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Copy file name to clipboardExpand all lines: .github/BECOMING_A_CORE_CONTRIBUTOR.md
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# How to become a core contributor
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# How to Become a Core Contributor
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Thanks for your interest in joining the Lightning team! We’re a rapidly growing project which is poised to become the go-to framework for DL researchers!
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As a core maintainer, you will have a strong say in the direction of the project. Big changes will require a majority of maintainers to agree.
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Our development is fully open, so you can still raise your voice just by commenting on issues and pull requests! Doing so is a big step in becoming part of core.
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## Code of conduct
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## Code of Conduct
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First and foremost, you'll be evaluated against [these core values](CONTRIBUTING.md). Any code we commit or feature we add needs to align with those core values.
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Any collaboration and communication must adhere to our [code of conduct](CODE_OF_CONDUCT.md).
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## The bar for joining the team
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## The Bar for Joining the Team
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Lightning is being used to solve really hard problems at the top AI labs in the world. As such, the bar for adding team members is extremely high. Candidates must have solid engineering skills, have a good eye for user experience, and must be a power user of Lightning and PyTorch.
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Here, we describe general expectations from core contributors:
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### Github issues
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### Github Issues
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- Our community is the main motivation for our work. Help them have an amazing experience. Issues range from answering questions from new people getting into deep learning to helping researchers doing something esoteric.
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They often require some sort of bug fix, document clarification, or new functionality to be scoped out. You can help them solve their issues and guide them to completion.
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- Help out with critical bugs. Nobody likes bugs so you'll be a hero if you fix them!
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### Pull requests (PRs)
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### Pull Requests (PRs)
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- Pull requests are the evolutionary mechanism of Lightning, so quality is extremely important. Make sure contributors adhere to the guidelines described in the [contributing section](CONTRIBUTING.md#Pull-request).
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# Contributing
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Welcome to the PyTorch Lightning community! We're building the most advanced research platform on the planet to implement the latest, best practices that the amazing PyTorch team rolls out!
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Welcome to the PyTorch Lightning community! We're building the most advanced research platform on the planet to implement the latest, best practices
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and integrations that the amazing PyTorch team and other research organization rolls out!
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If you are new to open source, check out [this blog to get started with your first Open Source contribution](https://devblog.pytorchlightning.ai/quick-contribution-guide-86d977171b3a).
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Simplify the API as much as possible from the user perspective.
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Any additions or improvements should minimize the things the user needs to remember.
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For example: One benefit of the validation_step is that the user doesn't have to remember to set the model to .eval().
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For example: One benefit of the `validation_step` is that the user doesn't have to remember to set the model to .eval().
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This helps users avoid all sorts of subtle errors.
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## Lightning Design Principles
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We encourage all sorts of contributions you're interested in adding! When coding for lightning, please follow these principles.
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We encourage all sorts of contributions you're interested in adding! When coding for Lightning, please follow these principles.
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### No PyTorch Interference
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Want to keep Lightning healthy? Love seeing those green tests? So do we! How to we keep it that way? We write tests! We value tests contribution even more than new features.
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Most of the tests in PyTorch Lightning train a trial MNIST model under various trainer conditions (ddp, ddp2+amp, etc...). The tests expect the model to perform to a reasonable degree of testing accuracy to pass. Want to add a new test case and not sure how? [Talk to us!](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)
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Most of the tests in PyTorch Lightning train a random `BoringModel`under various trainer conditions (ddp, ddp2+amp, etc...). Want to add a new test case and not sure how? [Talk to us!](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)
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```bash
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python -m pip install ".[dev, examples]"
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python -m pip install pre-commit
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pre-commit install
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```
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Additionally, for testing backward compatibility with older versions of PyTorch Lightning, you also need to download all saved version-checkpoints from the public AWS storage. Run the following script to get all saved version-checkpoints:
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