1. Detach the INDArray data to use it across workspaces:

INDArray array = Nd4j.rand(6, 6);
 INDArray mean = array.mean(1);
 INDArray result = mean.detach();

2. Remove all workspaces that were created during training/evaluation in case they are running short of RAM:

Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();

3. Leverage an array instance from another workspace in the current workspace by calling leverageTo():

LayerWorkspaceMgr.leverageTo(ArrayType.ACTIVATIONS, myArray);

4. Track the time spent on every iteration during training using PerformanceListener:

model.setListeners(new PerformanceListener(frequency,reportScore)); 

5. Add the following Maven dependency for cuDNN support:

<dependency>
   <groupId>org.deeplearning4j</groupId>
   <artifactId>deeplearning4j-cuda-x.x</artifactId> //cuda version to be specified
   <version>1.0.0-beta4</version>
 </dependency>

6. Configure DL4J/cuDNN to favor performance over memory:

MultiLayerNetwork config = new NeuralNetConfiguration.Builder()
 .cudnnAlgoMode(ConvolutionLayer.AlgoMode.PREFER_FASTEST) //prefer performance over memory
 .build();

7. Configure ParallelWrapper to support multi-GPU training/inferences:

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
 .prefetchBuffer(deviceCount)
.workers(Nd4j.getAffinityManager().getNumberOfDevices())
.trainingMode(ParallelWrapper.TrainingMode.SHARED_GRADIENTS)
.thresholdAlgorithm(new AdaptiveThresholdAlgorithm())
 .build();

8. Configure ParallelInference as follows:

ParallelInference inference = new ParallelInference.Builder(model)
 .inferenceMode(InferenceMode.BATCHED)
.batchLimit(maxBatchSize)
 .workers(workerCount)
 .build();