Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at https://github.com/dask/dask-image/issues.

If you are reporting a bug, please include:

  • Your operating system name and version.

  • Any details about your local setup that might be helpful in troubleshooting.

  • Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation

dask-image could always use more documentation, whether as part of the official dask-image docs, in docstrings, or even on the web in blog posts, articles, and such.

To build the documentation locally and preview your changes, first set up the conda environment for building the dask-image documentation:

$ conda env create -f continuous_integration/environment-doc.yml
$ conda activate dask_image_doc_env

This conda environment contains dask-image and its dependencies, sphinx, and the dask-sphinx-theme.

Next, build the documentation with sphinx:

$ cd dask-image/docs
$ make html

Now you can preview the html documentation in your browser by opening the file: dask-image/docs/_build/html/index.html

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/dask/dask-image/issues.

If you are proposing a feature:

  • Explain in detail how it would work.

  • Keep the scope as narrow as possible, to make it easier to implement.

  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here’s how to set up dask-image for local development.

  1. Fork the dask-image repo on GitHub.

  2. Clone your fork locally:

    $ git clone git@github.com:your_name_here/dask-image.git
    $ cd dask-image
    
  3. Install your local copy into an environment. Assuming you have conda installed, this is how you set up your fork for local development (on Windows drop source). Replace “<some version>” with the Python version used for testing.:

    $ conda create -n dask-image-env python="<some version>"
    $ source activate dask-image-env
    $ python -m pip install -e .
    
  4. Create a branch for local development:

    $ git checkout -b name-of-your-bugfix-or-feature
    

    Now you can make your changes locally.

  5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions:

    $ flake8 dask_image tests
    $ pytest
    

    To get flake8, just conda install it into your environment.

  6. Commit your changes and push your branch to GitHub:

    $ git add .
    $ git commit -m "Your detailed description of your changes."
    $ git push origin name-of-your-bugfix-or-feature
    
  7. Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

  1. The pull request should include tests.

  2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.

  3. The pull request should work for all supported Python versions. Check CIs and make sure that the tests pass for all supported Python versions and platforms.

Testing

Running tests locally

To setup a local testing environment that matches the test environments we use for our continuous integration services, you can use the .yml conda environment files included in the continuous_integration folder in the dask-image repository.

There is a separate environment file for each supported Python version.

We will use conda to create an environment from a file (conda env create -f name-of-environment-file.yml).

Note

If you do not have Anaconda/miniconda installed, please follow these instructions.

$ conda env create -f continuous_integration/environment-latest.yml

This command will create a new conda test environment called dask-image-testenv with all required dependencies.

Now you can activate your new testing environment with:

.. code-block:: console

$ conda activate dask-image-testenv

Finally, install the development version of dask-image:

.. code-block:: console

$ pip install -e “.[test]””

For local testing, please run pytest in the test environment:

.. code-block:: console

$ pytest

To run a subset of tests, for example all the tests for ndfourier:

$ pytest tests/test_dask_image/test_ndfourier

Continuous integration tests

Creating a pull request will automatically run the continuous integration tests with Github Actions.

Results from the continuous integration (CI) checks are shown linked at the bottom of your pull request, and also in the dask-image GitHub Actions tab: https://github.com/dask/dask-image/actions

To edit the CI checks, see the workflow scripts in the repository located in dask-image/.github/workflows

GPU continuous integration

We also have continuous integration to test GPU features!

This will run automatically, after a maintainer has approved it. Maintainers approve the GPU CI to run by replying to the gpuCI bot on the pull request, leaving a comment that says either “ok to test” or “add to allowlist”.

The gpuCI bot @GPUtester comment looks like this:

Can one of the admins verify this patch?

Admins can comment ok to test to allow this one PR to run or add to allowlist to allow all future PRs from the same author to run.

For more information about the GPU continuous integration provided by NVIIDIA, see the main Dask docs GPU CI section