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 <:ComputationalResources.AbstractResource
.
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 MLJ.default_resource()
.
!!! note
You cannot use `CPUThreads()` with models wrapping python code.