PoolModel

class pyapprox.interface.PoolModel(function, max_eval_concurrency, assert_omp=True, base_model=None)

Bases: object

Evaluate a function at multiple samples in parallel using multiprocessing.Pool

Methods Summary

__call__(samples[, verbose, return_grad])	Evaluate a function at multiple samples in parallel using multiprocessing.Pool
set_max_eval_concurrency(max_eval_concurrency)	Set the number of threads used to evaluate the function

Methods Documentation

__call__(samples, verbose=False, return_grad=False)

Evaluate a function at multiple samples in parallel using multiprocessing.Pool

Parameters:

samplesnp.ndarray (nvars,nsamples)

Samples used to evaluate self.function

set_max_eval_concurrency(max_eval_concurrency)

Set the number of threads used to evaluate the function

Parameters:

max_eval_concurrencyinteger

The maximum number of simulations that can be run in parallel. Should be no more than the maximum number of cores on the computer being used