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* ignore in setup * show report * abs imports * abstract pass * cover loggers * doctest trains * locals * pass * revert tensorboard * use tensorboardX * revert tensorboardX * fix trains * Add TrainsLogger.set_credentials (#1179) * Add TrainsLogger.set_credentials to control trains server configuration and authentication from code. Sync trains package version. Fix CI Trains tests * Add global TrainsLogger set_bypass_mode (#1187) * Add global TrainsLogger set_bypass_mode skips all external communication Co-authored-by: bmartinn <> * rm some no-cov Co-authored-by: Martin.B <51887611+bmartinn@users.noreply.github.com>
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/PyTorchLightning/pytorch-lightning
cd pytorch-lightning
# install module locally
pip install -e .
# install dev deps
pip install -r tests/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 (coverage is also installed as part of dev dependencies under tests/requirements.txt)
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