role": "user", "content": "Can you 'catch' feelings from someone else?"}
+{"role": "user", "content": "What do the signs + and \u2013 mean?"}
+{"role": "user", "content": "What do you wear on a rainy day to keep your feet dry?"}
+{"role": "user", "content": "Is it important to clean up spills right away?"}
+{"role": "user", "content": "Some cultures wear beautiful robes. Can you think of a country where people wear kimonos?"}
+{"role": "user", "content": "Can you name a fast swimmer who won lots of Olympic gold medals?"}
+{"role": "user", "content": "Can you name a famous tennis player known for her powerful serve?"}
+{"role": "user", "content": "Why does a spinning top stay upright?"}
+{"role": "user", "content": "Is it okay to feel frustrated when you have a problem to solve?"}
+{"role": "user", "content": "What is a machine that uses a big wheel and rope to lift heavy things?"}
+{"role": "user", "content": "Why do flowers smell nice?"}
+{"role": "user", "content": "Is it okay to ask for help when you don't understand a word?"}
+{"role": "user", "content": "What's something besides food that you can buy in bulk to reduce waste?"}
+{"role": "user", "content": "How does the internet work?"}
+{"role": "user", "content": "How do owls see so well at night?"}
+{"role": "user", "content": "What do we call a drawing of a person?"}
+{"role": "user", "content": "Can words have more than one meaning?"}
+{"role": "user", "content": "How are rocks made?"}
+{"role": "user", "content": "Why is buying fruits and veggies that are 'in season' a good idea?"}
+{"role": "user", "content": "What does a red traffic light mean?"}
+{"role": "user", "content": "Imagine a road stretching far away...things in the distance look tiny, right? What's that called in art?"}
+{"role": "user", "content": "How does a blender work?"}
+{"role": "user", "content": "If you have 3 crayons and your friend gives you 2 more, how many do you have in total?"}
+{"role": "user", "content": "What is a word for a really big and impressive building?"}
+{"role": "user", "content": "How does a car work?"}
+{"role": "user", "content": "What do your parents call their parents?"}
+{"role": "user", "content": "Why do we sometimes get muscle cramps?"}
+{"role": "user", "content": "If you see your dog or cat stretching, is that a kind of exercise for them too?"}
+{"role": "user", "content": "What happens if I eat too many sweets?"}
+{"role": "user", "content": "Where do babies come from?"}
+{"role": "user", "content": "Do poems always rhyme?"}
+{"role": "user", "content": "Why do I have to apologize when I do something wrong?"}
+{"role": "user", "content": "Can you write your own name?"}
+{"role": "user", "content": "Is exercise more fun by yourself, or with friends and family?"}
+{"role": "user", "content": "Why is it important to wash our hands before preparing food?"}
+{"role": "user", "content": "Is it okay to share food or drinks with a friend who is sick?"}
+{"role": "user", "content": "Why do we get scared?"}
+{"role": "user", "content": "Can you cut out pictures and glue them together to make a new silly picture?"}
+{"role": "user", "content": "If you help grow a vegetable, are you more likely to want to taste it?"}
+{"role": "user", "content": "Who was Marie Curie?"}
+{"role": "user", "content": "What are some different ways we can travel from one place to another?"}
+{"role": "user", "content": "Where is a fun place to play tag?"}
+{"role": "user", "content": "Can you hop on one foot? How about the other foot?"}
+{"role": "user", "content": "What makes someone a good friend?"}
+{"role": "user", "content": "How can I help someone who is being bullied?"}
+{"role": "user", "content": "Why do we burp?"}
+{"role": "user", "content": "How does a hug make someone feel?"}
+{"role": "user", "content": "Should you touch your eyes, nose, or mouth if your hands aren't clean?"}
+{"role": "user", "content": "Are there other planets like Earth?"}
+{"role": "user", "content": "Would a peanut butter and jelly sandwich be better on white bread or whole grain bread?"}
+{"role": "user", "content": "Why do swimmers wear tight swimsuits?"}
+{"role": "user", "content": "Are simple machines only found in old-fashioned things?"}
+{"role": "user", "content": "What do you call your aunt or uncle's children?"}
+{"role": "user", "content": "If there's a food you BEG your parents to buy, but they say 'no', is it okay to be a little disappointed?"}
+{"role": "user", "content": "How are the pieces of a shirt put together?"}
+{"role": "user", "content": "Is the number seven odd or even?"}
+{"role": "user", "content": "Why do we need to wear sunscreen?"}
+{"role": "user", "content": "Does flossing help get rid of germs hiding in your mouth?"}
+{"role": "user", "content": "What does our stomach do?"}
+{"role": "user", "content": "How do volcanoes work?"}
+{"role": "user", "content": "If a recipe calls for 1 cup, and you only need half as much, how much would you use?"}
+{"role": "user", "content": "How do cuts heal?"}
+{"role": "user", "content": "Which cartoon dog has a big red nose?"}
+{"role": "user", "content": "Can you name 3 different types of helpers?"}
+{"role": "user", "content": "How do high jumpers get so high?"}
+{"role": "user", "content": "Why is buying food from a local farmer's market a responsible choice?"}
+{"role": "user", "content": "Why do babies cry?"}
+{"role": "user", "content": "Why do we need to take a bath or shower?"}
+{"role": "user", "content": "What food group gives us strong bones and teeth?"}
+{"role": "user", "content": "What is a good 'first recipe' to learn how to cook all by yourself?"}
+{"role": "user", "content": "What does it mean to count?"}
+{"role": "user", "content": "What's another way to say 'throw'?"}
+{"role": "user", "content": "Why should we try to have a positive attitude?"}
+{"role": "user", "content": "What does a red and white sideways triangle mean?"}
+{"role": "user", "content": "Does helping prepare food in the kitchen sometimes make you want to try it?"}
+{"role": "user", "content": "Is ice cream a good way to get your dairy in?"}
+{"role": "user", "content": "What is the past tense of the verb 'eat'?"}
+{"role": "user", "content": "What are allergies?"}
+{"role": "user", "content": "Besides yummy food, what's the best part about cooking?"}
+{"role": "user", "content": "What happens when you mix a primary color and a secondary color together?"}
+{"role": "user", "content": "Where do germs like to hide?"}
+{"role": "user", "content": "Why do some people need glasses?"}
+{"role": "user", "content": "Can you build a simple machine using things from around your house?"}
+{"role": "user", "content": "If you want something really badly, how might you feel?"}
+{"role": "user", "content": "If something is 'sticky', what happens when you touch it?"}
+{"role": "user", "content": "Why are some rocks smooth and some rough?"}
+{"role": "user", "content": "What could you use to measure how heavy you are?"}
+{"role": "user", "content": "How many inches are in one foot?"}
+{"role": "user", "content": "There are lots of choices of cereal! How do you decide which one to try?"}
+{"role": "user", "content": "Does cheese come from plants or animals?"}
+{"role": "user", "content": "Is it okay to ask for a sample or taste of something at the grocery store before buying it?"}
+{"role": "user", "content": "If a table is 3 feet long, how many inches long is it?"}
+{"role": "user", "content": "Do you know a solid shape that looks like a party hat?"}
+{"role": "user", "content": "What is bread made from?"}
+{"role": "user", "content": "Should you wash your hands with hot or cold water?"}
+{"role": "user", "content": "What are the first ten numbers you learn to count?"}
+{"role": "user", "content": "Is a pencil longer or shorter than your foot?"}
+{"role": "user", "content": "Does practicing a sport over and over help you get better at it?"}
+{"role": "user", "content": "Is your mail carrier a helper in your community?"}
+{"role": "user", "content": "What do we call the shape of a stop sign?"}
+{"role": "user", "content": "Why do we pay taxes?"}
+{"role": "user", "content": "Can you draw a picture of yourself?"}
+{"role": "user", "content": "When it's cold outside, what does a thermometer measure?"}
+{"role": "user", "content": "What's another word for 'happy'?"}
+{"role": "user", "content": "Do builders have to work as a team?"}
+{"role": "user", "content": "Are quesadillas easy to make?"}
+{"role": "user", "content": "Where do apples come from?"}
+{"role": "user", "content": "Can you see a clock in your house? What parts of a clock help us tell time?"}
+{"role": "user", "content": "Can you use your fingers to paint?"}
+{"role": "user", "content": "Artists mix colors on a special flat board. What's it called?"}
+{"role": "user", "content": "If you want to build something, is it important to have a plan?"}
+{"role": "user", "content": "Why do we need to sleep?"}
+{"role": "user", "content": "Why does food cook faster in a pressure cooker?"}
+{"role": "user", "content": "What's the opposite of 'start'?"}
+{"role": "user", "content": "Do you have to be good at a sport to have fun playing?"}
+{"role": "user", "content": "Where can you find a ramp besides a slide at the playground?"}
+{"role": "user", "content": "Can you name some nouns in your room?"}
+{"role": "user", "content": "Name a food that's crunchy."}
+{"role": "user", "content": "Why do we say please and thank you?"}
+{"role": "user", "content": "If a word starts with a capital letter, what does that usually mean?"}
+{"role": "user", "content": "What happens to the food we eat?"}
+{"role": "user", "content": "Do you think playing video games can help you become a better problem-solver?"}
+{"role": "user", "content": "Can you find levers anywhere in your house?"}
+{"role": "user", "content": "Why do frogs have long, sticky tongues?"}
+{"role": "user", "content": "What's a good way to keep your immune system strong? "}
+{"role": "user", "content": "Can playing video games count as exercise?"}
+{"role": "user", "content": "Where can you find new, healthy recipes to try?"}
+{"role": "user", "content": "What do we call a big competition where athletes try to win medals?"}
+{"role": "user", "content": "Why does our hair grow long?"}
+{"role": "user", "content": "What is a vote, and why is it important?"}
+{"role": "user", "content": "Why do athletes need a good diet?"}
+{"role": "user", "content": "Why do grocery stores keep milk and cheese refrigerated?"}
