Welcome to DASF Documentation!

DASF is an Accelerated and Scalable Framework

DASF is a generic framework specialized in acceleration and scaling common techniques for Machine Learning. DASF uses most methods and function from the most common libraries to increase the speed up of most algorithms. Part of this is to use Dask data to scale computation and RAPIDS AI algorithms to extend the support to GPUs as well.

Contents

• Principles
  • DASF as a Simple API
• Installation Guide
  • Using Docker
  • Using Conda
  • Development version
  • Testing
• Overview
  • Implemented Machine Learning Algorithms
• Tutorials
  • Tutorial 1 - A Quick Demo
  • Tutorial 2 - How to extend DASF Datasets
  • Tutorial 3 - How Create Your Own Trasform
  • Tutorial 4 - How Create an Agnostic Pipeline
  • Tutorial 5 - Using profiler
  • Tutorial 6 - How Use the ApplyPatches Operator
  • Tutorial 7 - Trainig a PyTorch Lightning model
  • Tutorial 8 - HDBSCAN Differences between CPU and GPU versions
• DASF API Reference
  • dasf

Indices and tables

• Index

• Module Index

• Search Page