Build in g a FastAPI Inference Service: Async Handlers, Batch Processing, Health Checks, and Docker

FastAPI provides a minimal, high-performance framework for serving models. This post covers setting up async inference endpoints, batch processing requests, health checks, and Docker containerization.

FastAPI Server

from fastapi import FastAPI
import torch import torch.nn as nn
app = FastAPI() model = nn.Linear(10, 2)
@app.post("/predict") async def predict(data: list): x = torch.tensor(data, dtype=torch.float32) with torch.no_grad(): output = model(x) return {"prediction": output.tolist()}
@app.get("/health") async def health(): return {"status": "healthy"} 

Conclusion

FastAPI simplifies inference service deployment. Async handlers, batch processing, and health checks ensure production readiness. This completes the PyTorch mastery series—from tensors to production systems. Understanding each component enables building efficient, scalable deep learning applications.