[scikit-learn] 回覆： Using GPU in scikit learn

[blacklabel29]

Hi,

the scikit-learn random forest does not support GPUs. 
If you want to do image classification using GPU processing, the standard way 
in this day and age is to use a neural network library like TensorFlow/keras or 
pytorch.
GPUs can be faster than CPUs when the task is SIMD (single instruction multiple 
data), meaning the same calculation is done many times just on different 
datapoints. Neural networks are well-suited for such an architecture, decision 
trees not so much (even though there have been attempts to speed up decision 
trees using GPUs).
So my advice to you depends on how much time you have: If you are willing to 
invest time to learn about neural networks and the aforementioned libraries, 
then that is certainly a very valuable skill, especially when looking for a job 
later on. But if you just need to get your paper done as soon as possible, 
stick with random forest.

Greetings,Patrick


從我的 Samsung Galaxy 智慧型手機傳送。 原始訊息 自： hoang trung Ta 
 日期： 2018/8/9  02:50  (GMT+01:00) 至： 
scikit-learn@python.org 主旨： [scikit-learn] Using GPU in scikit learn 
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 studentGraduate School of Life and Environmental SciencesUniversity of 
Tsukuba, Japan
Mobile:  +81 70 3846 2993Email :  ta.hoang-trung...@alumni.tsukuba.ac.jp        
     tahoangtr...@gmail.com             s1626...@u.tsukuba.ac.jp
Mapping Technician
Department of Surveying and Mapping VietnamNo 2, Dang Thuy Tram street, Hanoi, 
Viet Nam
Mobile: +84 1255151344Email : tahoangtr...@gmail.com

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[scikit-learn] Fwd: BIC using GMM.fit and GaussianMixture.fit()

[Dixeena Lopez]

-- Forwarded message --
From: Dixeena Lopez 
Date: 2 August 2018 at 23:53
Subject: BIC using GMM.fit and GaussianMixture.fit()
To: scikit-learn@python.org


Dear Sir/Madam,

I have tried to fit the data using GaussianMixture.fit() and GMM.fit() and
calculated the BIC score. The BIC score value and the number of clusters I
got using each method is different. Do you have any idea?
‌

Sincerely,

Dixeena

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Re: [scikit-learn] 回覆： Using GPU in scikit learn

[hoang trung Ta]

Thank you very much for all of your information. Now I understand more
about Scikit learn.

On Thu, Aug 9, 2018 at 4:35 PM, blacklabel29  wrote:

> Hi,
>
>
> the scikit-learn random forest does not support GPUs.
>
> If you want to do image classification using GPU processing, the standard
> way in this day and age is to use a neural network library like
> TensorFlow/keras or pytorch.
>
> GPUs can be faster than CPUs when the task is SIMD (single instruction
> multiple data), meaning the same calculation is done many times just on
> different datapoints. Neural networks are well-suited for such an
> architecture, decision trees not so much (even though there have been
> attempts to speed up decision trees using GPUs).
>
> So my advice to you depends on how much time you have: If you are willing
> to invest time to learn about neural networks and the aforementioned
> libraries, then that is certainly a very valuable skill, especially when
> looking for a job later on. But if you just need to get your paper done as
> soon as possible, stick with random forest.
>
>
> Greetings,
> Patrick
>
>
>
> 從我的 Samsung Galaxy 智慧型手機傳送。
>  原始訊息 
> 自： hoang trung Ta 
> 日期： 2018/8/9 02:50 (GMT+01:00)
> 至： scikit-learn@python.org
> 主旨： [scikit-learn] Using GPU in scikit learn
>
> 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...@alumni.tsukuba.ac.jp
>  tahoangtr...@gmail.com
>  s1626...@u.tsukuba.ac.jp
>
> **
> *Mapping Technician*
> Department of Surveying and Mapping Vietnam
> No 2, Dang Thuy Tram street, Hanoi, Viet Nam
>
> Mobile: +84 1255151344
> Email : tahoangtr...@gmail.com
>
> ___
> scikit-learn mailing list
> scikit-learn@python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>


-- 
*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...@alumni.tsukuba.ac.jp
 tahoangtr...@gmail.com
 s1626...@u.tsukuba.ac.jp

**
*Mapping Technician*
Department of Surveying and Mapping Vietnam
No 2, Dang Thuy Tram street, Hanoi, Viet Nam

Mobile: +84 1255151344
Email : tahoangtr...@gmail.com
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[scikit-learn] GMM.fit and GaussianMixture.fit()

[Dixeena Lopez]

Dear Sir/Madam,

I have used GMM.fit() instead of GaussianMixture.fit() and got different
answers. Please gives the advantage and disadvantage of these two. Please
reply fast


‌Diixeena
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[scikit-learn] DBSCAN

[Prathusha Jonnagaddla Subramanyam Naidu]

Hi everyone,
  I'm trying to cluster 14000 samples using DBSCAN and want to know if
there is a way to display the index of each data point along with it's
label. I'm only able to access labels in the form of a list . When I look
at the graph and see outliers (black points) , I'm not able to pinpoint as
to which image/data that particular point belongs to.

Thank you


-- 
Regards,
Prathusha JS Naidu
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