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Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
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### Committing your change
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We use commit messages to update the project version number and generate changelog entries, so it's important for them to follow the right format. Valid commit messages include a prefix, separated from the rest of the message by a colon and a space. Here are a few examples:
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```
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feature: support VPC config for hyperparameter tuning
|`deprecation`| Deprecating an existing API or feature, or removing something that was previously deprecated. |
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|`feature`| Adding a new feature. |
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|`fix`| Bug fixes. |
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|`change`| Any other code change. |
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|`documentation`| Documentation changes. |
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Some of the prefixes allow abbreviation ; `break`, `feat`, `depr`, and `doc` are all valid. If you omit a prefix, the commit will be treated as a `change`.
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For the rest of the message, use imperative style and keep things concise but informative. See [How to Write a Git Commit Message](https://chris.beams.io/posts/git-commit/) for guidance.
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### Sending a pull request
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GitHub provides additional document on [creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
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Please remember to:
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* Use commit messages (and PR titles) that follow the guidelines under [Committing your change](#committing-your-change).
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* Send us a pull request, answering any default questions in the pull request interface.
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* Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
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## Finding contributions to work on
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Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels ((enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any ['help wanted'](https://github.com/aws/sagemaker-pytorch-training-toolkit/labels/help%20wanted) issues is a great place to start.
Copy file name to clipboardExpand all lines: README.rst
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SageMaker PyTorch Training Toolkit is an open-source library for using PyTorch to train models on Amazon SageMaker.
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This toolkit depends and extends the base `SageMaker Training Toolkit <https://github.com/aws/sagemaker-training-toolkit>`__ with PyTorch specific support.
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For inference, see `SageMaker PyTorch Inference Toolkit <https://github.com/aws/sagemaker-pytorch-serving-container>`__.
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For the Dockerfiles used for building SageMaker PyTorch Containers, see `AWS Deep Learning Containers <https://github.com/aws/deep-learning-containers>`__.
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