jupyterlab-bokeh-server

JupyterLab and Bokeh Server

A JupyterLab extension for displaying the contents of a Bokeh server.

This project serves as an example for how to integrate a Bokeh server application into JupyterLab. This makes it easy for Python developers to develop rich dashboards and integrate them directly into Jupyter environments.

Motivation

We want to give Jupyter users rich and real-time dashboards while they work. This can be useful for tracking quantities like the following:

• Computational resources like CPU, Memory, and Network
• External instruments or detectors
• Other resources like GPU accelerators
• Web APIs
• ...

JupyterLab extensions provide a platform for these dashboards, but require some JavaScript expertise. For Python-only developers, this requirement may prove to be a bottleneck. Fortunately the Bokeh plotting library makes it easy to produce rich and interactive browser-based visuals from Python, effectively crossing this boundary.

This repository serves as an example on how to create rich dashboards in Python with Bokeh and then smoothly integrate those dashboards into JupyterLab for a first-class dashboarding experience that is accessible to Python-only developers.

What's here

This repository contains two sets of code:

• Python code defining a Bokeh Server application that generates a couple of live plots in the jupyterlab_bokeh_server/ directory
• TypeScript code integrating these plots into JupyterLab in the src/ directory

You should be able to modify only the Python code to produce a dashboard system that works well for you without modifying the TypeScript code.

There are also two branches in this repository:

1. master contains a basic dashboard with two plots, a line plot and a histogram, that display randomly varying data: And is available in a live notebook here: TODO binder link

• system-resouces expands on the toy system above to create a real-world example that uses the psutil module to show CPU, memory, network, and storage activity: And is available in a live notebook here: TODO binder link

You can view the difference between these two branches. This should give a sense for what you need to do to construct your own JupyterLab enabled dashboard.

History

This project is a generalization of the approach taken by the Dask JupyterLab Extension which integrates a rich dashboard for distributed computing into JupyterLab, which has demonstrated value for many Dask and Jupyter users in the past.

Prerequisites

• JupyterLab 1.0.0a3
• bokeh

Installation

This extension has a server-side (Python) and a client-side (Typescript) component, and we must install both in order for it to work.

To install the server-side component, run the following in your terminal

pip install jupyterlab-bokeh-server

To install the client-side component, run

jupyter labextension install jupyterlab-bokeh-server

Development

To install the server-side part, run the following in your terminal from the repository directory:

pip install -e .

In order to install the client-side component (requires node version 8 or later), run the following in the repository directory:

jlpm install
jlpm run build
jupyter labextension install .

To rebuild the package and the JupyterLab app:

jlpm run build
jupyter lab build

Publishing

In order to distribute your bokeh dashboard application, you must publish the two subpackages. The JupyterLab frontend part should be published to npm, and the server-side part to PyPI or conda-forge (or both).

Instructions for publishing the JupyterLab extension can be found here. A nice write-up for how to publish a package to PyPI can be found in the nbconvert documentation.