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Difference between revisions of "SW:Scikit Learn"

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(Created page with "=Scikit-Learn= __TOC__ ==Description== Scikit-Learn provides simple and efficient tools for data mining and data analysis, and is accessible to everybody. It is built on Numpy...")
 
(Example Keras Script)
 
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Scikit-Learn provides simple and efficient tools for data mining and data analysis, and is accessible to everybody. It is built on Numpy, Scipy and Matplotlib.  
 
Scikit-Learn provides simple and efficient tools for data mining and data analysis, and is accessible to everybody. It is built on Numpy, Scipy and Matplotlib.  
 
* Homepage: [https://scikit-learn.org/ https://scikit-learn.org/]
 
* Homepage: [https://scikit-learn.org/ https://scikit-learn.org/]
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==Access==
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Scikit-Learn is open to all HPRC users.
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===Anaconda and Scikit-Learn Packages===
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TAMU HPRC currently supports the user of Scikit-Learn though the Anaconda modules. There are a variety of Anaconda modules available on Ada and Terra. An anaconda module may provides different version of scikit-learn. It is recommend to use the latest Anaconda module.
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To access scikit-learn on Ada or Terra:
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[NetID@terra ~]$ '''module load Anaconda/3-5.0.0.1'''
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You can learn more about the module system on our [[SW:Modules]] page.
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==Example Scikit-Learn Script==
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As with any job on the system, Scikit-Learn should be used via the submission of a job file. Scripts using Scikit-Learn are written in Python, and thus <font color=purple>Scikit-Learn scripts should not be written directly inside a job file or entered in the shell line by line</font>. Instead, a separate file for the Python/Scikit-Learn script should be created, which can then be executed by the job file.
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To create a new script file, simply open up the text editor of your choice.
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Below is an example script (for version 0.19.1) (entered in the text editor of your choice):
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from sklearn import datasets
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iris = datasets.load_iris()
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digits = datasets.load_digits()
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print(digits.data)
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digits.target
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digits.images[0]
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It is recommended to save this script with a .py file extension, but not necessary.
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Once saved, the script can be tested on a login node by entering:
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[NetID@terra ~]$ python testscript.py
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[[Category:Software]]
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[[Category:Machine Learning]]

Latest revision as of 09:49, 23 March 2018

Scikit-Learn

Description

Scikit-Learn provides simple and efficient tools for data mining and data analysis, and is accessible to everybody. It is built on Numpy, Scipy and Matplotlib.

Access

Scikit-Learn is open to all HPRC users.

Anaconda and Scikit-Learn Packages

TAMU HPRC currently supports the user of Scikit-Learn though the Anaconda modules. There are a variety of Anaconda modules available on Ada and Terra. An anaconda module may provides different version of scikit-learn. It is recommend to use the latest Anaconda module.

To access scikit-learn on Ada or Terra:

[NetID@terra ~]$ module load Anaconda/3-5.0.0.1

You can learn more about the module system on our SW:Modules page.

Example Scikit-Learn Script

As with any job on the system, Scikit-Learn should be used via the submission of a job file. Scripts using Scikit-Learn are written in Python, and thus Scikit-Learn scripts should not be written directly inside a job file or entered in the shell line by line. Instead, a separate file for the Python/Scikit-Learn script should be created, which can then be executed by the job file.

To create a new script file, simply open up the text editor of your choice.

Below is an example script (for version 0.19.1) (entered in the text editor of your choice):

from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()
print(digits.data)
digits.target
digits.images[0]

It is recommended to save this script with a .py file extension, but not necessary.

Once saved, the script can be tested on a login node by entering:

[NetID@terra ~]$ python testscript.py