This guide includes general guidelines on how to contribute to HoloViz. Read the Operating Guide if you are interested to learn how an Open-Source project like HoloViz operates.
Contributing? Yes! Wait, but why?#
There are many reasons why you would contribute to the HoloViz project, including:
You appreciate the HoloViz project, its mission and values, and would like to help
You are a user of one of the HoloViz packages and need a bug to be fixed or a new feature to be implemented, and you are willing to tackle that in some ways
You are looking for some open-source experience
You have been reading one of the HoloViz websites and found something to improve, even just a simple typo
These reasons, or whatever reason brought you here, are all valid to our eyes. We welcome anyone who wishes to contribute, and as you will see, there are many ways to contribute!
Contributing to the HoloViz project might even get you a job, which has been the case for a couple of HoloViz members.
What and how to contribute?#
If you end up on this page it is quite likely that you are interested in contributing code to HoloViz. However, as you will see, there are many other areas where you could contribute, and in some of these areas, the HoloViz project would really appreciate some help!
Each core HoloViz package has its own website. Maintaining the content of these websites is a lot of work, and you can easily help by:
submitting a PR to fix a typo
submitting a PR to clarify a section
submitting a PR to document an undocumented feature
submitting a PR to update a section that should have been updated
How the websites look and feel can also be improved. If you happen to have some front-end development expertise, your help would be greatly appreciated.
Finally, the documentation also lies within the code, and in Python, it is found in the so-called docstrings that accompany modules, classes, methods and functions. You can easily improve the docstrings by:
submitting a PR to fix a typo
submitting a PR to clarify a definition
submitting a PR to fix or document the default value of a parameter
Documentation fixes/improvements that are of a small scope usually do not need an issue to be opened beforehand. If you don’t have the time to open a pull request, we would appreciate it if you could record your suggestion in an issue on Github. That would already be an excellent contribution!
HoloViz users are recommended to ask their usage questions on the HoloViz Discourse Forum. This forum is only helpful if questions can find answers, and you can be the one writing that answer, which will make somebody else’s life so much easier! The HoloViz team members monitor the forum and try to contribute regularly, yet the more we are, the better.
Answering questions on the HoloViz Discourse Forum, or even just trying to answer some random questions, is a very good way to learn how to use the HoloViz tools.
Some users also ask questions on StackOverflow, which isn’t much monitored by the HoloViz team, so we would greatly appreciate any help there.
Before addressing how to contribute code to HoloViz, it is important to mention that a beneficial way to contribute to HoloViz is by communicating more about it. Tweeting, writing blog posts, creating Youtube videos, presenting your work with one or more of the HoloViz tools, etc., are all outreach activities that serve the purpose of HoloViz.
Contributing code includes:
fixing an existing bug: the recommended practice is to add one or more unit test(s) that would have failed before the bug fix was implemented.
adding a new feature: again, the recommended practice is to add unit test(s) to test the new feature’s functionality.
improving performance: while slow code isn’t necessarily a bug, users always appreciate faster code. Performance improvements should be demonstrated by some benchmark comparing the performance before and after the changes.
adding tests: test coverage can always be improved. Adding tests is a good way for beginners to contribute to a project and familiarize themselves with its code base and workflows.
refactoring: refactoring can be a good way to improve a code base.
The HoloViz projects usually provide specific instructions to set up your environment to be ready to contribute documentation or code to the project. The instructions provided hereafter are meant to be general enough to apply to each project. Refer to the documentation of the project you are working on for more details.
Before making anything beyond a minimal contribution (e.g. fixing a type), the first step is usually to open a GitHub Issue that documents what you are trying to fix/improve. Writing a good issue is important and will affect how it is going to be perceived by the project maintainers, contributors and watchers. Read for instance this blog post that describes what a good issue should look like. Once an issue is opened, a discussion can take place with the maintainers to see if what is suggested is relevant, which can orientate the action to take.
The first step to working on any source code is to install Git as the source codes are stored in Git source control repositories. To install Git on any platform, refer to the Installing Git section of the Pro Git Book.
As an external contributor, you do not have permission to submit code directly to any of the repositories. Github offers you to fork the repository, which will create a copy of the repository, on which you will have the right to work freely. Follow these instructions to find out how to fork and clone a repository.
The HoloViz developers rely on Conda to manage their virtual environments. We recommend you install Conda too to have an experience as close as possible to those of the core developers. To install Conda on any platform, you can install Miniconda.
The HoloViz developers rely on pyctdev, a custom developer tool that facilitates maintaining all of the holoviz projects. pyctdev is an extension of the task running tool doit. We recommend that you install
conda install -c pyviz/label/dev "pyctdev>0.5.0"
pyctdev is available and you are in the cloned (e.g. panel) repository, you can create a new virtual environment with the
env_create doit command with the appropriate channels:
doit env_create -c channel1 -c channel2 --name=dev-env --python=3.x
Don’t forget to activate this environment:
conda activate dev-env
To perform an editable install of the project you are working on, including all the dependencies required to run the full unit test suite, run the
develop_install with the appropriate channels and options:
doit develop_install -c channel1 -c channel2 -o tests -o ... -o ...
doit develop_install -c pyviz/label/dev -c conda-forge -o tests -o examples
Depending on the options you have chosen to install (usually
-o tests for the unit tests,
-o examples to run the examples notebooks, …), the following commands will run:
The unit tests:
The examples tests:
The options and commands are not standardized across the HoloViz projects. Refer to the project documentation for the exact commands to run.
The reference sources to find out which commands to run to install the correct dependencies and to execute the tests and other tasks are the Github Actions Workflows files that you can find in the
.github folder of each repository.
At this step, you should have your environment set up, being able to run tests and a clone of the source repository. The next step is to create a branch and start making changes. Before committing these changes, make sure the tests still pass by running them. The HoloViz source codes are stringent concerning styling (e.g., they’re not using black). Try to write code that follows the style of the surrounding code. Once you are done with your changes, you can commit them. It is good practice to break down big changes into smaller chunks, and each chunk has its own commit with a message that summarizes it. Now you can push the branch to your clone and create a pull request from your clone to the original repository. This Github Gist describes these GitHub steps in more detail.
The pull request you make should reference the issue you are attempting to close (i.e.
Fixes #issuenumber) and include a description of the changes you made. Changes affecting the visual properties should include screenshots or GIFs.
Your pull request should now be reviewed by one of the project’s maintainers. This may take a while given how busy the maintainers are, so try to be patient :). Your pull request will be evaluated to ensure that it is within the scope of the project (again, it’s a good idea to create an issue first to outline your intent and give the maintainers an opportunity to weigh in before you spend the effort creating a pull request). If it’s decided to proceed, the review will be conducted, and once the review is done you may be required to make some changes. You may also be asked to resolve conflicts if the changes you made appear to conflict with changes made to the source code after you submitted the pull request.
When your PR is merged, enjoy, and repeat!
The HoloViz project consists of:
Several core packages (including Panel, Lumen, HoloViews, GeoViews, Datashader, hvPlot, Colorcet, and Param) that must be maintained, tested, documented, etc.
A group of people collaborating to make the project better.
This section of the guide describes how that system works.
The HoloViz project was previously named PyViz but the Python community suggested that it wasn’t that appropriate and as such PyViz was renamed HoloViz, inspired by HoloViews. As renaming is a lot of work and can potentially break some systems, you will still find references to PyViz here and there. Notably, in the installation guide of each HoloViz package where the recommended installation command with conda is
conda install -c pyviz package.
PyViz, or PyViz.org, is now a community project of its own. It is a fully open guide to all Python visualization tools. The HoloViz group maintains the pyviz organization, but only for historical reasons, as it is meant to be shared by the whole Python visualization community.
Organisations and repositories#
HoloViz consists of several Python projects. All these projects are version-controlled with Git and hosted on GitHub. Having all the projects hosted on GitHub means that HoloViz can rely on all the excellent features Github freely provides to open-source projects, including easy collaboration through Issues and Pull Requests (PR), continuous integration (CI) with GitHub Actions (GHA) and documentation hosting with GitHub Pages.
It is crucial to keep in mind that nothing lasts forever and that, at some point, HoloViz may be forced to rely on other platforms or services. The more HoloViz relies on GitHub, the more difficult any transition to another system is.
The HoloViz group owns a few GitHub organizations:
holoviz is the main one where you are likely to contribute. It hosts the core packages maintained by the group.
holoviz-dev hosts two main types of repositories:
Packages that support maintaining the core HoloViz packages, including, for instance,
autover. These support packages were developed when no alternative, or satisfying alternative, was available at the time the group needed them.
