As the demand increases regarding the quantity of data and resource requirements for parallel computations, legacy approaches may not perform well. So far, we have seen how big data development has become famous and is the most followed approach by enterprises due to the same reasons. DL4J supports neural network training, evaluation, and inference on distributed clusters.
Modern approaches to heavy training, or output generation tasks, distribute training effort across multiple machines. This also brings additional challenges. We need to ensure that we have the following constraints checked before we use Spark to perform distributed training/evaluation/inference:
- Our data should be significantly large enough to justify the need for distributed clusters. Small network/data on Spark doesn't really gain any performance improvements...
