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‎CHANGELOG.md

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# Changelog
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## v2.54.0 (2021-08-16)
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### Features
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* add pytorch 1.5.1 eia configuration
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### Bug Fixes and Other Changes
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* issue #2253 where Processing job in Local mode would call Describe…
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## v2.53.0 (2021-08-12)
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### Features
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* support tuning step parameter range parameterization + support retry strategy in tuner
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## v2.52.2.post0 (2021-08-11)
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### Documentation Changes
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* clarify that default_bucket creates a bucket
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* Minor updates to Clarify API documentation
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## v2.52.2 (2021-08-10)
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### Bug Fixes and Other Changes
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* sklearn integ tests, remove swallowing exception on feature group delete attempt
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* sklearn integ test for custom bucket
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### Documentation Changes
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* Fix dataset_definition links
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* Document LambdaModel and LambdaPredictor classes
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## v2.52.1 (2021-08-06)
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### Bug Fixes and Other Changes
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* revert #2251 changes for sklearn processor
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## v2.52.0 (2021-08-05)
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### Features
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* processors that support multiple Python files, requirements.txt, and dependencies.
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* support step object in step depends on list
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### Bug Fixes and Other Changes
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* enable isolation while creating model from job
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* update `sagemaker.serverless` integration test
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* Use correct boto model name for RegisterModelStep properties
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## v2.51.0 (2021-08-03)
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### Features
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* add LambdaStep support for SageMaker Pipelines
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* support JsonGet for all step types
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## v2.50.1 (2021-08-02)
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### Bug Fixes and Other Changes
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* null checks for uploaded_code and entry_point
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### Documentation Changes
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* update sagemaker.estimator.EstimatorBase
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* Mark baseline as optional in KernelSHAP.
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## v2.50.0 (2021-07-28)
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### Features
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* add KIX region to image_uris
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### Bug Fixes and Other Changes
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* Rename `PredictorBase.delete_endpoint` as `PredictorBase.delete_predictor`
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* incorrect default argument for callback output parameter
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### Documentation Changes
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* Remove years from copyright boilerplate
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* Fix documentation formatting for PySpark and SparkJar processors
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### Testing and Release Infrastructure
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* enable py38 tox env
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## v2.49.2 (2021-07-21)
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### Bug Fixes and Other Changes

‎NOTICE.txt

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Amazon SageMaker Python SDK
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Copyright 2017-2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

‎README.rst

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Licensing
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~~~~~~~~~
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SageMaker Python SDK is licensed under the Apache 2.0 License. It is copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at:
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SageMaker Python SDK is licensed under the Apache 2.0 License. It is copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at:
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http://aws.amazon.com/apache2.0/
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Running tests

‎VERSION

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2.49.3.dev0
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2.54.1.dev0

‎ci-scripts/displaytime.sh

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#!/usr/bin/env bash
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# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"). You
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# may not use this file except in compliance with the License. A copy of

‎ci-scripts/queue_build.py

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# Copyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"). You
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# may not use this file except in compliance with the License. A copy of

‎doc/api/inference/model.rst

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:members:
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:undoc-members:
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:show-inheritance:
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.. autoclass:: sagemaker.serverless.model.LambdaModel
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/inference/predictors.rst

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:members:
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:undoc-members:
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:show-inheritance:
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.. autoclass:: sagemaker.serverless.predictor.LambdaPredictor
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/utility/lambda_helper.rst

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Lambda Utilities
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----------------
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.. automodule:: sagemaker.lambda_helper
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/conf.py

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# Copyright 2017-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"). You
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# may not use this file except in compliance with the License. A copy of

‎doc/overview.rst

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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>`__
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in the AWS documentation.
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********************
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Serverless Inference
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********************
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You can use the SageMaker Python SDK to perform serverless inference on Lambda.
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To deploy models to Lambda, you must complete the following prerequisites:
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- `Package your model and inference code as a container image. <https://docs.aws.amazon.com/lambda/latest/dg/images-create.html>`_
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- `Create a role that lists Lambda as a trusted entity. <https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html#permissions-executionrole-console>`_
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After completing the prerequisites, you can deploy your model to Lambda using
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the `LambdaModel`_ class.
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.. code:: python
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from sagemaker.serverless import LambdaModel
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image_uri = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-lambda-repository:latest"
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role = "arn:aws:iam::123456789012:role/MyLambdaExecutionRole"
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model = LambdaModel(image_uri=image_uri, role=role)
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predictor = model.deploy("my-lambda-function", timeout=20, memory_size=4092)
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The ``deploy`` method returns a `LambdaPredictor`_ instance. Use the
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`LambdaPredictor`_ ``predict`` method to perform inference on Lambda.
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.. code:: python
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url = "https://example.com/cat.jpeg"
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predictor.predict({"url": url}) # {'class': 'tabby'}
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Once you are done performing inference on Lambda, free the `LambdaModel`_ and
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`LambdaPredictor`_ resources using the ``delete_model`` and ``delete_predictor``
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methods.
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.. code:: python
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model.delete_model()
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predictor.delete_predictor()
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.. _LambdaModel : https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.serverless.model.LambdaModel
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.. _LambdaPredictor : https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.serverless.predictor.LambdaPredictor
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******************
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SageMaker Workflow
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******************

‎doc/workflows/pipelines/index.rst

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################
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Amazon Pipelines
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################
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###################
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SageMaker Pipelines
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###################
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SageMaker APIs for creating and managing Amazon Pipelines.
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SageMaker APIs for creating and managing SageMaker Pipelines.
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‎doc/workflows/pipelines/sagemaker.workflow.pipelines.rst

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-------------
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.. autoclass:: sagemaker.workflow.functions.JsonGet
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.. autoclass:: sagemaker.workflow.pipeline._PipelineExecution
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Pipeline Experiment Config
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--------------------------
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.. autoclass:: sagemaker.workflow.steps.CacheConfig
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‎setup.py

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License"). You
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‎src/sagemaker/__init__.py

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‎src/sagemaker/_studio.py

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‎src/sagemaker/algorithm.py

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‎src/sagemaker/amazon/__init__.py

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‎src/sagemaker/amazon/amazon_estimator.py

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‎src/sagemaker/amazon/common.py

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‎src/sagemaker/amazon/factorization_machines.py

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‎src/sagemaker/amazon/hyperparameter.py

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‎src/sagemaker/amazon/ipinsights.py

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‎src/sagemaker/amazon/kmeans.py

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‎src/sagemaker/amazon/knn.py

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‎src/sagemaker/amazon/lda.py

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‎src/sagemaker/amazon/linear_learner.py

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‎src/sagemaker/amazon/ntm.py

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‎src/sagemaker/amazon/object2vec.py

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‎src/sagemaker/amazon/pca.py

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‎src/sagemaker/amazon/randomcutforest.py

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