OpenML aims to democratize machine learning by creating an open, frictionless platform for accessing and sharing datasets, models, and experiments. Anytime, anywhere. It allows scientists to easily build on each other's work, learn from the past, and automate their workflows. Check our website to learn more.
If you are new to OpenML, please see our general contribution guide. We're so happy that you want to help! We are open to anyone getting involved, and are always seeking to increase diversity in AI.
You can talk to us in our Slack channel. Or, join us in one of our meetups.
Here's a brief overview of the repo's in OpenML and their status:
- openml.org: Our new frontend. Built on Flask, React, and Dash
- OpenML: Our older backend, with a lot of legacy code. We plan to rewrite this in 2023-2024
- openml-python: Our Python API, giving you access to everything OpenML has to offer
- openml-R: Our R API, covering the major OpenML use cases
- openml-java: Our Java API, covering the major OpenML use cases
- openml-tensorflow: Our TensorFlow integration, work in progress
- openml-pytorch: Our PyTorch integration, work in progress
- docs: Home of our documentation pages
- blog: Home of our blog
- benchmark-suites: Materials to help you build OpenML benchmark suites
- automlbenchmark: Our framework for benchmarking AutoML systems