You can contribute to this website either by submitting a new project or by updating an existing one.
Setup the managing environment#
We strongly recommend you to set up the managing environment to be able to validate and test a project locally, before submitting it to this repository. For that, clone the repository and run:
conda env create --file envs/environment.yml
conda activate examples-gallery-manage
Add a project#
Once you have an example (one or more notebook(s)) that you think is ready to be shared on this website follow these steps to set it up correctly.
1. Create the project directory#
Its name must be all lower-case, only including underscore as special characters. This name is important as you will have to re-use it as-is in multiple places.
2. Use the template#
Copy the template project from
your project. You will use this file as a basis to build your
own project. You can inspect more the
template example folder, which
contains a fully runnable and deployable project.
Do not forget to clean up the notes left in the template project file once you are done updating it for your own project.
3. Move your notebook(s) to your project directory#
If your project has a single notebook it must share the same name
as your project directory. If your project has more than one notebooks,
it must have an
index.ipynb notebook that will link to your other notebooks.
Your project may have notebooks that are not meant to be displayed on the website,
such as pre-processing notebooks. See the
skip option to declare them:
Your notebooks should be committed without output. The system will be in charge of executing your notebooks based on your project characteristics, to build the documentation.
In some cases it is impossible for the system to execute your notebooks. This
happens for instance if your notebooks are too long to run, require too much
data, require special hardware… In those cases, you should instead commit
your notebooks with output and set
4. List the dependencies#
Add to your
anaconda-project.yml file the dependencies required to run
your notebook(s) and application(s). The recommended practice is to pin
all your dependencies using either
>= (for easier updates) or
5. Manage the data#
There are multiple ways to manage the data required to run a project:
the project does not ingest any data, as it either generates it on the fly or as it just provides written indications on how to get it: set
the project needs to download some data, you have two options:
fill in the
downloadssection of the
or, create an Intake catalog and load it in your notebooks, the catalog must be named
catalog.ymland be at the root of your project
the project relies on some very light data, then it can be committed to the repository as is, it must be stored in a
data/subfolder of your project
When using an Intake catalog, point the cache to the data dir in the
project by defining the
INTAKE_CACHE_DIR variable in the
This way when the user runs the notebook, they will still be able to see the data from within the project directory
Your project will be automatically tested by the system:
the notebooks will be linted with
flake8. If you deem that the linter is too strict, you can add some more rules to ignore to the
the notebooks will be remotely executed with
nbval, the tests failing if an error occurs during their execution
If you project has some special testing needs, you can implement your own testing command by
test command to the
anaconda-project.yml file. You will presumably also need to
declare testing dependencies, you can do so by extending the default dependencies.
Projects that download data must provide a test dataset that is a reduced version of their full
dataset, which should be small enough to be committed to the repository. These datasets must be
saved in the
testdata folder in a folder named as the project. When the system will test your project,
it will move your test data to the right place, i.e. your datasets to the
data/ subfolder and
will replace your Intake catalog. This step allows automated tests to be run in a practical way,
exercising all of the example’s functionality but on a feasible subset of the data involved.
7. Complete the anaconda-project file#
Follow the instructions provided in the
8. Add a thumbnail#
Create a nice but lightweight PNG thumbnail, name it as your project if you have only one
notebook or as
index.png if you have multiple notebooks, and save it in the
subfolder of your project.
You must lock your project to capture the full set of dependencies that are required to execute it. Run:
10. Make sure it works#
If you have declared commands run them to check that they work as expected, as this is what the users of your project will ultimately use. Run:
anaconda-project run <commandname>
11. Validate, test and build#
You should run locally the steps that are going to be run on the CI.
Start by validating that your project is well specified by running
<projectname> by your project name):
Update your project until you have no validation warning left.
doit clean --clean-dep validate:<projectname> to clean up that step.
Now you will test your project by running:
Update your project until the tests pass.
doit clean --clean-dep test:<projectname> to clean up that step.
Finally you will build your project running:
After validating and testing the project, this step should succeeed.
doit clean --clean-dep build:<projectname> to clean up that step.
12. Open a Pull Request#
Open a Pull Request that adds your project to the repository. The CI will take care of validating, testing and building it. If these steps succeed the developer website will be built and you will be able to check a rendered version of your project at https://holoviz-dev.github.io/examples/.
When the review of your Pull Request is completed and it is merged, the CI will then take care of updating the main site. If you declared deployments, the CI will also take care of automatically starting them.
Update a project#