Docker Deployment for GPU-Accelerated Services

Containerizing standard Python web apps is easy. Containerizing Python apps that need to talk to NVIDIA GPUs, manage gRPC streams, handle WebSockets at scale, and integrate with complex monitoring stacks? That's a different beast. In this article, I'll share how we structured our Docker deployment for a GPU-accelerated Speech-to-Speech service, moving from a fragile "works on my machine" setup to a robust production infrastructure. The Challenge: GPU & Environment Complexity We faced three main challenges: Dual Deployment Modes : We needed to support both "Cloud" (Nvidia NVCF) and "Self-Hosted" (On-premise GPU) modes from the same image. Log Management : High-volume WebSocket traffic generates massive logs. We needed structured JSON for machines (Grafana/Loki) but readable text for developers. Process Management : Uvicorn needs careful tuning for async workloads to avoid blocking the event loop. 1. The Entrypoint Pattern Instead of a simple CMD ["uvicorn", ...] , we implemented a robus