Setup

note

Many developers are reporting issues with their environment (build failures...). This is inherent to the mixed Python and JavaScript build as dependency on Jupyter toolchain. We are putting this issue at the top of our priority to ease your pain.

1. Docker and devcontainer with VS Code are the preferred option.
2. We have migrated to Yarn v3 and latest Lerna to align with JupyterLab 4.
3. engines and os are enforced in package.json - We only support Yarn v3 on macOS and Linux.
4. We are offering hosted kernels so you don't need to run your local Jupyter Server. If you run yarn start, you will consume those hosted kernels from the cloud.
5. The default build should be faster and not build the examples, please give your opinion on https://github.com/datalayer/jupyter-ui/issues/63.
6. We are looking towards Pyodide kernels (already supported for the Console component).

👉 Please continue reporting any failure and idea on how to make a better experience. ❤️

Environment

caution

Only Yarn V3 is supported (Yarn V1 will not work, this is due to JupyterLab requirements).

Choose the way you want to develop Jupyter UI:

1. With VS Code Devcontainers
2. With Conda

Once you have applied the steps described in one of the above 2 options, you are ready to develop following the next steps.

Install and Build

Install the dependencies. This will take some time, be patient. 👍

yarn install

Build the source code. This will also take some time, be also patient. 🙂

yarn build

If you just want to develop the core package of the repository, the @datalayer/jupyter-react, just go to the sub folder and work there.

cd packages/react
yarn build