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| accelerators | ||
| base | ||
| benchmarks | ||
| callbacks | ||
| checkpointing | ||
| core | ||
| deprecated_api | ||
| helpers | ||
| lite | ||
| loggers | ||
| loops | ||
| models | ||
| overrides | ||
| plugins | ||
| profiler | ||
| trainer | ||
| tuner | ||
| utilities | ||
| README.md | ||
| __init__.py | ||
| conftest.py | ||
| mnode_tests.txt | ||
| standalone_tests.sh | ||
README.md
PyTorch-Lightning Tests
Most PL tests train a full MNIST model under various trainer conditions (ddp, ddp2+amp, etc...). This provides testing for most combinations of important settings. The tests expect the model to perform to a reasonable degree of testing accuracy to pass.
Running tests
git clone https://github.com/PyTorchLightning/pytorch-lightning
cd pytorch-lightning
# install dev deps
pip install -r requirements/devel.txt
# run tests
py.test -v
To test models that require GPU make sure to run the above command on a GPU machine. The GPU machine must have at least 2 GPUs to run distributed tests.
Note that this setup will not run tests that require specific packages installed such as Horovod, FairScale, NVIDIA/apex, NVIDIA/DALI, etc. You can rely on our CI to make sure all these tests pass.
Running Coverage
Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed.
cd pytorch-lightning
# generate coverage (coverage is also installed as part of dev dependencies under requirements/devel.txt)
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v
# print coverage stats
coverage report -m
# exporting results
coverage xml
Building test image
You can build it on your own, note it takes lots of time, be prepared.
git clone <git-repository>
docker image build -t pytorch_lightning:devel-torch1.9 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.9 .
To build other versions, select different Dockerfile.
docker image list
docker run --rm -it pytorch_lightning:devel-torch1.9 bash
docker image rm pytorch_lightning:devel-torch1.9