The acceleration API is experimental and may not work correctly in all cases, especially if trying to use an acceleration method that your version of Julia or installed packages cannot support. The API is also subject to breaking changes during minor or major releases without warning.
To enable composable, extensible acceleration of core MLJ methods, ComputationalResources.jl is utilized to provide some basic types and functions to make implementing acceleration easy. However, ambitious users or package authors have the option to define their own types to be passed as resources to
acceleration, which must be
Methods which support some form of acceleration support the
acceleration keyword argument, which can be passed a "resource" from
ComputationalResources. For example, passing
acceleration=CPUProcesses() will utilize
Distributed's multiprocessing functionality to accelerate the computation, while
acceleration=CPUThreads() will use Julia's PARTR threading model to perform acceleration.
The default computational resource is
CPU1(), which is simply serial processing via CPU. The default resource can be changed as in this example:
MLJ.default_resource(CPUProcesses()). The argument must always have type
<:ComputationalResource.AbstractResource. To inspect the current default, use
CPUThreads() resource is only available when running a version of Julia with
You cannot use
CPUThreads() with models wrapping python code.