History

Jirka Borovec eeae426b33 CI: skip hanging (#4943 ) * CI: try increase time limit * try min 3.8 * no ex * CI * dep * test * deps * deps * drop * drop Co-authored-by: chaton <thomas@grid.ai>		2020-12-02 16:18:14 +00:00
..
backends	Tpu save (#4309 )	2020-12-02 13:05:11 +00:00
base	CI cleaning (#4941 )	2020-12-02 10:00:05 +00:00
callbacks	ref: fix & simplify test callback (#4009 )	2020-11-27 19:12:56 +01:00
checkpointing	optimizer clean up (#4658 )	2020-12-01 00:09:46 +00:00
core	optimizer clean up (#4658 )	2020-12-01 00:09:46 +00:00
loggers	[bugfix] Accumulated_gradient and TensoBoard (#4738 )	2020-11-25 19:44:05 +00:00
metrics	Update test for logging a metric object and state reset (#4825 )	2020-11-24 11:28:02 +01:00
models	Tpu save (#4309 )	2020-12-02 13:05:11 +00:00
plugins	Allow string plugins (#4888 )	2020-12-01 20:30:49 +00:00
trainer	CI: skip hanging (#4943 )	2020-12-02 16:18:14 +00:00
tuner	fix: `nb` is set total number of devices, when nb is -1. (#4209 )	2020-10-29 10:50:37 +01:00
utilities	Tpu save (#4309 )	2020-12-02 13:05:11 +00:00
README.md	Horovod: fixed early stopping and added metrics aggregation (#3775 )	2020-11-05 12:52:02 -05:00
__init__.py	CI cleaning (#4941 )	2020-12-02 10:00:05 +00:00
collect_env_details.py	fix tensorboard version (#3132 )	2020-09-15 23:48:48 +02:00
conftest.py	Apply import formatting to files in the 2nd top level (#4717 )	2020-11-18 00:29:09 +01:00
test_deprecated.py	Deprecate prefix argument in ModelCheckpoint (#4765 )	2020-11-21 18:08:42 +05:30
test_profiler.py	update	2020-11-27 17:48:51 +00:00

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:

Install Open MPI or another MPI implementation. Learn how to install Open MPI on this page.

git clone https://github.com/PyTorchLightning/pytorch-lightning
cd pytorch-lightning

# install AMP support
bash requirements/install_AMP.sh

# install dev deps
pip install -r requirements/devel.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.
Horovod with NCCL support: HOROVOD_GPU_OPERATIONS=NCCL pip install horovod

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 requirements/devel.txt)
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v

# print coverage stats
coverage report -m

# exporting results
coverage xml

Building test image

You can build it on your own, note it takes lots of time, be prepared.

git clone <git-repository>
docker image build -t pytorch_lightning:devel-torch1.4 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.4 .

To build other versions, select different Dockerfile.

docker image list
docker run --rm -it pytorch_lightning:devel-torch1.4 bash
docker image rm pytorch_lightning:devel-torch1.4