adaptive.runner.BaseRunner¶
-
class
adaptive.runner.
BaseRunner
(learner, goal, *, executor=None, ntasks=None, log=False, shutdown_executor=False, retries=0, raise_if_retries_exceeded=True)[source]¶ Bases:
object
Base class for runners that use concurrent.futures.Executors.
- 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, oripyparallel.Client
, optional The executor in which to evaluate the function to be learned. If not provided, a newProcessPoolExecutor
is used on Unix systems while on Windows adistributed.Client
is used ifdistributed
is installed.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
inlearner.function(x)
. After retries is reached forx
the point is present inrunner.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)
.
-
to_retry
¶ Mapping of
{point: n_fails, ...}
. When a point has failedrunner.retries
times it is removed but will be present inrunner.tracebacks
.- Type
-
overhead : callable
The overhead in percent of using Adaptive. Essentially, this is
100 * (1 - total_elapsed_function_time / self.elapsed_time())
.
-
do_log
¶
-
elapsed_time
()[source]¶ Return the total time elapsed since the runner was started.
Is called in
overhead
.
-
failed
¶ Set of points that failed
runner.retries
times.
-
overhead
()[source]¶ Overhead of using Adaptive and the executor in percent.
This is measured as
100 * (1 - t_function / t_elapsed)
.Notes
This includes the overhead of the executor that is being used. The slower your function is, the lower the overhead will be. The learners take ~5-50 ms to suggest a point and sending that point to the executor also takes about ~5 ms, so you will benefit from using Adaptive whenever executing the function takes longer than 100 ms. This of course depends on the type of executor and the type of learner but is a rough rule of thumb.