CLAUDE: Add complete GPU transcoding solution for Tdarr containers
- Add working Podman-based GPU Tdarr startup script for Fedora systems - Document critical Docker Desktop GPU issues on Fedora/Nobara systems - Add comprehensive Tdarr configuration examples (CPU and GPU variants) - Add GPU acceleration patterns and troubleshooting documentation - Provide working solution for NVIDIA RTX GPU hardware transcoding Key insight: Podman works immediately for GPU access on Fedora systems where Docker Desktop fails due to virtualization layer conflicts. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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158
examples/docker/tdarr-node-configurations.md
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examples/docker/tdarr-node-configurations.md
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# Tdarr Node Container Configurations
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## Overview
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Complete examples for running Tdarr transcoding nodes in containers, covering both CPU-only and GPU-accelerated setups.
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## CPU-Only Configuration (Docker Compose)
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For systems without GPU or when GPU isn't needed:
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```yaml
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version: "3.4"
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services:
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tdarr-node:
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container_name: tdarr-node-cpu
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image: ghcr.io/haveagitgat/tdarr_node:latest
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restart: unless-stopped
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environment:
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- TZ=America/Chicago
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- UMASK_SET=002
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- nodeName=local-workstation-cpu
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- serverIP=YOUR_TDARR_SERVER_IP # Replace with your tdarr server IP
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- serverPort=8266
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- inContainer=true
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- ffmpegVersion=6
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volumes:
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# Mount your media from the same NAS share as the server
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- /path/to/your/media:/media # Replace with your local media mount
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# Temp directory for transcoding cache
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- ./temp:/temp
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```
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**Use case**:
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- CPU-only transcoding
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- Testing Tdarr functionality
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- Systems without dedicated GPU
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- When GPU drivers aren't available
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## GPU-Accelerated Configuration (Podman)
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**Recommended for Fedora/RHEL/CentOS/Nobara systems:**
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```bash
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podman run -d --name tdarr-node-gpu \
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--device nvidia.com/gpu=all \
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--restart unless-stopped \
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-e TZ=America/Chicago \
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-e UMASK_SET=002 \
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-e nodeName=local-workstation-gpu \
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-e serverIP=10.10.0.43 \
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-e serverPort=8266 \
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-e inContainer=true \
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-e ffmpegVersion=6 \
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-e NVIDIA_DRIVER_CAPABILITIES=all \
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-e NVIDIA_VISIBLE_DEVICES=all \
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-v ./media:/media \
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-v ./temp:/temp \
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ghcr.io/haveagitgat/tdarr_node:latest
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```
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**Use case**:
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- Hardware video encoding/decoding (NVENC/NVDEC)
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- High-performance transcoding
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- Multiple concurrent streams
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- Fedora-based systems where Podman works better than Docker
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## GPU-Accelerated Configuration (Docker)
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**For Ubuntu/Debian systems where Docker GPU support works:**
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```yaml
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version: "3.4"
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services:
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tdarr-node:
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container_name: tdarr-node-gpu
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image: ghcr.io/haveagitgat/tdarr_node:latest
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restart: unless-stopped
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environment:
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- TZ=America/Chicago
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- UMASK_SET=002
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- nodeName=local-workstation-gpu
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- serverIP=YOUR_TDARR_SERVER_IP
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- serverPort=8266
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- inContainer=true
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- ffmpegVersion=6
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- NVIDIA_DRIVER_CAPABILITIES=all
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- NVIDIA_VISIBLE_DEVICES=all
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volumes:
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- /path/to/your/media:/media
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- ./temp:/temp
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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```
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## Configuration Parameters
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### Required Environment Variables
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- `TZ`: Timezone (e.g., `America/Chicago`)
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- `nodeName`: Unique identifier for this node
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- `serverIP`: IP address of Tdarr server
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- `serverPort`: Tdarr server port (typically 8266)
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- `inContainer`: Set to `true` for containerized deployments
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- `ffmpegVersion`: FFmpeg version to use (6 recommended)
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### GPU-Specific Variables
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- `NVIDIA_DRIVER_CAPABILITIES`: Set to `all` for full GPU access
