I will be giving a talk in October at JupyterCon 2020 about how you and your team can get started with JupyterLab extensions.
I’ll post that talk once it’s available - but in the meantime, I’ve compiled the following list of tips and guides to get you started developing JupyterLab extensions quickly.
You’re going to need to get familiar with conda, fast.
Quick tips:
mambaconda package manager in C++. It solves environments quickly - it crushes conda’s performance.conda checks conda-forge for packages, it’s scanning tens (maybe hundreds at this point?) of thousands of packages each time. Using condametachannel, you can cut down on the number of packages being searched.environment.yaml files with pinned dependencies to your users, and instructions on how to use conda to install and activate the environment.It always helps to ensure that your conda installation is up to date.
# Update conda
conda update -n base conda
# If you run into issues, try fixes suggested here:
# https://github.com/conda/conda/issues/8051
conda clean --all
conda config --remove channels conda-forge
conda update -n base conda
Further reading on conda performance:
Use cookiecutter-ts to get started, generally.
You can try using my boilerplate repository as well - instructions and tutorial. It’s a bit dated at this point (a few months old) and I’ve learned a lot since then.
I will be announcing some news around a new boilerplate repository I’ve been working on. Stay tuned for that. It is much better than either option above.
When a JupyterLab file or directory has_underscores_in_the_name, it is related to Python/server extension side of things.
Files and directories with dashes-in-the-name are related to the TypeScript side of things.
| Naming Convention | Hyphenation | |
|---|---|---|
| Python | snake_case | _ |
| TypeScript | camelCase | - |
General guidelines:
/src/*.css files in style/* (and @import them into index.css)*.svg files in style/icons/Keep your labextension version (located in package.json) and your serverextension version (located in _version.py) in sync at all times.
I recommend pinning all @jupyterlab/* dependencies in package.json, as well as the version of jupyterlab required in setup.py.
It is very easy to accidentally break compatibility if version numbers are allowed to float. Even minor changes to the jupyterlab Python package can (and will) wreak havoc on your package.json dependencies.
Occasionally, @jupyterlab packages will disagree with each other on which packages they should rely on. The resolutions section of package.json is used to force the various packages to use the same versions of their dependencies.
@jupyterlab/* packages and how they work.Stay tuned for my upcoming JupyterCon 2020 talk :)
I will elaborate on best practices, future plans, and lessons learned over months and months of hard work.