[WIP]Enh/checkpointing #8
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mschmidt87
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Jul 31, 2018
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mschmidt87
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mschmidt87
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Looks good. Just some minor comments.
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| if load_existing_checkpoint: | ||
| state = load_checkpoint() | ||
| if state: |
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If state is None here, i.e. if there were no checkpoints that can be loaded, I suggest to raise an error. Otherwise checkpoint loading fails silently.
| from . import lib | ||
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| def load_checkpoint(): |
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What about enabling the user to specify a specific checkpoint, not only the most recent one? You could implement this by allowing load_existing_checkpoint to be specified as True or -1 (for the most recent one) or as an integer specifying the generation.
| pickle.dump(state, f) | ||
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| def extract_keys_from_dict(d, whitelist, blacklist=[]): |
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It appears to me that this function should be located in lib.py.
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This PR adds basic checkpointing by dumping all local variables that are changed during optimization to a pickle file and automatically loading the most recent checkpoint if any is found.
During the implementation, I found that it may be much easier to implement checkpointing if the optimization routine is a stateful object, so maybe your idea @mschmidt87 to turn all algorithms into classes should be done first and a new checkpointing mechanism added to that. Let me know what you think about this.
Branches of #4, so should not be merged before that.