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- `NVIDIA_VISIBLE_DEVICES`: `all` for all GPUs, or specific GPU IDs
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### Volume Mounts
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- `/media`: Mount point for media files (must match server configuration)
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- `/temp`: Temporary directory for transcoding cache
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## Platform-Specific Recommendations
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### Fedora/RHEL/CentOS/Nobara
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- **GPU**: Use Podman (Docker Desktop has GPU issues)
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- **CPU**: Docker or Podman both work fine
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### Ubuntu/Debian
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- **GPU**: Use Docker with nvidia-container-toolkit
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- **CPU**: Docker recommended
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### Testing GPU Functionality
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Verify GPU access inside container:
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```bash
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# For Podman
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podman exec tdarr-node-gpu nvidia-smi
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# For Docker
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docker exec tdarr-node-gpu nvidia-smi
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```
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Test NVENC encoding:
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```bash
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# For Podman
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podman exec tdarr-node-gpu /usr/local/bin/tdarr-ffmpeg -f lavfi -i testsrc2=duration=5:size=1920x1080:rate=30 -c:v h264_nvenc -t 5 /tmp/test.mp4
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# For Docker
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docker exec tdarr-node-gpu /usr/local/bin/tdarr-ffmpeg -f lavfi -i testsrc2=duration=5:size=1920x1080:rate=30 -c:v h264_nvenc -t 5 /tmp/test.mp4
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```
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## Troubleshooting
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- **GPU not detected**: See `reference/docker/nvidia-gpu-troubleshooting.md`
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- **Permission issues**: Ensure proper UMASK_SET and volume permissions
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- **Connection issues**: Verify serverIP and firewall settings
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- **Performance issues**: Monitor CPU/GPU utilization during transcoding
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## Related Documentation
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- `patterns/docker/gpu-acceleration.md` - GPU acceleration patterns
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- `reference/docker/nvidia-gpu-troubleshooting.md` - Detailed GPU troubleshooting
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- `start-tdarr-gpu-podman.sh` - Ready-to-use Podman startup script
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140
patterns/docker/gpu-acceleration.md
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patterns/docker/gpu-acceleration.md
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# GPU Acceleration in Docker Containers
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## Overview
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Patterns for enabling GPU acceleration in Docker containers, particularly for media transcoding workloads.
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## NVIDIA Container Toolkit Approach
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### Modern Method (CDI - Container Device Interface)
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```bash
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# Generate CDI configuration
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sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
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# Use in docker-compose
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services:
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app:
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devices:
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- nvidia.com/gpu=all
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```
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### Legacy Method (Runtime)
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```bash
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# Configure runtime
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sudo nvidia-ctk runtime configure --runtime=docker
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# Use in docker-compose
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services:
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app:
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runtime: nvidia
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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```
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### Compose v3 Method (Deploy)
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```yaml
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services:
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app:
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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```
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## Hardware Considerations
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### High-End Consumer GPUs (RTX 4080/4090)
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- Excellent NVENC/NVDEC performance
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- Multiple concurrent transcoding streams
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- High VRAM for large files
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### Multi-GPU Setups
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```yaml
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environment:
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- NVIDIA_VISIBLE_DEVICES=0,1 # Specific GPUs
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# or
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- NVIDIA_VISIBLE_DEVICES=all # All GPUs
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```
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## Troubleshooting Patterns
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### Gradual Enablement
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1. Start with CPU-only configuration
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2. Verify container functionality
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3. Add GPU support incrementally
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4. Test with simple workloads first
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### Fallback Strategy
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```yaml
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# Include both GPU and CPU fallback
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devices:
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- /dev/dri:/dev/dri # Intel/AMD GPU fallback
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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```
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## Common Issues
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- Docker service restart failures after toolkit install
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- CDI vs runtime configuration conflicts
