How do I enable NVIDIA GPU support in Docker FFmpeg?

You must install the NVIDIA Container Toolkit on the host and use the --gpus all flag in your docker run command, paired with a CUDA-enabled image like jrottenberg/ffmpeg:7.1-nvidia2404.

Can I use FFmpeg with Kubernetes?

Absolutely. This skill includes Kubernetes Job manifests and Pod specifications that handle persistent volume mounts and GPU resource limits for cluster-based processing.

Does this skill support FFmpeg on Windows Docker Desktop?

Yes, it provides specific instructions for PowerShell and Git Bash, including the mandatory requirement to use backslashes for Windows file paths.

What is the smallest FFmpeg Docker image available?

For a minimal footprint, the skill recommends jrottenberg/ffmpeg:7.1-alpine320 (approx. 100MB) or mwader/static-ffmpeg for a statically compiled binary.

Dockerized FFmpeg Deployment

Name: Dockerized FFmpeg Deployment
Author: JosiahSiegel

byJosiahSiegel

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Deployment & DevOps

Deploys and configures FFmpeg within Docker containers with full support for GPU acceleration, Kubernetes orchestration, and cross-platform path handling.

About

This skill provides a comprehensive framework for running FFmpeg in isolated, containerized environments, ensuring consistent media processing across development and production. It simplifies complex configurations such as NVIDIA, Intel, and AMD GPU passthrough, volume mounting for Windows and Linux, and building optimized custom Dockerfiles. Whether you are setting up a CI/CD pipeline, a Kubernetes transcoding job, or a simple watch-folder automation, this skill provides the production-ready patterns and troubleshooting guides needed for stable containerized video engineering.

Key Features

Optimized Docker image selection (Alpine, Ubuntu, and Static variants)
Cross-platform support for Windows, Linux, and macOS file path requirements
Production-ready Docker Compose and Kubernetes manifest templates
GPU passthrough configuration for NVIDIA (NVENC), Intel (QSV), and AMD (VAAPI)
Custom multi-stage Dockerfile recipes for specialized codec builds
7 GitHub stars

Use Cases

High-performance cloud transcoding services with hardware acceleration
Consistent media processing environments for automated CI/CD pipelines
Containerized 'watch folder' automation for distributed media workflows

About

Key Features

Optimized Docker image selection (Alpine, Ubuntu, and Static variants)
Cross-platform support for Windows, Linux, and macOS file path requirements
Production-ready Docker Compose and Kubernetes manifest templates
GPU passthrough configuration for NVIDIA (NVENC), Intel (QSV), and AMD (VAAPI)
Custom multi-stage Dockerfile recipes for specialized codec builds
7 GitHub stars

Use Cases

High-performance cloud transcoding services with hardware acceleration
Consistent media processing environments for automated CI/CD pipelines
Containerized 'watch folder' automation for distributed media workflows