# Development Guidelines

Dask is a community maintained project. We welcome contributions in the form of bug reports, documentation, code, design proposals, and more. This page provides resources on how best to contribute.

Note

Dask strives to be a welcoming community of individuals with diverse backgrounds. For more information on our values, please see our code of conduct and diversity statement

## Where to ask for help

Dask conversation happens in the following places:

1. Dask Discourse forum: for usage questions and general discussion

2. Stack Overflow #dask tag: for usage questions

3. GitHub Issue Tracker: for discussions around new features or established bugs

4. Dask Community Slack: for real-time discussion

For usage questions and bug reports we prefer the use of Discourse, Stack Overflow and GitHub issues over Slack chat. Discourse, GitHub and Stack Overflow are more easily searchable by future users, so conversations had there can be useful to many more people than just those directly involved.

## Separate Code Repositories

Dask maintains code and documentation in a few git repositories hosted on the GitHub `dask` organization, https://github.com/dask. This includes the primary repository and several other repositories for different components. A non-exhaustive list follows:

- https://github.com/dask/dask: The main code repository holding parallel algorithms, the single-machine scheduler, and most documentation

- https://github.com/dask/distributed: The distributed memory scheduler

- https://github.com/dask/dask-ml: Machine learning algorithms

- https://github.com/dask/s3fs: S3 Filesystem interface

- https://github.com/dask/gcsfs: GCS Filesystem interface

- https://github.com/dask/hdfs3: Hadoop Filesystem interface

- …

Git and GitHub can be challenging at first. Fortunately good materials exist on the internet. Rather than repeat these materials here, we refer you to pandas’ documentation and links on this subject at https://pandas.pydata.org/docs/dev/development/contributing.html

## Issues

The community discusses and tracks known bugs and potential features in the GitHub Issue Tracker. If you have a new idea or have identified a bug, then you should raise it there to start public discussion.

If you are looking for an introductory issue to get started with development, then check out the “good first issue” label, which contains issues that are good for starting developers. Generally, familiarity with Python, NumPy, pandas, and some parallel computing are assumed.

## Development Environment

### Download code

Make a fork of the main Dask repository and clone the fork:

    git clone https://github.com/<your-github-username>/dask.git
    cd dask

You should also pull the latest git tags (this ensures `pip`’s dependency resolver can successfully install Dask):

    git remote add upstream https://github.com/dask/dask.git
    git pull upstream main --tags

Contributions to Dask can then be made by submitting pull requests on GitHub.

### Install

From the top level of your cloned Dask repository you can install a local version of Dask, along with all necessary dependencies, using pip or conda

`pip`:

    python -m pip install -e ".[complete,test]"

`conda`:

    conda env create -n dask-dev -f continuous_integration/environment-3.12.yaml
    conda activate dask-dev
    python -m pip install --no-deps -e .

### Run Tests

Dask uses py.test for testing. You can run tests from the main dask directory as follows:

    py.test dask --verbose --doctest-modules

## Contributing to Code

Dask maintains development standards that are similar to most PyData projects. These standards include language support, testing, documentation, and style.

### Python Versions

Dask supports Python versions 3.9 to 3.12. Name changes are handled by the `dask/compatibility.py` file.

### Test

Dask employs extensive unit tests to ensure correctness of code both for today and for the future. Test coverage is expected for all code contributions.

Tests are written in a py.test style with bare functions:

    def test_fibonacci():
        assert fib(0) == 0
        assert fib(1) == 0
        assert fib(10) == 55
        assert fib(8) == fib(7) + fib(6)

        for x in [-3, 'cat', 1.5]:
            with pytest.raises(ValueError):
                fib(x)

These tests should compromise well between covering all branches and fail cases and running quickly (slow test suites get run less often).

You can run tests locally by running `py.test` in the local dask directory:

    py.test dask

You can also test certain modules or individual tests for faster response:

    py.test dask/dataframe

    py.test dask/dataframe/tests/test_dataframe.py::test_rename_index

If you want the tests to run faster, you can run them in parallel using `pytest-xdist`:

    py.test dask -n auto

Tests run automatically on GitHub Actions on every push to every pull request on GitHub.

