Many learners support training in parallel across multiple processes or machines using the parallelism built into Julia.

You can start Julia with extra worker processes using the p flag when running Julia from the terminal. For example, the following shell command will start Julia with three additional processes, for a total of four.

bash$ julia -p 3

You can also add additional worker processes to an existing Julia session using the addprocs function. The following Julia code adds three additional processes for a total of four:

using Distributed

The parallelism of the learner fitting algorithm can be controlled via the parallel_processes parameter on the learner (see Parameters). There are two options for specifying this parameter:

  • nothing will use all available processes during training
  • Specify a Vector containing the IDs of the processes to use during training. This needs to be a subset of the available processes, which can be found by running Distributed.procs().

Rich Multimedia Output Control

There are many learners and other objects that take advantage of Julia's rich multimedia output to produce interactive browser visualizations in Jupyter notebooks. Because these displays happen automatically, there is no opportunity to pass any desired keyword arguments to these display functions. If you would like to customize these visualizations with keyword arguments, you can instead use the set_rich_output_param! to specify the argument, which will be passed to the display function when automatically called.