Workgroup:DataScience: Difference between revisions
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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. | 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 the data science infra : | There have been some great examples of great work done on libraries: | ||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+nlp+ nlp] | |||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+sklearn scikit-learn] | |||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+tensorflow tensorflow] | |||
but notably also 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] | ||
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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 == | == Channels == | ||
Revision as of 23:01, 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:
but notably also 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.