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Adrian WälchliandJirka Borovec
b7de42f70d
* added custom mnist without torchvision dep * move files so it does not conflict with mnist gitignore * mock torchvision for tests * fix line too long * fix line too long * fix "module level import not at top of file" warning * move mock imports to __init__.py * simplify MNIST a lot and download directly the .pt files * further simplify and clean up mnist * revert import overrides * make as before * drop PIL requirement * move mnist.py to datasets subfolder * use logging instead of print * choose same name as in torchvision * remove torchvision and pillow also from yml file * refactor if train Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * capitalized class attr * moved mnist to models * re-added datsets ignore * better name for file variable * Update mnist.py * move dataset classes to datasets.py * new line * update * update * fix automerge * move to base folder * adapt testingmnist to new mnist base class * remove temporal fix * fix datatype * remove old testingmnist * readable * fix import * fix whitespace * docstring Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update tests/base/datasets.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * changelog * added types * Update CHANGELOG.md Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * exist->isfile Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * index -> idx * temporary fix for trains error * better changelog message Co-authored-by: Jirka Borovec <Borda@users.noreply.github.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.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.
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.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