Workgroup:DataScience: Difference between revisions

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* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+tensorflow tensorflow]
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+tensorflow tensorflow]


As well as notable work on the data science infra :
There has also been notable work on the data science infra :


* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+jupyter Jupyter]
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+jupyter Jupyter]

Revision as of 23:19, 1 June 2018

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.

There have been some great examples of great work done on libraries:

There has also been notable work on the data science infra :

with such highlights as @aborsu's 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

#nixos-data on Freenode

People

Ixxie