Repositories that are only used to host dev builds of the core packages websites, i.e., no actual work is done on these repositories. They just get updated automatically in a CI process.
holoviz-topics hosts repositories that demonstrate concrete usage of the HoloViz tools.
holoviz-demos hosts some demos, mostly Panel apps. It is meant to be removed.
holoviz-community is a place for the HoloViz community to host repositories that are going to be nicely exposed under the HoloViz umbrella
In more detail:
holoviz: HoloViz website and tutorial
hvPlot: A high-level plotting API for pandas, dask, xarray, and networkx built on HoloViews
Panel: A high-level app and dashboarding solution for Python
Lumen: Illuminate your data.
HoloViews: With Holoviews, your data visualizes itself.
GeoViews: Simple, concise geographical visualization in Python
Datashader: Quickly and accurately render even the largest data.
Colorcet: A set of useful perceptually uniform colormaps for plotting scientific data
Param: Make your Python code clearer and more reliable by declaring Parameters
Spatialpandas: Pandas extension arrays for spatial/geometric operations
pyviz_comms: Bidirectional communication for the HoloViz ecosystem
jupyter-panel-proxy: Jupyter Server Proxy for Panel
nbsite: Build a tested, sphinx-based website from notebooks
nbsmoke: Basic notebook checks. Do they run? Do they contain lint?
pyctdev: Python packaging Common Tasks for Developers
autover: Provides consistent and up-to-date version strings for Python packages.
pyct: Python packaging Common Tasks
blog: The HoloViz blog
status-dashboard: Status Dashboard for HoloViz Project
holoviz-dev.github.io: Index of all sites on holoviz-dev
holoviz_tasks: Shared GHA workflows and tasks used to maintain the HoloViz repositories
examples: Home for domain-specific narrative examples using multiple PyViz projects
earthsim: Project for developing Python-based workflows for specifying, launching, visualizing, and analyzing environmental simulations
earthml: Machine learning and visualization in Python for Earth science
holodoodler: Python application that allows interactive construction of sparse labeling for image segmentation using deep neural networks
holoviz organization has two teams:
holoviz-dev: Team that will manage the HoloViz contents
Triaging team: Contributors able to triage open issues on HoloViz projects
holoviz-dev organization has one team:
holoviz-dev: PyViz Developers
holoviz-topics organization has one team:
holoviz-dev: Developers on HoloViz Topics
The core packages have their repository that, except in a few cases, all share the same structure:
The tests are nested under the package directory, e.g., at
panel/tests. The tests are then automatically bundled in the source distribution, which makes it a little easier for repackagers to run the tests.
docfolder share the same structure, e.g. you will find
examplesfolder contains Jupyter Notebooks, while the
docfolder usually contains reStructuredText and Markdown files. The HoloViz packages generally have
pyctas a build dependency, which can be used to add an
examplescommand to a project (e.g.
panel examples) to make it easier for users to download the project notebooks. The
examplesfolder is bundled within the package source, so users running
panel examplesget the notebooks copied from the package to a local directory. The
docfolder isn’t bundled within the package source.
.githubdirectory contains GitHub specific configuration files, e.g., for GitHub Actions.
conda.recipedirectory contains a conda recipe template used by the building tooling when creating a conda package.
binderdirectory contains configuration files to setup binder
The HoloViz projects follow the standard Github workflow:
An issue should be created before submitting a pull request (PR) unless the scope of the planned PR is minimal, such as fixing a typo.
Pull requests must be based on the main (e.g., main) branch and kept up to date with this branch. Merging the main branch onto the working branch is recommended instead of rebasing, particularly if you are not working alone on that branch.
Pull requests must be reviewed before being merged. Each project has one or more reviewer(s) assigned.
The commits of a Pull request are automatically squashed on merge. This means that you don’t have to particularly well craft your commit messages in your branch as they won’t be part of the main git history.
The HoloViz group relies heavily on the conda package manager for the following reasons:
The HoloViz group consists of people who have a scientific background. Installing the Python scientific libraries was at some point a complicated task to achieve with
pipas these libraries usually depend on binary dependencies that need to be compiled, a step that pip delegates to whatever tool is installed on your machine.
condawas created to solve this exact problem and the HoloViz group was an early user of this solution. While it’s important to note that installing scientific libraries with
piphas become a smoother experience (notably because more wheels are being distributed), installing some packages like those of the Python geo-stack (
pyproj, etc.) still proves to be challenging, and
condausually provides a better experience.
