Skip to main content

An impatient start with Apache Ignite machine learning grid

Recently Apache Ignite 2.0 introduce a beta version of the in-memory machine learning grid, which is a distributed machine learning library built on top of the Apache IMDG. This beta release of ML library can perform local and distributed vector, decompositions and matrix algebra operations. The data structure can be stored in Java heap, off-heap or distributed Ignite caches. At this moment, the Apache Ignite ML grid doesn't support any prediction or recommendation analysis. In this short post, we are going to download the new Apache Ignite 2.0 release, build the example and run them.

1. Download and unpack the Apache Ignite 2.0 distribution.

Download the Apache Ignite 2.0 binary release version from the following link. Unpack the distribution somewhere in your workstation (e.g /home/ignite/2.0) and set the IGNITE_HOME path to the directory.

2. Start the Apache Ignite remote node

Run the following command in the terminal window.
ignite.sh examples/config/example-ignite.xml 

Note that, Remote nodes for examples should always be started with the special configuration file which enables P2P class loading: `examples/config/example-ignite.xml`.

Also, note that Apache Ignite version 2.0 needs Java version 1.8 or higher.

3. Build the machine learning examples

Go to the /examples folder of the Apache Ignite distribution. If you already installed and configure maven, run the following command from the examples folder.

mvn clean install -Pml

The above command will active the machine learning (ml) profile and build the project.

4. Run it

Lets run the simple local onheap version of the Vector example. Execute the following command in your terminal windows:

mvn exec:java -Dexec.mainClass=org.apache.ignite.examples.ml.math.vector.VectorExample

You should get the following logs in your console.


All the examples are autonomous, does't need any special configuration. Examples name with 'Cache' such as CacheMatrixExample or CacheVectorExample needs remote Ignite node with P2P class loading. Let's run the CacheMatrixExample with the following command.
mvn exec:java -Dexec.mainClass=org.apache.ignite.examples.ml.math.matrix.CacheMatrixExample

You should get the following output as shown below.


Additionally, Apache Ignite ML grid provides a simple utility class allows pretty-printing of matrices/vectors. You can run the TracerExample as follows:
mvn exec:java -Dexec.mainClass=org.apache.ignite.examples.ml.math.tracer.TracerExample

This above command will launch a web browser and provide some HTML output as follows:


This enough for now. If you like this post, you should also like the book.

Comments

Popular posts from this blog

Send e-mail with attachment through OSB

Oracle Service Bus (OSB) contains a good collection of adapter to integrate with any legacy application, including ftp, email, MQ, tuxedo. However e-mail still recognize as a stable protocol to integrate with any application asynchronously. Send e-mail with attachment is a common task of any business process. Inbound e-mail adapter which, integrated with OSB support attachment but outbound adapter doesn't. This post is all about sending attachment though JavaCallout action. There are two ways to handle attachment in OSB: 1) Use JavaCallout action to pass the binary data for further manipulation. It means write down a small java library which will get the attachment and send the e-mail. 2) Use integrated outbound e-mail adapter to send attachment, here you have to add a custom variable named attachment and assign the binary data to the body of the attachment variable. First option is very common and easy to implement through javax.mail api, however a much more developer manage t

Tip: SQL client for Apache Ignite cache

A new SQL client configuration described in  The Apache Ignite book . If it got you interested, check out the rest of the book for more helpful information. Apache Ignite provides SQL queries execution on the caches, SQL syntax is an ANSI-99 compliant. Therefore, you can execute SQL queries against any caches from any SQL client which supports JDBC thin client. This section is for those, who feels comfortable with SQL rather than execute a bunch of code to retrieve data from the cache. Apache Ignite out of the box shipped with JDBC driver that allows you to connect to Ignite caches and retrieve distributed data from the cache using standard SQL queries. Rest of the section of this chapter will describe how to connect SQL IDE (Integrated Development Environment) to Ignite cache and executes some SQL queries to play with the data. SQL IDE or SQL editor can simplify the development process and allow you to get productive much quicker. Most database vendors have their own front-en

Load balancing and fail over with scheduler

Every programmer at least develop one Scheduler or Job in their life time of programming. Nowadays writing or developing scheduler to get you job done is very simple, but when you are thinking about high availability or load balancing your scheduler or job it getting some tricky. Even more when you have a few instance of your scheduler but only one can be run at a time also need some tricks to done. A long time ago i used some data base table lock to achieved such a functionality as leader election. Around 2010 when Zookeeper comes into play, i always preferred to use Zookeeper to bring high availability and scalability. For using Zookeeper you have to need Zookeeper cluster with minimum 3 nodes and maintain the cluster. Our new customer denied to use such a open source product in their environment and i was definitely need to find something alternative. Definitely Quartz was the next choose. Quartz makes developing scheduler easy and simple. Quartz clustering feature brings the HA and