From Fedora Project Wiki
m (Improve intro to contain also ML related projects)
(Restructured and updated the wiki page)
Line 14: Line 14:
 
[[User:lbalhar|Lumír Balhar (lbalhar)]] <[mailto:lbalhar@redhat.com lbalhar@redhat.com]>
 
[[User:lbalhar|Lumír Balhar (lbalhar)]] <[mailto:lbalhar@redhat.com lbalhar@redhat.com]>
  
= Machine Learning Packages =
+
= Machine Learning Projects =
  
== Interesting software waiting for being packaged ==
+
== Thoth  ==
  
* [https://pypi.python.org/pypi/bob bob - free signal-processing and machine learning toolbox]
+
[https://thoth-station.ninja/ Thoth] is a promising project which uses artificial intelligence to analyze and recommend software stack for artificial intelligence applications. It's based on an assumption that the proper combination of tools and libraries can have a significant impact on the performance of your project.
* [https://pypi.python.org/pypi/copper copper - Fast, easy and intuitive machine learning prototyping]
 
* [https://pypi.python.org/pypi/ease ease - Machine learning based automated text classification library]
 
* [https://pypi.python.org/pypi/hyperspy hyperspy - Hyperspectral data analysis toolbox]
 
* [https://pypi.python.org/pypi/infer infer - machine learning toolkit for classification and assisted experimentation]
 
* [http://java-ml.sourceforge.net/ Java-ML]
 
* [https://pypi.python.org/pypi/milk milk - Machine Learning Toolkit]
 
* [https://pypi.python.org/pypi/MLizard MLizard - Machine Learning workflow automatization]
 
* [http://mlpy.sourceforge.net/ mlpy - Machine Learning Python]
 
* [https://pypi.python.org/pypi/mmlf Maja Machine Learning Framework]
 
* [https://pypi.python.org/pypi/Monte Monte - machine learning in pure Python]
 
* [https://pypi.python.org/pypi/nolearn nolearn - Miscellaneous utilities for machine learning]
 
* [https://pypi.python.org/pypi/pcSVM pcSVM]
 
* [https://pypi.python.org/pypi/Peach Peach - Python library for computational intelligence and machine learning]
 
* [http://pybrain.org/ PyBrain]
 
* [http://pyml.sourceforge.net/ PyML]
 
* [https://pypi.python.org/pypi/ramp Rapid Machine Learning Prototyping]
 
* [https://pypi.python.org/pypi/Reinforcement-Learning-Toolkit Reinforcement-Learning-Toolkit]
 
* [https://pypi.python.org/pypi/tradingmachine tradingmachine - backtester for financial algorithms]
 
* [http://sourceforge.net/projects/weka/ Weka - Machine Learning Software in Java]
 
* [https://pypi.python.org/pypi/zipline zipline - backtester for financial algorithms]
 
  
== Work in progress ==
+
Thoth is now in the alpha state but it should be soon ready for beta testers.
  
{|
+
= Machine Learning Packages =
! Package !! People involved !! Status !! Notes
 
|-
 
| [http://mahout.apache.org/ Apache Mahout] || [[User:Besser82 | besser82]]  || WIP ||
 
|-
 
| [https://pypi.python.org/pypi/astroML astroML] || [[User:lupinix|lupinix]] || DONE ||
 
|-
 
| [http://orange.biolab.si/ Orange] || [[User:Besser82 | besser82]] || WIP || depends on [[rhbug:1000829 | LINPACK]]
 
|-
 
| [http://www.clips.ua.ac.be/pages/pattern Pattern - Web mining module for Python] || [[User:kushal124|kushal124]] || WIP || depends on [[rhbug:1194576 | packaging]] and unbundling
 
|-
 
| [http://scikit-learn.org/ scikit-learn - Machine Learning in Python] ||  || review done, but needs unbundling  || depends on [[rhbug:999823 | unbundling]]
 
|-
 
| [http://shogun-toolbox.org/ SHOGUN] || [[User:Besser82 | besser82]] || DONE || [[User:Besser82/Changes/shogun | upcoming feature for F21]] Imported && Build
 
|}
 
  
 
== New packages ==
 
== New packages ==
Line 79: Line 45:
 
You can find them on the [[rhbug:ML-SIG|ML-SIG review-tracker]].
 