The core maintainers of the HoloViz group are employed by Anaconda, and as such, it makes sense for the group to use
Some HoloViz-maintained packages such as
Panelrequire software like
pipcannot install. In contrast, they can be installed with
condaallows to create a complete dev environment without having to install other software from another way, e.g., with
To contribute to HoloViz, we recommend that you use
conda. You would have a closer experience with the maintainers, and they will be in a better position to help you set up your dev environment and debug whatever issue you may encounter.
Of course, this is not a strict requirement, and you can decide to use
pyctdev stands for python packaging common tasks for developers, it is a developer tool built by the HoloViz group in an attempt to simplify managing Python projects, by providing a way to declare how to run the usual tasks required to maintain a project, such as running its unit tests or building its documentation, and by standardizing the way to execute these tasks with e.g.
doit test_unit or
pyctdev relies on doit to provide a command-line interface to developers (e.g.
doit test_unit) and can be as such seen as an extension of
doit allows to register tasks that consist of a sequence of actions, an action being either a command line instruction (e.g.
flake8 .) or the execution of a Python function.
pyctdev comes with various common Python tasks dedicated to manage a project, such as the
develop_install task (executed with
doit develop_install) that will install the project in development mode with its optional development dependencies.
By unifying the way to execute these common tasks,
pyctdev makes it easier to work across the HoloViz projects. It also makes it easier to work across environments, as the commands to execute should be the same whether you execute them locally or in a CI environment. In addition,
pyctdev supports developing with either pip or conda, a unique feature. In practice, however,
pyctdev is an ambitious project. While it has proven useful to the HoloViz group, it is yet another tool to learn for contributors and maintain for the group. We still recommend that you use
pyctdev to have a development experience closer to the core maintainers. If you feel constrained by the tooling abstraction that is
pyctdev, you can ignore it and reach out to the tools you are most comfortable with. In that case, you will have to inspect the files of the project you are contributing to and find what tasks you need to run. In each core HoloViz repository, you will find a few files that are of importance for
dodo.pyis a Python module that allows adding per-project
doittasks. Refer to its documentation to learn how to add or modify a task. A quick way to identify a task is to find a Python function named
doitmakes it available as a command line subcommand with
setup.pyis the classic install file of the setuptools build backend. It is the single source of truth of all the dependencies. In this file, you will find:
the list of the runtime dependencies required by the package in the
multiple extra dependency groups are required to run different tasks in the
extras_requirekey. For instance, the
docextra group would like the dependencies required to build the documentation site.
extras_requirekey, you will also find the dependencies required at the build time like
tox.iniis the configuration file of tox and where you will find the configuration of the
pyctdevcreates tasks out of the commands it contains and uses
toxdirectly in some cases (it vendors a version of it).
Now that the main files are defined, we can go through each step of a typical workflow with
pyctdev, assuming that you are in a cloned repository:
conda install -c pyviz/label/dev pyctdev: to start things off, you need to install
pyctdev. It is available on the
pyvizchannel, and we recommend installing a dev release from the dev channel
pyviz/label/devto get a more up-to-date version
doit env_create -c pyviz -c conda-forge --python=3.8 --name=my_dev_env: once
pyctdevis installed, you can run the
env_createcommand to create a conda environment that will have the Python version and the name you want, and will fetch packages from the channels you have listed.
conda activate my_dev_env: to activate the environment you’ve just created.
doit -c pyviz -c conda-forge develop_install -o tests -o examples: the
develop_installexecutes three actions, (1) it installs the build dependencies (including, e.g.,
setuptools), (2) it installs the runtime dependencies and the extra dependencies that are listed with the
-oflag (in the example that would be the tests and examples groups of dependencies), and (3) it installs the package you’re working on in editable mode (with
pip install --no-deps --no-build-isolation -e .).
doit test_flakes(or sometimes
doit test_lint): run linting on the project code source, e.g., with
doit test_unit: run the Python unit tests, i.e., the tests you will find in the
/testsfolder, most likely with
doit test_examples: smoke test the examples, i.e., the notebooks you will find in the
Most of the packages maintained by the HoloViz group have a website. Those not promoted for usage outside the group don’t have a website like
pyctdev. HoloViz being dedicated to making data visualization simpler in Python, it made sense for the group to develop a way to generate websites out of a collection of Jupyter Notebooks. As a result, nbsite was created to achieve that goal.
nbsite is based on sphinx and is the tool used by all the projects to build their site.
nbsite provides two important features:
NotebookDirectiveallows inserting an evaluated notebook in a document. It has a few useful parameters like
offsetthat takes a number that will be the number of top cells not rendered.
