Merge branch 'master' into master

Author: andre-marcos-perez

doc/conf.py

@@ -96,6 +96,13 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {"http://docs.python.org/": None}
 
+# -- Options for autodoc ----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#configuration
+
+# Automatically extract typehints when specified and place them in
+# descriptions of the relevant function/method.
+autodoc_typehints = "description"
+
 # autosummary
 autosummary_generate = True
 

doc/overview.rst

@@ -1565,6 +1565,8 @@ For detailed examples of running Docker in local mode, see:
 - `TensorFlow local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_script_mode_using_shell_commands/tensorflow_script_mode_using_shell_commands.ipynb>`__.
 - `MXNet local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon_local_mode.ipynb>`__.
 - `PyTorch local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/pytorch_cnn_cifar10/pytorch_local_mode_cifar10.ipynb>`__.
+- `Pipelines local mode example notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb>`__.
+
 
 You can also find these notebooks in the **SageMaker Python SDK** section of the **SageMaker Examples** section in a notebook instance.
 For information about using sample notebooks in a SageMaker notebook instance, see `Use Example Notebooks <https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-nbexamples.html>`__

doc/workflows/pipelines/sagemaker.workflow.pipelines.rst

@@ -46,6 +46,8 @@ Entities
 
 .. autoclass:: sagemaker.workflow.entities.Expression
 
+.. autoclass:: sagemaker.workflow.entities.PipelineVariable
+
 Execution Variables
 -------------------
 

src/sagemaker/amazon/amazon_estimator.py

@@ -316,16 +316,16 @@ def __init__(
         """A collection of Amazon :class:~`Record` objects serialized and stored in S3.
 
         Args:
-            s3_data (str): The S3 location of the training data
+            s3_data (str or PipelineVariable): The S3 location of the training data
             num_records (int): The number of records in the set.
             feature_dim (int): The dimensionality of "values" arrays in the
                 Record features, and label (if each Record is labeled).
-            s3_data_type (str): Valid values: 'S3Prefix', 'ManifestFile'. If
-                'S3Prefix', ``s3_data`` defines a prefix of s3 objects to train
+            s3_data_type (str or PipelineVariable): Valid values: 'S3Prefix', 'ManifestFile'.
+                If 'S3Prefix', ``s3_data`` defines a prefix of s3 objects to train
                 on. All objects with s3 keys beginning with ``s3_data`` will be
                 used to train. If 'ManifestFile', then ``s3_data`` defines a
                 single s3 manifest file, listing each s3 object to train on.
-            channel (str): The SageMaker Training Job channel this RecordSet
+            channel (str or PipelineVariable): The SageMaker Training Job channel this RecordSet
                 should be bound to
         """
         self.s3_data = s3_data

src/sagemaker/amazon/factorization_machines.py

@@ -333,7 +333,7 @@ def __init__(
         """Initialization for FactorizationMachinesModel class.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker

src/sagemaker/amazon/ipinsights.py

@@ -236,7 +236,7 @@ def __init__(
         """Creates object to get insights on S3 model data.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker

src/sagemaker/amazon/kmeans.py

@@ -260,7 +260,7 @@ def __init__(
         """Initialization for KMeansModel class.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker

src/sagemaker/amazon/knn.py

@@ -252,7 +252,7 @@ def __init__(
         """Function to initialize KNNModel.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker

src/sagemaker/amazon/lda.py

@@ -234,7 +234,7 @@ def __init__(
         """Initialization for LDAModel class.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker

src/sagemaker/amazon/linear_learner.py

@@ -495,7 +495,7 @@ def __init__(
         """Initialization for LinearLearnerModel.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
                 ``.tar.gz`` file.
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker