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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import warnings |
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def try_ray_import(): |
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try: |
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import ray |
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if ray.is_initialized(): |
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rayInit = True |
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else: |
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rayInit = False |
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except ImportError: |
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warnings.warn("failed to import ray", ImportWarning) |
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ray = None |
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rayInit = False |
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return ray, rayInit |
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class Distribution: |
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def dist(self, optimizer_class, _main_args_, _opt_args_): |
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ray, rayInit = try_ray_import() |
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if rayInit: |
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self.dist_ray(optimizer_class, _main_args_, _opt_args_, ray) |
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else: |
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self.dist_default(optimizer_class, _main_args_, _opt_args_) |
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def dist_default(self, optimizer_class, _main_args_, _opt_args_): |
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_optimizer_ = optimizer_class(_main_args_, _opt_args_) |
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self.results, self.pos, self.scores, self.eval_times, self.opt_times = ( |
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_optimizer_.search() |
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) |
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def dist_ray(self, optimizer_class, _main_args_, _opt_args_, ray): |
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optimizer_class = ray.remote(optimizer_class) |
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opts = [ |
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optimizer_class.remote(_main_args_, _opt_args_) |
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for job in range(_main_args_.n_jobs) |
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] |
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searches = [ |
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opt.search.remote(job, rayInit=True) for job, opt in enumerate(opts) |
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] |
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self.results, self.pos, self.scores, self.eval_times, self.opt_times = ray.get( |
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searches |
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)[0] |
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ray.shutdown() |
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SimonBlanke /
Hyperactive
