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* Initial commit of Horovod distributed backend implementation * Update distrib_data_parallel.py * Update distrib_data_parallel.py * Update tests/models/test_horovod.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update tests/models/test_horovod.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Fixed tests * Added six * tests * Install tox for GitHub CI * Retry tests * Catch all exceptions * Skip cache * Remove tox * Restore pip cache * Remove the cache * Restore pip cache * Remove AMP Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
50 lines
1.5 KiB
Markdown
50 lines
1.5 KiB
Markdown
# PyTorch-Lightning Tests
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Most PL tests train a full MNIST model under various trainer conditions (ddp, ddp2+amp, etc...).
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This provides testing for most combinations of important settings.
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The tests expect the model to perform to a reasonable degree of testing accuracy to pass.
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## Running tests
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The automatic travis tests ONLY run CPU-based tests. Although these cover most of the use cases,
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run on a 2-GPU machine to validate the full test-suite.
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To run all tests do the following:
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```bash
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git clone https://github.com/PyTorchLightning/pytorch-lightning
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cd pytorch-lightning
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# install AMP support
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bash tests/install_AMP.sh
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# install dev deps
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pip install -r tests/requirements.txt
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# run tests
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py.test -v
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```
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To test models that require GPU make sure to run the above command on a GPU machine.
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The GPU machine must have:
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1. At least 2 GPUs.
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2. [NVIDIA-apex](https://github.com/NVIDIA/apex#linux) installed.
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3. [Horovod with NCCL](https://horovod.readthedocs.io/en/stable/gpus_include.html) support: `HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL pip install horovod`
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## Running Coverage
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Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed.
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```bash
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cd pytorch-lightning
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# generate coverage (coverage is also installed as part of dev dependencies under tests/requirements.txt)
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coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
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# print coverage stats
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coverage report -m
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# exporting results
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coverage xml
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```
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