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- Distribution-specific package differences
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- Permission issues with device access
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## Critical Fedora/Nobara GPU Issue
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### Problem: Docker Desktop GPU Integration Failure
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On Fedora-based systems (Fedora, RHEL, CentOS, Nobara), Docker Desktop has significant compatibility issues with NVIDIA Container Toolkit, resulting in:
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- `CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected`
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- `unknown or invalid runtime name: nvidia`
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- Manual device mounting works but CUDA runtime fails
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### Solution: Use Podman Instead
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```bash
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# Podman works immediately on Fedora systems
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podman run -d --name container-name \
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--device nvidia.com/gpu=all \
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--restart unless-stopped \
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-e NVIDIA_DRIVER_CAPABILITIES=all \
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-e NVIDIA_VISIBLE_DEVICES=all \
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image:tag
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```
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### Why Podman Works Better on Fedora
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- Native systemd integration
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- Direct hardware access (no VM layer)
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- Default container engine for RHEL/Fedora
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- Superior NVIDIA Container Toolkit compatibility
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### Testing Commands
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```bash
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# Test Docker (often fails on Fedora)
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docker run --rm --gpus all ubuntu:20.04 nvidia-smi
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# Test Podman (works on Fedora)
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podman run --rm --device nvidia.com/gpu=all ubuntu:20.04 nvidia-smi
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```
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### Recommendation by OS
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- **Fedora/RHEL/CentOS/Nobara**: Use Podman
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- **Ubuntu/Debian**: Use Docker
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- **When in doubt**: Test both, use what works
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## Media Transcoding Example (Tdarr)
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```bash
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# Working Podman command for Tdarr on Fedora
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podman run -d --name tdarr-node-gpu \
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--device nvidia.com/gpu=all \
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--restart unless-stopped \
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-e nodeName=workstation-gpu \
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-e serverIP=10.10.0.43 \
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-e NVIDIA_VISIBLE_DEVICES=all \
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-v ./media:/media \
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-v ./tmp:/temp \
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ghcr.io/haveagitgat/tdarr_node:latest
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```
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161
reference/docker/nvidia-gpu-troubleshooting.md
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reference/docker/nvidia-gpu-troubleshooting.md
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# NVIDIA GPU Container Troubleshooting Guide
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## Key Insights from Fedora/Nobara GPU Container Issues
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### Problem: Docker Desktop vs Podman GPU Support on Fedora-based Systems
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**Issue**: Docker Desktop on Fedora/Nobara systems has significant compatibility issues with NVIDIA Container Toolkit integration, even when properly configured.
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**Symptoms**:
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- `CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected`
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- `unknown or invalid runtime name: nvidia`
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- Device nodes created but CUDA runtime fails to initialize
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- Manual device creation (`mknod`) works but CUDA still fails
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**Root Cause**: Docker Desktop's virtualization layer interferes with direct hardware access on Fedora-based systems.
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## Solution: Use Podman Instead of Docker
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### Why Podman Works Better on Fedora
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- **Native integration**: Better integration with systemd and Linux security contexts
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- **Direct hardware access**: No VM layer interfering with GPU communication
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- **Superior NVIDIA toolkit support**: Works with same nvidia-container-toolkit installation
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- **Built for Fedora**: Designed as the default container engine for RHEL/Fedora systems
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### Verification Commands
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```bash
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# Test basic GPU access with Podman (should work)
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podman run --rm --device nvidia.com/gpu=all ubuntu:20.04 nvidia-smi
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# Test basic GPU access with Docker (often fails on Fedora)
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docker run --rm --gpus all ubuntu:20.04 nvidia-smi
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```
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## Complete GPU Container Setup for Fedora/Nobara
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### Prerequisites
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1. NVIDIA drivers installed and working (`nvidia-smi` functional)
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2. nvidia-container-toolkit installed via DNF
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3. Podman installed (`dnf install podman`)
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### NVIDIA Container Toolkit Installation
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```bash
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# Install NVIDIA container toolkit
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sudo dnf install nvidia-container-toolkit
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# Configure Docker runtime (may not work but worth trying)
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sudo nvidia-ctk runtime configure --runtime=docker
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# The key insight: Podman works without additional configuration!
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```
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### Working Podman Command Template
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```bash
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podman run -d --name container-name \
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--device nvidia.com/gpu=all \
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--restart unless-stopped \
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-e NVIDIA_DRIVER_CAPABILITIES=all \
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-e NVIDIA_VISIBLE_DEVICES=all \
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[other options] \
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image:tag
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```
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## Troubleshooting Steps (In Order)
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### 1. Verify Host GPU Access
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```bash
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nvidia-smi # Should show GPU info
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lsmod | grep nvidia # Should show nvidia modules loaded
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ls -la /dev/nvidia* # Should show device files
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```
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### 2. Test Container Runtime
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```bash
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# Try Podman first (recommended for Fedora)
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podman run --rm --device nvidia.com/gpu=all ubuntu:20.04 nvidia-smi
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# If Podman works but Docker doesn't, use Podman for production
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```
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### 3. Check NVIDIA Container Toolkit
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```bash
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rpm -qa | grep nvidia-container-toolkit
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nvidia-ctk --version
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```
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### 4. Verify CUDA Library Locations
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```bash
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# Find CUDA libraries
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rpm -ql nvidia-driver-cuda-libs | grep libcuda
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ldconfig -p | grep cuda
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# Common locations:
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# /usr/lib64/libcuda.so*
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# /usr/lib64/libnvidia-encode.so*
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```
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## Common Misconceptions
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### ❌ Docker Should Always Work
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**Wrong**: Docker Desktop has known issues with GPU access on some Linux distributions, especially Fedora-based systems.
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### ❌ More Privileges = Better GPU Access
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**Wrong**: Adding `privileged: true` or manual device mounting doesn't solve Docker Desktop's fundamental GPU integration issues.
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### ❌ NVIDIA Container Toolkit Problems
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**Wrong**: The toolkit works fine - the issue is Docker Desktop's compatibility with it on Fedora systems.
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## Best Practices
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### For Fedora/RHEL/CentOS Systems
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1. **Use Podman by default** for GPU containers
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2. Test Docker as fallback, but expect issues
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3. Podman Compose works for orchestration
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4. No special configuration needed beyond nvidia-container-toolkit
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### For Production Deployments
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1. Test both Docker and Podman in your environment
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2. Use whichever works reliably (often Podman on Fedora)
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3. Document which container runtime is used
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4. Include runtime in deployment scripts
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## Success Indicators
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### GPU Container Working Correctly
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- `nvidia-smi` runs inside container
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- NVENC/CUDA applications detect GPU
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- No "CUDA_ERROR_NO_DEVICE" errors
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- Hardware encoder shows as available in applications
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### Example: Successful Tdarr Node
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```bash
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# Container logs should show:
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# h264_nvenc-true-true,hevc_nvenc-true-true,av1_nvenc-true-true
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# FFmpeg test should succeed:
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podman exec container-name ffmpeg -f lavfi -i testsrc2=duration=1:size=320x240:rate=1 -c:v h264_nvenc -t 1 /tmp/test.mp4
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```
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## System-Specific Notes
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### Nobara/Fedora 42
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- Docker Desktop: ❌ GPU support problematic
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- Podman: ✅ GPU support works out of the box
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- NVIDIA Driver version: 570.169 (tested working)
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- Container Toolkit version: 1.17.8 (tested working)
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### Key Files and Locations
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- GPU devices: `/dev/nvidia*` (auto-created)
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- CUDA libraries: `/usr/lib64/libcuda.so*` (via nvidia-driver-cuda-libs package)
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- Container toolkit: `nvidia-ctk` command available
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- Docker daemon config: `/etc/docker/daemon.json` (may not help)
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## Future Reference
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When encountering GPU container issues on Fedora-based systems:
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1. Try Podman first - it likely works immediately
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2. Don't waste time troubleshooting Docker Desktop GPU issues
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3. Use the same container images and configurations
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4. Podman commands are nearly identical to Docker commands
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This approach saves hours of debugging Docker Desktop GPU integration issues on Fedora systems.