+{"role": "user", "content": "What simple salad dressings can you make by whisking things together?"}
+{"role": "user", "content": "Why do some people have freckles?"}
+{"role": "user", "content": "What are some ways to show your family you love them?"}
+{"role": "user", "content": "Why do some animals sleep during the winter?"}
+{"role": "user", "content": "What is the capital of France?"}
+{"role": "user", "content": "Where does our garbage go?"}
+{"role": "user", "content": "Why do people wear different traditional clothing?"}
+{"role": "user", "content": "Why do we sometimes get bruises?"}
+{"role": "user", "content": "What are some adjectives to describe a tree?"}
+{"role": "user", "content": "Can rocks change?"}
+{"role": "user", "content": "Can animals talk to each other?"}
+{"role": "user", "content": "Are plastic water bottles a responsible choice?"}
+{"role": "user", "content": "What is whole grain bread made from?"}
+{"role": "user", "content": "Which Disney princess has a pet tiger named Rajah?"}
+{"role": "user", "content": "What do you need to wear on your feet to go play in the snow?"}
+{"role": "user", "content": "If it's raining outside, how could we measure how much rain has fallen?"}
+{"role": "user", "content": "Name something we can grow in a garden."}
+{"role": "user", "content": "Why do astronauts wear spacesuits?"}
+{"role": "user", "content": "Is it important to listen to your body when you're feeling full?"}
+{"role": "user", "content": "How many continents are there?"}
+{"role": "user", "content": "What is a problem?"}
+{"role": "user", "content": "Photos can be beautiful art too! What would you like to take a picture of?"}
+{"role": "user", "content": "Why does being strong help you climb up on the playground?"}
+{"role": "user", "content": "Is it okay to hit someone back if they hit me?"}
+{"role": "user", "content": "Why is ice slippery?"}
+{"role": "user", "content": "What color do you get when you mix blue and yellow?"}
+{"role": "user", "content": "Is it okay to make a mess sometimes when you're cooking?"}
+{"role": "user", "content": "Do penguins live in the North Pole or South Pole?"}
+{"role": "user", "content": "Why is it good to have a variety of colors on your plate?"}
+{"role": "user", "content": "What are some words that rhyme with 'cat'?"}
+{"role": "user", "content": "Can sharing toys spread germs?"}
+{"role": "user", "content": "Do your clothes look the same as clothes kids in other countries wear?"}
+{"role": "user", "content": "Have you seen a painting with a magical night sky filled with swirls? What is it called?"}
+{"role": "user", "content": "When you tie your shoes, what kind of problem are you solving?"}
+{"role": "user", "content": "Should you always try new foods, even once?"}
+{"role": "user", "content": "Which is longer, a sentence or a paragraph?"}
+{"role": "user", "content": "What's more fun: following a recipe exactly, or experimenting a little with flavors you like?"}
+{"role": "user", "content": "How many ounces are in one pound?"}
+{"role": "user", "content": "If you get sick at night, can you still go to the doctor?"}
+{"role": "user", "content": "What is an architect?"}
+{"role": "user", "content": "What does a 'helper' do?"}
+{"role": "user", "content": "What were some inventions from ancient China?"}
+{"role": "user", "content": "How do plants help us breathe?"}
+{"role": "user", "content": "Sketching is like a quick drawing to capture an idea. What happens in a detailed drawing?"}
+{"role": "user", "content": "What solid shape looks like a box?"}
+{"role": "user", "content": "Where do you keep foods that need to stay cold?"}
+{"role": "user", "content": "Can you name some healthy snacks?"}
+{"role": "user", "content": "What do we use to talk to each other?"}
+{"role": "user", "content": "Why was the Titanic a famous ship?"}
+{"role": "user", "content": "What is a synonym? "}
+{"role": "user", "content": "What clothes do you put on first when you get dressed?"}
+{"role": "user", "content": "Where does rain come from?"}
+{"role": "user", "content": "Why can we stand on the ground without sinking?"}
+{"role": "user", "content": "What should be the biggest part of a healthy meal?"}
+{"role": "user", "content": "What do teachers do?"}
+{"role": "user", "content": "Why is drinking water important?"}
+{"role": "user", "content": "Can you use your favorite book to practice your reading?"}
+{"role": "user", "content": "Is being patient important for both engineers and doctors?"}
+{"role": "user", "content": "Have you ever seen a train? What kind of tracks does it travel on?"}
+{"role": "user", "content": "What is a job, and why do people work?"}
+{"role": "user", "content": "Would you rather make a sweet treat or a savory snack to cook?"}
+{"role": "user", "content": "Is it harder to learn a sport when you're younger or older?"}
+{"role": "user", "content": "What are shapes?"}
+{"role": "user", "content": "Can solving a problem sometimes involve teamwork?"}
+{"role": "user", "content": "Can you name 3 red fruits or vegetables?"}
+{"role": "user", "content": "What kind of vehicles do you see on the road most often?"}
+{"role": "user", "content": "If you break a bone, what kind of doctor might help fix it?"}
+{"role": "user", "content": "Why do we get stronger when we exercise?"}
+{"role": "user", "content": "When you're swinging on a swingset, what simple machine are you using?"}
+{"role": "user", "content": "Which word means happy and excited?"}
+{"role": "user", "content": "Can gardening be a form of exercise?"}
+{"role": "user", "content": "Why do we see rainbows after it rains?"}
+{"role": "user", "content": "What makes ice skates glide on the ice so well?"}
+{"role": "user", "content": "Are there foods from other countries you'd like to try?"}
+{"role": "user", "content": "What are some important kitchen safety rules?"}
+{"role": "user", "content": "What does an electrician do?"}
+{"role": "user", "content": "When something is 'rough', how does it feel?"}
+{"role": "user", "content": "Can people really kill each other? Like in movies?"}
+{"role": "user", "content": "Why do we sometimes get scars?"}
+{"role": "user", "content": "What's a different word for 'small'?"}
+{"role": "user", "content": "When you're jumping on a trampoline, what kind of exercise are you doing?"}
+{"role": "user", "content": "Can food be healthy AND fun?"}
+{"role": "user", "content": "Knives and axes have a type of simple machine that helps split things. What is it called?"}
+{"role": "user", "content": "What does 'swear word' mean?"}
+{"role": "user", "content": "Why do we need exercise?"}
+{"role": "user", "content": "What are the names of the Teenage Mutant Ninja Turtles?"}
+{"role": "user", "content": "What if you're playing a game and keep losing? What are some problem-solving things you can try?"}
+{"role": "user", "content": "What does a blue sign with a white 'P' mean? "}
+{"role": "user", "content": "Is a plate full of only french fries a balanced meal?"}
+{"role": "user", "content": "Do famous athletes always win?"}
+{"role": "user", "content": "Why can't we hear sounds in space?"}
+{"role": "user", "content": "Can Bugs Bunny fly?"}
+{"role": "user", "content": "What does a sign with a curved arrow and a line through it mean? "}
+{"role": "user", "content": "Do you need to wash your hands after playing with stuffed animals?"}
+{"role": "user", "content": "What word means to move back and forth in a playful way?"}
+{"role": "user", "content": "Why does dough rise?"}
+{"role": "user", "content": "Did you know some types of clothes were originally made for practical reasons, but became traditional?"}
+{"role": "user", "content": "What makes some people more flexible than others?"}
+{"role": "user", "content": "Can we find rocks from space on Earth?"}
+{"role": "user", "content": "Should you always carry hand sanitizer with you?"}
+{"role": "user", "content": "Why do leaves change color in the fall?"}
+{"role": "user", "content": "Which famous baseball player was known for hitting lots of home runs?"}
+{"role": "user", "content": "Is the word 'skip' a noun, verb, or adjective?"}
+{"role": "user", "content": "Can engineers help design things that protect the environment?"}
+{"role": "user", "content": "Who was Albert Einstein?"}
+{"role": "user", "content": "Is a pound heavier or lighter than an ounce?"}
+{"role": "user", "content": "Can germs make us cough or sneeze?"}
+{"role": "user", "content": "Is being brave a part of some helper jobs?"}
+{"role": "user", "content": "Why is it a good idea to celebrate when you solve a difficult problem?"}
+{"role": "user", "content": "Why do athletes practice so much?"}
+{"role": "user", "content": "Can you exercise along with your favorite cartoon characters?"}
+{"role": "user", "content": "What are some ways to reduce food waste at home?"}
+{"role": "user", "content": "What makes a silly sentence? "}
+{"role": "user", "content": "Do carrots grow on trees, or under the ground?"}
+{"role": "user", "content": "What rhymes with 'dog'?"}
+{"role": "user", "content": "Have you ever worn clothes from a different culture?"}
+{"role": "user", "content": "Someone with a growth mindset sees a difficult problem and thinks...?"}
+{"role": "user", "content": "How many sides does a triangle have?"}
+{"role": "user", "content": "How does a refrigerator keep things cold?"}
+{"role": "user", "content": "Instead of getting upset when you make a mistake, what can you try to do?"}
+{"role": "user", "content": "What is the opposite of 'tiny'?"}
+{"role": "user", "content": "What's better for getting rid of germs on dishes: washing by hand in the sink or using the dishwasher?"}
+{"role": "user", "content": "Why do we need street signs?"}
+{"role": "user", "content": "What are germs?"}
+{"role": "user", "content": "What does 'responsible shopping' mean?"}
+{"role": "user", "content": "What does a white rectangle with 'Speed Limit 25' mean?"}
+{"role": "user", "content": "What is a question mark for?"}
+{"role": "user", "content": "What should you always do before crossing the street?"}
+{"role": "user", "content": "Have you ever seen art made from unusual things?"}
+{"role": "user", "content": "Can you compost food scraps instead of throwing them in the trash?"}
+{"role": "user", "content": "Why does ice cream melt?"}
+{"role": "user", "content": "Does food sometimes look or smell different than it tastes?"}
+{"role": "user", "content": "Can you name 3 fruits?"}
