unioaz / mlops-mara-sample-public

Sample solution for MLOps Marathon 2023

MLOps Marathon 2023 - Sample solution

This repository is the sample solution for MLOps Marathon 2023.

Quickstart

1. Prepare environment

   # Install python 3.9
   # Install docker version 20.10.17
   # Install docker-compose version v2.6.1
   pip install -r requirements.txt
   make mlflow_up

2. Train model

   • Download data, ./data/raw_data dir should look like

     data/raw_data
     ├── .gitkeep
     └── phase-1
         └── prob-1
             ├── features_config.json
             └── raw_train.parquet

   • Process data

     python src/raw_data_processor.py --phase-id phase-1 --prob-id prob-1

   • After processing data, ./data/train_data dir should look like

     data/train_data
     ├── .gitkeep
     └── phase-1
         └── prob-1
             ├── category_index.pickle
             ├── test_x.parquet
             ├── test_y.parquet
             ├── train_x.parquet
             └── train_y.parquet

   • Train model

     export MLFLOW_TRACKING_URI=http://localhost:5000
     python src/model_trainer.py --phase-id phase-1 --prob-id prob-1

   • Register model: Go to mlflow UI at http://localhost:5000 and register a new model named phase-1_prob-1_model-1

3. Deploy model predictor

   • Create model config at data/model_config/phase-1/prob-1/model-1.yaml with content:

     phase_id: "phase-1"
     prob_id: "prob-1"
     model_name: "phase-1_prob-1_model-1"
     model_version: "1"

   • Test model predictor

     # run model predictor
     export MLFLOW_TRACKING_URI=http://localhost:5000
     python src/model_predictor.py --config-path data/model_config/phase-1/prob-1/model-1.yaml --port 8000
     
     # curl in another terminal
     curl -X POST http://localhost:8000/phase-1/prob-1/predict -H "Content-Type: application/json" -d @data/curl/phase-1/prob-1/payload-1.json
     
     # stop the predictor above

   • Deploy model predictor

     make predictor_up
     make predictor_curl

   • After running make predictor_curl to send requests to the server, ./data/captured_data dir should look like:

      data/captured_data
      ├── .gitkeep
      └── phase-1
          └── prob-1
              ├── 123.parquet
              └── 456.parquet

4. Improve model

   • The technique to improve model by using the prediction data is described in improve_model.md.

   • Label the captured data, taking around 3 minutes

     python src/label_captured_data.py --phase-id phase-1 --prob-id prob-1

   • After label the captured data, ./data/captured_data dir should look like:

     data/captured_data
     ├── .gitkeep
     └── phase-1
         └── prob-1
             ├── 123.parquet
             ├── 456.parquet
             └── processed
                 ├── captured_x.parquet
                 └── uncertain_y.parquet

   • Improve model with updated data

     export MLFLOW_TRACKING_URI=http://localhost:5000
     python src/model_trainer.py --phase-id phase-1 --prob-id prob-1 --add-captured-data true

   • Register model: Go to mlflow UI at http://localhost:5000 and register model using the existing name phase-1_prob-1_model-1. The latest model version now should be 2.

   • Update model config at data/model_config/phase-1/prob-1/model-1.yaml to:

     phase_id: "phase-1"
     prob_id: "prob-1"
     model_name: "phase-1_prob-1_model-1"
     model_version: "2"

   • Deploy new model version

     make predictor_restart
     make predictor_curl

5. Teardown

   make teardown