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Merge branch 'master' into fix/minor-jumpstart-dev-ex-improvements
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‎doc/frameworks/pytorch/using_pytorch.rst

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---------------------------
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When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvision``, and ``numpy``.
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For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`_.
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If there are other packages you want to use with your script, you can include a ``requirements.txt`` file in the same directory as your training script to install other dependencies at runtime. Both ``requirements.txt`` and your training script should be put in the same folder. You must specify this folder in ``source_dir`` argument when creating PyTorch estimator.
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The function of installing packages using ``requirements.txt`` is supported for all PyTorch versions during training. When serving a PyTorch model, support for this function varies with PyTorch versions. For PyTorch 1.3.1 or newer, ``requirements.txt`` must be under folder ``code``. The SageMaker PyTorch Estimator will automatically save ``code`` in ``model.tar.gz`` after training (assuming you set up your script and ``requirements.txt`` correctly as stipulated in the previous paragraph). In the case of bringing your own trained model for deployment, you must save ``requirements.txt`` under folder ``code`` in ``model.tar.gz`` yourself or specify it through ``dependencies``. For PyTorch 1.2.0, ``requirements.txt`` is not supported for inference. For PyTorch 0.4.0 to 1.1.0, ``requirements.txt`` must be in ``source_dir``.
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A ``requirements.txt`` file is a text file that contains a list of items that are installed by using ``pip install``. You can also specify the version of an item to install. For information about the format of a ``requirements.txt`` file, see `Requirements Files <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ in the pip documentation.
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If you were to use your own custom Docker Image, the `SageMaker Python SDK` and the `SageMaker Training Toolkit <https://github.com/aws/sagemaker-training-toolkit/>`__ need to be installed.
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To do so, you can add the following lines to your ``requirements.txt`` file:
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.. code:: text
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sagemaker
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sagemaker-training
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Deep Learning Framework-Specific SageMaker Toolkits and Containers
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------------------------------------------------------------------
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Framework-specific Toolkits exist. You might want to use them in your applications for framework-specific features.
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For Training Toolkits, see:
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- `SageMaker PyTorch Training Toolkit <https://github.com/aws/sagemaker-pytorch-training-toolkit>`__
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- `SageMaker MXNet Training Toolkit <https://github.com/aws/sagemaker-mxnet-training-toolkit>`__
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- `SageMaker TensorFlow Training Toolkit <https://github.com/aws/sagemaker-tensorflow-training-toolkit>`__
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For Inference Toolkits, see:
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- `SageMaker PyTorch Inference Toolkit <https://github.com/aws/sagemaker-pytorch-inference-toolkit>`__
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- `SageMaker MXNet Inference Toolkit <https://github.com/aws/sagemaker-mxnet-training-toolkit>`__
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- `SageMaker TensorFlow Inference Toolkit <https://github.com/aws/sagemaker-tensorflow-inference-toolkit>`__
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- `SageMaker HuggingFace Inference Toolkit <https://github.com/aws/sagemaker-huggingface-inference-toolkit>`__
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Moreover, for more information on the container runtime environment, including specific framework versions and configurations,
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see `AWS Deep Learning Containers <https://github.com/aws/deep-learning-containers/>`_. More specifically, see:
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- `Images for PyTorch <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`__
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- `Images for MXNet <https://github.com/aws/deep-learning-containers/tree/master/mxnet>`__
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- `Images for TensorFlow <https://github.com/aws/deep-learning-containers/tree/master/tensorflow>`__
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- `Images for HuggingFace <https://github.com/aws/deep-learning-containers/tree/master/huggingface>`__
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Create an Estimator
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===================
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‎inference-experience-dev-tester.sh

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‎requirements/extras/test_requirements.txt

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black==22.3.0
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stopit==1.1.2
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# Update tox.ini to have correct version of airflow constraints file
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apache-airflow==2.7.2
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apache-airflow==2.7.3
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apache-airflow-providers-amazon==7.2.1
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attrs>=23.1.0,<24
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fabric==2.6.0
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sagemaker-feature-store-pyspark-3.3
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# TODO find workaround
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xgboost>=1.6.2,<=1.7.6
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pillow>=9.5.0,<=10.0.0
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pillow>=10.0.1,<=11
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transformers==4.32.0
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sentencepiece==0.1.99
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# https://github.com/triton-inference-server/server/issues/6246

‎src/sagemaker/image_uri_config/djl-neuronx.json

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"versions": {
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"il-central-1": "780543022126",
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"ap-east-1": "871362719292",
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"ap-northeast-1": "763104351884",
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"ap-northeast-2": "763104351884",
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"ap-northeast-3": "364406365360",
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"ap-south-1": "763104351884",
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"ap-southeast-1": "763104351884",
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"ap-southeast-2": "763104351884",
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"ap-southeast-3": "907027046896",
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"ca-central-1": "763104351884",
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"cn-north-1": "727897471807",
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"cn-northwest-1": "727897471807",
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"eu-north-1": "763104351884",
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"eu-west-2": "763104351884",
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"eu-south-1": "692866216735",
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"me-south-1": "217643126080",
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"us-west-1": "763104351884",
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},
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"repository": "djl-inference",
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"tag_prefix": "0.25.0-neuronx-sdk2.15.0"
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},
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"0.24.0": {
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"cn-north-1": "727897471807",
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‎src/sagemaker/image_uri_config/huggingface-neuron.json

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‎src/sagemaker/image_uri_config/huggingface-neuronx.json

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