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summarize total size of model params in bytes (#5590) 
* simplified model size calc

* fix spaces

* fix newlines

* minor refactor

* Update pytorch_lightning/core/memory.py

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

* make model size property

* fix doctest

* Update pytorch_lightning/core/memory.py

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* remove explicit doctest from file

* better docs

* model precalculate size 1.0 mbs

* better comment

* Update tests/core/test_memory.py

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

* Update tests/core/test_memory.py

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

* merge _model_size into model_size property itself

* minor comment fix

* add feature to changelog

* added precision test

* isort

* minor def name typo

* remove monkeypath set env as boringmodel wont need any torch hub cache

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
 		2021-01-25 09:35:29 +01:00
..
backends Fix pre-commit isort failure on tests/backends/*.py (#5430) 2021-01-14 19:32:41 -05:00
base Fix pre-commit isort failure on tests/base/*.py (#5429) 2021-01-15 03:01:57 -05:00
callbacks Set progressbar refresh rate in Google Colab (#5516) 2021-01-19 12:47:14 -05:00
checkpointing Fix pre-commit isort failure on tests/checkpointing/*.py (#5427) 2021-01-12 03:31:51 -05:00
core summarize total size of model params in bytes (#5590) 2021-01-25 09:35:29 +01:00
deprecated_api clarify Trainer running state atribs. (#5589) 2021-01-24 10:45:42 +00:00
loggers feat(wandb): add sync_step (#5351) 2021-01-24 17:44:09 -05:00
metrics Classification metrics overhaul: precision & recall (4/n) (#4842) 2021-01-18 03:24:13 -05:00
models simple tests restructure (#5452) 2021-01-15 20:58:20 -05:00
overrides Refactor LightningDistributedDataParallel (#5185) 2021-01-13 14:35:42 -05:00
plugins Fix pre-commit isort failure on tests/plugins/*.py (#5422) 2021-01-14 03:57:16 -05:00
trainer add possibility for nested loaders (#5404) 2021-01-24 07:32:02 -05:00
tuner refactor - check F841 (#5202) 2020-12-21 11:10:55 +05:30
utilities Fix pre-commit isort failure on tests/utilities/*.py (#5420) 2021-01-11 14:00:39 -05:00
README.md Horovod: fixed early stopping and added metrics aggregation (#3775) 2020-11-05 12:52:02 -05:00
__init__.py flake8 ++ 2021-01-05 09:58:37 +01:00
collect_env_details.py add copyright to tests (#5143) 2021-01-05 09:57:37 +01:00
conftest.py update isort config (#5335) 2021-01-06 12:49:23 +01:00
special_tests.sh simple tests restructure (#5452) 2021-01-15 20:58:20 -05:00
test_profiler.py update isort config (#5335) 2021-01-06 12:49:23 +01:00

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