ddbf7de6dc
* `add_argparse_args` method fixed (argument types added) * autopep8 fixes * --gpus=0 removed from test (for ci tests) * Update pytorch_lightning/trainer/trainer.py Co-Authored-By: Joe Davison <joe@huggingface.co> * test_with_accumulate_grad_batches added * agg_and_log_metrics logic added to the base logger class * small format fix * agg metrics strategies removed (not to complicate stuff) * agg metrics: handle zero step * autopep8 * changelog upd * flake fix * metrics aggregators factored out, metrics_agg.py added + tests * metrics agg default value added * Update pytorch_lightning/loggers/metrics_agg.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * metrics aggregators factored out, metrics_agg.py added + tests * metrics agg default value added * Update pytorch_lightning/loggers/metrics_agg.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * remove .item which causes sync issues (#1254) * remove .item which causes sync issues * fixed gradient acc sched * fixed gradient acc sched * test_metrics_agg.py removed (all tested in doctrings), agg metrics refactored * test_metrics_agg.py removed (all tested in doctrings), agg metrics refactored * autopep8 * loggers base.py types fixed * test * test * metrics aggregation for loggers: each key now has a specific function (or default one) * metrics aggregation for loggers: each key now has a specific function (or default one) * docstrings upd * manual typehints removed from docstrings * batch_size decreased for test `test_with_accumulate_grad_batches` * extend running accum * refactor * fix tests * fix tests * allowed_types generator scoped * trainer.py distutils was imported twice, fixed * TensorRunningAccum refactored * TensorRunningAccum added to change log (Changed) * change log pull link added Co-authored-by: Joe Davison <joe@huggingface.co> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: J. Borovec <jirka.borovec@seznam.cz> |
||
|---|---|---|
| .. | ||
| base | ||
| loggers | ||
| models | ||
| trainer | ||
| Dockerfile | ||
| README.md | ||
| __init__.py | ||
| collect_env_details.py | ||
| conftest.py | ||
| install_AMP.sh | ||
| requirements.txt | ||
| test_deprecated.py | ||
| test_profiler.py | ||
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:
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