ND4J backends for GPUs and CPUs
You can choose GPUs or native CPUs for your backend linear algebra operations by changing the dependencies in ND4J’s POM.xml file. Your selection will affect both ND4J and DL4J being used in your application.
If you have CUDA v9.2+ installed and NVIDIA-compatible hardware, then your dependency declaration will look like:
<dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-cuda-10.1</artifactId> <version>1.0.0-beta4</version> </dependency>
As of now, the
artifactId for the CUDA versions can be one of
Otherwise you will need to use the native implementation of ND4J as a CPU backend:
<dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-native</artifactId> <version>1.0.0-beta4</version> </dependency>
If you are developing your project on multiple operating systems/system architectures, you can add
-platform to the end of your
artifactId which will download binaries for most major systems.
<dependency> ... <artifactId>nd4j-native-platform</artifactId> ... </dependency>
If you have several GPUs, but your system is forcing you to use just one, you can use the helper
CudaEnvironment.getInstance().getConfiguration().allowMultiGPU(true); as first line of your
See our page on CuDNN.
Check the NVIDIA guides for instructions on setting up CUDA on the NVIDIA website.