Tests are organized within the various modules’ subdirectories:

    dask/array/tests/test_*.py
    dask/bag/tests/test_*.py
    dask/bytes/tests/test_*.py
    dask/dataframe/tests/test_*.py
    dask/diagnostics/tests/test_*.py

For the Dask collections like Dask Array and Dask DataFrame, behavior is typically tested directly against the NumPy or pandas libraries using the `assert_eq` functions:

    import numpy as np
    import dask.array as da
    from dask.array.utils import assert_eq

    def test_aggregations():
        rng = np.random.default_rng()
        nx = rng.random(100)
        dx = da.from_array(nx, chunks=(10,))

        assert_eq(nx.sum(), dx.sum())
        assert_eq(nx.min(), dx.min())
        assert_eq(nx.max(), dx.max())
        ...

This technique helps to ensure compatibility with upstream libraries and tends to be simpler than testing correctness directly. Additionally, by passing Dask collections directly to the `assert_eq` function rather than call compute manually, the testing suite is able to run a number of checks on the lazy collections themselves.

### Docstrings

User facing functions should roughly follow the numpydoc standard, including sections for `Parameters`, `Examples`, and general explanatory prose.

By default, examples will be doc-tested. Reproducible examples in documentation is valuable both for testing and, more importantly, for communication of common usage to the user. Documentation trumps testing in this case and clear examples should take precedence over using the docstring as testing space. To skip a test in the examples add the comment `# doctest: +SKIP` directly after the line.

    def fib(i):
        """ A single line with a brief explanation

        A more thorough description of the function, consisting of multiple
        lines or paragraphs.

        Parameters
        ----------
        i: int
             A short description of the argument if not immediately clear

        Examples
        --------
        >>> fib(4)
        3
        >>> fib(5)
        5
        >>> fib(6)
        8
        >>> fib(-1)  # Robust to bad inputs
        ValueError(...)
        """

Docstrings are tested under Python 3.12 on GitHub Actions. You can test docstrings with pytest as follows:

    py.test dask --doctest-modules

Docstring testing requires `graphviz` to be installed. This can be done via:

    conda install -y graphviz

### Code Formatting

Dask uses several code linters (flake8, black, isort, pyupgrade, mypy), which are enforced by CI. Developers should run them locally before they submit a PR, through the single command `pre-commit run --all-files`. This makes sure that linter versions and options are aligned for all developers.

Optionally, you may wish to setup the pre-commit hooks to run automatically when you make a git commit. This can be done by running:

    pre-commit install

from the root of the Dask repository. Now the code linters will be run each time you commit changes. You can skip these checks with `git commit --no-verify` or with the short version `git commit -n`.

## Contributing to Documentation

Dask uses Sphinx for documentation, hosted on https://readthedocs.org . Documentation is maintained in the RestructuredText markup language (`.rst` files) in `dask/docs/source`. The documentation consists both of prose and API documentation.

The documentation is automatically built, and a live preview is available, for each pull request submitted to Dask. Additionally, you may also build the documentation yourself locally by following the instructions outlined below.

### How to build the Dask documentation

To build the documentation locally, make a fork of the main Dask repository, clone the fork:

    git clone https://github.com/<your-github-username>/dask.git
    cd dask/docs

Install the packages in `requirements-docs.txt`.

Optionally create and activate a `conda` environment first:

    conda create -n daskdocs -c conda-forge python=3.8
    conda activate daskdocs

Install the dependencies with `pip`:

    python -m pip install -r requirements-docs.txt

Then build the documentation with `make`:

    make html

The resulting HTML files end up in the `build/html` directory.

You can now make edits to rst files and run `make html` again to update the affected pages.

## Dask CI Infrastructure

### Github Actions

Dask uses Github Actions for Continuous Integration (CI) testing for each PR. These CI builds will run the test suite across a variety of Python versions, operating systems, and package dependency versions. Additionally, if a commit message includes the phrase `test-upstream`, then an additional CI build will be triggered which uses the development versions of several dependencies including: NumPy, pandas, fsspec, etc.

The CI workflows for Github Actions are defined in .github/workflows with additional scripts and metadata located in continuous_integration