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Adrian Wälchli a6aa1a0f82
make gpus=str in Trainer consistent with command line parsing of string (#6388) 
* string gpu input

* update docs

* deprecation warning

* Revert "update docs"

This reverts commit c5f3893413.

* deprecation

* add changelog

* update parser

* update warning

* implement v1.5 behavior ahead of time

* formatting

* set accelerator in test to avoid different warning

* add warning

* remove todo warn

* Update pytorch_lightning/utilities/device_parser.py

Co-authored-by: Kaushik B <45285388+kaushikb11@users.noreply.github.com>

* resolve flake8

Co-authored-by: Kaushik B <45285388+kaushikb11@users.noreply.github.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: tchaton <thomas@grid.ai>
 		2021-05-04 09:56:27 +00:00
..
accelerators Remove `trainer.fit` return value [2/n] (#7237) 2021-04-28 19:11:32 +01:00
base Update LR schedulers only when their corresponding Optimizer is being… (#4868) 2021-05-04 09:37:40 +00:00
callbacks [bugfix] Add reloading support using BaseFinetuning (#7253) 2021-04-30 11:14:43 -04:00
checkpointing Remove `trainer.fit` return value [2/n] (#7237) 2021-04-28 19:11:32 +01:00
core Replace `_DataModuleWrapper` with `__new__` [1/2] (#7289) 2021-05-04 08:00:24 +00:00
deprecated_api make gpus=str in Trainer consistent with command line parsing of string (#6388) 2021-05-04 09:56:27 +00:00
helpers [bugfix] Fix dataloading for iterable datasets and limit_train_batches (#7306) 2021-05-03 19:50:26 +01:00
loggers fix(wandb): allow custom init args (#6989) 2021-05-04 09:45:36 +00:00
metrics Simplify deprecations (#6620) 2021-03-25 15:26:38 +01:00
models make gpus=str in Trainer consistent with command line parsing of string (#6388) 2021-05-04 09:56:27 +00:00
overrides [feat] Add better support for predict + ddp 2/3 (#7215) 2021-04-27 08:46:45 -04:00
plugins Update Accelerator Connector for Registry (#7214) 2021-05-03 21:03:21 +00:00
trainer Update LR schedulers only when their corresponding Optimizer is being… (#4868) 2021-05-04 09:37:40 +00:00
tuner Attach data refactor and tuner bugs [4/n] (#7258) 2021-04-30 13:54:58 +00:00
utilities Fix `Trainer.plugins` type declaration (#7288) 2021-05-04 08:42:57 +02:00
README.md Delete unused CI scripts (#7152) 2021-04-22 03:07:48 +02:00
__init__.py fixing examples (#6600) 2021-03-20 18:58:59 +00:00
collect_env_details.py add copyright to tests (#5143) 2021-01-05 09:57:37 +01:00
conftest.py CI: fixture for global rank variable reset (#6839) 2021-04-06 09:37:17 -07:00
mnode_tests.txt Mnodes (#5020) 2021-02-04 20:55:40 +01:00
special_tests.sh DeepSpeed ZeRO Update (#6546) 2021-03-30 13:39:02 -04:00
test_profiler.py fix version + yapf (#6999) 2021-04-22 18:25:51 +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

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

# 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 to run distributed tests.

Note that this setup will not run tests that require specific packages installed such as Horovod, FairScale, NVIDIA/apex, NVIDIA/DALI, etc. You can rely on our CI to make sure all these tests pass.

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