It can build a gallery from an organized collection of Notebooks.
Building a site with
nbsite is usually a two-step process:
nbsite generate-rst ...looks for notebooks in the
examplesfolder and generates their corresponding reStructuredText files. For instance, if the notebook
examples/user_guide/Data.ipynbis found, then the corresponding file
doc/user_guide/Data.rstis created and includes the
NotebookDirectivethat points to the notebook file to insert it in this document. A similar process applies to the notebooks found in a gallery.
nbsite build ...executes the notebooks and builds the website.
After these steps, you should find a
builtdocs folder in the repository that contains the static site built by nbsite/sphinx. When the websites are built in the continuous integration system, the content of the
builtdocs folder is pushed to a
gh-pages branch. The details of this process can be found in the
docs.yaml Github Action workflow of each project, located in the
The documentation is currently written in a mix of three file formats:
Jupyter Notebooks (.ipynb): Notebooks are saved in the
examplesfolder and are meant to be executed when the documentation is built, which means that the system that builds the documentation must have all the dependencies and data required to run them. Notebooks must be cleared before being committed to a repository. The team uses a custom shell script that leverages
jqto strip out some data and metadata from the notebook JSON file. Thanks to MyST-NB, on which
nbsitedepends, MyST Markdown is correctly handled in notebooks.
reStructuredText (.rst): Original file format supported by Sphinx and in which the Python documentation is written. They should be gradually replaced by MyST Markdown files.
Markdown (.md): MyST Markdown is a Markdown extension focused on scientific and technical documentation authoring. It is easier to write and read compared to reStructuredText, which should be favored.
The HoloViz sites rely on the PyData Sphinx Theme for their theme.
Currently, most of the websites gather analytics via Google Analytics.
The HoloViz websites are usually hosted on GitHub Pages. The deployment process involves pushing the website built to the
gh-pages branch, which is watched by GitHub and will trigger a site update on change. This process is based on Git and comes with some size limitations, which means that it wasn’t possible to deploy HoloViews’ website this way, which is instead hosted on AWS S3.
Most HoloViz projects have both the main site - the one meant to be visited by users and that is in line with the latest official release - and a dev site that can be updated at any time and is meant to be used by the HoloViz developers for testing purposes, and in particular making sure that the documentation build works as expected before making a new release.
This page references all the HoloViz dev sites: https://holoviz-dev.github.io/. A link to the dev site is also usually available in the README file of each project.
Status dashboard and scheduled workflows#
The HoloViz Status dashboard is a site that lets you glance at the status of the packages and repositories maintained by the HoloViz group. It also includes the status of other packages important to HoloViz, e.g., Bokeh. The dashboard primarily consists of badges that report various indicators, such as the status of the latest CI test runs, the latest version available on PyPi, whether the documentation is up or not, etc.
Scheduled Github actions have been set up to run on Sundays on most of the packages maintained by the group. This means that checking the Status Dashboard on a Monday morning is the right time to get an appreciation of the state of the test/build/docs workflows across the projects.
Lumen AE5 Monitor#
The Lumen AE5 Monitor is a dashboard that helps monitor the state and performance of the deployments.
All the HoloViz websites are static websites. Yet many of their pages would actually require an active Python kernel to be fully interactive. I.e., any datashader examples on HoloViews’ website would require a kernel to show users that data shading is done every time their plot changes. Some websites have implemented deployments to show users how the tools behave in a fully interactive environment. As the core HoloViz members are employed by Anaconda, they have access to an Anaconda product named Enterprise Data Science Platform (also called AE5) that is a platform to, among other things, allow to easily develop and deploy projects, like for instance Jupyter Notebooks or Panel apps. The HoloViz group has set up an AE5 instance and used it to deploy applications for the following websites:
It is sometimes convenient to have a place where to store data. This happens when the data is too large to be stored on a GitHub repository (storing data there isn’t recommended anyway), which is pretty standard when creating a tutorial that relies on an actual data set. The HoloViz group makes use of the following AWS S3 buckets:
These buckets are managed by @jlstevens and @philippjfr.
