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cuml 25.10.00 documentation

  • Introduction
  • User Guide
  • Zero Code Change Acceleration
  • API Reference
  • FIL - RAPIDS Forest Inference Library
    • Blogs and other references
  • GitHub
  • Twitter
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nightly (25.10)
nightly (25.10)stable (25.08)legacy (25.06)
  • Introduction
  • User Guide
  • Zero Code Change Acceleration
  • API Reference
  • FIL - RAPIDS Forest Inference Library
  • Blogs and other references
  • GitHub
  • Twitter

Section Navigation

  • Blogs and other references

Blogs and other references#

The RAPIDS team blogs at https://medium.com/rapids-ai, and many of these blog posts provide deeper dives into models or key features from cuML. Here, we’ve selected just a few that are of particular interest to cuML users:

Integrations, applications, and general concepts#

  • RAPIDS Configurable Input and Output Types

  • RAPIDS on AWS Sagemaker

Tree and forest models#

  • Accelerating Random Forests up to 45x using cuML

  • RAPIDS Forest Inference Library: Prediction at 100 million rows per second

  • Sparse Forests with FIL

Other popular models#

  • Accelerating TSNE with GPUs: From hours to seconds

  • Combining Speed and Scale to Accelerate K-Means in RAPIDS cuML

  • Accelerating k-nearest neighbors 600x using RAPIDS cuML

Academic Papers#

  • Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (Sebastian Raschka, Joshua Patterson, Corey Nolet)

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FIL - RAPIDS Forest Inference Library

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  • Tree and forest models
  • Other popular models
  • Academic Papers

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