54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
# PyTorch-Lightning Tests
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Most PL tests train a full MNIST model under various trainer conditions (ddp, ddp2+amp, etc...).
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This provides testing for most combinations of important settings.
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The tests expect the model to perform to a reasonable degree of testing accuracy to pass.
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## Running tests
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```bash
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git clone https://github.com/PyTorchLightning/pytorch-lightning
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cd pytorch-lightning
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# install dev deps
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pip install -r requirements/devel.txt
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# run tests
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py.test -v
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```
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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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such as Horovod, FairScale, NVIDIA/apex, NVIDIA/DALI, etc.
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You can rely on our CI to make sure all these tests pass.
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## Running Coverage
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Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed.
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```bash
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cd pytorch-lightning
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# generate coverage (coverage is also installed as part of dev dependencies under requirements/devel.txt)
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coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -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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## Building test image
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You can build it on your own, note it takes lots of time, be prepared.
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```bash
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git clone <git-repository>
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docker image build -t pytorch_lightning:devel-torch1.9 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.9 .
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```
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To build other versions, select different Dockerfile.
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```bash
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docker image list
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docker run --rm -it pytorch_lightning:devel-torch1.9 bash
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docker image rm pytorch_lightning:devel-torch1.9
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```
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