Some of the sites have their own domain name:
While others are available as subdomains of holoviz.org:
pyctdev acts as the task runner, other tools run the tests. There are four main kinds of tests that a HoloViz project may run:
Linters: running programs that check Python source files for errors and styling issues. Most HoloViz projects rely on Flake8 and use the
doit test_flakescommand. Some projects may rely on pre-commit to run the linters on every commit to avoid having the CI fail in linting issues, which are best found locally. Notebooks can also be linted, this is done either by nbsmoke or nbqa.
Unit tests: the HoloViz projects rely on pytest to run their unit tests, sometimes with some additional pytest plugins. pytest-cov usage is pretty standard across the projects, as it provides an easy way to produce coverage reports that can then automatically be uploaded to the Codecov service. The
doit test_unitcommand is usually the one that will run these tests.
Example tests: the examples tests and the notebooks tests, in which the notebooks found in the
/examplesfolder are all executed. Note that their output is not compared with any reference. Instead, the tests only fail if an error is raised while running the notebooks. The projects rely on pytest and either nbsmoke or nbval. The
doit test_examplescommand is usually the one that will run these tests.
UI tests: some projects may rely on Playwright and pytest-playwright to run tests that check that things get displayed as expected in a browser and those interactions between the client and the backend work as expected. The
doit test_uicommand is usually the one that will run these tests.
We would like not to have to maintain nbsmoke anymore and instead rely on nbqa and nbval.
Releasing a new version of a package is an operation that needs to be done with care, a broken release can adversely affect many users.
Making a new release is a cumbersome and delicate operation. It implies many manual steps and has a cost on the general Python ecosystem, e.g., someone needs to update the conda-forge release). So make sure that a release is needed before making one, and try not to mess it up too badly ;)
Releasing a new package version is, in practice, very easy:
a commit must be tagged (e.g.
git tag -m "Version 1.9.6 alpha1" v1.9.6a1 main)
that tag must be pushed (e.g.
git push origin v1.9.6a1)
And that’s it, as soon as a new tag is pushed, the Packages and Documentation Github Actions get triggered, and start building the packages and the documentation, and deploy them.
Version tags must start with a lowercase
v and have a period in them, e.g.
v0.1 and may include the PEP440 prerelease identifiers of
b (beta) or
rc (release candidate) allowing tags such as
The goal of making a release is to distribute a new package version.
The HoloViz group automatically builds and publishes package distributions directly to two platforms:
Conda packages are distributed to the pyviz channel. This allows the HoloViz group to be sure that new packages are available instantaneously to the users as soon as they are published.
The core HoloViz packages are also made available on two other conda channels:
defaults: most of the core HoloViz packages are made available on this channel managed by Anaconda (the company)
conda-forge: the HoloViz members, helped by other contributors, maintain the conda-forge recipes of the packages they publish.
Development releases are only available on PyPi and the conda
pyviz channel. To install the latest development release, e.g., Panel execute
pip install panel --pre or
conda install -c pyviz/label/dev panel.
There are a few tasks that are worth paying attention to before making a release:
You should have made some decisions about what should go in that release and check later that these decided changes (bug fixes, new features, documentation, etc.) are indeed merged. This is best managed by setting Github Milestones to issues.
You should make sure that the test suite passes.
A new release is a good opportunity to check that there are no alarming warnings emitted while the tests suite runs. Missing a deprecation warning could mean that you would have to make a new release soon after this one!
Development releases can have different goals. Sometimes they are only meant to be pre-releases made right before an actual release to make sure everything is alright. Sometimes they are made to be shared with stakeholders (e.g., a specific contributor, a customer, a dependent project, etc.). Depending on your situation, you might decide to pause the release process in the procedure detailed below between two development releases, waiting for feedback.
Before making a proper release, you should start by making a development release. This is the first one that would be an alpha release. Bump the version too, e.g.,
v1.9.6a1and commit the new tag.
After pushing the new tag, you can monitor the Packages and Documentation workflows. If one fails, you will have to fix that as that would be a release blocker.
