Development Guidelines
Contents
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:
Dask Discourse forum: for usage questions and general discussion
Stack Overflow #dask tag: for usage questions
GitHub Issue Tracker: for discussions around new features or established bugs
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 .
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