1  train:
 2  	uv run  src/training_pipeline/training.py 
 3  
 4  training-data:
 5  	uv run  src/feature_pipeline/preprocessing/core.py 
 6  
 7  frontend:
 8  	uv run  streamlit run src/inference_pipeline/frontend/main.py --server.port 8510
 9  
10  
11  # Backfilling the Feature Store
12  backfill-features:
13  	uv run  src/inference_pipeline/backend/backfill_feature_store.py --scenarios start end --target features 
14  	
15  backfill-predictions:
16  	uv run  src/inference_pipeline/backend/backfill_feature_store.py --scenarios start end --target predictions
17  
18  backfill-all: backfill-features backfill-predictions	
19  	
20  
21  # Docker
22  start-docker:
23  	sudo systemctl start docker
24  
25  image:
26  	docker build -t divvy-hourly .
27  
28  container:
29  	docker run  -it --env-file .env -p 8501:8501/tcp divvy-hourly:latest 
30  
31  
32  # Git
33  push-main:
34  	git push -u github main 
35  	git push -u codeberg main 
36  	git push rad main 
37