Thanks Abhishek. I am glad it was helpful :) Luciano: I agree with you about having a central place for documentation. Before cleaning up the tutorial and putting it into our documentation, I wanted to: 1. Have a discussion about which setup should we use to introduce SystemML: command-line standalone, command-line spark/pyspark REPL (yarn/standalone), command-line hadoop, scala/python notebook (online notebook or require user to setup jupyter/zeppelin). 2. Encourage other contributors to come up with intellectually simulating tutorial using real world dataset and our existing DML algorithms. This means creating JIRAs that people can work on. My repository is only a POC to facilitate discussion and will be deleted after that. 3. If we do decide to go with online notebook based tutorial, have a discussion on how to structure the tutorial: - so as to support variety of hosting sites (bluemix / datascientist workbench / databricks cloud / azureml / aws / ...). - Python or Scala as primary language. - Jupyter or Zeppelin as primary notebook. - DML kernel or MLContext-based or JMLC-based example. - Any standard tutorial (or textbook) we should use as example for choosing the dataset. - Whether the emphasis should be on learning DML or on building larger data pipeline (for example: our MLPipeline-wrapper).
Thanks, Niketan Pansare IBM Almaden Research Center E-mail: npansar At us.ibm.com http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar From: Abhishek Srivastava <abhisheksrivasta...@gmail.com> To: dev@systemml.incubator.apache.org Date: 04/08/2016 08:55 AM Subject: Re: Updating documentation for notebook Great job Niketan , I had been searching for such document off late. Regards, Abhishek Srivastava Fellowship Scholar , IIM Ranchi Skype : abhi.sri3 On Fri, Apr 8, 2016 at 6:34 AM, Niketan Pansare <npan...@us.ibm.com> wrote: > > > Hi all, > > Here is a suggestion for reducing the barrier to entry for SystemML: "Have > a detailed quickstart guide/video using Notebook on free (or trial-based) > hosting solution like IBM Bluemix or Data Scientist Workbench". > > I have create a sample tutorial: > https://github.com/niketanpansare/systemml_tutorial > > Missing items in above tutorial: > 1. Create a separate section for Notebook rather than have it hidden under > MLContext Programming guide ( > > http://apache.github.io/incubator-systemml/spark-mlcontext-programming-guide.html > ). > 2. Add Python Notebooks (This requires attaching both jars and python > MLContext to Zeppelin or Jupyter context). > 3. Allow users to use jars from our nightly build (see my jupyter example) > as well as released version (see my zeppelin example). > 4. Tutorials for all our algorithms using real world dataset. Example: > > https://www.ibm.com/support/knowledgecenter/SSPT3X_2.1.2/com.ibm.swg.im.infosphere.biginsights.tut.doc/doc/tut_Mod_BigR.html > . > 5. DML Kernel for Zeppelin (see > https://issues.apache.org/jira/browse/SYSTEMML-542). > 6. Other hosting services such as AzureML. > 7. Tutorial that shows SystemML's integration with MLPipeline. > > These missing items can be broken down into relatively small tasks with > detailed specification that external contributors can work on. Any > thoughts ? > > Thanks, > > Niketan Pansare > IBM Almaden Research Center > E-mail: npansar At us.ibm.com > http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar >
