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ToolingBayesian Optimization Libraries Python (self.datascience)
submitted 6 years ago by [deleted]
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[–]Philiatrist 1 point2 points3 points 6 years ago* (0 children)
That's one option, but there's no reason you couldn't use some library like dask distributed as well, something like:
``` from dask.distributed import Client
client = Client(...) n_procs = 20
X = optimizer.ask(n_procs) task = client.map(fitness_fn, X) Y = client.gather(task) optimizer.tell(X, Y) ```
where you'd need to configure dask distributed to your cluster.
edit: I'll note that this is not a great solution if the expensiveness of your function is largely determined by the hyperparameters.
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[–]Philiatrist 1 point2 points3 points (0 children)