Merge branch 'master' into fix/remove-env-vars

Author: BasilBeirouti

CHANGELOG.md

@@ -1,5 +1,29 @@
 # Changelog
 
+## v2.104.0 (2022-08-17)
+
+### Features
+
+ * local mode executor implementation
+ * Pipelines local mode setup
+ * Add PT 1.12 support
+ * added _AnalysisConfigGenerator for clarify
+
+### Bug Fixes and Other Changes
+
+ * yaml safe_load sagemaker config
+ * pipelines local mode minor bug fixes
+ * add local mode integ tests
+ * implement local JsonGet function
+ * Add Pipeline annotation in model base class and tensorflow estimator
+ * Allow users to customize trial component display names for pipeline launched jobs
+ * Update localmode code to decode urllib response as UTF8
+
+### Documentation Changes
+
+ * New content for Pipelines local mode
+ * Correct documentation error
+
 ## v2.103.0 (2022-08-05)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.103.1.dev0
+2.104.1.dev0

doc/Makefile

@@ -3,7 +3,7 @@
 
 # You can set these variables from the command line.
 SPHINXOPTS    = -W
-SPHINXBUILD   = python -msphinx
+SPHINXBUILD   = python3 -msphinx
 SPHINXPROJ    = sagemaker
 SOURCEDIR     = .
 BUILDDIR      = _build

doc/algorithms/index.rst

@@ -1,20 +1,15 @@
 ######################
-First-Party Algorithms
+Built-in Algorithms
 ######################
 
 Amazon SageMaker provides implementations of some common machine learning algorithms optimized for GPU architecture and massive datasets.
 
 .. toctree::
     :maxdepth: 2
 
-    sagemaker.amazon.amazon_estimator
-    factorization_machines
-    ipinsights
-    kmeans
-    knn
-    lda
-    linear_learner
-    ntm
-    object2vec
-    pca
-    randomcutforest
+    tabular/index
+    text/index
+    time_series/index
+    unsupervised/index
+    vision/index
+    other/index

doc/algorithms/other/index.rst

@@ -0,0 +1,10 @@
+######################
+Other
+######################
+
+:ref:`All Pre-trained Models <all-pretrained-models>`
+
+.. toctree::
+    :maxdepth: 2
+
+    sagemaker.amazon.amazon_estimator

doc/algorithms/sagemaker.amazon.amazon_estimator.rst renamed to doc/algorithms/other/sagemaker.amazon.amazon_estimator.rst

@@ -0,0 +1,28 @@
+############
+AutoGluon
+############
+
+`AutoGluon-Tabular <https://auto.gluon.ai/stable/index.html>`__ is a popular open-source AutoML framework that trains highly accurate machine learning models on an unprocessed tabular dataset.
+Unlike existing AutoML frameworks that primarily focus on model and hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers.
+
+
+The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker AutoGluon-Tabular algorithm.
+
+.. list-table::
+   :widths: 25 25
+   :header-rows: 1
+
+   * - Notebook Title
+     - Description
+   * - `Tabular classification with Amazon SageMaker AutoGluon-Tabular algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/autogluon_tabular/Amazon_Tabular_Classification_AutoGluon.ipynb>`__
+     - This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular classification model.
+   * - `Tabular regression with Amazon SageMaker AutoGluon-Tabular algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/autogluon_tabular/Amazon_Tabular_Regression_AutoGluon.ipynb>`__
+     - This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular regression model.
+
+
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
+Use tab and choose Create copy.
+
+For detailed documentation, please refer to the `Sagemaker AutoGluon-Tabular Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/autogluon-tabular.html>`__.

doc/algorithms/tabular/autogluon.rst

@@ -0,0 +1,37 @@
+############
+CatBoost
+############
+
+
+`CatBoost <https://catboost.ai/>`__ is a popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT)
+algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of
+estimates from a set of simpler and weaker models.
+
+CatBoost introduces two critical algorithmic advances to GBDT:
+
+* The implementation of ordered boosting, a permutation-driven alternative to the classic algorithm
+
+* An innovative algorithm for processing categorical features
+
+Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing
+implementations of gradient boosting algorithms.
+
+The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker CatBoost algorithm.
+
+.. list-table::
+   :widths: 25 25
+   :header-rows: 1
+
+   * - Notebook Title
+     - Description
+   * - `Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Classification_LightGBM_CatBoost.ipynb>`__
+     - This notebook demonstrates the use of the Amazon SageMaker CatBoost algorithm to train and host a tabular classification model.
+   * - `Tabular regression with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Regression_LightGBM_CatBoost.ipynb>`__
+     - This notebook demonstrates the use of the Amazon SageMaker CatBoost algorithm to train and host a tabular regression model.
+
+For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
+`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
+instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
+Use tab and choose Create copy.
+
+For detailed documentation, please refer to the `Sagemaker CatBoost Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/catboost.html>`__.

doc/algorithms/tabular/catboost.rst

@@ -1,4 +1,4 @@
-FactorizationMachines
+Factorization Machines
 -------------------------
 
 The Amazon SageMaker Factorization Machines algorithm.

doc/algorithms/factorization_machines.rst renamed to doc/algorithms/tabular/factorization_machines.rst

@@ -0,0 +1,18 @@
+######################
+Tabular
+######################
+
+Amazon SageMaker provides built-in algorithms that are tailored to the analysis of tabular data. The built-in SageMaker algorithms for tabular data can be used for either classification or regression problems.
+
+.. toctree::
+    :maxdepth: 2
+
+    autogluon
+    catboost
+    factorization_machines
+    knn
+    lightgbm
+    linear_learner
+    tabtransformer
+    xgboost
+    object2vec