Please follow our established coding style including variable names, module imports, and function definitions.
The Pyro codebase follows the PEP8 style guide
(which you can check with make lint
) and follows
isort
import order (which you can enforce with make format
).
When creating new files please add a license header; this can be done automatically via make license
or simply make format
.
First install PyTorch.
Then, install all the dev dependencies for Pyro.
make install
or explicitly
pip install -e .[dev]
Before submitting a pull request, please autoformat code and ensure that unit tests pass locally
make format # runs isort
make test # linting and unit tests
If you've modified core pyro code, examples, or tutorials, you can run more comprehensive tests locally (after first adding any new files to the appropriate tests/
script)
make test-examples # test examples/
make integration-test # longer-running tests (may take hours)
make test-cuda # runs unit tests in cuda mode
To run all tests locally in parallel, use the pytest-xdist
package
pip install pytest-xdist
pytest -vs -n auto
To run a single test from the command line
pytest -vs {path_to_test}::{test_name}
# or in cuda mode
CUDA_TEST=1 PYRO_TENSOR_TYPE=torch.cuda.DoubleTensor pytest -vs {path_to_test}::{test_name}
To ensure documentation builds correctly, run
make docs
We run some tutorials on travis to avoid bit rot.
Before submitting a new tutorial, please run make scrub
from
the top-level pyro directory in order to scrub the metadata in
the notebooks.
To enable a tutorial for testing
- Add a line
smoke_test = ('CI' in os.environ)
to your tutorial. Our test scripts only test tutorials that contain the stringsmoke_test
. - Each time you do something expensive for many iterations, set the number
of iterations like this:
for epoch in range(200 if not smoke_test else 1): ...
You can test locally by running make test-tutorials
.
The profiler module contains scripts to support profiling different Pyro modules, as well as test for performance regression.
To run the profiling utilities, ensure that all dependencies for profiling are satisfied,
by running make install
, or more specifically, pip install -e .[profile]
.
There are some generic test cases available in the profiler
module. Currently, this supports
only the distributions
library, but we will be adding test cases for inference methods
soon.
To get help on the parameters that the profiling script takes, run:
python -m profiler.distributions --help
To run the profiler on all the distributions, simply run:
python -m profiler.distributions
To run the profiler on a few distributions by varying the batch size, run:
python -m profiler.distributions --dist bernoulli normal --batch_sizes 1000 100000
To get more details on the potential sources of slowdown, use the cProfile
tool
as follows:
python -m profiler.distributions --dist bernoulli --tool cprofile
For larger changes, please open an issue for discussion before submitting a pull request. For relevant design questions to consider, see past design documents.
In your pull request description on github, please note:
- Proposed changes
- Links to related issues/PRs
- New and existing tests
Before submitting, please run make format
, make lint
, and running tests as described above.
For speculative changes meant for early-stage review, include [WIP]
in the PR's title.
(One of the maintainers will add the WIP
tag.)