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Vadim BereznyukandWilliam Falcon 50881c0b31 Check early stopping metric in the beginning of the training (#542 )

* Early stopping fix

* Update trainer.py

* Don't force validation sanity check

* fix tests

* update

* Added early_stopping check_metrics

* Updated docs

* Update docs

* Do not call early stopping when validation is disabled

Co-authored-by: William Falcon <waf2107@columbia.edu>

2020-01-23 11:12:51 -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

README.md

update org paths & convert logos (#685 )

2020-01-20 14:50:31 -05:00

requirements.txt

Update requirements.txt

2020-01-21 08:11:22 -05:00

test_amp.py

Fix amp tests (#661 )

2020-01-05 14:34:25 -05:00

test_cpu_models.py

Check early stopping metric in the beginning of the training (#542 )

2020-01-23 11:12:51 -05:00

test_gpu_models.py

Fix failing GPU tests (#722 )

2020-01-21 14:26:43 -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

Check early stopping metric in the beginning of the training (#542 )

2020-01-23 11:12:51 -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