def set_num_threads(nthreads):
"""
Sets a number of threads to be used in operations.
DEPRECATED: returns the previous setting for the number of threads.
During initialization time NumExpr sets this number to the number
of detected cores in the system (see `detect_number_of_cores()`).
"""
old_nthreads = _set_num_threads(nthreads)
return old_nthreads
def set_num_threads(nthreads):
"""
Sets a number of threads to be used in operations.
Returns the previous setting for the number of threads.
During initialization time Numexpr sets this number to the number
of detected cores in the system (see `detect_number_of_cores()`).
If you are using Intel's VML, you may want to use
`set_vml_num_threads(nthreads)` to perform the parallel job with
VML instead. However, you should get very similar performance
with VML-optimized functions, and VML's parallelizer cannot deal
with common expresions like `(x+1)*(x-2)`, while Numexpr's one
can.
"""
old_nthreads = _set_num_threads(nthreads)
return old_nthreads
