本文整理了Java中org.nd4j.linalg.factory.Nd4j.getBackend()方法的一些代码示例，展示了Nd4j.getBackend()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Nd4j.getBackend()方法的具体详情如下：
包路径：org.nd4j.linalg.factory.Nd4j
类名称：Nd4j
方法名：getBackend

Nd4j.getBackend介绍

暂无

代码示例

代码示例来源：origin: deeplearning4j/nd4j

public static Cloner newCloner() {
  Cloner cloner = new Cloner();
  //Implement custom cloning for INDArrays (default can have problems with off-heap and pointers)
  //Sadly: the cloner library does NOT support interfaces here, hence we need to use the actual classes
  //cloner.registerFastCloner(INDArray.class, new INDArrayFastCloner());  //Does not work due to interface
  IFastCloner fc = new INDArrayFastCloner();
  cloner.registerFastCloner(Nd4j.getBackend().getNDArrayClass(), fc);
  cloner.registerFastCloner(Nd4j.getBackend().getComplexNDArrayClass(), fc);
  //Same thing with DataBuffers: off heap -> cloner library chokes on them, but need to know the concrete
  // buffer classes, not just the interface
  IFastCloner fc2 = new DataBufferFastCloner();
  DataBufferFactory d = Nd4j.getDataBufferFactory();
  doReg(cloner, fc2, d.intBufferClass());
  doReg(cloner, fc2, d.longBufferClass());
  doReg(cloner, fc2, d.halfBufferClass());
  doReg(cloner, fc2, d.floatBufferClass());
  doReg(cloner, fc2, d.doubleBufferClass());
  doReg(cloner, fc2, CompressedDataBuffer.class);
  return cloner;
}

代码示例来源：origin: org.nd4j/nd4j-tensorflow

/**
 * Infers the device for the given thread
 * based on the {@link Nd4j#getAffinityManager()}
 * Usually, this will either be a gpu or cpu
 * reserved for the current device.
 * You can think of the "current thread"
 * as a worker. This is mainly useful with multiple gpus
 * @return
 */
public static String defaultDeviceForThread() {
  Integer deviceForThread = Nd4j.getAffinityManager().getDeviceForThread(Thread.currentThread());
  String deviceName = null;
  //gpu
  if(Nd4j.getBackend().getClass().getName().contains("JCublasBackend")) {
    deviceName = "/device:gpu:" + deviceForThread;
  }
  else {
    deviceName = "/device:cpu:" + deviceForThread;
  }
  return deviceName;
}

代码示例来源：origin: org.nd4j/nd4j-kryo_2.11

@Override
  public void registerClasses(Kryo kryo) {
    kryo.register(Nd4j.getBackend().getNDArrayClass(), new Nd4jSerializer());
    kryo.register(Nd4j.getBackend().getComplexNDArrayClass(), new Nd4jSerializer());
    //Also register Java types (synchronized/unmodifiable collections), which will fail by default
    UnmodifiableCollectionsSerializer.registerSerializers(kryo);
    SynchronizedCollectionsSerializer.registerSerializers(kryo);
  }
}

代码示例来源：origin: org.nd4j/nd4j-tensorflow

public static org.tensorflow.framework.ConfigProto getAlignedWithNd4j() {
  org.tensorflow.framework.ConfigProto configProto = org.tensorflow.framework.ConfigProto.getDefaultInstance();
  ConfigProto.Builder builder1 = configProto.toBuilder().addDeviceFilters(TensorflowConversion.defaultDeviceForThread());
  try {
    //cuda
    if(Nd4j.getBackend().getClass().getName().toLowerCase().contains("jcu")) {
      builder1.setGpuOptions(GPUOptions.newBuilder()
          .setAllowGrowth(true)
          .setPerProcessGpuMemoryFraction(0.5)
          .build());
    }
    //cpu
    else {
    }
  } catch (Exception e) {
    e.printStackTrace();
  }
  return builder1.build();
}