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76
start-tdarr-gpu-podman.sh
Executable file
76
start-tdarr-gpu-podman.sh
Executable file
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#!/bin/bash
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# Tdarr Node with GPU Support - Podman Script
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# This script starts a Tdarr node container with NVIDIA GPU acceleration using Podman
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set -e
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CONTAINER_NAME="tdarr-node-gpu"
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SERVER_IP="10.10.0.43"
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SERVER_PORT="8266"
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NODE_NAME="local-workstation-gpu"
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echo "🚀 Starting Tdarr Node with GPU support using Podman..."
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# Stop and remove existing container if it exists
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if podman ps -a --format "{{.Names}}" | grep -q "^${CONTAINER_NAME}$"; then
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echo "🛑 Stopping existing container: ${CONTAINER_NAME}"
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podman stop "${CONTAINER_NAME}" 2>/dev/null || true
|
||||
podman rm "${CONTAINER_NAME}" 2>/dev/null || true
|
||||
fi
|
||||
|
||||
# Create required directories
|
||||
echo "📁 Creating required directories..."
|
||||
mkdir -p ./media ./tmp
|
||||
|
||||
# Start Tdarr node with GPU support
|
||||
echo "🎬 Starting Tdarr Node container..."
|
||||
podman run -d --name "${CONTAINER_NAME}" \
|
||||
--device nvidia.com/gpu=all \
|
||||
--restart unless-stopped \
|
||||
-e TZ=America/Chicago \
|
||||
-e UMASK_SET=002 \
|
||||
-e nodeName="${NODE_NAME}" \
|
||||
-e serverIP="${SERVER_IP}" \
|
||||
-e serverPort="${SERVER_PORT}" \
|
||||
-e inContainer=true \
|
||||
-e ffmpegVersion=6 \
|
||||
-e NVIDIA_DRIVER_CAPABILITIES=all \
|
||||
-e NVIDIA_VISIBLE_DEVICES=all \
|
||||
-v "$(pwd)/media:/media" \
|
||||
-v "$(pwd)/tmp:/temp" \
|
||||
ghcr.io/haveagitgat/tdarr_node:latest
|
||||
|
||||
echo "⏳ Waiting for container to initialize..."
|
||||
sleep 5
|
||||
|
||||
# Check container status
|
||||
if podman ps --format "{{.Names}}" | grep -q "^${CONTAINER_NAME}$"; then
|
||||
echo "✅ Tdarr Node is running successfully!"
|
||||
echo ""
|
||||
echo "📊 Container Status:"
|
||||
podman ps --filter "name=${CONTAINER_NAME}" --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
|
||||
echo ""
|
||||
echo "🔍 Testing GPU Access:"
|
||||
if podman exec "${CONTAINER_NAME}" nvidia-smi --query-gpu=name --format=csv,noheader,nounits 2>/dev/null; then
|
||||
echo "🎉 GPU is accessible in container!"
|
||||
else
|
||||
echo "⚠️ GPU test failed, but container is running"
|
||||
fi
|
||||
echo ""
|
||||
echo "🌐 Connection Details:"
|
||||
echo " Server: ${SERVER_IP}:${SERVER_PORT}"
|
||||
echo " Node Name: ${NODE_NAME}"
|
||||
echo ""
|
||||
echo "🧪 Test NVENC encoding:"
|
||||
echo " podman exec ${CONTAINER_NAME} /usr/local/bin/tdarr-ffmpeg -f lavfi -i testsrc2=duration=5:size=1920x1080:rate=30 -c:v h264_nvenc -preset fast -t 5 /tmp/test.mp4"
|
||||
echo ""
|
||||
echo "📋 Container Management:"
|
||||
echo " View logs: podman logs ${CONTAINER_NAME}"
|
||||
echo " Stop: podman stop ${CONTAINER_NAME}"
|
||||
echo " Remove: podman rm ${CONTAINER_NAME}"
|
||||
else
|
||||
echo "❌ Failed to start container"
|
||||
echo "📋 Checking logs..."
|
||||
podman logs "${CONTAINER_NAME}" --tail 10
|
||||
exit 1
|
||||
fi
|
||||
Loading…
Reference in New Issue
Block a user