CPU/GPU-specific configuration for training

Hardware-specific changes are generic configurations that can't be ignored in a distributed environment. DL4J supports GPU-accelerated training in NVIDIA GPUs with CUDA/cuDNN enabled. We can also perform Spark distributed training using GPUs.

In this recipe, we will configure CPU/GPU-specific changes.

How to do it...

1. Download, install, and set up the CUDA toolkit from https://developer.nvidia.com/cuda-downloads. OS-specific setup instructions are available at the NVIDIA CUDA official website.
2. Configure the GPU for Spark distributed training by adding a Maven dependency for ND4J's CUDA backend:

<dependency>
   <groupId>org.nd4j</groupId>
   <artifactId...