Inference Pipeline

Creates an Airflow container that can call an inference container from its DAGs.

Design Rationale

This project relies on Airflow's ability to launch external containers through DockerOperator.

The functionality is:

• Airflow is launched.
• When a task is triggered, a DockerOperator task is launched.
• The specific container will launch, with its associated command:
  • e.g., docker run inference:latest python3 main_inference.py -t <INFERENCE TYPE>
  • <INFERENCE_TYPE> can be any inference activity added to inference/main_inference.py

Project Structure

• dags: contains two example DAGs: one for testing nvidia-smi inside of the built container, and one for running inference.
• data: contains example data from Kaggle.
• docker: contains the Dockerfiles to build the inference containers. These containers get an inference type as an incoming command. Copies over main_inference.py.
• docker-compose.yaml: Contains the main entrypoint. Intended to launch Airflow.
• inference: contains main_inference.py, the main file to be copied inside the inference container.