lightning/tests
Alexey Karnachev 4c34d16a34
Fixed configure optimizer from dict without "scheduler" key (#1443)
* `configure_optimizer` from dict with only "optimizer" key. bug fixed

* autopep8

* pep8speaks suggested fixes

* CHANGELOG.md upd
2020-04-10 11:43:06 -04:00
..
base add rank warning (#1428) 2020-04-09 14:05:46 -04:00
loggers Added accumulation of loggers' metrics for the same steps (#1278) 2020-04-08 08:35:47 -04:00
models add rank warning (#1428) 2020-04-09 14:05:46 -04:00
trainer Fixed configure optimizer from dict without "scheduler" key (#1443) 2020-04-10 11:43:06 -04:00
Dockerfile CI: split tests-examples (#990) 2020-03-25 07:46:27 -04:00
README.md CI: split tests-examples (#990) 2020-03-25 07:46:27 -04:00
__init__.py faster CI testing (#1323) 2020-04-02 12:28:44 -04:00
collect_env_details.py fix incomplete RunningMean (#1309) 2020-03-30 18:28:31 -04:00
conftest.py Fix amp tests (#661) 2020-01-05 14:34:25 -05:00
install_AMP.sh CI: split tests-examples (#990) 2020-03-25 07:46:27 -04:00
requirements.txt Add MNIST dataset & drop torchvision dep. from tests (#986) 2020-03-30 18:25:37 -04:00
test_deprecated.py Simplify progress bar args (#1108) 2020-04-03 00:53:00 +02:00
test_profiler.py Profiler summary (#1259) 2020-03-31 08:57:48 -04:00

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 AMP support
bash tests/install_AMP.sh

# 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:

  1. At least 2 GPUs.
  2. 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)
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules

# print coverage stats
coverage report -m

# exporting results
coverage xml