Phase 8: Serverless GPU Deployment & Infrastructure · 55 min · Python · Docker · NVIDIA Container Toolkit

Docker Packaging for GPU Models

Docker + CUDA base image + model weights + inference script = reproducible GPU deployment.

Real-world application: Docker packaging for GPU models (CUDA base images, model weight management, multi-stage builds) is a core infrastructure skill for generative media deployment roles.

What you will learn

Create Docker images for GPU models: nvidia-runtime, CUDA base images, model file management
Package FLUX.1 Schnell: base image + model weights + inference script
Package ComfyUI: ComfyUI + custom nodes + models + handler script
Optimize image size: multi-stage builds, model weight layering, squash

Objective

Operate media generation under real packaging, cold-start, storage, throughput, and cost constraints. In the paired studio lab, create a Dockerfile that packages FLUX.1 Schnell with a Python inference handler. Use multi-stage build to optimize image size. Write a handler.py that accepts a prompt via HTTP, generates an image, and returns it. Build the image and document its size, layers, and estimated cold start time.

The Problem

A team needs to deploy a FLUX.1 Schnell model to RunPod Serverless. They need:

A Docker image with CUDA, PyTorch, Diffusers, and model weights
An inference script that receives jobs and returns generated images
Optimized image size (model weights are 20+ GB)
Reproducible builds (same Dockerfile → same image)

Docker packaging for GPU models is a core infrastructure skill for generative media deployment.

What you'll build
Create Docker images for GPU models using NVIDIA CUDA base images, package FLUX.1 Schnell and ComfyUI, optimize image size with multi-stage builds, and write handler scripts for serverless deployment.

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Key terms in this lesson

Inference