a5b44de0f6
- shared mode uses one optimizer with shared results - multi mode runs as many optimizers as jobs and results are only shared on ask - a flag to override the strategy when asking more points (--lie-strat) - make sure to ask with n_points `None` to avoid computing more points than needed in shared mode - reduce n of models to 1 in multi mode - don't load more than the specified number of jobs when loading previous optimizers - stretch the batch length to reach the epochs limit - a warning for when no epochs are logged
14 lines
371 B
Python
14 lines
371 B
Python
from typing import Any
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from queue import Queue
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from multiprocessing.managers import SyncManager
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hyperopt: Any = None
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manager: SyncManager
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# stores the optimizers in multi opt mode
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optimizers: Queue
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# stores a list of the results to share between optimizers
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# in the form of dict[tuple(Xi)] = yi
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results_board: Queue
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# store the results in single opt mode
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results: Queue
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