lightning/tests
Carlos Mocholí cf0d362658
Delete deprecated `TrainerTrainingTricksMixin` ()
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accelerators Fix distributed types support for CPUs () 2021-08-02 16:42:28 +05:30
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callbacks Deprecate LightningModule.model_size () 2021-07-30 13:53:40 +00:00
checkpointing [1 / 3] improvements to saving and loading callback state () 2021-07-29 00:12:32 +02:00
core Fix references for `ResultCollection.extra` and improve `str` and `repr` () 2021-07-30 12:47:34 +02:00
deprecated_api Delete deprecated `TrainerTrainingTricksMixin` () 2021-08-02 18:00:32 +02:00
helpers Prune deprecated metrics () 2021-07-28 16:57:31 +00:00
loggers Add `pyupgrade` to `pre-commit` () 2021-07-26 14:38:12 +02:00
loops Replace `iteration_count` and other index attributes in the loops with progress dataclasses () 2021-07-27 18:36:20 +02:00
models Un-skip some Horovod tests () 2021-08-02 17:54:05 +02:00
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plugins Add property to skip restoring optimizers and schedulers via plugin () 2021-07-31 10:08:10 +02:00
profiler Fix profiler test on Windows minimal () 2021-07-26 13:25:24 +00:00
trainer Fix distributed types support for CPUs () 2021-08-02 16:42:28 +05:30
tuner Replace `yapf` with `black` () 2021-07-26 13:37:35 +02:00
utilities Deprecate LightningModule.model_size () 2021-07-30 13:53:40 +00:00
README.md
__init__.py Replace `yapf` with `black` () 2021-07-26 13:37:35 +02:00
conftest.py Add `pyupgrade` to `pre-commit` () 2021-07-26 14:38:12 +02:00
mnode_tests.txt
special_tests.sh support launching Lightning ddp with traditional command () 2021-07-14 11:25:36 +00:00

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