mlpack is a community-led effort, and so the code is not possible without the community. Since mlpack is an open-source project, anyone is welcome to become a part of the community and contribute. There is no need to be a machine learning expert to participate—often, there are many tasks to be done that don't require in-depth knowledge.
All mlpack development is done on GitHub. In addition to that, there are a few channels of communication:
Everyone is welcome to contribute to mlpack. But before becoming a contributor, it's often useful to understand mlpack as a user. So, a good place to start is to download mlpack and use the Python bindings, Julia bindings or the command-line programs to perform machine learning tasks. You can also write C++ programs to perform machine learning tasks with mlpack; the examples repository has many examples.
Once you have an idea of what's included in mlpack and how a user might use it, then a good next step would be to set up a development environment. Once you have that set up, you can build mlpack from source and explore the codebase to see how it's organized. It may even be useful to try and make small changes to the code, then rebuild the command-line programs and see what your changes did.
Now you're set up to contribute! There are lots of ways you can contribute. Here are a couple ideas:
• Help others figure out their mlpack issues and questions. Here is a list of Github issues tagged . Helping others figure out their problems is really one of the best ways to learn about the library.
• Read through the vision document to learn about the development goals of the mlpack community and see the high-level tasks that need to be done to accomplish that vision.
• Find an issue that needs implementation help; here is a list of Github issues tagged ; and here is a list of Github issues tagged . Once you open a PR that passes all the continuous integration tests, the community will review it and get it merged into the codebase.
• Find an abandoned pull request; here is a list of pull requests that were closed for inactivity. Often these have comments that need to be addressed, but the original author didn't have time to finish the work. So, you can pick up where they left off!
• Implement a new machine learning algorithm that mlpack doesn't currently have.
• Take a look at the ideas on the Google Summer of Code Ideas List and see if you find any of them interesting or exciting. Even if you're not planning to do Summer of Code, it's okay to take these ideas and implement them separately.
• Do some timing on an existing machine learning algorithm implemented by mlpack, and try to make it faster.
• If you find a bug or have a problem, open an issue for it—issue templates are provided to make reporting easier.
Two mailing lists are used for mlpack discussion and development:
• For email discussion and announcements: mlpack mailing list →
• Which sends emails for all mlpack-related activity: mlpack-git mailing list →
The mlpack community stays in touch in real-time via several chat services that are all linked together thanks to the Matrix project. You can choose any of the services below and will be able to communicate with anyone who is using any of the other services (assuming that the inter-service bridges are correctly operating).
IRC via libera.chat: #mlpack; you can use webchat (the link) and specify the channel #mlpack, or set up an account on libera.chat and use a client of your choice.
mlpack/mlpack; you will need a Github account to join.
On the first and third Friday of every month, at 1700 UTC on Fridays, we have casual video meetups with no particular agenda. Feel free to join up! We often talk about code changes that we are working on, issues people are having with mlpack, general design direction, and whatever else might be on our mind. We use this Zoom room. For security, we use a password for the meeting to keep malicious bots out; the password is simple---it's just the name of the library (in all lowercase).
Blog for mlpack development and updates and blog posts. Read More →
Organizations that have supported mlpack's development or currently support it.
mlpack is fiscally sponsored by NumFOCUS.
Over the past several years, mlpack has participated in Google Summer of Code.
Originally, mlpack was produced by the FASTLab at Georgia Tech.
Developer Ryan Curtin received partial support to work on mlpack at Symantec.
Developer Ryan Curtin received partial support to work on mlpack in 2015 from the US National Science Foundation (NSF) under Award 1339745 (SI2-SSI: The XScala Project).
Developer Marcus Edel received partial support to work on mlpack at Free University of Berlin.
Below is a list of contributors to mlpack. Without their hard work, this software would not be possible.
Haritha Sreedharan Nair
Mudit Raj Gupta
Tan Jun An
Mehul Kumar Nirala
Sriram S K
Muhammad Fawwaz Mayda
Sagar B Hathwar
N Rajiv Vaidyanathan
B Kartheek Reddy
Yashwant Singh Parihar
Vikas S Shetty
Manoranjan Kumar Bharti
Roshan Nrusing Swain
Sri Madhan M