+{"role": "user", "content": "What if you start with five crayons, and someone gives you two more? How many would you have?"}
+{"role": "user", "content": "Why would someone use a wedge to hold a door open?"}
+{"role": "user", "content": "Can engineers design things that help people with disabilities?"}
+{"role": "user", "content": "Why do stars twinkle?"}
+{"role": "user", "content": "Why do we have to go to school?"}
+{"role": "user", "content": "Why is sleep important for athletes?"}
+{"role": "user", "content": "Why do we need bones?"}
+{"role": "user", "content": "How many inches are in one foot?"}
+{"role": "user", "content": "Instead of a glass of milk, what's another way to get your calcium?"}
+{"role": "user", "content": "Have you ever grown any of your own food, even in a small pot?"}
+{"role": "user", "content": "What is a 'growth mindset'?"}
+{"role": "user", "content": "How does a whisk make whipped cream?"}
+{"role": "user", "content": "What is the sun?"}
+{"role": "user", "content": "Why is it important to put groceries away when you get home, especially things that need to stay cold?"}
+{"role": "user", "content": "Is it okay to taste a little bit of your food as you're cooking it?"}
+{"role": "user", "content": "When you run really fast, what does your heart do?"}
+{"role": "user", "content": "What parts of your hands should you scrub when washing?"}
+{"role": "user", "content": "Are there ways to save money at the grocery store?"}
+{"role": "user", "content": "Is a ball a flat shape or a solid shape?"}
+{"role": "user", "content": "What do you call a word that means the opposite of another word?"}
+{"role": "user", "content": "Why do we breathe heavier during exercise?"}
+{"role": "user", "content": "Why can't I eat candy all the time?"}
+{"role": "user", "content": "Where can you find the Amazon rainforest?"}
+{"role": "user", "content": "What is lightning?"}
+{"role": "user", "content": "Who is a famous soccer player known for his amazing goals and skills?"}
+{"role": "user", "content": "Is pizza a healthy food to eat every day?"}
+{"role": "user", "content": "Do you need to wash fruits and vegetables with skins before eating them?"}
+{"role": "user", "content": "Are monsters under my bed?"}
+{"role": "user", "content": "Can you do 5 jumping jacks?"}
+{"role": "user", "content": "Does going for a walk count as exercise?"}
+{"role": "user", "content": "If you have 8 stickers and you give 5 away, how many stickers would you have left?"}
+{"role": "user", "content": "What does a red rectangle with 'Wrong Way' written on it mean? "}
+{"role": "user", "content": "Why do we get vaccines?"}
+{"role": "user", "content": "What do you do if a recipe says 'add a tablespoon' of something?"}
+{"role": "user", "content": "When you make a mistake, does it mean you're not smart?"}
+{"role": "user", "content": "Is the sun a planet?"}
+{"role": "user", "content": "Does eating lots of colorful fruits and veggies help your body fight off getting sick?"}
+{"role": "user", "content": "When you're doing a jigsaw puzzle, what's a good problem-solving strategy?"}
+{"role": "user", "content": "Why is it important to wear a hard hat on a construction site?"}
+{"role": "user", "content": "Is getting dressed in the morning a form of problem-solving?"}
+{"role": "user", "content": "Are reusable bags better for the environment than plastic bags from the grocery store?"}
+{"role": "user", "content": "What was life like in ancient Rome?"}
+{"role": "user", "content": "What is one of the BEST ways to fight off germs?"}
+{"role": "user", "content": "What kind of vehicles can travel on water?"}
+{"role": "user", "content": "What color is Garfield the cat?"}
+{"role": "user", "content": "What do we use to measure how much liquid is in a cup?"}
+{"role": "user", "content": "If you spill something while cooking, what should you do?"}
+{"role": "user", "content": "Are food allergies the same as just not liking a food?"}
+{"role": "user", "content": "If reading is hard for you, does a growth mindset mean believing you CAN get better at it with practice?"}
+{"role": "user", "content": "Is buying the biggest container of something ALWAYS the most responsible choice?"}
+{"role": "user", "content": "I have a face, hands, and numbers, but I can't tell you how you look. What am I?"}
+{"role": "user", "content": "Do vegetables from the store need to be washed?"}
+{"role": "user", "content": "Can you think of a word that rhymes with 'cat'?"}
+{"role": "user", "content": "Why is the wind sometimes strong and sometimes gentle?"}
+{"role": "user", "content": "If you see someone who looks lost or needs help, what should you do?"}
+{"role": "user", "content": "What foods change when you heat them up?"}
+{"role": "user", "content": "Can you name a road sign that is red and shaped like an octagon (eight sides)?"}
+{"role": "user", "content": "Why do we dream?"}
+{"role": "user", "content": "How do we turn sheep's wool into yarn for knitting a sweater?"}
+{"role": "user", "content": "Which country is famous for maple syrup?"}
+{"role": "user", "content": "Why is it important to be on time?"}
+{"role": "user", "content": "What's a yummy topping to make plain oatmeal more exciting?"}
+{"role": "user", "content": "What food do we get from cows?"}
+{"role": "user", "content": "If you try something to solve a problem and it doesn't work, what should you do?"}
+{"role": "user", "content": "Have you ever accidentally used salt instead of sugar in a recipe? How did it taste?"}
+{"role": "user", "content": "What is a sentence?"}
+{"role": "user", "content": "What do doctors and nurses do?"}
+{"role": "user", "content": "Can you name a simple machine that helps you lift heavy things?"}
+{"role": "user", "content": "What sport uses a ball and a net, where you hit the ball over with your hands?"}
+{"role": "user", "content": "What kind of animal is Scooby-Doo?"}
+{"role": "user", "content": "Why might fruits and vegetables sometimes be cheaper at a farmer's market than in a big grocery store?"}
+{"role": "user", "content": "Why is it a good idea to wear sneakers when you're playing outside?"}
+{"role": "user", "content": "Whose job is it to decide what foods are served at home?"}
+{"role": "user", "content": "Why do mosquitoes bite us?"}
+{"role": "user", "content": "What is the fancy hat called that some people in Mexico wear, which is wide and colorful?"}
+{"role": "user", "content": "What kind of fun shapes can you make sandwiches with?"}
+{"role": "user", "content": "What does the word 'tiny' mean?"}
+{"role": "user", "content": "Can you stretch your arms up towards the sky as high as you can?"}
+{"role": "user", "content": "Is a whisper loud or quiet?"}
+{"role": "user", "content": "Why are some rocks shiny?"}
+{"role": "user", "content": "What are some fun toppings for pancakes or waffles?"}
+{"role": "user", "content": "Why do we wear different clothes in the summer and winter?"}
+{"role": "user", "content": "How does a microwave oven heat food?"}
+{"role": "user", "content": "What does a red light mean?"}
+{"role": "user", "content": "Why does a ball bounce?"}
+{"role": "user", "content": "After we have fabric, what's the next step in making a t-shirt?"}
+{"role": "user", "content": "What is an adjective?"}
+{"role": "user", "content": "Can you name something that floats on water?"}
+{"role": "user", "content": "When you're really hungry, is an apple or a small cookie going to fill you up more?"}
+{"role": "user", "content": "What do plants need to grow?"}
+{"role": "user", "content": "Does someone make clothes all by themselves?"}
+{"role": "user", "content": "What word means a loud, sudden sound that might scare you?"}
+{"role": "user", "content": "What do you call your father's brother?"}
+{"role": "user", "content": "Why do we need traffic signs?"}
+{"role": "user", "content": "What is a construction site?"}
+{"role": "user", "content": "What are some different types of engineers?"}
+{"role": "user", "content": "Why do we sweat when we're hot?"}
+{"role": "user", "content": "What color are the Minions?"}
+{"role": "user", "content": "Why is too much screen time bad?"}
+{"role": "user", "content": "Why does our heart rate go back down after exercising?"}
+{"role": "user", "content": "Does everyone make mistakes sometimes?"}
+{"role": "user", "content": "Do you smoke/drink?"}
+{"role": "user", "content": "When is it SUPER important to wash your hands?"}
+{"role": "user", "content": "Can you name 2 green vegetables?"}
+{"role": "user", "content": "Can you count backwards from 10?"}
+{"role": "user", "content": "What's the difference between the regular checkout line and the self-checkout at the grocery store?"}
+{"role": "user", "content": "Do you have a favorite food you'd like to learn to make yourself?"}
+{"role": "user", "content": "Which famous baseball player was known for hitting lots of home runs?"}
+{"role": "user", "content": "Why is it important to walk on the sidewalk?"}
+{"role": "user", "content": "Let's build a sculpture! What can you use?"}
+{"role": "user", "content": "Why do we get goosebumps?"}
+{"role": "user", "content": "Why do we have two eyes?"}
+{"role": "user", "content": "How do you feel after reading a funny story?"}
+{"role": "user", "content": "Does food you make yourself sometimes taste even better than store-bought?"}
+{"role": "user", "content": "If your friends are arguing over what game to play, can you use problem-solving to help?"}
+{"role": "user", "content": "Do you know what a bicycle is powered by?"}
+{"role": "user", "content": "Whose job is it to learn to like lots of different healthy foods"}
+{"role": "user", "content": "Where are the tags on your clothes usually found?"}
+{"role": "user", "content": "What's a word that means the opposite of 'fast'?"}
+{"role": "user", "content": "Why is it important to respect people who are different from us?"}
+{"role": "user", "content": "What's the special tool doctors use to listen to your heartbeat?"}
+{"role": "user", "content": "Why can some bugs walk on water?"}
+{"role": "user", "content": "Which number is smaller, 2 or 7?"}
+{"role": "user", "content": "Should you always follow a recipe exactly, or is it okay to experiment a little bit?"}
+{"role": "user", "content": "What makes popcorn pop?"}
+{"role": "user", "content": "Can you do push-ups against the wall?"}
+{"role": "user", "content": "What are some different holidays celebrated around the world?"}
+{"role": "user", "content": "What do you call your sister's son?"}
