note:
this is using a pre-existing utility called Model_Evaluator which parallelizes requests across multiple processes/machines, it has been used extensively in optimizations and has never given any problems. In this example it is set to work with 40 processes. The threadPoolExecutor is just a device to forward multiple calls to the evaluator.
The error implies that if there is too high a degree of parallelization, the logic inside minimize fails, because the function requests (ask) somehow get out of sync with the results returned (tell). The same error occurs whether batch mode is True or False.
Observed Results
the optimizer runs for a while, probably running several hundred evaluations, then produces regular errors like:
RuntimeError: Mean shift samples are expected but missing.
This happens if, for example, ask is called more than once, without calling tell
(because the first call removes the samples from the injection list).
cma.sigma_adaptation.CMAAdaptSigmaTPA step-size adaptation generates mean shift
samples and relies on them.
Expected Results
The optimizer should work
Relevant Code
# Set up the optimizer
optimizer = ng.optimizers.CMA(parametrization=num_weights, budget=1000000, num_workers=num_workers)
model_eval = Model_Evaluator(config)
with futures.ThreadPoolExecutor(max_workers=optimizer.num_workers) as executor:
recommendation = optimizer.minimize(model_eval.f_eval, executor=executor, batch_mode=True)
Steps to reproduce
note: this is using a pre-existing utility called Model_Evaluator which parallelizes requests across multiple processes/machines, it has been used extensively in optimizations and has never given any problems. In this example it is set to work with 40 processes. The threadPoolExecutor is just a device to forward multiple calls to the evaluator.
The error implies that if there is too high a degree of parallelization, the logic inside minimize fails, because the function requests (ask) somehow get out of sync with the results returned (tell). The same error occurs whether batch mode is True or False.
Observed Results
the optimizer runs for a while, probably running several hundred evaluations, then produces regular errors like:
evolution_strategy.py:2981: InjectionWarning: orphanated injected solution
then fails with error:
RuntimeError: Mean shift samples are expected but missing. This happens if, for example,
askis called more than once, without callingtell(because the first call removes the samples from the injection list).cma.sigma_adaptation.CMAAdaptSigmaTPAstep-size adaptation generates mean shift samples and relies on them.Expected Results
The optimizer should work
Relevant Code