Name: SystemML_Usage
Owner: International Business Machines
Description: Demonstrate how to perform a Machine Learning exercise using Apache SystemML
Created: 2017-08-22 22:38:31.0
Updated: 2018-03-22 16:14:31.0
Pushed: 2018-03-22 16:14:33.0
Homepage: https://developer.ibm.com/code/patterns/perform-a-machine-learning-exercise/
Size: 1570
Language: Jupyter Notebook
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Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work.
In this Code Pattern we will use Apache SystemML running on IBM Watson Studio to perform a Machine Learning exercise. Watson Studio is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data. Apache SystemML is a flexible machine learning platform that is optimized to scale with large data sets.
When you have completed this Code Pattern, you will understand how to:
The intended audience for this Code Pattern is both application developers and other stakeholders who wish to utilize the power of Data Science quickly and effectively to solve machine learning problems using Apache SystemML. Although Apache SystemML provides various out-of-the box algorithms to experiment with, this specific Code Pattern will provide a Linear Regression example to demonstrate the ease and power of Apache SystemML. Additionally, users can develop their own algorithms using Apache SystemML's Declarative Machine Language (DML) which has R or Python like syntax, or customize any algorithm provided in the package. For more information about additional functionality support, documentation, and the roadmap, please visit Apache SystemML.
Typically data scientist writes an algorithm on subset of dataset which can be fit on the workstation (laptop) disk/memory. Once he/she is satisfied with the results on a workstation, he/she approach system engineer to implement same algorithm in the distributed environment with much bigger dataset. It may takes weeks if not months to go back and forth between data scientist and system engineer to have equivalent algorithm gets implemented in distributed environment on bigger dataset. As human intervention gets involved there is a potential for introduction of bugs in an implementation of equivalent algorithm. When final algorithm is ready it cannot be determined if final algorithm is equivalent to that of an algorithm which was implemented to run it on a workstation. Its hard to determine if any issues found are due to implementation of algorithm in distributed environment or due to an original algorithm itself.
There comes the ?State of the Art? from SystemML. With SystemML data scientist has to write an algorithm only once. With in-built optimizer from SystemML, any algorithm written will have dynamic runtime plan based on data characteristics and runtime environment such as single machine or cluster with multiple nodes. Data Scientist can save lot of time and possible error injection while transforming algorithm implemented to run on single machine to algorithm to be run in a distributed environment.
Follow these steps to setup and run this Code Pattern. These steps are described in detail below.
Sign up for IBM's Watson Studio. By creating a project in Watson Studio a free tier Object Storage service will be created in your IBM Cloud account. Take note of your service names as you will need to select them in the following steps.
Note: When creating your Object Storage service, select the
Freestorage type in order to avoid having to pay an upgrade fee.
To create these services:
Apache Spark so that you can keep track of it.Watson Studio-ObjectStorage so that you can keep track of it.Note: When creating your Object Storage service, select the
Swiftstorage type in order to avoid having to pay an upgrade fee.
Take note of your service names as you will need to select them in the following steps.
Create the Notebook:
Create notebook to create a notebook.Assets tab, select the Create notebook option.From URL tab.Notebook URL enter: https://github.com/IBM/SystemML_Usage/blob/master/notebooks/Machine-Learning-Using-Apache-SystemML.ipynbCreate Notebook.
When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom.
Each code cell is selectable and is preceded by a tag in the left margin. The tag
format is In [x]:. Depending on the state of the notebook, the x can be:
*, which indicates that the cell is currently executing.There are several ways to execute the code cells in your notebook:
Play button in the toolbar.Cell menu bar, there are several options available. For example, you
can Run All cells in your notebook, or you can Run All Below, that will
start executing from the first cell under the currently selected cell, and then
continue executing all cells that follow.Schedule button located in the top right section of your notebook
panel. Here you can schedule your notebook to be executed once at some future
time, or repeatedly at your specified interval.Under the File menu, there are several ways to save your notebook:
Save will simply save the current state of your notebook, without any version
information.Save Version will save your current state of your notebook with a version tag
that contains a date and time stamp. Up to 10 versions of your notebook can be
saved, each one retrievable by selecting the Revert To Version menu item.You can share your notebook by selecting the Share button located in the top
right section of your notebook panel. The end result of this action will be a URL
link that will display a ?read-only? version of your notebook. You have several
options to specify exactly what you want shared from your notebook:
Only text and output: will remove all code cells from the notebook view.All content excluding sensitive code cells: will remove any code cells
that contain a sensitive tag. For example, # @hidden_cell is used to protect
your dashDB credentials from being shared.All content, including code: displays the notebook as is.download as options are also available in the menu.