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https://github.com/webclinic017/drift.git
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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>
20 lines
815 B
Python
20 lines
815 B
Python
from training.inference import run_inference_pipeline
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from models.saving import load_models
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from run_pipeline import run_pipeline
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from config.config import get_dev_config, get_default_ensemble_config, get_lightweight_ensemble_config
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from typing import Callable
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def run_inference(preload_models:bool, get_config:Callable):
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if preload_models:
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all_models_all_assets, data_config, training_config = load_models(None)
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else:
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all_models_all_assets, data_config, training_config, _, _, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_config)
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run_inference_pipeline(data_config, training_config, all_models_all_assets)
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if __name__ == '__main__':
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run_inference(preload_models=False, get_config=get_lightweight_ensemble_config) |