An open, automated, and frictionless machine learning environment.
1000s of data sets, uniformly formatted, easy to load, organized online
Models and pipelines automatically uploaded from machine learning libraries
Extensive APIs to integrate OpenML into your tools and scripts
Easily reproducible results (e.g. models, evaluations) for comparison and reuse
Stand on the shoulders of giants, and collaborate in real time
Make your work more visible and reusable
Built for automation: streamline your experiments and model building
Installation¶
The OpenML package is available in many languages and across libraries. For more information about them, see the Integrations page.
- Python/sklearn repository
pip install openml
- Pytorch repository
pip install openml-pytorch
- Keras repository
pip install openml-keras
- TensorFlow repository
pip install openml-tensorflow
- R repository
install.packages("mlr3oml")
- Julia repository
using Pkg;Pkg.add("OpenML")
- RUST repository
- Install from source
- .Net repository
Install-Package openMl
You might also need to set up the API key. For more information, see the API key page
Learning OpenML¶
Aside from the individual package documentations, you can learn more about OpenML through the following resources:
The core concepts of OpenML are explained in the Concepts page. These concepts include the principle behind using Datasets, Runs, Tasks, Flows, Benchmarking and much more. Going through them will help you leverage OpenML even better in your work.
Contributing to OpenML¶
OpenML is an open source project, hosted on GitHub. We welcome everybody to help improve OpenML, and make it more useful for everyone. For more information on how to contribute, see the Contributing page.
We want to make machine learning and data analysis simple, accessible, collaborative and open with an optimal division of labour between computers and humans.
Want to get involved?¶
Awesome, we're happy to have you! :tada:
OpenML is dependent on the community. If you want to help, please email us ([email protected]). If you feel already comfortable you can help by opening issues or make a pull request on GitHub. We also have regular workshops you can join (they are announced on openml.org).