Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).īy default, the jlpm run build command generates the source maps for this extension to make it easier to debug using the browser dev tools. With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. # Watch the source directory in one terminal, automatically rebuilding when needed You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension. # Rebuild extension Typescript source after making changes # Link your development version of the extension with JupyterLab # Clone the repo to your local environment # Change directory to the jupyter_bokeh directory # Install package in development mode ![]() The jlpm command is JupyterLab's pinned version of Note: You will need NodeJS to build the extension package. Compatible JupyterLab and jupyter_bokeh versions JupyterLab Installation may refer to the below table. ![]() Track that of JupyterLab, so users seeking to find extension releases that are compatible with their JupyterLab We've been previously inconsistent with having the extension release minor version bumps Made to follow JupyterLab minor release bumps, while micro releases are for new jupyter_bokeh features Our goal is that jupyter_bokeh minor releases (using the SemVer pattern) are JupyterLab and this jupyter_bokeh extension Jupyter labextension install labextension install install a specific version: jupyter labextension install core Bokeh library is generally version independent of ![]() Separately: conda install -c conda-forge jupyter_bokeh Or conda install -c conda-forge jupyter_bokehįor versions of Jupyter Lab older than 3.0, you must install the labextension Jupyter_bokeh with either pip or conda: pip install jupyter_bokeh Document User A User controls authentication information at the user level and both models combined determines the authorization information regarding user documents that are private, so can be accessed only by the user, or public. Installįor versions 3.0 and newer of JupyterLab, you have the option to install The core architecture of bokeh-server develops around 2 core models. See also the separate ipywidgets_bokeh library for support for using Jupyter widgets/ipywidgets objects within Bokeh applications. A Jupyter extension for rendering Bokeh content within Jupyter.
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