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

8 things every developer should know about the Apache Ignite caching

Any technology, no matter how advanced it is, will not be able to solve your problems if you implement it improperly. Caching, precisely when it comes to the use of a distributed caching, can only accelerate your application with the proper use and configurations of it. From this point of view, Apache Ignite is no different, and there are a few steps to consider before using it in the production environment. In this article, we describe various technics that can help you to plan and adequately use of Apache Ignite as cutting-edge caching technology. Do proper capacity planning before using Ignite cluster. Do paperwork for understanding the size of the cache, number of CPUs or how many JVMs will be required. Let’s assume that you are using Hibernate as an ORM in 10 application servers and wish to use Ignite as an L2 cache. Calculate the total memory usages and the number of Ignite nodes you have to need for maintaining your SLA. An incorrect number of the Ignite nodes can become a b...

Analyse with ANT - a sonar way

After the Javaone conference in Moscow, i have found some free hours to play with Sonar . Here is a quick steps to start analyzing with ANT projects. Sonar provides Analyze with ANT document to play around with ANT, i have just modify some parts. Here is it. 1) Download the Sonar Ant Task and put it in your ${ANT_HOME}/lib directory 2) Modify your ANT build.xml as follows: <?xml version = '1.0' encoding = 'windows-1251'?> <project name="abc" default="build" basedir="."> <!-- Define the Sonar task if this hasn't been done in a common script --> <taskdef uri="antlib:org.sonar.ant" resource="org/sonar/ant/antlib.xml"> <classpath path="E:\java\ant\1.8\apache-ant-1.8.0\lib" /> </taskdef> <!-- Out-of-the-box those parameters are optional --> <property name="sonar.jdbc.url" value="jdbc:oracle:thin:@xyz/sirius.xyz" /> <property na...

Writing weblogic logs to database table

By default, oracle weblogic server logging service uses an implementation, based on the Java Logging APIs by using the LogMBean.isLog4jLoggingEnabled attribute. With a few effort you can use log4j with weblogic logging service. In the Administration Console, you can specify Log4j or keep the default Java Logging implementation. In this blog i will describe how to configure log4j with weblogic logging service and writes all the logs messages to database table. Most of all cases it's sufficient to writes log on files, however it's better to get all the logs on table to query on it. In our case we have 3 different web logic servers in our project and our consumer need to get all the logs in one central place to diagnose if something goes wrong. First of all we will create a simple table on our oracle database schema and next configure all other parts. Here we go: 1) CREATE TABLE LOGS (USER_ID VARCHAR2(20), DOMAIN varchar2(50), DATED DATE NOT NULL, LOGGER VARCHAR2(500) NOT...