Hardware setup for Eclipse Deeplearning4j, including GPUs and CUDA.
ND4J works atop so-called backends, or linear-algebra libraries, such as Native nd4j-native and nd4j-cuda-10.2 (GPUs), which you can select by pasting the right dependency into your project’s POM.xml file.
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:
As of now, the
artifactIdfor the CUDA versions can be one of
Otherwise you will need to use the native implementation of ND4J as a CPU backend:
If you are developing your project on multiple operating systems/system architectures, you can add
-platformto the end of your
artifactIdwhich will download binaries for most major systems.
For enabling different backends at runtime, you set the priority with your environment via the environment variable
Relative to the priority, it will allow you to dynamically set the backend type.
org.nd4j.linalg.factory.Nd4jBackend$NoAvailableBackendException: Please ensure that you have an nd4j backend on your classpath. Please see: https://deeplearning4j.konduit.ai/nd4j/backend
... 2 more
There are multiple reasons why you might run into this error message.
- 1.You haven't configured an ND4J backend at all.
- 2.You have a jar file that doesn't contain a backend for your platform.
- 3.You have a jar file that doesn't contain service loader files.
Read this page and add a ND4J Backend to your dependencies:
This happens when you use a non
-platformtype backend dependency definition. In this case, only the Backend for the system that the jar file was built on will be included.
To solve this issue, use
nd4j-native, if you are running on CPU and
nd4j-cuda-10.2when using the GPU backend.
If the jar file only contains the GPU backend, but your system has no CUDA capable (CC >= 3.5) GPU or CUDA isn't installed on the system, the CPU Backend should be used instead.
ND4J uses the Java ServiceLoader in order to detect which backends are available on the class path. Depending on your uberjar packaging configuration, those files might be stripped away or broken.
To double check that the required files are included, open your uberjar and make sure it contains
/META-INF/services/org.nd4j.linalg.factory.Nd4jBackend. Then open the file, and make sure there are entries for all of your configured backends.
If your uberjar does not contain that file, or if not all of the configured backends are listed there, you will have to reconfigure your shade plugin. See ServicesResourceTransformer documentation for how to do that.