+{"role": "user", "content": "What's one easy recipe you could make with minimal help?"}
+{"role": "user", "content": "Why does our heart beat?"}
+{"role": "user", "content": "Why is it important to try and understand how other people feel?"}
+{"role": "user", "content": "How many cups are in a pint?"}
+{"role": "user", "content": "How many stars are there?"}
+{"role": "user", "content": "What are letters?"}
+{"role": "user", "content": "Are foods with lots of packaging good for the environment?"}
+{"role": "user", "content": "Is your brain like a muscle?"}
+{"role": "user", "content": "Can we break a bone?"}
+{"role": "user", "content": "What is hand-eye coordination?"}
+{"role": "user", "content": "Who was the first woman to fly solo across the Atlantic Ocean?"}
+{"role": "user", "content": "What can make it harder for our body to fight off germs and viruses?"}
+{"role": "user", "content": "Do engineers need to be good at math?"}
+{"role": "user", "content": "What kind of machine is used to make cloth out of cotton or yarn?"}
+{"role": "user", "content": "What are muscles, and why are they important?"}
+{"role": "user", "content": "Why is cooking sometimes called a 'science experiment'?"}
+{"role": "user", "content": "What's the opposite of 'wet'?"}
+{"role": "user", "content": "Is it okay to ask for help after you've tried to solve something on your own?"}
+{"role": "user", "content": "What should make up the biggest part of a healthy meal?"}
+{"role": "user", "content": "If someone is hurt, but it's not a big emergency, where could you take them for help?"}
+{"role": "user", "content": "Can you pack your own lunch for school sometimes?"}
+{"role": "user", "content": "Why do we have joints?"}
+{"role": "user", "content": "Why is staying hydrated important for athletes?"}
+{"role": "user", "content": "What did Leonardo da Vinci do?"}
+{"role": "user", "content": "What are some traditional foods from different countries?"}
+{"role": "user", "content": "What is a family?"}
+{"role": "user", "content": "Why do some plants smell bad?"}
+{"role": "user", "content": "Should we drink lots of water or sugary drinks like soda?"}
+{"role": "user", "content": "Why do we need to follow rules?"}
+{"role": "user", "content": "What are some healthy snacks you can assemble with no cooking required?"}
+{"role": "user", "content": "What's a fastener that helps keep our pants up?"}
+{"role": "user", "content": "How can you make your writing more exciting?"}
+{"role": "user", "content": "Can watching TV count as exercise?"}
+{"role": "user", "content": "Is a bus driver a helper?"}
+{"role": "user", "content": "What is the very first word many babies learn to say?"}
+{"role": "user", "content": "Sometimes foods come in glass jars instead of plastic. Is this a more responsible choice?"}
+{"role": "user", "content": "What does a red circle with a white line through it mean?"}
+{"role": "user", "content": "Do engineers help design our phones and computers?"}
+{"role": "user", "content": "Why do we have belly buttons?"}
+{"role": "user", "content": "Have you ever twisted something into wood, or used a jar lid? What simple machine does that use?"}
+{"role": "user", "content": "What do builders do?"}
+{"role": "user", "content": "Can drawing or sketching out your ideas help you when solving a problem?"}
+{"role": "user", "content": "How does your body feel when you've had enough exercise for the day?"}
+{"role": "user", "content": "If your friend makes a mistake, what's a helpful thing you can do?"}
+{"role": "user", "content": "Why do wheels make things easier to move?"}
+{"role": "user", "content": "When you learn to ride a bike, do you get it perfect on the first try?"}
+{"role": "user", "content": "What are some foods that are mostly sugar, and not so healthy?"}
+{"role": "user", "content": "How does our brain work?"}
+{"role": "user", "content": "What if a sentence is talking about something happening right NOW? Do we use past or present tense?"}
+{"role": "user", "content": "Why do some plants have thorns?"}
+{"role": "user", "content": "What kind of food group is peanut butter in?"}
+{"role": "user", "content": "Do helpers have to go to school to learn how to do their jobs?"}
+{"role": "user", "content": "How do seeds become plants?"}
+{"role": "user", "content": "Who was the 16th president of the United States?"}
+{"role": "user", "content": "What does a sign with a person in a wheelchair mean?"}
+{"role": "user", "content": "How does a straw work?"}
+{"role": "user", "content": "Why does my friend use a wheelchair?"}
+{"role": "user", "content": "What do you call your mother's sister?"}
+{"role": "user", "content": "Can plants move?"}
+{"role": "user", "content": "How does our nose smell things?"}
+{"role": "user", "content": "Before it's turned into cloth, what does cotton look like?"}
+{"role": "user", "content": "What does it feel like to be drunk?"}
+{"role": "user", "content": "What are some things families do together?"}
+{"role": "user", "content": "Why do some things float in water?"}
+{"role": "user", "content": "Why do we yawn?"}
+{"role": "user", "content": "Why did someone steal from our neighbor?"}
+{"role": "user", "content": "Why do we get fevers?"}
+{"role": "user", "content": "Does food that looks delicious in commercials or on the box always taste as good?"}
+{"role": "user", "content": "Who was the first person to walk on the moon?"}
+{"role": "user", "content": "Why is teamwork important in sports? "}
+{"role": "user", "content": "How is snow made?"}
+{"role": "user", "content": "How can you tell if your friend is feeling sad?"}
+{"role": "user", "content": "What are some healthy foods?"}
+{"role": "user", "content": "Why did dinosaurs go extinct?"}
+{"role": "user", "content": "What color is SpongeBob SquarePants?"}
+{"role": "user", "content": "Name a food that's soft."}
+{"role": "user", "content": "Sometimes clothes have pictures or words on them, how does that get there?"}
+{"role": "user", "content": "If you ask for a 'treat' at the grocery store and a grown-up offers you a healthy snack instead, is it okay to try it even if you're not sure you'll like it?"}
diff --git a/ archived/llm_monitor_byoc/requirements.txt b/ archived/llm_monitor_byoc/requirements.txt
new file mode 100644
index 0000000000..085fbd1862
--- /dev/null
+++ b/ archived/llm_monitor_byoc/requirements.txt
@@ -0,0 +1,3 @@
+python-dotenv==1.0.1
+pytest==8.2.2
+fmeval==1.0.3
diff --git a/ prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/api_workforce_show/__init__.py b/ archived/llm_monitor_byoc/src/components/__init__.py
similarity index 100%
rename from prepare_data/sm-ground_truth_multi_modal_parallel_with_step_functions/src/lambda_src/api_workforce_show/__init__.py
rename to archived/llm_monitor_byoc/src/components/__init__.py
diff --git a/ archived/llm_monitor_byoc/src/components/cloudwatch_logger.py b/ archived/llm_monitor_byoc/src/components/cloudwatch_logger.py
new file mode 100644
index 0000000000..0e120c97ee
--- /dev/null
+++ b/ archived/llm_monitor_byoc/src/components/cloudwatch_logger.py
@@ -0,0 +1,106 @@
+from typing import Dict
+import logging
+import json
+import datetime
+import os
+
+logger = logging.getLogger(__name__)
+
+PROCESSING_JOB_CONFIG_FILE = '/opt/ml/config/processingjobconfig.json'
+
+DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE = ('byoc_llm_default_endpoint', 'byoc_llm_default_monitoring_schedule')
+
+
+class CloudWatchLogger:
+ """
+ The CloudWatchLogger is a service that writes evaluation metrics to CloudWatch.
+ """
+
+ def __init__(self):
+ """
+ Constructor.
+ """
+
+ def log(self, eval_results: Dict, destination: str):
+ """
+ Log the evaluation results to CloudWatch.
+ :param eval_results: A dictionary of evaluation results.
+ :param destination: The path to the file where the evaluation results will be written.
+ :raises: ValueError if eval_results is not a dictionary.
+
+ For formatting and other information, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html
+ """
+
+ if eval_results is not None and not isinstance(eval_results, dict):
+ raise ValueError("eval_results must be a dictionary")
+
+
+ now = datetime.datetime.now(datetime.timezone.utc)
+ metric_timestamp = now.strftime("%Y-%m-%dT%H:%M:%SZ")
+
+
+ endpoint_name, monitoring_schedule_name = get_endpoint_and_monitoring_schedule()
+ logger.info(f"Endpoint: {endpoint_name}, Monitoring Schedule: {monitoring_schedule_name}")
+
+ # Create the output directory if it doesn't exist
+ formatted_data_dir = os.path.dirname(destination)
+ if not os.path.exists(formatted_data_dir):
+ os.makedirs(formatted_data_dir, exist_ok=True)
+
+ try:
+ with open(destination, 'w') as file:
+ for metric_name, metric_value in eval_results.items():
+ metric_data = {
+ "MetricName": metric_name,
+ "Timestamp": metric_timestamp,
+ "Dimensions": [
+ {"Name": "Endpoint", "Value": endpoint_name},
+ {"Name": "MonitoringSchedule", "Value": monitoring_schedule_name}
+ ],
+ "Value": metric_value
+ }
+ file.write(json.dumps(metric_data) + '\n')
+
+ logger.info(f"Logged metrics: {json.dumps(metric_data)}")
+ logger.info(f"Logged to {destination}")
+ except PermissionError as e:
+
+ print(f"Error: {e}")
+
+ print(f"Evaluation results logged to: {destination}")
+
+
+def is_running_in_docker():
+ """
+ Checks whether we are running in a Docker container or not.
+ :returns True if DOCKER_CONTAINER env variable is present, False otherwise.
+ """
+ return 'DOCKER_CONTAINER' in os.environ
+
+
+def get_endpoint_and_monitoring_schedule():
+ """
+ Retrieves the endpoint name and monitoring schedule name from the processing job config file.
+ If we are in a docker container, we are running a monitoring job, and the config file has
+ the endpoint name and monitoring schedule name.
+
+ For information about processingjobcongfig.json file, see here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-contract-inputs.html
+
+ :returns A tuple containing the endpoint name and monitoring schedule name.
+ """
+
+ if is_running_in_docker():
+ try:
+ with open(PROCESSING_JOB_CONFIG_FILE, 'r') as config:
+ params = json.load(config)
+ logger.info("Reading Env params")
+ endpoint_name = params["Environment"]["sagemaker_endpoint_name"]
+ monitoring_schedule_name = params["Environment"]["sagemaker_monitoring_schedule_name"]
+
+ return endpoint_name, monitoring_schedule_name
+ except KeyError:
+ logger.error(f"Environment does not have endpoint or monitoring schedule name. Ensure that this processing job is initiated by a monitoring schedule.")
