feature(Inference): Created the inference process, added model saving. (#153)

* 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>

run_inference.py

@@ -0,0 +1,20 @@
+from training.inference import run_inference_pipeline
+from models.saving import load_models
+
+from run_pipeline import run_pipeline
+from config.config import get_dev_config, get_default_ensemble_config, get_lightweight_ensemble_config
+
+from typing import Callable
+
+
+def run_inference(preload_models:bool, get_config:Callable):
+    if preload_models:
+        all_models_all_assets, data_config, training_config = load_models(None)
+    else:
+        all_models_all_assets, data_config, training_config, _, _, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_config)
+    
+    run_inference_pipeline(data_config, training_config, all_models_all_assets)
+    
+
+if __name__ == '__main__':
+    run_inference(preload_models=False, get_config=get_lightweight_ensemble_config)