If the alpha release succeeded, it is now time to check a few things:
A new version of the dev (built on the corresponding
gh-pagesbranch) site should have been built. You should spot-check it, trying to find errors that could have occurred while the notebooks ran, for instance.
Some packages have implemented downstream tests. When they do, these tests run only when a release is made. They trigger the test suite of their downstream packages (e.g., a Panel release would trigger the test suite of Param). This ensures that the release you just made didn’t break some other packages of the HoloViz ecosystem. To find the results of these downstream tests, check the Github Actions page of the released package.
Optionally, and to make sure that the release went well, you could install the package you’ve released (e.g.
conda install -c pyviz/label/dev panel) and check there’s no embarrassing issue.
Pause the release process if you expect feedback from others. If not, keep going.
In practice making beta releases appear to be quite rare. However, making a release candidate, in particular before making a release that incorporates breaking changes, is recommended. After making a release candidate you should announce it (e.g., on Discourse, Discord, Twitter) so that users can try it out and provide feedback.
It is not required to update the changelog for a development release. However, a final release should come with an updated changelog, which means that at some point before making the last development release, you will have to merge an updated changelog. Updating the changelog is a process that depends on the package being released. Look for files such as
CHANGELOG.mdin the repository and update them accordingly to their format. Crafting a good changelog is an important step in the release process. Users will read it to find out what’s new and, in particular, what may potentially break their code. Don’t forget to mention all the people who have contributed to the release since the last one.
Once the last development release has been confirmed to be in good shape, by yourself and preferably by others too (in particular, when it comes to spot-checking the website, it is best to have more than one pair of eyes looking into that!), you are ready to make the final release. You will have to bump the version again to its final number (e.g., to
v1.9.6). Note that you may need to make another development release before the final one if you’ve made changes after the latest development version that are worth mentioning in the changelog.
Optionally again, and to ensure the release went well, you could install the package you’ve released (e.g.
conda install -c pyviz panel) and check there’s no embarrassing issue.
Create a Github Release. It should contain the same changelog as the one published on the website (the formatting can change).
Go to the Github repository
Click the most recent tag that you just added
Click Create a new release
Add release notes and publish the release
Find the conda-forge recipe of the package you released and update it if required. Pay attention to the build and runtime dependencies and their version pins. If you’re not yet a maintainer, add yourself to the list of maintainers and ping an existing maintainer, letting them know the PR is ready and that you have added yourself as a maintainer.
Announce the release (e.g., on Discourse, Discord, Twitter).
If the release is important (e.g., not a bug fix release), it may be worth a blog post.
Bumping the version of a package depends on the package’s nature:
package-lock.jsonfiles to be updated, manually bumping the version in those files (e.g., from
1.9.6-a.1). Note that the version scheme isn’t the same as the Python version scheme.
Pure Python packages (and non-pure Python packages once manually bumped) just need a new tag, done with
git tag -m "Version 1.9.6 alpha1" v1.9.6a1 main.
Development releases are cheap, don’t hesitate to make as many as required! It’s also fine if a development release only makes it to one of the two distribution platforms (PyPi or Anaconda.org). They’re not meant to be for end-users but development purposes only.
You can tag a release from any branch, not necessarily from the main one. This is useful if you have to maintain multiple versions simultaneously (e.g., 2.* and 3.*).
If you push a tag by mistake or the wrong tag and are lucky enough to spot that instantly, you should hurry up (really!) and cancel the Build and Documentation workflows before anything gets published/deployed. If you manage to do that, you can then remove the faulty tag.
HoloViz Users are meant to ask their questions on the HoloViz Discourse forum. This Discourse instance is managed by @philippjfr.
HoloViz Contributors chat on multiple open channels:
Directly on Github via issues and pull requests
On the Discord, which is meant for brainstorming and casual chatting. An example of a discussion on Discord would be when the maintainer of a library that relies on a HoloViz package has in mind a suggestion for an improvement and requires a first assessment before opening an issue.
HoloViz Contributors also have regular online meetings. These meetings are open to anyone. Please see the Community page for a calendar and description of the meetings.
The HoloViz project maintains a blog at https://blog.holoviz.org/ where new major releases are announced.
The former PyViz-named blog is still alive at http://blog.pyviz.org/.
There are four more or less active Twitter accounts: