remove limit_ram_use
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parent
b421521be3
commit
96fafb7f56
@ -98,7 +98,6 @@ class FreqaiDataDrawer:
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self.empty_pair_dict: pair_info = {
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"model_filename": "", "trained_timestamp": 0,
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"data_path": "", "extras": {}}
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self.limit_ram_use = self.freqai_info.get('limit_ram_usage', False)
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if 'Reinforcement' in self.config['freqaimodel']:
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logger.warning('User passed a ReinforcementLearner model, FreqAI will '
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'now use stable_baselines3 to save models.')
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@ -514,10 +513,10 @@ class FreqaiDataDrawer:
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dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
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)
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if not self.limit_ram_use:
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self.model_dictionary[coin] = model
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self.model_dictionary[coin] = model
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self.pair_dict[coin]["model_filename"] = dk.model_filename
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self.pair_dict[coin]["data_path"] = str(dk.data_path)
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if coin not in self.meta_data_dictionary:
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self.meta_data_dictionary[coin] = {}
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self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
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@ -565,7 +564,7 @@ class FreqaiDataDrawer:
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dk.label_list = dk.data["label_list"]
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# try to access model in memory instead of loading object from disk to save time
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if dk.live and coin in self.model_dictionary and not self.limit_ram_use:
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if dk.live and coin in self.model_dictionary:
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model = self.model_dictionary[coin]
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elif self.model_type == 'joblib':
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model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
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@ -587,7 +586,7 @@ class FreqaiDataDrawer:
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)
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# load it into ram if it was loaded from disk
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if coin not in self.model_dictionary and not self.limit_ram_use:
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if coin not in self.model_dictionary:
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self.model_dictionary[coin] = model
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if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
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