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Carlos Mocholí 0327f6b4c2
Do not warn when the name key is used in the lr_scheduler dict (#5057) 
* Do not warn when the name key is used

* Missing line

* Consistency

* Update pytorch_lightning/callbacks/lr_monitor.py

* Update docs

* Update pytorch_lightning/core/lightning.py

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>

* Update CHANGELOG

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
 		2020-12-14 08:38:10 +01:00
..
backends update usage of deprecated checkpoint_callback (#5006) 2020-12-09 14:14:34 -05:00
base Refactor load in checkpoint connector (#4593) 2020-12-14 00:13:50 +08:00
callbacks Do not warn when the name key is used in the lr_scheduler dict (#5057) 2020-12-14 08:38:10 +01:00
checkpointing Improve some tests (#5049) 2020-12-13 23:04:16 +08:00
core drop unused test with result api (#5058) 2020-12-12 21:51:19 +05:30
deprecated_api split tests for deprecated api (#5071) 2020-12-12 20:25:11 +05:30
loggers drop usage of deprecated distributed_backend (#5009) 2020-12-09 09:18:23 +01:00
metrics drop duplicate metrics (#5014) 2020-12-11 18:42:53 +01:00
models Refactor load in checkpoint connector (#4593) 2020-12-14 00:13:50 +08:00
plugins [feat] pp 2/n (#5026) 2020-12-09 12:56:51 +00:00
trainer Do not warn when the name key is used in the lr_scheduler dict (#5057) 2020-12-14 08:38:10 +01:00
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
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
special_tests.sh [feat] pp 2/n (#5026) 2020-12-09 12:56:51 +00:00
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