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https://github.com/wassname/pytorch-lightning.git
synced 2026-10-17 13:50:25 +08:00
Peter IzsakandWilliam Falcon
054a35312d
* Added max number of steps in Trainer * Added docstring * Fix flake8 errors * Clarified docstrings * Fixed flake8 error * Added min_steps to Trainer * Added steps and epochs test * flake8 * minor fix * fix steps test in test_trainer * Split steps test into 2 tests * Refactor steps test * Update test_trainer.py * Minor in test_trainer.py * Update test_trainer.py * Address PR comments * Minor Co-authored-by: William Falcon <waf2107@columbia.edu>
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 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