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Adrian Wälchli 5db42a413d make a special test 2021-06-29 13:09:22 +02:00
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accelerators make a special test 2021-06-29 13:09:22 +02:00
base Merge pull request #7872 from PyTorchLightning/refactor/logger-poc-changes 2021-06-08 09:04:16 -04:00
callbacks Bugfix/swa iterable dset (#8172) 2021-06-28 21:18:25 +00:00
checkpointing Iterate dictionary directly (#8155) 2021-06-27 21:55:16 +02:00
core Support state restoration of logged results 2/2(#7966) 2021-06-25 19:16:11 +00:00
deprecated_api `pytorch_lightning.loops` file structure: group by dataloader, epoch, and batch loop (#8077) 2021-06-24 23:40:46 +02:00
helpers Revert "Skip test due to 'Python bus error'" 2021-06-29 13:09:22 +02:00
loggers rename old `Trainer.train_loop` -> `Trainer.fit_loop` (#8025) 2021-06-22 11:49:32 +02:00
metrics Fix metric attribute lookup (#8181) 2021-06-28 20:17:43 +00:00
models [bugfix] Clean Validation Sanity Checking metrics (#8171) 2021-06-28 13:49:56 -04:00
overrides Remove unnecessary use of comprehension (#8149) 2021-06-27 10:00:02 +01:00
plugins fix NCCL error with non-consecutive trainer gpus (#8165) 2021-06-28 22:08:10 +02:00
profiler XLA Profiler integration (#8014) 2021-06-29 00:58:05 +05:30
trainer training loop refactor - move val loop (#8120) 2021-06-29 09:06:44 +00:00
tuner Remove unnecessary use of comprehension (#8149) 2021-06-27 10:00:02 +01:00
utilities Iterate dictionary directly (#8155) 2021-06-27 21:55:16 +02:00
README.md Delete unused CI scripts (#7152) 2021-04-22 03:07:48 +02:00
__init__.py fixing examples (#6600) 2021-03-20 18:58:59 +00:00
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conftest.py CI: fixture for global rank variable reset (#6839) 2021-04-06 09:37:17 -07:00
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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-torch1.4 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.4 .

To build other versions, select different Dockerfile.

docker image list
docker run --rm -it pytorch_lightning:devel-torch1.4 bash
docker image rm pytorch_lightning:devel-torch1.4