Help Make zamba Better

zamba is an open source project, which means you can help make it better!

Develop the Github Repository

To get involved, check out the Github code repository. There you can find open issues , project goals, and plenty of comments and links to help you along.

zamba uses continuous integration and test-driven-development to ensure that we always have a working project. So what are you waiting for? git going!

Installation for development

To install zamba for development, you need to clone the git repository and then install the cloned version of the library for local development.

To install for development:

$ git clone
$ cd zamba
$ pip install --editable .[cpu]

** Note: You can change cpu to gpu to develop against a gpu version on tensorflow. **

Running the zamba test suite

The included Makefile contains code that uses pytest to run all tests in zamba/tests.

The command is (from the project root),

$ make test

Testing End-To-End Prediction With

The test tests/ runs an end-to-end prediction with CnnEnsemble.predict(data_dir) using a video that automatically gets downloaded along with the input directory (this and all required directories are downloaded upon instantiation of CnnEnsemble if they are not already present in the project).

This test takes a longer time to execute than is possible on continuous integration, so by default this test is skipped due to the pytest decorator:

@pytest.mark.skip(reason="This test takes hours to run, makes network calls, and is really for local dev only.")
def test_predict():
    data_dir = Path(__file__).parent.parent / "models" / "cnnensemble" / "input" / "raw_test"

    manager = ModelManager('', model_class='cnnensemble', proba_threshold=0.5)
    manager.predict(data_dir, save=True)

This test is important during local development, so it is recommended that the decorator be commented out to test end-to-end prediction locally. However, this change should never be pushed, as the lightweight machines on codeship will not be happy, or able, to complete the end-to-end prediction.

To test end-to-end prediction using make test on a different set of videos, simply edit data_dir.

The included Makefile contains code that uses pytest to run all tests in zamba/tests.

Packaging and pushing to PyPI

If you want to update the packaging, you must update the version number in and docs/ To test packaging you can run:

$ make build

If you have credentials for PyPI in a ~/.pypirc file then you can push to PyPI. To upload a new version, you can use the make commands:

$ make distribute_testpypi

and, once tested:

$ make distribute_pypi