[scikit-learn] Using GPU in scikit learn

Tommy Tracy tjt7a at virginia.edu
Wed Aug 8 22:01:57 EDT 2018

Dear Ta Hoang,

Accelerating decision tree ensembles (including Random Forest) is actually
a current area of computer architecture research; in fact it is a principle
component of my dissertation. Like Sebastian Raschka said, the GPU is not
an ideal architecture for decision tree inference because at its core it is
a pointer-chasing algorithm (low computation per memory access) that shows
low memory locality. Scikit-Learn has done an excellent job with their von
Neumann implementation utilizing things like predication and vectorization.
If you're looking to go beyond what the CPU can give you, I would point you
to FPGAs. If you're interested in discussing this further, let me know.

Tommy James Tracy II
     Ph.D Candidate
Computer Engineering
  University of Virginia

On Wed, Aug 8, 2018 at 8:50 PM, hoang trung Ta <tahoangtrung at gmail.com>

> Dear all members,
> I am using Random forest for classification satellite images. I have a
> bunch of images, thus the processing is quite slow. I searched on the
> Internet and they said that GPU can accelerate the process.
> I have GPU NDVIA Geforce GTX 1080 Ti installed in the computer
> Do you know how to use GPU in Scikit learn, I mean the packages to use and
> sample code that used GPU in random forest classification?
> Thank you very much
> --
> *Ta Hoang Trung (Mr)*
> *Master student*
> Graduate School of Life and Environmental Sciences
> University of Tsukuba, Japan
> Mobile:  +81 70 3846 2993
> Email :  ta.hoang-trung.xm at alumni.tsukuba.ac.jp
>              tahoangtrung at gmail.com
>              s1626066 at u.tsukuba.ac.jp
> *----*
> *Mapping Technician*
> Department of Surveying and Mapping Vietnam
> No 2, Dang Thuy Tram street, Hanoi, Viet Nam
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> Email : tahoangtrung at gmail.com
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