mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Daniel Szemerey
55f083638f
* feat: Basic scaffolding up for inference process after training. * feat: Saving and loading models works. Inference works nearly. * feat: Added inference pipeline. * feat: Saving model now accoring to date and time; loading models now selects from latest file. Fixed the creation of dictionary of models. * feat: Added lightweight asset config, but full pipeline. * feat: Added new naming for dictionary. * fix: Fixed dictionary naming convention. * fix: Fixed naming again, now the model structure is good * fix: Changed the output path and the return values from run_pipeline. * feat: Added function to make sure folder exists for output models. Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
import pickle
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import datetime
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from typing import Optional, Union
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import os
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import warnings
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def save_models(all_models_for_all_assets: dict, data_config:dict, training_config:dict) -> None:
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all_models_for_all_assets['training_config'] = training_config
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all_models_for_all_assets['data_config'] = data_config
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date_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
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if not os.path.exists('output/models'):
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warnings.warn("No folder exists, creating one.")
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os.makedirs('output/models')
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pickle.dump( all_models_for_all_assets, open( "output/models/{}.p".format(date_string), "wb" ) )
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def load_models(file_name:Union[str, None]) -> tuple[dict, dict, dict]:
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if file_name is None:
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warnings.warn("No file name provided, will load latest models and configurations.")
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files_in_directory:list = os.listdir('output/models')
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assert len(files_in_directory) > 0, "No models found in output/models."
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file_name = sorted(files_in_directory)[-1]
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all_models_for_all_assets = pickle.load( open( "output/models/{}".format(file_name), "rb" ) )
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data_config = all_models_for_all_assets['data_config']
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training_config = all_models_for_all_assets['training_config']
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return all_models_for_all_assets, data_config, training_config
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