hubs-predictor 0.1.0

Price predictions for 3DHubs

Price Prediction

Replicate / Reproduce Whole Process

Training Process

• Pre-requirements:

  1. Install conda: link
  2. Install MLFlow:

     pip install mlflow
     

• Run Mlflow UI server to track the training experiments:

  cd train/
  mlflow server --backend-store-uri ./mlruns/
  

• Train:

  # working dir: "ml-engineer-assignment-bendangnuksung/train/"
  
  # modify "train/MLproject" file, update parameters such as:
  # 'datapath' -> path to your data CSV file (important)
  # 'kfolds'   -> N kfolds you want
  # 'lr'       -> Set your own learning rate
  
  # Run training 
  mlflow run --experiment-name hubs_price_prediction .
  

Deployment Process

• Pre-requirements:

  1. Install Docker. Link
  2. Install Docker Compose:

     pip install docker-compose
     

• Build and Start Docker:

  # working dir: "ml-engineer-assignment-bendangnuksung/"
  
  # Modify "docker-compose.yml" if:
  # 1. Wants to change PORT
  # 2. Change the volumes if model stored in different directory. (Default is: "./train/models" because models are stored there after training) 
  
  sudo docker-compose up