lightning/.github/workflows

Continuous Integration and Delivery

Brief description of all our automation tools used for boosting development performances.

Unit and Integration Testing

workflow file	action	accelerator
.github/workflows/ci-tests-fabric.yml	Run all tests except for accelerator-specific and standalone.	CPU
.github/workflows/ci-tests-pytorch.yml	Run all tests except for accelerator-specific and standalone.	CPU
.github/workflows/ci-tests-data.yml	Run unit and integration tests with data pipelining.	CPU
.azure-pipelines/gpu-tests-fabric.yml	Run only GPU-specific tests, standalone*, and examples.	GPU
.azure-pipelines/gpu-tests-pytorch.yml	Run only GPU-specific tests, standalone*, and examples.	GPU
.azure-pipelines/gpu-benchmarks.yml	Run speed/memory benchmarks for parity with vanila PyTorch.	GPU
.github/workflows/ci-flagship-apps.yml	Run end-2-end tests with full applications, including deployment to the production cloud.	CPU
.github/workflows/ci-tests-pytorch.yml	Run all tests except for accelerator-specific, standalone and slow tests.	CPU
.github/workflows/tpu-tests.yml	Run only TPU-specific tests. Requires that the PR title contains '[TPU]'	TPU

* Each standalone test needs to be run in separate processes to avoid unwanted interactions between test cases.

• Accelerators used in CI

  • GPU: 2 x NVIDIA RTX 3090
  • TPU: Google TPU v4-8

• To check which versions of Python or PyTorch are used for testing in our CI, see the corresponding workflow files or checkgroup config file at .github/checkgroup.yml.

Documentation

workflow file	action
.github/workflows/docs-build.yml	Run doctest, linkcheck and full HTML build.
.github/workflows/ci-rtfd.yml	Append link to the PR description with temporaty ReadTheDocs build docs.
.github/workflows/ci-check-md-links.yml .github/markdown.links.config.json	Validate links in markdown files.

Code Quality

workflow file	action
.codecov.yml	Measure test coverage with codecov.io
.github/workflows/code-checks.yml	Check Python typing with MyPy.
.github/workflows/ci-schema.yml	Validate the syntax of workflow files.

Others

workflow file	action
.github/workflows/docker-build.yml	Build docker images used for testing in CI. If run on nightly schedule, push to the Docker Hub.
.github/workflows/ci-pkg-install.yml	Test if pytorch-lightning is successfully installed using pip.
.github/workflows/ci-checkpoints.yml	Build checkpoints that are will be tested on release to ensure backwards-compatibility

The published Docker Hub project is https://hub.docker.com/r/pytorchlightning/pytorch_lightning.

Deployment

workflow file	action
.github/workflows/docs-build.yml	Build the docs for each project and puch it to GCS with automatics deployment.
.github/workflows/docker-build.yml	Build docker images used for releases and push them to the Docker Hub.
.github/workflows/release-pkg.yml	Publish a release to PyPI and upload to the GH release page as artifact.
.github/workflows/_legacy-checkpoints.yml	Add on request generate legacy checkpoints and upload them to AWS S3.

Bots

workflow file	action
.github/mergify.yml	Label PRs as conflicts or ready, and request reviews if needed.
.github/stale.yml	Close inactive issues/PRs sometimes after adding the "won't fix" label to them.
.github/workflows/probot-auto-cc.yml .github/lightning-probot.yml	Notify maintainers of interest depending on labels added to an issue We utilize lightning-probot forked from PyTorch’s probot.
.github/workflows/probot-check-group.yml .github/checkgroup.yml	Checks whether the relevant jobs were successfully run based on the changed files in the PR
.pre-commit-config.yaml	It applies a set of linters and formatters and can be registered with your local dev. If needed bot pushc changes to each PRs.
.github/workflows/labeler-pr.yml, .github/label-change.yml	Integration of https://github.com/actions/labeler
.github/workflows/labeler-issue.yml	Parse user provided lightning version and set it as label.