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Copy file name to clipboardExpand all lines: .azure-pipelines/gpu-tests.yml
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- bash: |
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python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)"
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pip install fairscale>=0.3.4
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pip install deepspeed>=0.4.0 -U
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pip install "deepspeed>=0.4.3, !=0.4.4" # FIXME: bug with 0.4.4
Copy file name to clipboardExpand all lines: .github/BECOMING_A_CORE_CONTRIBUTOR.md
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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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## Code of conduct
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First and foremost, you'll be evaluated against [these core values](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/.github/CONTRIBUTING.md). Any code we commit or feature we add needs to align with those core values.
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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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With that said, the Lightning team will be diverse and a reflection of an inclusive AI community. You don't have to be an engineer to contribute! Scientists with great usability intuition and PyTorch ninja skills are welcomed!
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## Responsibilities:
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The responsibilities mainly revolve around 3 things.
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### Github issues
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- Here we want to help users have an amazing experience. These range from questions from new people getting into DL to questions from researchers about doing something esoteric with Lightning
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Often, these issues require some sort of bug fix, document clarification or new functionality to be scoped out.
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Often, these issues require some sort of bug fix, document clarification or new functionality to be scoped out.
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- To become a core member you must resolve at least 10 Github issues which align with the API design goals for Lightning. By the end of these 10 issues I should feel comfortable in the way you answer user questions
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Pleasant/helpful tone.
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Pleasant/helpful tone.
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- Can abstract from that issue or bug into functionality that might solve other related issues or makes the platform more flexible.
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- Ask yourself, could a non-engineer understand what’s happening here?
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- Make sure new tests are written
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- Is this NECESSARY for Lightning? There are some PRs which are just purely about adding engineering complexity which have no place in Lightning.
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Guidance
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Guidance
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- Some other PRs are for people who are wanting to get involved and add something unnecessary. We do want their help though! So don’t approve the PR, but direct them to a Github issue that they might be interested in helping with instead!
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- To be considered for core contributor, please review 10 PRs and help the authors land it on master. Once you've finished the review, ping me
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for a sanity check. At the end of 10 PRs if your PR reviews are inline with expectations described above, then you can merge PRs on your own going forward,
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otherwise we'll do a few more until we're both comfortable :)
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for a sanity check. At the end of 10 PRs if your PR reviews are inline with expectations described above, then you can merge PRs on your own going forward,
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otherwise we'll do a few more until we're both comfortable :)
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### Project directions
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There are some big decisions which the project must make. For these I expect core contributors to have something meaningful to add if it’s their area of expertise.
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### Diversity
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Lightning should reflect the broader community it serves. As such we should have scientists/researchers from
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different fields contributing!
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The first 5 core contributors will fit this profile. Thus if you overlap strongly with experiences and expertise as someone else on the team, you might have to wait until the next set of contributors are added.
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### Summary: Requirements to apply
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The goal is to be inline with expectations for solving issues by the last one so you can do them on your own. If not, I might ask you to solve a few more specific ones.
Copy file name to clipboardExpand all lines: .github/CONTRIBUTING.md
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Have a favorite feature from other libraries like fast.ai or transformers? Those should just work with lightning as well. Grab your favorite model or learning rate scheduler from your favorite library and run it in Lightning.
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- Add details on how to reproduce the issue - a minimal test case is always best, colab is also great.
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Note, that the sample code shall be minimal and if needed with publicly available data.
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2. Try to fix it or recommend a solution. We highly recommend to use test-driven approach:
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1. Try to fix it or recommend a solution. We highly recommend to use test-driven approach:
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- Convert your minimal code example to a unit/integration test with assert on expected results.
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- Start by debugging the issue... You can run just this particular test in your IDE and draft a fix.
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- Verify that your test case fails on the master branch and only passes with the fix applied.
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3. Submit a PR!
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1. Submit a PR!
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_**Note**, even if you do not find the solution, sending a PR with a test covering the issue is a valid contribution, and we can help you or finish it with you :]_
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_**Note**, even if you do not find the solution, sending a PR with a test covering the issue is a valid contribution, and we can help you or finish it with you :\]_
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### New Features:
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1. Submit a GitHub issue - describe what is the motivation of such feature (adding the use case, or an example is helpful).
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2. Determine the feature scope with us.
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3. Submit a PR! We recommend test driven approach to adding new features as well:
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1. Determine the feature scope with us.
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1. Submit a PR! We recommend test driven approach to adding new features as well:
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- Write a test for the functionality you want to add.
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- Write the functional code until the test passes.
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4. Add/update the relevant tests!
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1. Add/update the relevant tests!
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-[This PR](https://github.com/PyTorchLightning/pytorch-lightning/pull/2671) is a good example for adding a new metric, and [this one for a new logger](https://github.com/PyTorchLightning/pytorch-lightning/pull/2721).
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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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