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 a machine learning expert to participate; often, there are many tasks to be done that don’t require in-depth knowledge.

Over the past several years, mlpack has participated in Google Summer of Code. For more information, see this page.

All mlpack development is done on GitHub. Commits and issue comments can be tracked via the mlpack-git list (graciously hosted by FreeLists. Communication is generally either via issues on GitHub, or via chat:

🔗 Real-time chat

🔗 Video meetup

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 that 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).

🔗 Getting involved

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:

There is also the examples repository that contains many examples you can build and play around with.

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.

Try making small changes to the code, or adding new tests to the test suite, and then rebuild to see how your changes work.

Now you’re set up to contribute! There are lots of ways you can contribute. Here are a couple ideas: