Building consists of:
- Populating the staging directory using template files.
- Handling any locally installed packages.
- Ensuring all installed assets are available.
- Bundling the assets.
- Copying the bundled assets to the static directory.
# Set yarn cache dir. yarn cache dir yarn config set cache-folder ~/.yarn-cache
# Install prerequisites. git clone https://github.com/jupyterlab/jupyterlab.git && \ cd jupyterlab && \ pip install -e . && \ yarn install
# Build more accurate sourcemaps that show the original Typescript code when debugging. # However, it takes a bit longer to build the sources, so is used only to build for production by default. cd $DLAHOME/repos/jupyterlab && \ yarn run build:dev:prod && \ ls -alph dev_mode/static && \ du -h dev_mode/static
# Build the core mode assets (optional). yarn run build:core
# extensions schemas settings staging static themes ls $DLAHOME/opt/miniconda3/envs/datalayer/share/jupyter/lab
jupyter lab --dev-mode --watch --browser chromium-browser # If you wish to run JupyterLab with the set of pinned requirements that was shipped with the Python package, you can launch as. jupyter lab --core-mode
cd $DLAHOME/repos/jupyterlab && \ yarn build:test && \ yarn test
echo file://$PWD/docs/index.html echo file://$PWD/docs/api/index.html cd $DLAHOME/repos/jupyterlab && \ yarn docs
# At times, it may be necessary to clean your local repo with the command yarn run clean:slate. # This will clean the repository, and re-install and rebuild. yarn clean:slate # Deletes the lib directory. yarn clean
Try the Datalayer Lab JupyterLab examples.