Skip to content

daxiongshu/rapids-demos

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 

Repository files navigation

rapids-demos

Introduction

In this demo, we show an end-to-end workflow of rapids dask-cudf + dask-glm (doc) LogisticRegression on mutliple GPUs. Experiments are done on a DGX-1 with 8xGPUs and 40-core CPUs. With the HIGGS dataset, the GPU solution achieves 14x speedup over CPU using the lbfgs solver. To get a more comprehensive speedup measurement, we run dask-glm on vared sizes of random synthetic data and the GPU solution achieves up to 27x speedup over CPU.

Background

Multi-GPU support of dask-glm is enabled by recent efforts of allowing cupy dask arrays as inputs. dask/dask-glm#87 and dask/dask-glm#89

dask-glm offers 3 estimators:

  • LinearRegression
  • LogisticRegression
  • PoissonRegression

and 5 solvers:

  • admm,
  • gradient_descent,
  • newton,
  • lbfgs,
  • proximal_grad

Currently, all 3 estimators and 5 algorithms work seamlessly with dask cupy arrays and dask-cudf on multiple GPUs.

Install instructions:

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published