Comments (2)
I managed to implement the shared memory dictionary for multiprocessing in 5439daf. It requires >=0.3.3 of Gradient-Free-Optimizers (because I needed to enable the pass through of the shared memory dict).
The shared memory dictionary is automatically activated if memory=True and is shared between all objective functions that are equal. There is no additional input required by the user. Compatibility with the memory_warm_start parameter is also ensured.
The shared memory is tested with multiple tests that confirm the speedup for n_jobs>1.
Offtopic @0liu: The early stopping also works for Hyperactive v3.3.0
This feature was a great idea! Thank you @0liu for suggesting it.
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Hello @0liu,
this is a great suggestion! I did some reading about this topic and I think it should not be that difficult to implement (but maybe I am wrong with that ;-) ).
I will do some testing/prototyping for this feature in the next few days. I'll get back to you when I have an update.
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Related Issues (20)
- ValueError: assignment destination is read-only HOT 3
- Dynamic inertia in ParticleSwarmOptimizer HOT 4
- Feature: Passing extra parameters to the optimization function HOT 5
- Optimization in serial? HOT 4
- New feature: save optimizer object to continue optimization run at a later time.
- hyper.results(model) HOT 1
- New feature: Optimization Strategies HOT 1
- add ray multiprocessing support
- Change Optimization paramters at runtime
- Show speed difference between python version
- Question of Particle Swarm Optimizer HOT 6
- Progress Bar visual error when running in parallel HOT 2
- Error when creating shared memory HOT 1
- TypeError: cannot pickle '_thread.RLock' object HOT 5
- Redesign command-line output of optimization run HOT 1
- Add early stopping feature to custom optimization strategies HOT 1
- Add type hints to hyperactive-api HOT 1
- add `prune_search_space`-method to optimization strategies HOT 1
- add constrained optimization to API HOT 1
- Stop verbosity of search HOT 3
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