backend_server.qmd

---
title: Bootstrapping an API using FastAPI and Kedro Boot
order: 7
---

# Bootstrapping an API using FastAPI and Kedro Boot

The final stage of this pod involves bootstrapping all the nodes across the inference pipelines to create a FastAPI server. This enables easy deployment and interaction with your inference workflow as an API.

---

## Key Steps

1. **Tagging Nodes**  
   Ensure that all the nodes you want to include in the server are tagged with `"inference"`. This allows seamless integration into the FastAPI server.

2. **Using Kedro Boot**  
   We use **Kedro Boot**, a Kedro plugin that preloads components in a modular pipeline. This minimizes runtime for every API request, enhancing performance.

---

## Launching the API

The relevant code is located in the folder:  
`image_ml_pod/src/image_ml_pod_fastapi`.

### To launch the API server locally:
```bash
uvicorn src.image_ml_pod_fastapi.app:app --host 0.0.0.0 --port 8000
```

---

## Docker Integration

We have provided a **Dockerfile** for easy deployment of the FastAPI server.

### To build the Docker image:
```bash
docker build -t image-ml-pod .
```

### To run the Docker image with GPU support:
```bash
docker run -p 8000:8000 --gpus all image-ml-pod
```

> **Note**: The current Docker image is designed for rapid prototyping and may not be fully optimized. You can refine it further for production use.

---

## Scalability Considerations

While the current implementation is monolithic, consider using **microservices** for better scalability:  
- Each of the three pipelines involved in inference (`inf_data_preprocessing`, `model_inference`, and `inf_pred_postprocessing`) could be deployed as separate microservices.
- This would allow scaling relevant hardware (e.g., GPU nodes for inference) independently.

---