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Nic Eggert
019f612204
* Run AMP tests in their own process With opt_level="O1" (the default), AMP patches many torch functions, which breaks any tests that run afterwards. This patch introduces a pytest extension that lets tests be marked with @pytest.mark.spawn so that they are run in their own process using torch.multiprocessing.spawn so that the main python interpreter stays un-patched. Note that tests using DDP already run AMP in its own process, so they don't need this annotation. * Fix AMP tests Since AMP defaults to O1 now, DP tests no longer throw exceptions. Since AMP patches torch functions, CPU inference no longer works. Skip prediction step for AMP tests. * typo
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
The automatic travis tests ONLY run CPU-based tests. Although these cover most of the use cases, run on a 2-GPU machine to validate the full test-suite.
To run all tests do the following:
git clone https://github.com/williamFalcon/pytorch-lightning
cd pytorch-lightning
# install module locally
pip install -e .
# install dev deps
pip install -r requirements.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.
- NVIDIA-apex installed.
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
pip install coverage
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
# print coverage stats
coverage report -m
# exporting resulys
coverage xml
codecov -t 17327163-8cca-4a5d-86c8-ca5f2ef700bc -v