You can find them on the [[rhbug:ML-SIG|ML-SIG review-tracker]].
  
We would be glad, if you would take one or a few.  :)
+
We would be glad if you would take one or a few.  :)
  
 
== Existing packages ==
 
== Existing packages ==
  
You can find the existing [https://admin.fedoraproject.org/pkgdb/users/packages/ml-sig ml-related packages on the PkgDB].
+
Unfortunately, there is no FAS group for Machine learning SIG so it's not easy to find all the packages we participate on. But we are working to fix it and create a FAS group which will help us to find all related packages and make our contributions easier.
 
 
== Categories ==
 
 
 
more to come soon.
 
  
 
= What are we going to do? =
 
= What are we going to do? =
  
more to come soon.
+
* We're gonna update this wiki page so it can serve as a hub of interesting projects, links to important content, RPM packages etc. Do you know about anything we should have here? Let us know!
 +
* We'll help ML developers as much as we can. Do you need some help or some new RPM package in Fedora? Let us know!
 +
* We'll try to make Fedora the best distribution for AI/ML developers and users. Do you know how? Let us know!
  
 
= Participation =
 
= Participation =
Line 102: Line 66:
  
 
* Join: {{fplist|ml}}
 
* Join: {{fplist|ml}}
* Archives: [http://lists.fedoraproject.org/pipermail/ml/ read]
+
* Archives: [https://lists.fedoraproject.org/archives/list/ml@lists.fedoraproject.org/ read]
  
 
== IRC ==
 
== IRC ==

Revision as of 12:43, 28 June 2019

Machine Learning SIG

The Machine Learning SIG's goal is to make Fedora the best platform for all things related to Machine Learning. We aim to act a hub in the gap between the Astronomy, Bigdata, Fedora Medical and Science and Technology SIGs and also between all interesting projects related to machine learning in Fedora.

Members

Björn Esser (besser82) <besser82@fedoraproject.org>

Kushal Khandelwal (kushal124) <kushalkhandelwal10@gmail.com>

Christian Dersch (lupinix) <lupinix@fedoraproject.org>

Dhanesh B. Sabane (dhanesh95) <dhanesh95@fedoraproject.org>

Lumír Balhar (lbalhar) <lbalhar@redhat.com>

Machine Learning Projects

Thoth

Thoth is a promising project which uses artificial intelligence to analyze and recommend software stack for artificial intelligence applications. It's based on an assumption that the proper combination of tools and libraries can have a significant impact on the performance of your project.

Thoth is now in the alpha state but it should be soon ready for beta testers.

Machine Learning Packages

New packages

When submitting a new ml-related package for review, please add "Blocks: ML-SIG" to your review-request. After the review has been granted don't forget to remove it, when filing the SCM-request, please.

When you are filing your SCM-admin-request, you should make sure to request InitialCC for "ml-sig".

Example:

New Package SCM Request
=======================
Package Name: pkgname
Short Description: summary of package
Owners: foo bar
Branches: f18 f19 f20 el5 el6
InitialCC: ml-sig

Packages waiting for your review

You can find them on the ML-SIG review-tracker.

We would be glad if you would take one or a few. :)

Existing packages

Unfortunately, there is no FAS group for Machine learning SIG so it's not easy to find all the packages we participate on. But we are working to fix it and create a FAS group which will help us to find all related packages and make our contributions easier.

What are we going to do?

  • We're gonna update this wiki page so it can serve as a hub of interesting projects, links to important content, RPM packages etc. Do you know about anything we should have here? Let us know!
  • We'll help ML developers as much as we can. Do you need some help or some new RPM package in Fedora? Let us know!
  • We'll try to make Fedora the best distribution for AI/ML developers and users. Do you know how? Let us know!

Participation

There is no formal process for participating; joining the mailing list, hanging out on IRC, or participating in meetings are all fantastic ways to get involved.

A little self-introduction on the mailing list would be nice, too. And, if you want to, add yourself to our members-section above.

Mailing list

IRC

We will likely hang out on irc.freenode.net at #fedora-ml. German members may want to come into #fedora-ml-de, too.

Haven't used IRC for communication before? More information on how to use IRC is available here.

Meetings

We shall have them, and see how it goes.

more to come soon.