df3d22b218
- Add comprehensive Tdarr troubleshooting and GPU transcoding documentation - Create /scripts directory for active operational scripts - Archive mapped node example in /examples for reference - Update CLAUDE.md with scripts directory context triggers - Add distributed transcoding patterns and NVIDIA troubleshooting guides - Enhance documentation structure with clear directory usage guidelines 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
45 lines
1.2 KiB
YAML
45 lines
1.2 KiB
YAML
version: "3.4"
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services:
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tdarr-node:
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container_name: tdarr-node-local-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=192.168.1.100 # 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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# NVIDIA environment variables
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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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# Media access (same as server)
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- /mnt/media:/media # Replace with your media path
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# Local transcoding cache
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- ./temp:/temp
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devices:
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- /dev/dri:/dev/dri # Intel/AMD GPU fallback
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# GPU configuration - choose ONE method:
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# Method 1: Deploy syntax (recommended)
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deploy:
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resources:
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limits:
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memory: 16G # GPU transcoding uses less RAM
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reservations:
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memory: 8G
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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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# Method 2: Runtime (alternative)
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# runtime: nvidia
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# Method 3: CDI (future)
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# devices:
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# - nvidia.com/gpu=all |