How to Deploy FastAPI Applications with GPU Access in the Cloud

Deploying a FastAPI application with GPU acceleration, such as using PyTorch, involves creating a simple FastAPI API for GPU-based inference, packaging it into a Docker container with CUDA support, and deploying it on Runpod's GPU cloud platform. The process entails setting up a Dockerfile to include CUDA drivers, PyTorch, and the FastAPI code, and ensuring the application is configured to run with Uvicorn. After building and testing the Docker image locally, it can be deployed on Runpod via the web UI or CLI, which involves selecting the appropriate GPU type, configuring the container image, and exposing the necessary HTTP ports. The guide also emphasizes best practices for port configuration, environment variables, security, logging, and performance optimization, such as using asynchronous endpoints and preloading the model to reduce latency. Additionally, it provides tips on managing persistent storage, scaling applications, and verifying GPU usage, ultimately illustrating how to efficiently run a FastAPI application on cloud GPU infrastructure.