History

Jeremy Jordan 1cf430f7bc new feature for profiling training runs (#782 ) * initial implementation * formatting, pass through profiler, docstring * call profiler during training * add initial tests * report stats when training is done * fix formatting * error handling, bugfix in passthroughprofiler * finish documenting profiler arg in Trainer * relax required precision for profiling tests * option to dump cProfiler results to text file * use logging, format with black * include profiler in docs * improved logging and better docs * appease the linter * better summaries, wrapper for iterables * fix typo * allow profiler=True creation * more documentation * add tests for advanced profiler * Update trainer.py * make profilers accessible in pl.utilities * reorg profiler files * change import for profiler tests Co-authored-by: William Falcon <waf2107@columbia.edu>		2020-02-06 22:01:21 -05:00
..
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	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	rename logging -> loggers (#767 )	2020-02-01 15:47:58 -05:00
test_profiler.py	new feature for profiling training runs (#782 )	2020-02-06 22:01:21 -05:00
test_restore_models.py	Resolve some codefactor issues (#756 )	2020-02-01 18:44:05 -05:00
test_trainer.py	Resolve some codefactor issues (#756 )	2020-02-01 18:44:05 -05:00
utils.py	rename logging -> loggers (#767 )	2020-02-01 15:47:58 -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