Java Deep Learning Cookbook

How it works...

A workspace is a memory management model that enables the reuse of memory for cyclic workloads without having to introduce a JVM garbage collector. INDArray memory content is invalidated once in every workspace loop. Workspaces can be integrated for training or inference.

In step 1, we start with workspace benchmarking. The detach() method will detach the specific INDArray from the workspace and will return a copy. So, how do we enable workspace modes for our training instance? Well, if you're using the latest DL4J version (from 1.0.0-alpha onwards), then this feature is enabled by default. We target version 1.0.0-beta 3 in this book.

In step 2, we removed workspaces from the memory, as shown here:

Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread(); ...