Make a fork of the main `Dask repository <https://github.com/dask/dask>`_ 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.

.. _develop-install:

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_

.. _conda: https://conda.io/

``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

.. _py.test: https://docs.pytest.org/en/latest/


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 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:

.. code-block:: python

   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:

.. code-block:: python

   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

   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(...)
   """

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 <https://pre-commit.com/>`_ 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

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 <https://github.com/dask/dask/tree/main/.github/workflows>`_
with additional scripts and metadata located in `continuous_integration
<https://github.com/dask/dask/tree/main/continuous_integration>`_


.. _Sphinx: https://www.sphinx-doc.org/