86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
# PyTorch-Lightning Tests
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Most of the tests in PyTorch Lightning train a [BoringModel](https://github.com/Lightning-AI/lightning/blob/master/src/lightning/pytorch/demos/boring_classes.py) under various trainer conditions (ddp, amp, etc...). Want to add a new test case and not sure how? [Talk to us!](https://www.pytorchlightning.ai/community)
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## Running tests
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**Local:** Testing your work locally will help you speed up the process since it allows you to focus on particular (failing) test-cases.
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To setup a local development environment, install both local and test dependencies:
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```bash
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# clone the repo
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git clone https://github.com/Lightning-AI/lightning.git
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cd lightning
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# install required dependencies
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export PACKAGE_NAME=pytorch
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python -m pip install ".[dev, examples]"
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# install pre-commit (optional)
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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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```bash
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bash .actions/pull_legacy_checkpoints.sh
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```
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Note: These checkpoints are generated to set baselines for maintaining backward compatibility with legacy versions of PyTorch Lightning. Details of checkpoints for back-compatibility can be found [here](https://github.com/Lightning-AI/lightning/blob/master/tests/legacy/README.md).
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You can run the full test suite in your terminal via this make script:
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```bash
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make test
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```
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Note: if your computer does not have multi-GPU or TPU these tests are skipped.
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**GitHub Actions:** For convenience, you can also use your own GHActions building which will be triggered with each commit.
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This is useful if you do not test against all required dependency versions.
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**Docker:** Another option is to utilize the [pytorch lightning cuda base docker image](https://hub.docker.com/repository/docker/pytorchlightning/pytorch_lightning/tags?page=1&name=cuda). You can then run:
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```bash
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python -m pytest src/lightning/pytorch tests/tests_pytorch -v
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```
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You can also run a single test as follows:
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```bash
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python -m pytest -v tests/tests_pytorch/trainer/test_trainer_cli.py::test_default_args
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```
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### Conditional Tests
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To test models that require GPU make sure to run the above command on a GPU machine.
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The GPU machine must have at least 2 GPUs to run distributed tests.
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Note that this setup will not run tests that require specific packages installed
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You can rely on our CI to make sure all these tests pass.
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### Standalone Tests
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There are certain standalone tests, which you can run using:
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```bash
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./tests/run_standalone_tests.sh tests/tests_pytorch/trainer/
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# or run a specific test
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./tests/run_standalone_tests.sh -k test_multi_gpu_model_ddp
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```
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## Running Coverage
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Make sure to run coverage on a GPU machine with at least 2 GPUs.
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```bash
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# generate coverage (coverage is also installed as part of dev dependencies)
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coverage run --source src/lightning/pytorch -m pytest src/lightning/pytorch tests/tests_pytorch -v
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# print coverage stats
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coverage report -m
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# exporting results
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coverage xml
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```
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