This method allows to configure number of network training threads per cluster node.
Default value: -1, which defines automated number of workers selection, based on hardware present in system (i.e., number of GPUs, if training on a GPU enabled system).
When training on GPUs, you should use 1 worker per GPU (which is the default). For CPUs, 1 worker per node is usually preferred, though multi-CPU (i.e., multiple physical CPUs) or CPUs with large core counts may have better throughput (i.e., more examples per second) when increasing the number of workers, at the expense of more memory consumed. Note that if you increase the number of workers on a CPU system, you should set the number of OpenMP threads using the {- code OMP_NUM_THREADS} property - see {- link org.nd4j.config.ND4JEnvironmentVars#OMP_NUM_THREADS} for more details. For example, a machine with 32 physical cores could use 4 workers with {- code OMP_NUM_THREADS=8}