Workgroup:DataScience
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This workgroup is dedicated towards improving the state of the data science stack in Nixpkgs. This includes work on packages and modules for scientific computation, artificial intelligence and data processing, as well as data science IDEs.
JupyterLab
The JupyterWith repo "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."
Alternatively, there is an unmerged pull request with work to easily deploy arbitrary kernels and jupyter extensions with nix. There are some limitations due to jupyterlab extensions relying heavily on npm and webpack to compile the javascript modules. Thus an unpure setup was considered easiest to get it working. If the pull request were merged, the following `default.nix` shell would install 20 jupyerlab extension + 4 kernels (c, python, go, and ansible). All that would need to be edited by user would be kernels, additionalExtensions, and buildInputs. The rest would be automatic and would launch a jupyterlab instance for you.
{ pkgs ? import <nixpkgs> {}, pythonPackages ? pkgs.python36Packages }:
let kernels = [
pkgs.python36Packages.ansible-kernel
pythonPackages.jupyter-c-kernel
pkgs.gophernotes
];
additionalExtensions = [
"@jupyterlab/toc"
"@jupyterlab/fasta-extension"
"@jupyterlab/geojson-extension"
"@jupyterlab/katex-extension"
"@jupyterlab/mathjax3-extension"
"@jupyterlab/plotly-extension"
"@jupyterlab/vega2-extension"
"@jupyterlab/vega3-extension"
"@jupyterlab/xkcd-extension"
"jupyterlab-drawio"
"@jupyterlab/hub-extension"
"jupyterlab_bokeh"
];
in
pkgs.mkShell rec {
buildInputs = [
### Base Packages
pythonPackages.jupyterlab pkgs.nodejs
### Extensions
pythonPackages.ipywidgets
pythonPackages.ipydatawidgets
pythonPackages.ipywebrtc
pythonPackages.pythreejs
pythonPackages.ipyvolume
pythonPackages.jupyterlab-git
pythonPackages.jupyterlab-latex
pythonPackages.ipyleaflet
pythonPackages.ipympl
] ++ kernels;
shellHook = ''
TEMPDIR=$(mktemp -d -p /tmp)
mkdir -p $TEMPDIR
cp -r ${pkgs.python36Packages.jupyterlab}/share/jupyter/lab/* $TEMPDIR
chmod -R 755 $TEMPDIR
echo "$TEMPDIR is the app directory"
# kernels
export JUPYTER_PATH="${pkgs.lib.concatMapStringsSep ":" (p: "${p}/share/jupyter/") kernels}"
# labextensions
${pkgs.lib.concatMapStrings
(s: "jupyter labextension install --no-build --app-dir=$TEMPDIR ${s}; ")
(pkgs.lib.unique
((pkgs.lib.concatMap
(d: pkgs.lib.attrByPath ["passthru" "jupyterlabExtensions"] [] d)
buildInputs) ++ additionalExtensions)) }
jupyter lab build --app-dir=$TEMPDIR
# start jupyterlab
jupyter lab --app-dir=$TEMPDIR
'';
}
Some recent examples of work done on libraries:
There has also been notable work on the data science infra :
with such highlights as Jupyter kernels written in Nix:
./modules/datasci.nix
...
python3kernel = let
env = (pkgs.python3.withPackages
(pythonPackages: with pythonPackages; [
ipykernel
pandas
scikitlearn
]));
in {
displayName = "Python 3 for machine learning";
argv = [
"$ {env.interpreter}"
"-m"
"ipykernel_launcher"
"-f"
"{connection_file}"
];
language = "python";
logo32 = "$ {env.sitePackages}/ipykernel/resources/logo-32x32.png";
logo64 = "$ {env.sitePackages}/ipykernel/resources/logo-64x64.png";
};
...
It looks like NixOS is well on its way to becoming a solid data science platform; the reproducible and language agnostic approach is a natural match to the task. But perhaps a coordinated effort be fruitful step up the game?
Lets continue the discussion here and at #nixos-data.
Channels
#datascience:nixos.org on Matrix