emmaggie / nd4j

nd4j site

ND4j

A Numpy and matlab like environment for cross platform scientific computing.

Supports GPUS via CUDA and Native via jblas.

All of this is wrapped in a unifying interface.

The api is a mix of numpy and jblas.

An example creation:

      INDArray arr = Nd4j.create(new float[]{1,2,3,4},new int[]{2,2});

This will create a 2 x 2 ndarray.

The way the project works as follows:

Include the following in your pom.xml:

   <dependency>
    <artifactId>nd4j</artifactId>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-api</artifactId>
    <version>0.0.1-SNAPSHOT</version>
   </dependency>

From here, you need to pick an implementation suitable for your needs. This can be either jblas for native or cuda for GPUs.

Jblas:

         <dependency>
            <artifactId>nd4j</artifactId>
            <groupId>org.nd4j</groupId>
            <artifactId>nd4j-jblas</artifactId>
            <version>0.0.1-SNAPSHOT</version>
           </dependency>

Jcuda:

                <dependency>
                   <artifactId>nd4j</artifactId>
                   <groupId>org.nd4j</groupId>
                   <artifactId>nd4j-jcublas</artifactId>
                   <version>0.0.1-SNAPSHOT</version>
                  </dependency>

For jcuda, we are still in the process of streamlining the release for this one. For now, please do the following:

              git clone https://github.com/SkymindIO/mavenized-jcuda
              cd mavenized-jcuda
              mvn clean install

This will install the jcuda jar files.

You need to specify a version of jcuda to use as well. The version will depend on your GPU. Amazon supports 0.5.5.

We will be streamllining this process soon as well.

Basics:

In place operations:

     INDArray arr = Nd4j.create(new float[]{1,2,3,4},new int[]{2,2});
     //scalar operation
     arr.addi(1);

     //element wise operations
     INDArray arr2 = ND4j.create(new float[]{5,6,7,8},new int[]{2,2});
     arr.addi(arr2);

   
     Duplication operations:
            
             //clone then add
              arr.add(1);
              //clone then add
              arr.add(arr2);
             
    
     Dimension wise operations (column and row order depending on the implementation chosen)
     
     arr.sum(0);