+ return DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE
+
+ else:
+ return DEFAULT_ENDPOINT_AND_MONITORING_SCHEDULE
\ No newline at end of file
diff --git a/ archived/llm_monitor_byoc/src/components/data_loader.py b/ archived/llm_monitor_byoc/src/components/data_loader.py
new file mode 100644
index 0000000000..560139fde1
--- /dev/null
+++ b/ archived/llm_monitor_byoc/src/components/data_loader.py
@@ -0,0 +1,178 @@
+import os
+import json
+import logging
+import base64
+import jsonschema
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+SCHEMA_FILE = '../utils/jsonl-capture-data.schema'
+
+class DataLoader:
+ """
+ The DataLoader is a service that recursively searches all subdirectories of
+ the '/opt/ml/processing/input_data' directory for JSONL files and subsequently executes an
+ ETL (Extract, Transform, Load) process. The DataLoader completes its job when all data has
+ been extracted, formatted, and loaded into '/opt/ml/processing/formatted_data/data.jsonl'.
+ """
+
+ def __init__(self):
+ """
+ Constructor. No parameters.
+
+ """
+ self.transformed_data = []
+
+ def extract(self, file_path: str):
+ """
+ Extracts data from a JSONL file.
+
+ :param file_path: The path to the JSONL file.
+ :raises: ValueError if file_path is not a valid string.
+ :returns: A list of data records extracted from the file. If file does not exist, returns empty list.
+ """
+
+ if not isinstance(file_path, str):
+ raise ValueError("file_path must be a string")
+
+ schema_filepath = os.path.join(os.path.dirname(__file__), SCHEMA_FILE)
+
+ logger.info(f"Extracting data from file: {file_path}")
+ extracted_data = []
+ try:
+ with open(file_path, 'r') as file:
+ for line in file:
+ try:
+ data = json.loads(line)
+ validate_json_against_schema(data, schema_filepath)
+ except json.JSONDecodeError:
+ logger.info(f"Invalid JSON data: {line}")
+ continue
+ except jsonschema.ValidationError as e:
+ logger.info(f"Validation error: {e}")
+ continue
+ extracted_data.append(data)
+ return extracted_data
+ except:
+ return []
+
+
+ def transform(self, data: list):
+ """
+ Applies transformation rules to the extracted data. The current rules format the data to be used with FMEval.
+
+ :param data: A list of data records to be transformed. Each item is a dictionary.
+ :raises: ValueError if data is not a list.
+ :raises: Warning if invalid data is provided.
+ :returns: The transformed data records.
+ """
+ logger.info("Transforming data...")
+
+ if not isinstance(data, list):
+ raise ValueError("data must be a list")
+
+ transformed_data = []
+ for record in data:
+ try:
+ content = json.loads(record["captureData"]["endpointInput"]["data"])["inputs"][0][0]["content"]
+ model_output = json.loads(base64.b64decode(record["captureData"]["endpointOutput"]["data"]).decode("utf-8"))[0]["generation"]["content"]
+
+ # Create the transformed data
+ transformed_record = {
+ "content": content,
+ "answer": model_output
+ }
+ transformed_data.append(transformed_record)
+ except (KeyError, IndexError, json.JSONDecodeError, UnicodeDecodeError) as e:
+ logger.warning(f"Error transforming record: {e}")
+ continue
+
+ return transformed_data
+
+ def load(self, destination: str):
+ """
+ Loads the transformed data into a single JSONL file.
+ :param destination: The destination filepath of the JSONL file.
+ :raises: ValueError if destination is not a valid string.
+ :returns: None.
+ """
+
+ if not isinstance(destination, str):
+ raise ValueError("destination must be a string")
+
+
+ logger.info(f"Loading data to: {destination}")
+
+ # Create the directory if it doesn't exist
+ formatted_data_dir = os.path.dirname(destination)
+ if not os.path.exists(formatted_data_dir):
+ os.makedirs(formatted_data_dir, exist_ok=True)
+
+ # Open the file and write the data
+ try:
+ with open(destination, 'w') as file:
+ for data_record in self.transformed_data:
+ file.write(json.dumps(data_record) + '\n')
+ except PermissionError as e:
+
+ logger.error(f"Permission error: {e}")
+
+
+
+ def execute_etl(self, directory: str, destination: str):
+ """
+ Executes the ETL (Extract, Transform, Load) process. This function recursively searches the input data directory and performs
+ ETL on all .jsonl files found.
+
+ :param directory: The directory to search for capture data.
+ :param destination: The destination filepath of the transformed data.
+ :raises: ValueError if directory is not a valid string.
+ :raises: ValueError if destination is not a valid string.
+ :raises: Warning if invalid directory provided.
+ :returns: None.
+ """
+
+ if not isinstance(directory, str):
+ raise ValueError("directory must be a string")
+ if not isinstance(destination, str):
+ raise ValueError("destination must be a string")
+
+
+ logger.info(f"current dir: {os.getcwd()}")
+ logger.info(f"Executing ETL process for directory: {directory}")
+ if os.path.exists(directory) and os.path.isdir(directory):
+ # Iterate over each file and directory in the directory
+ for item in os.listdir(directory):
+ item_path = os.path.join(directory, item)
+ if os.path.isdir(item_path):
+ # Recursively call the function for subdirectories
+ self.execute_etl(item_path, destination)
+ else:
+ # Check if the file is a .jsonl file and process it
+ if item.endswith(".jsonl"):
+ logger.info(f"Processing file: {item_path}")
+ extracted_data = self.extract(item_path)
+ transformed_data = self.transform(extracted_data)
+ self.transformed_data.extend(transformed_data)
+ else:
+ logger.info(f"Found file: {item_path}")
+
+ else:
+ logger.warning(f"The directory {directory} does not exist or is not a directory.")
+
+ # Load the transformed data into a single JSONL file
+ self.load(destination)
+
+
+def validate_json_against_schema(data, schema_filepath):
+ """
+ Validates that the data fits the schema defined in the schema file.
+
+ :param data: The data to validate.
+ :param schema_filepath: The path to the schema file.
+ :raises: jsonschema.ValidationError if the data does not match the schema.
+ """
+ with open(schema_filepath) as sf:
+ schema = json.load(sf)
+ jsonschema.validate(instance=data, schema=schema)
\ No newline at end of file
diff --git a/ archived/llm_monitor_byoc/src/components/evaluator.py b/ archived/llm_monitor_byoc/src/components/evaluator.py
new file mode 100644
index 0000000000..e3f06a28cd
--- /dev/null
+++ b/ archived/llm_monitor_byoc/src/components/evaluator.py
@@ -0,0 +1,84 @@
+from typing import Set, Optional
+import logging
+import json
+from fmeval.eval_algorithms.toxicity import Toxicity, ToxicityConfig, DataConfig
+from fmeval.exceptions import EvalAlgorithmClientError
+
+# Model Input/Output specify which fields FMEVal looks in our dataset.
+# Reference https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-foundation-model-evaluate-auto-lib-custom.html
+DATASET_NAME = "custom_dataset"
+DATASET_MIME_TYPE = "application/jsonlines"
+MODEL_INPUT_LOCATION = "content"
+MODEL_OUTPUT_LOCATION = "answer"
+
+
+TOXICITY_EVALUATOR_MODEL = "detoxify"
+DEFAULT_EVALUATIONS = {'toxicity', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat', 'sexual_explicit'}
+
+logger = logging.getLogger(__name__)
+
+class Evaluator:
+ """
+ The Evaluator is a service that assesses the performance of Large Language Models by running a set
+ of evaluation algorithms specified by a configuration set. It reads formatted data from
+ the /opt/ml/processing/output/data.jsonl file and uses the FMEval open-source library to
+ execute the specified evaluation tasks.
+ """
+ def __init__(self, eval_config: Optional[Set[str]] = None):
+ """
+ Constructor
+ :param eval_config: A Set of evaluation tasks to run. If not provided, all evaluation tasks will be run.
+ :raises: ValueError if eval_config is not a set or a list of strings.
+ """
+ self.eval_config = eval_config
+ if eval_config is not None:
+ if isinstance(eval_config, set):
+ self.eval_config = eval_config
+ elif isinstance(eval_config, list):
+ self.eval_config = set(eval_config)
+ else:
+ raise ValueError("eval_config must be a set or a list of strings")
+
+ def evaluate(self, dataset_uri: str):
+ """
+ Evaluate the data using the configured settings.
+
+ :param dataset_uri: The path to the dataset file.
+ :raises: ValueError if the dataset_uri is not a valid string.
+ :return: A dictionary containing the evaluation results. If data is empty/malformed, returns an empty dictionary.
+ """
+
+ if not isinstance(dataset_uri, str):
+ raise ValueError("dataset_uri must be a valid string")
+
+ config = DataConfig(
+ dataset_name=DATASET_NAME,
+ dataset_uri=dataset_uri,
+ dataset_mime_type=DATASET_MIME_TYPE,
+ model_input_location=MODEL_INPUT_LOCATION,
+ model_output_location=MODEL_OUTPUT_LOCATION,
+ )
+
+ if not self.eval_config:
+ configured_evals = DEFAULT_EVALUATIONS
+ else:
+ configured_evals = set(self.eval_config)
+
+ eval_algo = Toxicity(ToxicityConfig(model_type=TOXICITY_EVALUATOR_MODEL))
+
+ try:
+ eval_output = eval_algo.evaluate(dataset_config=config, save=True)
+ except (json.JSONDecodeError, EvalAlgorithmClientError) as e:
+ # If we evaluate an empty/malformed file, return an empty dict
+ logger.warning("Evaluated data malformed.")
