RAPIDS cuML is a suite of fast, GPU-accelerated machine learning algorithms designed for data science and analytical tasks. The API mirrors scikit-learn, providing 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 become slow and cumbersome. RAPIDS provides users a streamlined approach where data is initially 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.

Features

• Zero Code Change Acceleration: cuML.accel provides zero code change acceleration for Scikit-Learn, UMAP, and HDBSCAN, allowing users to leverage GPU acceleration without modifying their existing code.
• Comprehensive Algorithm Support: Includes a wide range of algorithms for regression, classification, clustering, dimensionality reduction, manifold learning, neighbors, time series, and model explainability.
• Multi-Node, Multi-GPU Support: Supports multi-node, multi-GPU algorithms, enabling large-scale machine learning tasks.
• Integration with Scikit-Learn: Mirrors the scikit-learn API, making it easy for users to transition to GPU-accelerated workflows.
• Open Source: Fully open source, encouraging contributions from the community.

Benefits

• High Performance: Accelerates machine learning tasks, significantly reducing processing time and improving efficiency.
• Ease of Use: Provides a familiar API, making it easy for users to adopt GPU-accelerated workflows without extensive learning curves.
• Scalability: Supports large-scale machine learning tasks with multi-node, multi-GPU capabilities.
• Community-Driven: Open source nature encourages community contributions, fostering innovation and collaboration.