We configured NVIDIA CUDA using steps 1 to 4. For more detailed OS-specific instructions, refer to the official NVIDIA CUDA website at https://developer.nvidia.com/cuda-downloads.

Depending on your OS, installation instructions will be displayed on the website. DL4J version 1.0.0-beta 3 currently supports CUDA installation versions 9.0, 9.2, and 10.0. For instance, if you need to install CUDA v10.0 for Ubuntu 16.04, you should navigate the CUDA website as shown here:

Note that step 3 is not applicable to newer versions of DL4J. For of 1.0.0-beta and later versions, the necessary CUDA libraries are bundled with DL4J. However, this is not applicable for step 7.

Additionally, before proceeding with steps 5 and 6, make sure that there are no redundant dependencies (such as CPU-specific dependencies) present in pom.xml.

DL4J supports CUDA, but performance can be further accelerated by adding a cuDNN library. cuDNN does not show up as a bundled package in DL4J. Hence, make sure you download and install NVIDIA cuDNN from the NVIDIA developer website. Once cuDNN is installed and configured, we can follow step 7 to add support for cuDNN in the DL4J application.