+ return {}
+
+ eval_results = {}
+ for eval_score in eval_output[0].dataset_scores:
+ if eval_score.name in configured_evals:
+ eval_results[eval_score.name] = eval_score.value
+
+ logger.info(f"Evaluation Results: {eval_results}")
+
+ return eval_results
+
\ No newline at end of file
diff --git a/ archived/llm_monitor_byoc/src/main.py b/ archived/llm_monitor_byoc/src/main.py
new file mode 100644
index 0000000000..758932e787
--- /dev/null
+++ b/ archived/llm_monitor_byoc/src/main.py
@@ -0,0 +1,44 @@
+import logging
+import sys
+import site
+from components.data_loader import DataLoader
+from components.evaluator import Evaluator
+from components.cloudwatch_logger import CloudWatchLogger
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# This is where our capture data is loaded to. MUST be same as "destination" field in EndointInput for deployed model.
+INPUT_DATA_SOURCE = '/opt/ml/processing/input_data'
+
+# Destination for formatted and cleaned data in the container for evaluation.
+CLEANED_DATA_DESTINATION = '/opt/ml/processing/internal/data.jsonl'
+
+# Destination for metrics. These metrics MUST be stored at this location if they are to be published.
+# See https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-byoc-cloudwatch.html
+CLOUDWATCH_METRICS_DESTINATION = '/opt/ml/output/metrics/cloudwatch/cloudwatch_metrics.jsonl'
+
+# These are all of the evaluations we can run.
+EVALUATIONS = {
+ "toxicity",
+ "severe_toxicity",
+ "obscene",
+ "identity_attack",
+ "insult",
+ "threat",
+ "sexual_explicit"
+ }
+
+if __name__ == "__main__":
+ try:
+ data_loader = DataLoader()
+ evaluator = Evaluator(EVALUATIONS)
+ cloudwatch_logger = CloudWatchLogger()
+
+ data_loader.execute_etl(INPUT_DATA_SOURCE, CLEANED_DATA_DESTINATION)
+ eval_results = evaluator.evaluate(CLEANED_DATA_DESTINATION)
+ cloudwatch_logger.log(eval_results, CLOUDWATCH_METRICS_DESTINATION)
+
+ except Exception as e:
+ logger.exception("Exception performing analysis: " + str(e))
+ sys.exit(255)
diff --git a/ archived/llm_monitor_byoc/src/utils/jsonl-capture-data.schema b/ archived/llm_monitor_byoc/src/utils/jsonl-capture-data.schema
new file mode 100644
index 0000000000..af48e7da17
--- /dev/null
+++ b/ archived/llm_monitor_byoc/src/utils/jsonl-capture-data.schema
@@ -0,0 +1,86 @@
+{
+ "$schema": "http://json-schema.org/draft-04/schema#",
+ "type": "object",
+ "properties": {
+ "captureData": {
+ "type": "object",
+ "properties": {
+ "endpointInput": {
+ "type": "object",
+ "properties": {
+ "observedContentType": {
+ "type": "string"
+ },
+ "mode": {
+ "type": "string"
+ },
+ "data": {
+ "type": "string"
+ },
+ "encoding": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "observedContentType",
+ "mode",
+ "data",
+ "encoding"
+ ]
+ },
+ "endpointOutput": {
+ "type": "object",
+ "properties": {
+ "observedContentType": {
+ "type": "null"
+ },
+ "mode": {
+ "type": "string"
+ },
+ "data": {
+ "type": "string"
+ },
+ "encoding": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "observedContentType",
+ "mode",
+ "data",
+ "encoding"
+ ]
+ }
+ },
+ "required": [
+ "endpointInput",
+ "endpointOutput"
+ ]
+ },
+ "eventMetadata": {
+ "type": "object",
+ "properties": {
+ "eventId": {
+ "type": "string"
+ },
+ "customAttributes": {
+ "type": "array",
+ "items": [
+ {
+ "type": "string"
+ }
+ ]
+ },
+ "inferenceTime": {
+ "type": "string"
+ }
+ }
+ },
+ "eventVersion": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "captureData"
+ ]
+}
diff --git a/ build_and_train_models/sm-experiments_local_experiment_tracking_pytorch/images/charts_overview.png b/ archived/local_experiment_tracking/images/charts_overview.png
similarity index 100%
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rename from build_and_train_models/sm-visual_object_detection_finetuning/sm-visual_object_detection_finetuning.ipynb
rename to archived/visual_object_detection/visual_object_detection.ipynb
diff --git a/ deploy_and_monitor/sm-model_monitor_visualizing_monitoring_results/sm-model_monitor_visualizing_monitoring_results.ipynb b/ archived/visualization/SageMaker-Model-Monitor-Visualize.ipynb
similarity index 100%
rename from deploy_and_monitor/sm-model_monitor_visualizing_monitoring_results/sm-model_monitor_visualizing_monitoring_results.ipynb
rename to archived/visualization/SageMaker-Model-Monitor-Visualize.ipynb
diff --git a/ deploy_and_monitor/sm-model_monitor_visualizing_monitoring_results/utils.py b/ archived/visualization/utils.py
similarity index 100%
rename from deploy_and_monitor/sm-model_monitor_visualizing_monitoring_results/utils.py
rename to archived/visualization/utils.py
diff --git a/ deploy_and_monitor/sm-inference_recommender_xgboost.ipynb b/ archived/xgboost-inference-recommender.ipynb
similarity index 100%
rename from deploy_and_monitor/sm-inference_recommender_xgboost.ipynb
rename to archived/xgboost-inference-recommender.ipynb
diff --git a/ build_and_train_models/sm-distributed_script_mode_xgboost/abalone.py b/ archived/xgboost_abalone/abalone.py
similarity index 100%
rename from build_and_train_models/sm-distributed_script_mode_xgboost/abalone.py
rename to archived/xgboost_abalone/abalone.py
diff --git a/ build_and_train_models/sm-remote_function_xgboost/config.yaml b/ archived/xgboost_abalone/config.yaml
similarity index 100%
rename from build_and_train_models/sm-remote_function_xgboost/config.yaml
rename to archived/xgboost_abalone/config.yaml
diff --git a/ build_and_train_models/sm-distributed_script_mode_xgboost/images/shap_old_abalone.png b/ archived/xgboost_abalone/images/shap_old_abalone.png
similarity index 100%
rename from build_and_train_models/sm-distributed_script_mode_xgboost/images/shap_old_abalone.png
rename to archived/xgboost_abalone/images/shap_old_abalone.png
diff --git a/ build_and_train_models/sm-distributed_script_mode_xgboost/images/shap_young_abalone.png b/ archived/xgboost_abalone/images/shap_young_abalone.png
similarity index 100%
rename from build_and_train_models/sm-distributed_script_mode_xgboost/images/shap_young_abalone.png
rename to archived/xgboost_abalone/images/shap_young_abalone.png
diff --git a/ build_and_train_models/sm-distributed_script_mode_xgboost/inference.py b/ archived/xgboost_abalone/inference.py
similarity index 100%
rename from build_and_train_models/sm-distributed_script_mode_xgboost/inference.py
rename to archived/xgboost_abalone/inference.py
diff --git a/ build_and_train_models/sm-remote_function_xgboost/requirements.txt b/ archived/xgboost_abalone/requirements.txt
similarity index 100%
rename from build_and_train_models/sm-remote_function_xgboost/requirements.txt
rename to archived/xgboost_abalone/requirements.txt
diff --git a/ build_and_train_models/sm-remote_function_xgboost/sm-remote_function_xgboost.ipynb b/ archived/xgboost_abalone/xgboost_abalone.ipynb
similarity index 100%
rename from build_and_train_models/sm-remote_function_xgboost/sm-remote_function_xgboost.ipynb
rename to archived/xgboost_abalone/xgboost_abalone.ipynb
diff --git a/ build_and_train_models/sm-distributed_script_mode_xgboost/sm-distributed_script_mode_xgboost.ipynb b/ archived/xgboost_abalone/xgboost_abalone_dist_script_mode.ipynb
similarity index 100%
rename from build_and_train_models/sm-distributed_script_mode_xgboost/sm-distributed_script_mode_xgboost.ipynb
rename to archived/xgboost_abalone/xgboost_abalone_dist_script_mode.ipynb
diff --git a/ build_and_train_models/sm-inference_script_mode_xgboost/sm-inference_script_mode_xgboost.ipynb b/ archived/xgboost_abalone/xgboost_inferenece_script_mode.ipynb
similarity index 100%
rename from build_and_train_models/sm-inference_script_mode_xgboost/sm-inference_script_mode_xgboost.ipynb
rename to archived/xgboost_abalone/xgboost_inferenece_script_mode.ipynb
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/sm-xgboost_bring_your_own_container_batch_transform.ipynb b/ archived/xgboost_bring_your_own/Batch_Transform_BYO_XGB.ipynb
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/sm-xgboost_bring_your_own_container_batch_transform.ipynb
rename to archived/xgboost_bring_your_own/Batch_Transform_BYO_XGB.ipynb
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/Dockerfile b/ archived/xgboost_bring_your_own/Dockerfile
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/Dockerfile
rename to archived/xgboost_bring_your_own/Dockerfile
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/data.xls b/ archived/xgboost_bring_your_own/data.xls
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/data.xls
rename to archived/xgboost_bring_your_own/data.xls
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/nginx.conf b/ archived/xgboost_bring_your_own/xgboost/nginx.conf
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/nginx.conf
rename to archived/xgboost_bring_your_own/xgboost/nginx.conf
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/predictor.py b/ archived/xgboost_bring_your_own/xgboost/predictor.py
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/predictor.py
rename to archived/xgboost_bring_your_own/xgboost/predictor.py
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/serve b/ archived/xgboost_bring_your_own/xgboost/serve
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/serve
rename to archived/xgboost_bring_your_own/xgboost/serve
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/train b/ archived/xgboost_bring_your_own/xgboost/train
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/train
rename to archived/xgboost_bring_your_own/xgboost/train
diff --git a/ end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/wsgi.py b/ archived/xgboost_bring_your_own/xgboost/wsgi.py
similarity index 100%
rename from end_to_end_ml_lifecycle/sm-xgboost_bring_your_own_container_batch_transform/xgboost/wsgi.py
rename to archived/xgboost_bring_your_own/xgboost/wsgi.py
diff --git a/ build_and_train_models/sm-xgboost_customer_churn.ipynb b/ archived/xgboost_customer_churn_outputs.ipynb
similarity index 100%
rename from build_and_train_models/sm-xgboost_customer_churn.ipynb
rename to archived/xgboost_customer_churn_outputs.ipynb
diff --git a/ build_and_train_models/sm-multi_class_classification_xgboost.ipynb b/ archived/xgboost_mnist.ipynb
similarity index 100%
rename from build_and_train_models/sm-multi_class_classification_xgboost.ipynb
rename to archived/xgboost_mnist.ipynb
diff --git a/ deploy_and_monitor/sm-mme_xgboost.ipynb b/ archived/xgboost_multi_model_endpoint_home_value.ipynb
similarity index 100%
rename from deploy_and_monitor/sm-mme_xgboost.ipynb
rename to archived/xgboost_multi_model_endpoint_home_value.ipynb
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
new file mode 100644
index 0000000000..a7bfd1a46e
--- /dev/null
+++ b/.github/CODEOWNERS
@@ -0,0 +1,14 @@
+# Each line is a file pattern followed by one or more owners (@username or
+# email@address).
