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Vismantas
2aba70e228
* Unit tests for num_gpu property as proxy for __parse_gpu_ids.
* Refactoring __parse_gpu_ids
* Moved the function outside the class as it is
an utility function and did not depend on class in any way.
* Added unit tests for it.
* Mocked torch.cuda.device_count function in tests.
This allows the tests to be run on machines that do not have gpus.
* Fixed the parse_gpu_ids function to handle -1 case.
Function now handles -1 the same way as it does for '-1'.
* Unit tests for root_gpu added.
Added backend as a parameter as currently depending on backend set
or not, code fails with exception in certain circumstances, before
giving a wrong answer.
* Moved __set_root_gpu function out of the class.
This function does not depend on the class and can be tested
more easily this way.
Also added unit tests for this function. They simply reuse
data for the root_gpu property.
* determine_root_gpu_device passes unit tests.
* num_gpus passes unit tests.
Also added a None test for this function.
* parse_gpu_ids tests changed to reflect desired state after refactoring.
Planning to refactor parse_gpu_ids to always return list of ints.
This will simplify code that use output of this function.
* * parse_gpu_ids always returns lists
* parse_gpu_ids checks given ids against available ids
* parse_gpu_ids raises exception for non existant ids
* parse_gpu_ids returns None when no gpus are available
* cleaned up determine_root_gpu_device
* cleaned up num_gpus property
* Updated unit tests to reflect changes in the functions
* Flake8 fixes
* Moved fixture code up before where it is used.
* Updated documentation.
* Changed tests to match the API:
* gpus=-1 or gpus='-1' should use all available gpu devices
* gpus=N
* N=0: no gpus should be used.
* N>0: N gpus should be used
* gpus=list of ints or a comma separated string of numbers:
Use the gpus indicated by the list or the string.
* Fixed code to pass all the changed tests for parsing gpus param.
* Refactoring parse_gpu_ids function.
* flake8 fixes.
* Updating documentation.
* flake8 fixes.
* flake8 fixes.
* flake8 fixes
* Update trainer.py
* Update dp_mixin.py
* Make reduce_distributed_output a stand alone function.
Fix imports.
Fix flake8.
* Add comet_ml dependency to tests requirements.txt
* Revert "Make reduce_distributed_output a stand alone function. Fix imports. Fix flake8."
This reverts commit eac0338
* Merge with master.
PyTorch-Lightning Tests
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/williamFalcon/pytorch-lightning
cd pytorch-lightning
# install module locally
pip install -e .
# install dev deps
pip install -r 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.
test_models.py
This file fits a tiny model on MNIST using these different set-ups.
- CPU only.
- Single GPU with DP.
- Multiple (2) GPUs using DP.
- Multiple (2) GPUs using DDP.
- Multiple (2) GPUs using DP + apex (for 16-bit precision).
- Multiple (2) GPUs using DDP + apex (for 16-bit precision).
For each set up it also tests:
- model saving.
- model loading.
- predicting with a loaded model.
- simulated save from HPC signal.
- simulated load from HPC signal.
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
pip install coverage
coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
# print coverage stats
coverage report -m
# exporting resulys
coverage xml
codecov -t 17327163-8cca-4a5d-86c8-ca5f2ef700bc -v