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Merge branch 'master' into feat/tgi-optimum-release
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‎.github/workflows/codebuild-ci-health.yml

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name: CI Health
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on:
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schedule:
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- cron: "0 */3 * * *"
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workflow_dispatch:
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permissions:
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id-token: write # This is required for requesting the JWT
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jobs:
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codestyle-doc-tests:
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runs-on: ubuntu-latest
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steps:
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: ${{ secrets.CI_AWS_ROLE_ARN }}
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aws-region: us-west-2
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role-duration-seconds: 10800
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- name: Run Codestyle & Doc Tests
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uses: aws-actions/aws-codebuild-run-build@v1
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with:
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project-name: sagemaker-python-sdk-ci-health-codestyle-doc-tests
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unit-tests:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["py38", "py39", "py310"]
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steps:
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: ${{ secrets.CI_AWS_ROLE_ARN }}
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aws-region: us-west-2
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role-duration-seconds: 10800
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- name: Run Unit Tests
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uses: aws-actions/aws-codebuild-run-build@v1
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with:
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project-name: sagemaker-python-sdk-ci-health-unit-tests
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env-vars-for-codebuild: |
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PY_VERSION
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env:
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PY_VERSION: ${{ matrix.python-version }}
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integ-tests:
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runs-on: ubuntu-latest
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steps:
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: ${{ secrets.CI_AWS_ROLE_ARN }}
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aws-region: us-west-2
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role-duration-seconds: 10800
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- name: Run Integ Tests
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uses: aws-actions/aws-codebuild-run-build@v1
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id: codebuild
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with:
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project-name: sagemaker-python-sdk-ci-health-integ-tests
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slow-tests:
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runs-on: ubuntu-latest
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steps:
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: ${{ secrets.CI_AWS_ROLE_ARN }}
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aws-region: us-west-2
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role-duration-seconds: 10800
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- name: Run Slow Tests
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uses: aws-actions/aws-codebuild-run-build@v1
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with:
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project-name: sagemaker-python-sdk-ci-health-slow-tests
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localmode-tests:
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runs-on: ubuntu-latest
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steps:
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: ${{ secrets.CI_AWS_ROLE_ARN }}
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aws-region: us-west-2
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role-duration-seconds: 10800
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- name: Run Local Mode Tests
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uses: aws-actions/aws-codebuild-run-build@v1
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with:
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project-name: sagemaker-python-sdk-ci-health-localmode-tests
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notebook-tests:
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runs-on: ubuntu-latest
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steps:
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- name: Configure AWS Credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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role-to-assume: ${{ secrets.CI_AWS_ROLE_ARN }}
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aws-region: us-west-2
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role-duration-seconds: 10800
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- name: Run Notebook Tests
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uses: aws-actions/aws-codebuild-run-build@v1
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with:
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project-name: sagemaker-python-sdk-ci-health-notebook-tests

‎CONTRIBUTING.md

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1. Install coverage using `pip install .[test]`
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1. cd into the sagemaker-python-sdk folder: `cd sagemaker-python-sdk` or `cd /environment/sagemaker-python-sdk`
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1. Run the following tox command and verify that all code checks and unit tests pass: `tox tests/unit`
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You can also run a single test with the following command: `tox -e py310 -- -s -vv <path_to_file><file_name>::<test_function_name>`
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1. You can also run a single test with the following command: `tox -e py310 -- -s -vv <path_to_file><file_name>::<test_function_name>`
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1. You can run coverage via runcvoerage env : `tox -e runcoverage -- tests/unit` or `tox -e py310 -- tests/unit --cov=sagemaker --cov-append --cov-report xml`
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* Note that the coverage test will fail if you only run a single test, so make sure to surround the command with `export IGNORE_COVERAGE=-` and `unset IGNORE_COVERAGE`
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* Example: `export IGNORE_COVERAGE=- ; tox -e py310 -- -s -vv tests/unit/test_estimator.py::test_sagemaker_model_s3_uri_invalid ; unset IGNORE_COVERAGE`
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‎README.rst

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:target: https://sagemaker.readthedocs.io/en/stable/
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:alt: Documentation Status
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.. image:: https://github.com/benieric/sagemaker-python-sdk/actions/workflows/codebuild-ci-health.yml/badge.svg
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:target: https://github.com/benieric/sagemaker-python-sdk/actions/workflows/codebuild-ci-health.yml
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:alt: CI Health
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SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
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With the SDK, you can train and deploy models using popular deep learning frameworks **Apache MXNet** and **TensorFlow**.

‎src/sagemaker/estimator.py

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"""
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_framework_name = None
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UNSUPPORTED_DLC_IMAGE_FOR_SM_PARALLELISM = (
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"2.0.1-gpu-py310-cu121",
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"2.0-gpu-py310-cu121",
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)
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UNSUPPORTED_DLC_IMAGE_FOR_SM_PARALLELISM = ("2.0.1-gpu-py310-cu121",)
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def __init__(
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self,

‎src/sagemaker/fw_utils.py

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"2.2.0",
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]
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TORCH_DISTRIBUTED_GPU_SUPPORTED_FRAMEWORK_VERSIONS = [
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"1.13.1",
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"2.0.0",

‎tests/unit/test_fw_utils.py

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("ml.p3.16xlarge", "pytorch", "2.0.0", "py310", smdataparallel_enabled),
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("ml.p3.16xlarge", "pytorch", "2.0.1", "py310", smdataparallel_enabled),
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("ml.p3.16xlarge", "pytorch", "2.1.0", "py310", smdataparallel_enabled),
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("ml.p3.16xlarge", "pytorch", "2.2.0", "py310", smdataparallel_enabled),
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("ml.p3.16xlarge", "tensorflow", "2.4.1", "py3", smdataparallel_enabled_custom_mpi),
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("ml.p3.16xlarge", "tensorflow", "2.4.1", "py37", smdataparallel_enabled_custom_mpi),
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("ml.p3.16xlarge", "tensorflow", "2.4.3", "py3", smdataparallel_enabled_custom_mpi),
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("ml.p3.16xlarge", "pytorch", "2.0.0", "py310", smdataparallel_enabled_custom_mpi),
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("ml.p3.16xlarge", "pytorch", "2.0.1", "py310", smdataparallel_enabled_custom_mpi),
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("ml.p3.16xlarge", "pytorch", "2.1.0", "py310", smdataparallel_enabled_custom_mpi),
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("ml.p3.16xlarge", "pytorch", "2.2.0", "py310", smdataparallel_enabled_custom_mpi),
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]
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for instance_type, framework_name, framework_version, py_version, distribution in good_args:
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fw_utils._validate_smdataparallel_args(

‎tox.ini

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pip install 'torchvision==0.15.2+cpu' -f 'https://download.pytorch.org/whl/torch_stable.html'
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pip install 'dill>=0.3.8'
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pytest --cov=sagemaker --cov-append {posargs}
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{env:IGNORE_COVERAGE:} coverage report -i --fail-under=86
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pytest {posargs}
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deps = .[test]
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depends =
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{py38,py39,py310,p311}: clean
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[testenv:runcoverage]
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description = run unit tests with coverage
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commands =
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pytest --cov=sagemaker --cov-append {posargs}
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{env:IGNORE_COVERAGE:} coverage report -i --fail-under=86
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[testenv:flake8]
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skipdist = true
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skip_install = true

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