Using Remote Kernels with Jupyter Notebook Server

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Jupyter Notebook uses kernels to execute code interactively. The Jupyter Notebook server runs kernels as separate processes on the same host by default. However, there are scenarios where it would be necessary or beneficial to have the Notebook server use kernels that run remotely.

A good example is when you want to use notebooks to explore and analyze large data sets on an Apache Spark cluster that runs in the cloud. In this case, the notebook kernel becomes the Spark driver program, and the Spark architecture dictates that drivers run as close as possible to the cluster, preferably on the same local area network [1].

To run Jupyter Notebook with remote kernels, first you need a kernel server that exposes an API to manage and communicate with kernels. Second, you need to modify the default behavior of the Notebook server, which is to spawn kernels as local processes on the same host.

The Jupyter Kernel Gateway satisifies the first need. It allows clients to provision and communicate with kernels using HTTP and web socket protocols.

Jupyter Notebook 4.2 introduced server extensions to make it possible to extend (or modify) the Notebook server behavior. I leveraged this new capabilitiy and created a demo server extension to modify the Notebook server to use remote kernels hosted by the Kernel Gateway. For lack of a better name, I called it nb2kg.

We can visualize the Jupyter Notebook components in the diagram below, which was taken from the Jupyter documentation.

The key points are:

  • The Notebook web UI (browser) and the Notebook server communicate using HTTP and web socket protocols.
  • By default, the Notebook server spawns kernels on the same host as the server.
  • The server and kernel processes communicate using ZeroMQ.

The nb2kg extension essentially proxies all kernel requests and web socket communication from the notebook web UI to a Kernel Gateway. Using the extension, the browser to server to kernel communication looks like this:

Try It

This section provides an example of running a Jupyter Kernel Gateway and the Jupyter Notebook server with the nb2kg extension in a conda environment. (The nb2kg Kernel Gateway demo also includes Dockerfiles and a docker-compose recipe if you care to try it using Docker).

First, open a terminal and create and activate a new conda environment. Install Jupyter Notebook version 4.2 or later.

Use pip to install the Kernel Gateway, and start it on port 8889.

Open a second terminal and attach to the nb2kg conda environment.

Install and enable the nb2kg Jupyter Notebook server extension.

You can ensure that the extension is enabled using:

Finally, start the notebook server. The nb2kg extension requires that you set the KG_URL environment variable to the URL of the Kernel Gateway. It also requires that you override the default session, kernel, and kernel spec managers when you start the notebook.

If things are working properly, you should see output similar to:

A web browser window should open to the notebook server home page (in this case http://localhost:8888/tree). You should be able to create and use notebooks as normal, with the following caveats:

  • When you enable the nb2kg extension, all kernels run on the configured Kernel Gateway, instead of on the Notebook server host. (The extension does not support local kernels).
  • Keep in mind that notebooks and other files reside on the Notebook server, and remote kernels may not be able to access them.

[1] Apache Spark cluster documentation

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3 comments

  • Pav

    pip install “git+https://github.com/jupyter/kernel_gateway_demos.git#egg=nb2kg&subdirectory=nb2kg”
    should instead be
    pip install “git+https://github.com/jupyter/kernel_gateway_demos.git#egg=nb2kg&subdirectory=nb2kg”

    No HTML escaping 🙂

  • Dharmesh

    Looks like notebook version 4.2 is not compatible with nb2kg extension. I update the notebook using command ‘conda update notebook’, which upgraded notebook to version 4.3.1, which seemed to be compatible with nb2kg extension.

    With this update I could connect my notebook to remote kernel.

    Cheers
    Dharmesh

    • Yes, Dharmesh, you are correct. Some of the notebook APIs changed from 4.2 (when the original post was written) to 4.3. I will update the post content to reflect the current state of things (use 4.3).

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