Benchmarking is a standard against which we compare solutions to find out whether they are good or not. In the context of deep learning, we might set benchmarks for an existing model that is performing pretty well. We might test our model against factors such as accuracy, the amount of data handled, memory consumption, and JVM garbage collection tuning. In this chapter, we briefly talk about the benchmarking possibilities with your DL4J applications. We will start with general guidelines and then move on to more DL4J-specific benchmarking settings. At the end of the chapter, we will look at a hyperparameter tuning example that shows how to find the best neural network parameters in order to yield the best results. 

In this chapter, we will cover the following recipes:

• DL4J/ND4J specific configuration
• Setting up heap spaces and garbage collection
• Using asynchronous ETL
• Using arbiter to monitor neural network behavior
• Performing hyperparameter tuning