adaptive.BlockingRunner

class adaptive.BlockingRunner(learner, goal, *, executor=None, ntasks=None, log=False, shutdown_executor=False, retries=0, raise_if_retries_exceeded=True)[source]

Bases: adaptive.runner.BaseRunner

Run a learner synchronously in an executor.

Parameters

  • learner (BaseLearner instance) –

  • goal (callable) – The end condition for the calculation. This function must take the learner as its sole argument, and return True when we should stop requesting more points.

  • executor (concurrent.futures.Executor, distributed.Client, mpi4py.futures.MPIPoolExecutor, ipyparallel.Client or loky.get_reusable_executor, optional) – The executor in which to evaluate the function to be learned. If not provided, a new ProcessPoolExecutor.

  • ntasks (int, optional) – The number of concurrent function evaluations. Defaults to the number of cores available in executor.

  • log (bool, default: False) – If True, record the method calls made to the learner by this runner.

  • shutdown_executor (bool, default: False) – If True, shutdown the executor when the runner has completed. If executor is not provided then the executor created internally by the runner is shut down, regardless of this parameter.

  • retries (int, default: 0) – Maximum amount of retries of a certain point x in learner.function(x). After retries is reached for x the point is present in runner.failed.

  • raise_if_retries_exceeded (bool, default: True) – Raise the error after a point x failed retries.

learner

The underlying learner. May be queried for its state.

Type

BaseLearner instance

log

Record of the method calls made to the learner, in the format (method_name, *args).

Type

list or None

to_retry

List of (point, n_fails). When a point has failed runner.retries times it is removed but will be present in runner.tracebacks.

Type

list of tuples

tracebacks

List of of (point, tb) for points that failed.

Type

list of tuples

pending_points

A list of tuples with (concurrent.futures.Future, point).

Type

list of tuples

elapsed_time : callable

A method that returns the time elapsed since the runner was started.

overhead : callable

The overhead in percent of using Adaptive. This includes the overhead of the executor. Essentially, this is 100 * (1 - total_elapsed_function_time / self.elapsed_time()).

elapsed_time()[source]

Return the total time elapsed since the runner was started.