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Jirka Borovec ea59a99426 update org paths & convert logos (#685 ) * fix typos * update org paths * update links from READMe to docs * add svg logo * add svg logo-text * update logos * testing temp paths * prune links from readme * optimize imports * update logo * update paths in README * missing imports		2020-01-20 14:50:31 -05:00
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README.md	update org paths & convert logos (#685 )	2020-01-20 14:50:31 -05:00
__init__.py	added init to test folder	2019-07-24 21:32:31 -04:00
conftest.py	Fix amp tests (#661 )	2020-01-05 14:34:25 -05:00
debug.py	rename variables nb -> num (#567 )	2019-12-04 06:57:10 -05:00
requirements.txt	fixing TensorBoard (#687 )	2020-01-16 07:22:29 -05:00
test_amp.py	Fix amp tests (#661 )	2020-01-05 14:34:25 -05:00
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test_gpu_models.py	Simplify variables: step, epoch, max_epochs, min_epochs (#589 )	2019-12-07 08:50:21 -05:00
test_logging.py	add version_ prefix to log_dir (#706 )	2020-01-18 07:17:53 -05:00
test_restore_models.py	unify model test acc (#696 )	2020-01-17 05:50:26 -05:00
test_trainer.py	clean v2 docs (#691 )	2020-01-17 06:03:31 -05:00
utils.py	unify model test acc (#696 )	2020-01-17 05:50:26 -05:00

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 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