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DJL (Deep Java Library) has supported MPS since version 0.20.0.
I couldn't find any samples, so I decided to try it out and take some notes.
The sample code is here. I tested it on an Apple M2 Pro, with 32 GB of memory, running macOS 13.5.2.
It seems you can create a Device instance with Device.of("mps", 0) as shown below:
import ai.djl.Device;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.types.Shape;
public class Main {
public static void main(String[] args) {
int dimension = 1024;
Device device = Device.of("mps", 0);
//Device device = Device.cpu();
System.out.println(device.isGpu()); // false
try (NDManager manager = NDManager.newBaseManager(device)) {
NDArray array1 = manager.randomUniform(0, 1, new Shape(dimension, dimension));
NDArray array2 = manager.randomUniform(0, 1, new Shape(dimension, dimension));
NDArray result = array1.add(array2).mul(10).matMul(array1.transpose()).div(5);
System.out.println(result);
}
}
}
Although MPS itself is not a GPU, using the MPS API allows the GPU to be utilized. Therefore, when you run code that uses MPS, the % GPU number in the Activity Monitor will be greater than 0.