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cuml 23.02.00 documentation

Site Navigation

 * Introduction
 * API Reference
 * User Guide
 * Blogs and other references

 * GitHub
 * Twitter


Home
cuml
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stable (23.02)
nightly (23.04)stable (23.02)legacy (22.12)

Site Navigation

 * Introduction
 * API Reference
 * User Guide
 * Blogs and other references

 * GitHub
 * Twitter





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.

Contents:

 * Introduction
   * 1. Where possible, match the scikit-learn API
   * 2. Accept flexible input types, return predictable output types
   * 3. Be fast!
   * Learn more
 * API Reference
   * Module Configuration
   * Preprocessing, Metrics, and Utilities
   * Regression and Classification
   * Clustering
   * Dimensionality Reduction and Manifold Learning
   * Neighbors
   * Time Series
   * Model Explainability
   * Multi-Node, Multi-GPU Algorithms
   * Experimental
 * User Guide
   * Training and Evaluating Machine Learning Models
   * Pickling Models for Persistence
 * Blogs and other references
   * Integrations, applications, and general concepts
   * Tree and forest models
   * Other popular models
   * Academic Papers


INDICES AND TABLES#

 * Index

 * Module Index

 * Search Page

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Introduction

On this page
 * Welcome to cuML’s documentation!
 * Indices and tables



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