+#
+# Order is important. The last matching pattern has the most precedence.
+# So if a pull request only touches a specific folder, only the respective owners
+# will be requested to review.
+#
+# @See https://help.github.com/articles/about-codeowners/
+
+/sagemaker-experiments/* @aws/sagemakerexperimentsadmin
+/sagemaker-lineage/* @aws/sagemakerexperimentsadmin
+
+# Community contributed
+/contrib/ @aws/sagemaker-notebook-sas
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000000..99394f4660
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,21 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[Bug Report]"
+labels: ''
+assignees: ''
+
+---
+
+**Link to the notebook**
+Add the link to the notebook.
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To reproduce**
+A clear, step-by-step set of instructions to reproduce the bug.
+
+**Logs**
+If applicable, add logs to help explain your problem.
+You may also attach an `.ipynb` file to this issue if it includes relevant logs or output.
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000000..9df79c9079
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,5 @@
+blank_issues_enabled: false
+contact_links:
+ - name: Ask a question
+ url: https://stackoverflow.com/questions/tagged/amazon-sagemaker
+ about: Use Stack Overflow to ask and answer questions
diff --git a/.github/ISSUE_TEMPLATE/content-improvement.md b/.github/ISSUE_TEMPLATE/content-improvement.md
new file mode 100644
index 0000000000..3390ef51b6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/content-improvement.md
@@ -0,0 +1,16 @@
+---
+name: Content improvement
+about: Suggest changes to a notebook to make it more educational
+title: "[Content Improvement]"
+labels: ''
+assignees: ''
+
+---
+
+**Link to the notebook**
+Add the link to the notebook.
+
+**What aspects of the notebook can be improved?**
+
+
+**What are your suggestions?**
diff --git a/.github/ISSUE_TEMPLATE/example-request.md b/.github/ISSUE_TEMPLATE/example-request.md
new file mode 100644
index 0000000000..4a02f80224
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/example-request.md
@@ -0,0 +1,23 @@
+---
+name: Example request
+about: Suggest an use case example you want to see
+title: "[Example Request]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the use case example you want to see**
+A clear and concise description of the functionality you want.
+
+**How would this example be used? Please describe.**
+A clear and concise description of the use case for this example.
+
+**Describe which SageMaker services are involved**
+
+
+**Describe what other services (other than SageMaker) are involved***
+
+
+**Describe which dataset could be used. Provide its location in s3://sagemaker-sample-files or another source.**
+
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000000..baf083ee0b
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,17 @@
+*Issue #, if available:*
+
+*Description of changes:*
+
+*Testing done:*
+
+## Merge Checklist
+
+_Put an `x` in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request._
+
+- [ ] I have verified that my PR does not contain any new notebook/s which demonstrate a SageMaker functionality already showcased by another existing notebook in the repository
+- [ ] I have read the [CONTRIBUTING](https://github.com/aws/amazon-sagemaker-examples/blob/default/CONTRIBUTING.md) doc and adhered to the guidelines regarding folder placement, notebook naming convention and example notebook best practices
+- [ ] I have updated the necessary documentation, including the README of the appropriate folder as well as the index.rst file
+- [ ] I have tested my notebook(s) and ensured it runs end-to-end
+- [ ] I have linted my notebook(s) and code using `black-nb -l 100 {path}/{notebook-name}.ipynb`
+
+By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index fa96541fa6..334742fd57 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -6,7 +6,6 @@ documentation, we greatly value feedback and contributions from our community.
Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
information to effectively respond to your bug report or contribution.
-
## Report Bugs/Feature Requests
We welcome you to use the GitHub issue tracker to report bugs or suggest features.
@@ -18,7 +17,6 @@ reported the issue. Please try to include as much information as you can. Detail
* Any modifications you've made relevant to the bug.
* A description of your environment or deployment.
-
## Contribute via Pull Requests (PRs)
Before sending us a pull request, please ensure that:
@@ -26,8 +24,7 @@ Before sending us a pull request, please ensure that:
* You are working against the latest source on the *main* branch.
* You check the existing open and recently merged pull requests to make sure someone else hasn't already addressed the problem.
* You open an issue to discuss any significant work - we would hate for your time to be wasted.
-* **NOTE:: If you are submitting an entirely new notebook, please ensure it demonstrates a functionality of SageMaker not yet showcased by any other existing notebook in this repository. If you don't meet this criteria, your PR will be rejected.**
-
+* **NOTE: If you are submitting an entirely new notebook, please ensure it demonstrates a functionality of SageMaker not yet showcased by any other existing notebook in this repository. If you don't meet this criteria, your PR will be rejected.**
### Pull Down the Code
@@ -36,7 +33,6 @@ Before sending us a pull request, please ensure that:
1. Follow the instructions at [Fork a Repo](https://help.github.com/en/articles/fork-a-repo) to fork a GitHub repository.
1. Clone your fork of the repository: `git clone https://github.com//amazon-sagemaker-examples` where `` is your github username.
-
### Run the Linter
Apply Python code formatting to Jupyter notebook files using [black](https://pypi.org/project/black/).
@@ -45,7 +41,6 @@ Apply Python code formatting to Jupyter notebook files using [black](https://pyp
1. In terminal, run the following black command on each of your ipynb notebook files and verify that the linter passes: `python3 -m black -l 100 {path}/{notebook-name}.ipynb`
1. Some notebook features such as `%` bash commands or `%%` cell magic cause black to fail. As long as you run the above command to format as much as possible, that is sufficient, even if the check fails
-
### Test Your Notebook End-to-End
Our [CI system](https://github.com/aws/sagemaker-example-notebooks-testing) runs modified or added notebooks, in parallel, for every Pull Request.
@@ -54,8 +49,8 @@ Please ensure that your notebook runs end-to-end so that it passes our CI.
The `sagemaker-bot` will comment on your PR with a link for `Build logs`.
If your PR does not pass CI, you can view the logs to understand how to fix your notebook(s) and code.
-
### Add CI badges to your notebook
+
Our [CI system](https://github.com/aws/sagemaker-example-notebooks-testing) tests each notebook in this repo everyday to see if it is fully functional. We provide badges to display the results of these daily tests so you and your customers can see if your notebook is working or needs to be fixed. **It is required that all notebooks have these badges.**
The badges should be added using the following steps:
@@ -109,6 +104,26 @@ This notebook was tested in multiple regions. The test results are as follows, e
```
+### Name your notebook
+
+We have migrated to a new standarized naming convention for all notebooks with the repository. The naming format follows the pattern: sm - {name_of_sagemaker_feature} _ {any_key_secondary_feature} _ {detailed_description_of_notebook_focus} . ipynb
+
+Examples:
+- sm-jumpstart_foundation_trainium_inferentia_finetuning_deployment.ipynb
+- sm-training_compiler_language_modeling_multi_gpu_multi_node.ipynb
+- sm-clarify_text_explainability_text_sentiment_analysis.ipynb
+
+### Place your notebook into the correct folder
+
+We have implemented a flattened directory structure in order to increase the discoverability of notebooks within the repository. Once you have completed the notebook, place it into the folder that best corresponds with the primary functionality that you highlighting within your example notebook. Here is a list of the folders and a brief description of their primary purposes:
+
+- end_to_end_ml_lifecycle - end-to-end notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker
+- prepare_data - noteboooks that showcase Amazon SageMaker's data preparation capabilities
+- building_and_train_models - notebooks that highlight Amazon SageMaker tools to build and train ML models at scale
+- deploy_and_monitor - notebooks that demonstrate Amazon SageMaker's ML infrastructure and model deployment options as well as SageMaker's ability to monitor the quality of your machine learning models in real time
+- responsible_ai - notebooks that highlight Amazon SageMaker's abilities to improve your machine learning models by detecting potential bias and helping to explain the predictions that your models make from your tabular, computer vision, natural processing, or time series datasets
+- ml_ops - notebooks that feature Amazon SageMaker's ability to implement machine learning models in production environments with continuous integration and deployment
+- generative_ai - notebooks that demonstate Amazon SageMaker's generative AI capabilities to create new, synthetic data across various modalities, such as text, images, audio, and video, based on the patterns and relationships learned from training data
### Add Your Notebook to the Website
@@ -168,28 +183,30 @@ python -m http.server 8000
#### Add a notebook to the website
-You will typically modify an index.rst file and add the notebook by name, minus the extension. For example, if the new notebook is in a subfolder in the `aws_marketplace` folder:
-https://github.com/aws/amazon-sagemaker-examples/blob/main/aws_marketplace/creating_marketplace_products/algorithms/Bring_Your_Own-Creating_Algorithm_and_Model_Package.ipynb
-You would modify this file: https://github.com/aws/amazon-sagemaker-examples/blob/main/aws_marketplace/index.rst
+You will modify the index.rst file at the highest level of the directory and add the notebook by name, minus the extension into the section that corresponds to the folder in which you added the notebook. For example, if the new notebook is in a subfolder in the `generative_ai` folder:
+https://github.com/aws/amazon-sagemaker-examples/blob/default/generative_ai/sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb
+You would modify this file: https://github.com/aws/amazon-sagemaker-examples/blob/default/index.rst
-1. Look for the table of contents directive, `toctree` :
+1. Look for the table of contents directive, `toctree` with the caption that matches the subfolder you placed the notebook into:
```
.. toctree::
:maxdepth: 1
+ :caption: Generatative AI
```
-1. Add an entry for the new notebook:
+2. Add an entry for the new notebook:
```
.. toctree::
:maxdepth: 1
+ :caption: Generatative AI
- creating_marketplace_products/algorithms/Bring_Your_Own-Creating_Algorithm_and_Model_Package
+ generative_ai/sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation
```
#### Adjusting navigation
diff --git a/README.md b/README.md
index aefad6e360..b1cbeeb326 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,9 @@
+# :exclamation::fire: Announcement: New Folder Structure :fire::exclamation:
+
+Starting 7 August, 2024, we are introducing a new flattened folder structure within the SageMaker Example Notebooks repository in order to improve the discoverability of the notebooks. The new structure uses standardized folder and notebook naming conventions to align with common workflows and SageMaker services. It also includes an archived folder for redundant, outdated, and low-viewed notebooks. To ease the transition, we have created an [excel sheet](new_file_structure_updated_notebook_names_and_folders.xlsx) showing the old naming convention and its new corresponding name.
