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William Falcon a1705441a9
ref: remove _evaluate fx (#3197) 
* remove _evaluate

* remove _evaluate

* remove _evaluate

* remove _evaluate

* remove _evaluate

* remove _evaluate

* remove _evaluate

* remove _evaluate
 		2020-08-26 12:28:14 -04:00
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base New modular metric interface (#2528) 2020-08-26 13:01:29 +02:00
callbacks ref: remove _evaluate fx (#3197) 2020-08-26 12:28:14 -04:00
core ref: refactored gpu backend __step (#3120) 2020-08-24 09:22:05 -04:00
loggers ref: remove _evaluate fx (#3197) 2020-08-26 12:28:14 -04:00
metrics Fix RMSLE metric (#3188) 2020-08-26 08:02:53 -04:00
models fix ONNX model save on GPU (#3145) 2020-08-26 16:22:19 +00:00
trainer ref: restore on_eval_start hook (#3183) 2020-08-26 00:45:43 -04:00
utilities updated hooks (#2850) 2020-08-07 09:29:57 -04:00
README.md test dockers & add AMP in pt-1.6 (#1584) 2020-07-31 08:23:13 -04:00
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test_deprecated.py remove deprecated in v0.9 (#2760) 2020-07-30 23:19:28 +02:00
test_profiler.py RC & Docs/changelog (#1776) 2020-05-11 21:57:53 -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:

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 tests/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_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=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