# Image Tests We greatly appreciate pull requests that extend the automated tests that vet the basic functionality of the Docker images. ## How the Tests Work GitHub Action executes `make build-test-all` against pull requests submitted to the `jupyter/docker-stacks` repository. This `make` command builds and then tests every docker image. We use `pytest` module to run tests on the image. `conftest.py` and `pytest.ini` in the `tests` folder define the environment in which tests are run. More info on `pytest` can be found [here](https://docs.pytest.org/en/latest/contents.html). All the actual test files are located in folders like `tests/-notebook`. ```{note} If your test is located in `tests/-notebook`, it will be run against `jupyter/-notebook` image and against all the images inherited from this image. ``` Many tests make use of global [pytest fixtures](https://docs.pytest.org/en/latest/reference/fixtures.html) defined in the [conftest.py](https://github.com/jupyter/docker-stacks/blob/master/tests/conftest.py) file. ## Unit tests If you want to run a python script in one of our images, you could add a unit test. You can do this by creating a `tests/-notebook/units/` directory, if it doesn't already exist and put your file there. Files in this folder will run automatically when tests are run. You could see an example for tensorflow package [here](https://github.com/jupyter/docker-stacks/blob/HEAD/tests/tensorflow-notebook/units/unit_tensorflow.py). ## Contributing New Tests Please follow the process below to add new tests: 1. Add your test code to one of the modules in `-notebook/tests/` directory or create a new module. 2. Build one or more images you intend to test and run the tests locally. If you use `make`, call: ```bash make build/somestack-notebook make test/somestack-notebook ``` 3. [Submit a pull request](https://github.com/PointCloudLibrary/pcl/wiki/A-step-by-step-guide-on-preparing-and-submitting-a-pull-request) (PR) with your changes. 4. Watch for GitHub to report a build success or failure for your PR on GitHub. 5. Discuss changes with the maintainers and address any issues running the tests on GitHub.