[jira] [Resolved] (SYSTEMML-855) Add a "Get Started" tutorial for Python users

["Niketan Pansare (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Niketan Pansare resolved SYSTEMML-855.
--------------------------------------
       Resolution: Fixed
         Assignee: Niketan Pansare
    Fix Version/s: SystemML 0.11

> Add a "Get Started" tutorial for Python users
> ---------------------------------------------
>
>                 Key: SYSTEMML-855
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-855
>             Project: SystemML
>          Issue Type: Task
>            Reporter: Niketan Pansare
>            Assignee: Niketan Pansare
>             Fix For: SystemML 0.11
>
>
> As an example, this tutorial could have following sections:
> 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support:
> wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py
> wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar
> pyspark --master local[*] --driver-class-path SystemML.jar
> OR
> Use pip installer.
> 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description
> 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL:
> https://github.com/apache/incubator-systemml/pull/197
> import numpy as np
> from sklearn import datasets
> diabetes = datasets.load_diabetes()
> diabetes_X = diabetes.data[:, np.newaxis, 2]
> diabetes_X_train = diabetes_X[:-20]
> diabetes_X_test = diabetes_X[-20:]
> diabetes_y_train = diabetes.target[:-20]
> diabetes_y_test = diabetes.target[-20:]
> 4. Explain how to use our algorithm instead:
> http://apache.github.io/incubator-systemml/algorithms-regression.html#examples
> 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed.
> By the end of tutorial, the programmer should understand at very high-level:
> 1. Moving to SystemML is painless. Almost as simple as changing "import"
> 2. SystemML has a sophisticated optimizer that allows it to adapt to different data/cluster characteristics and allows the code and algorithm to scale.



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