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Adrian WälchliandJirka Borovec
732eaee4d7
* check for nan values * test nan detection on loss * sys.exit * whitespace * detect nan and inf values in loss and params * update * added documentation * moved detect nan to training loop, remove flag for print * blank line * test * rename * deprecate print_nan_grads * deprecated print_nan_grads * remove unused imports * update changelog * fix line too long * correct deprecated version Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * raise exception instead of sysexit Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * raise exception instead of sysexit Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update pytorch_lightning/trainer/training_tricks.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update pytorch_lightning/trainer/training_tricks.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * fix test Co-authored-by: Jirka Borovec <Borda@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