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da61398835
* Added support for non-primitive types to tensorboard logger * added EOF newline * PEP8 * Updated CHANGELOG for PR #1130. Moved _sanitize_params to base logger. Cleaned up _sanitize_params * Updated CHANGELOG for PR #1130. Moved _sanitize_params to base logger. Cleaned up _sanitize_params * changed convert_params to static method * PEP8 * Cleanup Doctest for _sanitize_params Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Removed OrderedDict import * Updated import order to conventions Co-authored-by: Manbir Gulati <manbirgulati@Manbirs-MBP.hsd1.md.comcast.net> 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