Configuring Maven for DL4J

We need to add DL4J/ND4J Maven dependencies to leverage DL4J capabilities. ND4J is a scientific computation library dedicated to DL4J. It is necessary to mention the ND4J backend dependency in your pom.xml file. In this recipe, we will add a CPU-specific Maven configuration in pom.xml.

Getting ready

Let's discuss the required Maven dependencies. We assume you have already done the following:

• JDK 1.7, or higher, is installed and the PATH variable is set.
• Maven is installed and the PATH variable is set.

A 64-bit JVM is required to run DL4J.

Set the PATH variable for JDK and Maven:

• On Linux: Use the export command to add Maven and JDK to the PATH variable:
  

export PATH=/opt/apache-maven-3.x.x/bin:$PATH
export PATH=${PATH}:/usr/java/jdk1.x.x/bin

Replace the version number as per the installation.

• On Windows: Set System Environment variables from system Properties:

set PATH="C:/Program Files/Apache Software Foundation/apache-maven-3.x.x/bin:%PATH%"
 set PATH="C:/Program Files/Java/jdk1.x.x/bin:%PATH%"

Replace the JDK version number as per the installation.

How to do it...

1. Add the DL4J core dependency:

<dependency>
 <groupId>org.deeplearning4j</groupId>
 <artifactId>deeplearning4j-core</artifactId>
 <version>1.0.0-beta3</version>
 </dependency>
 

2. Add the ND4J native dependency:

<dependency>
 <groupId>org.nd4j</groupId>
 <artifactId>nd4j-native-platform</artifactId>
 <version>1.0.0-beta3</version>
 </dependency>
 
 

3. Add the DataVec dependency to perform ETL (short for Extract, Transform and Load) operations:

<dependency>
 <groupId>org.datavec</groupId>
 <artifactId>datavec-api</artifactId>
 <version>1.0.0-beta3</version>
 </dependency>

4. Enable logging for debugging:

<dependency>
 <groupId>org.slf4j</groupId>
 <artifactId>slf4j-simple</artifactId>
 <version>1.7.25</version> //change to latest version
 </dependency> 

Note that 1.0.0-beta 3 is the current DL4J release version at the time of writing this book, and is the official version used in this cookbook. Also, note that DL4J relies on an ND4J backend for hardware-specific implementations.

How it works...

After adding DL4J core dependency and ND4J dependencies, as mentioned in step 1 and step 2, we are able to create neural networks. In step 2, the ND4J maven configuration is mentioned as a necessary backend dependency for Deeplearnign4j. ND4J is the scientific computation library for Deeplearning4j.

ND4J is a scientific computing library written for Java, just like NumPy is for Python.

Step 3 is very crucial for the ETL process: that is, data extraction, transformation, and loading. So, we definitely need this as well in order to train the neural network using data.

Step 4 is optional but recommended, since logging will reducee the effort involved in debugging.