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* Add support for hierarchical dict * Support nested Namespace * Add docstring * Migrate hparam flattening to each logger * Modify URLs in CHANGELOG * typo * Simplify the conditional branch about Namespace Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update CHANGELOG.md Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * added examples section to docstring * renamed _dict -> input_dict * mata_tags.csv -> hparams.yaml * code style fixes * add pyyaml * remove unused import * create the member NAME_HPARAMS_FILE * improve tests * Update tensorboard.py * pass the local test w/o relavents of Horovod * formatting * update dependencies * fix dependencies * Apply suggestions from code review * add savings * warn * docstrings * tests * Apply suggestions from code review * saving * Apply suggestions from code review * use default * remove logging * typo fixes * update docs * update CHANGELOG * clean imports * add blank lines * Update pytorch_lightning/core/lightning.py Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> * Update pytorch_lightning/core/lightning.py Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> * back to namespace * add docs * test fix * update dependencies * add space Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
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:
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 tests/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.
- NVIDIA-apex installed.
- 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 tests/requirements-devel.txt)
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
# 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-pt_1_4 -f tests/Dockerfile --build-arg TORCH_VERSION=1.4 .
To build other versions, select different Dockerfile.
docker image list
docker run --rm -it pytorch_lightning:devel-pt_1_4 bash
docker image rm pytorch_lightning:devel-pt_1_4