JupyterWith

This repository provides a Nix-based framework for the definition of declarative and reproducible Jupyter environments. These environments include JupyterLab - configurable with extensions - the classic notebook, and configurable Jupyter kernels.

In practice, a Jupyter environment is defined in a single shell.nix file which can be distributed together with a notebook as a self-contained reproducible package.

These kernels are currently included by default:

• IPython
• IHaskell (long build time)
• CKernel
• IRuby
• Juniper RKernel (limited jupyterlab support)
• Ansible Kernel
• Xeus-Cling CPP (experimental, not yet configurable with packages, long build time)
• IJavascript (not yet configurable with packages)
• gophernotes (not yet configurable with packages)

Example notebooks are here.

Getting started

Nix must be installed in order to use JupyterWith. A simple JupyterLab environment with kernels can be defined in a shell.nix file such as:

let
  jupyter = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    rev = "";
  }) {};

  iPython = jupyter.kernels.iPythonWith {
    name = "python";
    packages = p: with p; [ numpy ];
  };

  iHaskell = jupyter.kernels.iHaskellWith {
    name = "haskell";
    packages = p: with p; [ hvega formatting ];
  };

  jupyterEnvironment =
    jupyter.jupyterlabWith {
      kernels = [ iPython iHaskell ];
    };
in
  jupyterEnvironment.env

JupyterLab can then be started by running:

nix-shell --command "jupyter lab"

This can take a while, especially when it is run for the first time because all dependencies of JupyterLab have to be downloaded, built and installed. Subsequent runs are instantaneous for the same environment, or much faster even when some packages or kernels are changed, since a lot will already be cached.

This process can be largely accelerated by using cachix:

cachix use jupyterwith

Using JupyterLab extensions

Lab extensions can be added by generating a JupyterLab frontend directory. This can be done by running nix-shell from the folder with the shell.nix file and then using the generate-directory executable that is available from inside the shell.

$ generate-directory [EXTENSIONS]
$ generate-directory jupyterlab-ihaskell jupyterlab_bokeh

This will generate a folder called jupyterlab that can then be passed to jupyterWith. With extensions, the example above becomes:

let
  jupyter = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    rev = "";
  }) {};

  iPython = jupyter.kernels.iPythonWith {
    name = "python";
    packages = p: with p; [ numpy ];
  };

  iHaskell = jupyter.kernels.iHaskellWith {
    name = "haskell";
    packages = p: with p; [ hvega formatting ];
  };

  jupyterEnvironment =
    jupyter.jupyterlabWith {
      kernels = [ iPython iHaskell ];
      ## The generated directory goes here
      directory = ./jupyterlab;
    };
in
  jupyterEnvironment.env

Another option is to use the impure mkDirectoryWith Nix function that comes with this repo:

{
  jupyterEnvironment =
    jupyter.jupyterlabWith {
      kernels = [ iPython iHaskell ];
      ## The directory is generated here
      directory = mkDirectoryWith {
        extensions = [
          "jupyterlab-ihaskell"
          "jupyterlab_bokeh"
        ];
      };
    };
}

In this case, you must make sure that sandboxing is disabled in your Nix configuration. Newer Nix versions have it enabled by default. Sandboxing can be disabled:

• either by running nix-shell --option sandbox false; or
• by setting sandbox = false in /etc/nix/nix.conf.

For this to work, your user must be in the nix.trustedUsers list in configuration.nix.

Changes to the default package sets

The kernel environments rely on the default package sets that are provided by the Nixpkgs repository that is defined in the nix folder. These package sets can be modified using overlays, for example to add a new Python package from PIP. You can see examples of this in the ./nix/python-overlay.nix and ./nix/haskell-overlay.nix files. You can also modify the package set directly in the shell.nix file, as demonstrated in this example that adds a new Haskell package to the package set.

Building the Docker images

One can easily generate Docker images from Jupyter environments defined with JupyterWith with a docker.nix file:

let
  jupyter = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    rev = "";
  }) {};

  jupyterEnvironment = jupyter.jupyterlabWith {};
in
  jupyter.mkDockerImage {
    name = "jupyter-image";
    jupyterlab = jupyterEnvironment;
  }

nix-build docker.nix builds the image and it can be passed to Docker with:

$ cat result | docker load
$ docker run -v $(pwd)/example:/data -p 8888:8888 jupyter-image:latest

Adding packages to the environment

It is possible to add extra packages to the JupyterWith environment. For example, if you want to add pandoc to the environment in order to convert notebooks to PDF, you can do the following.

let
  jupyter = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    rev = "";
  }) {};

  jupyterEnvironment = jupyter.jupyterlabWith {
    extraPackages = p: [p.pandoc];
  };

Here, the p argument corresponds to Nixpkgs checkout being used.

Using as an overlay

You can import JupyterWith as an overlay as follows:

let
  path = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    rev = "";
  }) {};

  overlays = [
    # Only necessary for Haskell kernel
    (import (path ++ /nix/haskell-overlay.nix))
    # Necessary for Jupyter
    (import (path ++ /nix/python-overlay.nix))
    (import (path ++ /nix/overlay.nix))
  ];

  pkgs = import <nixpkgs> { inherit overlays; };
in
  pkgs.jupyterWith;

Contributing

Kernels

New kernels are easy to add to jupyterWith. Kernels are derivations that expose a kernel.json file with all information that is required to run a kernel to the main Jupyter derivation. Examples can be found in the kernels folder.

About extensions

In order to install extensions, JupyterLab runs yarn to resolve the precise compatible versions of the JupyterLab core modules, extensions, and all of their dependencies. This resolution process is difficult to replicate with Nix. We therefore decided to use the JupyterLab build system for now to prebuild a custom JupyterLab version with extensions.

If you have ideas on how to make this process more declarative, feel free to create an issue or PR.

Nixpkgs

The final goal of this project is to be completely integrated into Nixpkgs eventually. However, the migration path, in part due to extensions, is not completely clear.

If you have ideas, feel free to create an issue so that we can discuss.

License

This project is licensed under the MIT License. See the LICENSE file for details.