Welcome to cuML’s documentation!¶
cuML is a suite of fast, GPU-accelerated machine learning algorithms designed for data science and analytical tasks. Our API mirrors Sklearn’s, and we provide practitioners with the easy fit-predict-transform paradigm without ever having to program on a GPU.
As data gets larger, algorithms running on a CPU becomes slow and cumbersome. RAPIDS provides users a streamlined approach where data is intially loaded in the GPU, and compute tasks can be performed on it directly.
cuML is fully open source, and the RAPIDS team welcomes new and seasoned contributors, users and hobbyists! Thank you for your wonderful support!
An installation requirement for cuML is that your system must be Linux-like. Support for Windows is possible in the near future.
- cuML API Reference
- Intro and key concepts for cuML
- cuML blogs and other references
- Training and Evaluating Machine Learning Models in cuML
- Pickling cuML Models for Persistence