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‎CHANGELOG.md‎

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# Changelog
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## v2.104.0 (2022-08-17)
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### Features
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* local mode executor implementation
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* Pipelines local mode setup
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* Add PT 1.12 support
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* added _AnalysisConfigGenerator for clarify
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### Bug Fixes and Other Changes
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* yaml safe_load sagemaker config
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* pipelines local mode minor bug fixes
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* add local mode integ tests
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* implement local JsonGet function
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* Add Pipeline annotation in model base class and tensorflow estimator
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* Allow users to customize trial component display names for pipeline launched jobs
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* Update localmode code to decode urllib response as UTF8
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### Documentation Changes
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* New content for Pipelines local mode
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* Correct documentation error
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## v2.103.0 (2022-08-05)
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### Features
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* AutoGluon 0.4.3 and 0.5.2 image_uris
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### Bug Fixes and Other Changes
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* Revert "change: add a check to prevent launching a modelparallel job on CPU only instances"
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* Add gpu capability to local
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* Link PyTorch 1.11 to 1.11.0
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## v2.102.0 (2022-08-04)
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### Features
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* add warnings for xgboost specific rules in debugger rules
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* Add PyTorch DDP distribution support
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* Add test for profiler enablement with debugger_hook false
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### Bug Fixes and Other Changes
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* Two letter language code must be supported
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* add a check to prevent launching a modelparallel job on CPU only instances
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* Allow StepCollection added in ConditionStep to be depended on
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* Add PipelineVariable annotation in framework models
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* skip managed spot training mxnet nb
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### Documentation Changes
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* smdistributed libraries currency updates
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## v2.101.1 (2022-07-28)
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### Bug Fixes and Other Changes

‎VERSION‎

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2.101.2.dev0
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2.104.1.dev0

‎doc/Makefile‎

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

‎doc/algorithms/index.rst‎

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

‎doc/algorithms/other/index.rst‎

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######################
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Other
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######################
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:ref:`All Pre-trained Models <all-pretrained-models>`
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.. toctree::
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:maxdepth: 2
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sagemaker.amazon.amazon_estimator

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

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‎doc/algorithms/tabular/autogluon.rst‎

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############
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AutoGluon
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############
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`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.
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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.
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The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker AutoGluon-Tabular algorithm.
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.. list-table::
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:widths: 25 25
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:header-rows: 1
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* - Notebook Title
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- Description
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* - `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>`__
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- This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular classification model.
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* - `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>`__
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- This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular regression model.
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For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
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`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
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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
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Use tab and choose Create copy.
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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/catboost.rst‎

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

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

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FactorizationMachines
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Factorization Machines
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-------------------------
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The Amazon SageMaker Factorization Machines algorithm.

‎doc/algorithms/tabular/index.rst‎

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######################
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Tabular
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######################
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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.
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.. toctree::
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:maxdepth: 2
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autogluon
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catboost
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factorization_machines
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knn
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lightgbm
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linear_learner
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tabtransformer
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xgboost
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object2vec

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