969e929a48
* remove deprecated args to learning rate step function * step based scheduler * mixing models for testing * fix styling * tests * update documentation * smaller fix * update to dict structure * updated test * update documentation * update CHANGELOG.md * fix styling * fix problems with trainer io * fix tests * simplification of code * fix styling * change from batch to step * update to tests * fix styling * fixed some logic * Update pytorch_lightning/core/lightning.py * duplicated test * fix test on amp * small update to tests * added monitor key for ReduceLROnPlateau * Update trainer.py * Update training_loop.py * fix test after introducing monitor keyword Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: William Falcon <waf2107@columbia.edu> |
||
|---|---|---|
| .. | ||
| loggers | ||
| models | ||
| trainer | ||
| README.md | ||
| __init__.py | ||
| conftest.py | ||
| requirements.txt | ||
| test_amp.py | ||
| test_cpu_models.py | ||
| test_gpu_models.py | ||
| test_profiler.py | ||
| test_restore_models.py | ||
README.md
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