+
# Amazon SageMaker Examples
Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.
@@ -34,8 +38,6 @@ They can be accessed by clicking on the `SageMaker Examples` tab in Jupyter or t
Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
-As of February 7, 2022, the default branch is named "main". See our [announcement](https://github.com/aws/amazon-sagemaker-examples/discussions/3131) for details and how to update your existing clone.
-
## :notebook: Example Notebook Categories
### End-to-End ML Lifecycle
@@ -80,5 +82,5 @@ For more details, please take a look at the [LICENSE](https://github.com/aws/ama
## :handshake: Contributing
Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed.
-Please read our [contributing guidelines](https://github.com/aws/amazon-sagemaker-examples/blob/master/CONTRIBUTING.md)
+Please read our [contributing guidelines](https://github.com/aws/amazon-sagemaker-examples/blob/default/CONTRIBUTING.md)
if you'd like to open an issue or submit a pull request.
diff --git a/_static/kendrasearchtools.js b/_static/kendrasearchtools.js
new file mode 100644
index 0000000000..4920607010
--- /dev/null
+++ b/_static/kendrasearchtools.js
@@ -0,0 +1,700 @@
+/*
+ * kendrasearchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * A modification of searchtools.js (https://github.com/sphinx-doc/sphinx/blob/275d9/sphinx/themes/basic/static/searchtools.js)
+ * where the default full-text search implemented in searchtools.js is replaced with AWS Kendra searching over multiple
+ * websites. The default full-text search is still kept and implemented as a fallback in the case that the Kendra search doesn't work.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+if (!Scorer) {
+ /**
+ * Simple result scoring code.
+ */
+ var Scorer = {
+ // Implement the following function to further tweak the score for each result
+ // The function takes a result array [filename, title, anchor, descr, score]
+ // and returns the new score.
+ /*
+ score: function(result) {
+ return result[4];
+ },
+ */
+
+ // query matches the full name of an object
+ objNameMatch: 11,
+ // or matches in the last dotted part of the object name
+ objPartialMatch: 6,
+ // Additive scores depending on the priority of the object
+ objPrio: {0: 15, // used to be importantResults
+ 1: 5, // used to be objectResults
+ 2: -5}, // used to be unimportantResults
+ // Used when the priority is not in the mapping.
+ objPrioDefault: 0,
+
+ // query found in title
+ title: 15,
+ partialTitle: 7,
+ // query found in terms
+ term: 5,
+ partialTerm: 2
+ };
+}
+
+if (!splitQuery) {
+ function splitQuery(query) {
+ return query.split(/\s+/);
+ }
+}
+
+/**
+ * default rtd search (used as fallback)
+ */
+var Search = {
+
+ _index : null,
+ _queued_query : null,
+ _pulse_status : -1,
+
+ htmlToText : function(htmlString) {
+ var virtualDocument = document.implementation.createHTMLDocument('virtual');
+ var htmlElement = $(htmlString, virtualDocument);
+ htmlElement.find('.headerlink').remove();
+ docContent = htmlElement.find('[role=main]')[0];
+ if(docContent === undefined) {
+ console.warn("Content block not found. Sphinx search tries to obtain it " +
+ "via '[role=main]'. Could you check your theme or template.");
+ return "";
+ }
+ return docContent.textContent || docContent.innerText;
+ },
+
+ init : function() {
+ var params = $.getQueryParameters();
+ if (params.q) {
+ var query = params.q[0];
+ $('input[name="q"]')[0].value = query;
+ // this.performSearch(query);
+ }
+ },
+
+ loadIndex : function(url) {
+ $.ajax({type: "GET", url: url, data: null,
+ dataType: "script", cache: true,
+ complete: function(jqxhr, textstatus) {
+ if (textstatus != "success") {
+ document.getElementById("searchindexloader").src = url;
+ }
+ }});
+ },
+
+ setIndex : function(index) {
+ var q;
+ this._index = index;
+ if ((q = this._queued_query) !== null) {
+ this._queued_query = null;
+ Search.query(q);
+ }
+ },
+
+ hasIndex : function() {
+ return this._index !== null;
+ },
+
+ deferQuery : function(query) {
+ this._queued_query = query;
+ },
+
+ stopPulse : function() {
+ this._pulse_status = 0;
+ },
+
+ startPulse : function() {
+ if (this._pulse_status >= 0)
+ return;
+ function pulse() {
+ var i;
+ Search._pulse_status = (Search._pulse_status + 1) % 4;
+ var dotString = '';
+ for (i = 0; i < Search._pulse_status; i++)
+ dotString += '.';
+ Search.dots.text(dotString);
+ if (Search._pulse_status > -1)
+ window.setTimeout(pulse, 500);
+ }
+ pulse();
+ },
+
+ /**
+ * perform a search for something (or wait until index is loaded)
+ */
+ performSearch : function(query) {
+ // create the required interface elements
+ this.out = $('#search-results');
+ this.title = $('#search-results h2:first'); // $('
').appendTo(this.out);
+ this.out.css("margin", "auto");
+
+ $('#search-progress').text(_('Preparing search...'));
+ this.startPulse();
+
+ this.query(query, 1, pageSize=10, filters=filters)
+ },
+
+};
+
+$(document).ready(function() {
+ KendraSearch.init();
+});
diff --git a/_static/pagination.css b/_static/pagination.css
new file mode 100644
index 0000000000..7584510574
--- /dev/null
+++ b/_static/pagination.css
@@ -0,0 +1,17 @@
+.pagination {
+ display: inline-block;
+}
+
+.pagination a {
+ color: black;
+ float: left;
+ padding: 8px 16px;
+ text-decoration: none;
+}
+
+.pagination a.active {
+ background-color: #2a80b9;
+ color: white;
+}
+
+.pagination a:hover:not(.active) {background-color: #ddd;}
\ No newline at end of file
diff --git a/_static/product-icon_Amazon_SageMaker_lockup_centered_squid_ink.png b/_static/product-icon_Amazon_SageMaker_lockup_centered_squid_ink.png
new file mode 100755
index 0000000000..ef72b10d52
Binary files /dev/null and b/_static/product-icon_Amazon_SageMaker_lockup_centered_squid_ink.png differ
diff --git a/_static/sagemaker-banner.png b/_static/sagemaker-banner.png
new file mode 100644
index 0000000000..0abe07a78f
Binary files /dev/null and b/_static/sagemaker-banner.png differ
diff --git a/_static/sagemaker_gears.jpg b/_static/sagemaker_gears.jpg
new file mode 100644
index 0000000000..c1783c9977
Binary files /dev/null and b/_static/sagemaker_gears.jpg differ
diff --git a/_static/search_accessories.css b/_static/search_accessories.css
new file mode 100644
index 0000000000..c7e09e1f06
--- /dev/null
+++ b/_static/search_accessories.css
@@ -0,0 +1,29 @@
+.example-badge {
+ background-color: #c63340;
+ color: white;
+ padding: 0.25rem 0.5rem;
+ text-align: center;
+ border-radius: 5px;
+ font-size: 0.8rem;
+ display: inline-block;
+}
+
+.aws-doc-badge {
+ background-color: #e18b50;
+ color: white;
+ padding: 0.25rem 0.5rem;
+ text-align: center;
+ border-radius: 5px;
+ font-size: 0.8rem;
+ display: inline-block;
+}
+
+.sdk-doc-badge {
+ background-color: #4c968f;
+ color: white;
+ padding: 0.25rem 0.5rem;
+ text-align: center;
+ border-radius: 5px;
+ font-size: 0.8rem;
+ display: inline-block;
+}
\ No newline at end of file
diff --git a/_templates/search.html b/_templates/search.html
new file mode 100644
index 0000000000..93c01c7799
--- /dev/null
+++ b/_templates/search.html
@@ -0,0 +1,56 @@
+{#
+ basic/search.html
+ ~~~~~~~~~~~~~~~~~
+
+ Template for the search page.
+
+ :copyright: Copyright 2007-2013 by the Sphinx team, see AUTHORS.
+ :license: BSD, see https://github.com/sphinx-doc/sphinx/blob/master/LICENSE for details.
+#}
+{%- extends "layout.html" %}
+{% set title = _('Search') %}
+{% set display_vcs_links = False %}
+{%- block scripts %}
+ {{ super() }}
+
+
+{%- endblock %}
+{% block footer %}
+{# this is used when loading the search index using $.ajax fails,
+ such as on Chrome for documents on localhost #}
+
+
+{{ super() }}
+{% endblock %}
+{% block body %}
+
+
+{% if search_performed %}
+ {# Translators: Search is a noun, not a verb #}
+
{{ _('Search Results') }}
+ {% if not search_results %}
+
{{ _('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve used the correct terminology.') }}
+ {% endif %}
+{% endif %}
+
+{% if search_results %}
+
+ {% for href, caption, context in search_results %}
+