Add a parallel algorithm for TPE and related research blog #1081
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| @@ -190,7 +190,7 @@ class HyperoptTuner(Tuner): | |||
| HyperoptTuner is a tuner which using hyperopt algorithm. | |||
| """ | |||
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can you give me a comparison between this PR and the current method in our example?
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Not clear, compare what?
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compare with the current TPE version in NNI.
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@PurityFan cannot access PAI anymore, @Crysple will compare the performance.
| @@ -374,8 +401,18 @@ def get_suggestion(self, random_search=False): | |||
| total_params : dict | |||
| parameter suggestion | |||
| """ | |||
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| rval = self.rval | |||
| if self.parallel == True and len(self.running_data): | |||
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Suggest changing "if self.parallel and len(self.runing_data)" to avoid pylint warning.
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| rval = self.rval | ||
| if self.parallel == True and len(self.running_data): | ||
| self.CL_rval = copy.deepcopy(self.rval) |
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Every time we copy a "self.rval" to "self.CL_rval". Shall we need to consider the memory recycle here?
| @@ -204,7 +204,12 @@ def __init__(self, algorithm_name, optimize_mode='minimize'): | |||
| self.json = None | |||
| self.total_data = {} | |||
| self.rval = None | |||
| self.CL_rval = None | |||
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what's the "CL" means? What's the "self.CL_rval" represent?
| 'classArgs': { | ||
| Optional('optimize_mode'): setChoice('optimize_mode', 'maximize', 'minimize'), | ||
| Optional('parallel_optimize'): setType('parallel_optimize', bool), | ||
| Optional('constant_liar_type'): setChoice('min', 'max', 'mean') |
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This is inconsistent with your API in the config file
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BTW, the name should also be the first arg of setChoice
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closed, this pr is replaced with another pr #1373 |
#1052