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Jirka Borovec ce9179591d
ref: clean config [1/n] add intermediate setters (#4990) 
* add intermediate setters

* show inputs

* fix options

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Co-authored-by: chaton <thomas@grid.ai>
 		2020-12-09 14:13:57 -05:00
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backends [feat] pp 2/n (#5026) 2020-12-09 12:56:51 +00:00
base CI cleaning (#4941) 2020-12-02 10:00:05 +00:00
callbacks Simplify optimization Logic (#4984) 2020-12-07 12:55:49 +00:00
checkpointing Added changeable extension variable for model checkpoints (#4977) 2020-12-06 22:58:50 +05:30
core drop usage of deprecated distributed_backend (#5009) 2020-12-09 09:18:23 +01:00
loggers drop usage of deprecated distributed_backend (#5009) 2020-12-09 09:18:23 +01:00
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trainer ref: clean config [1/n] add intermediate setters (#4990) 2020-12-09 14:13:57 -05:00
tuner fix: `nb` is set total number of devices, when nb is -1. (#4209) 2020-10-29 10:50:37 +01:00
utilities drop usage of deprecated distributed_backend (#5009) 2020-12-09 09:18:23 +01:00
README.md Horovod: fixed early stopping and added metrics aggregation (#3775) 2020-11-05 12:52:02 -05:00
__init__.py simplify CI horovod (#4951) 2020-12-07 10:31:33 +01:00
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special_tests.sh [feat] pp 2/n (#5026) 2020-12-09 12:56:51 +00:00
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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:

  1. At least 2 GPUs.
  2. NVIDIA-apex installed.
  3. 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