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https://github.com/wassname/pytorch-lightning.git
synced 2026-10-28 13:20:31 +08:00
JakubandWilliam Falcon
8dc8a8bfd3
* added neptune integration * added tests for NeptuneLogger, added neptune to docs * updated link to neptune support * fixed docstrings, fixed try/except in tests, changed append_tags input * fixed docstrings line lenght * bumped epoch nr in model restore tests * added tags support for single strings * fixed passing neptune token to backend * fixed project name in offline mode * added save_top_k=-1 to checkpoint callback * reformated initialization of neptune in online mode * bumped epoch nr to 4 in test_load_model_from_checkpoint * bumped epoch nr to 5 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/williamFalcon/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