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>
83 lines
3.2 KiB
Bash
Executable File
83 lines
3.2 KiB
Bash
Executable File
#!/bin/bash
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# Tdarr Mapped Node with GPU Support - Example Script
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# This script starts a MAPPED Tdarr node container with NVIDIA GPU acceleration using Podman
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#
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# MAPPED NODES: Direct access to media files via volume mounts
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# Use this approach when you want the node to directly access your media library
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# for local processing without server coordination for file transfers
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#
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# Configure these variables for your setup:
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set -e
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CONTAINER_NAME="tdarr-node-gpu-mapped"
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SERVER_IP="YOUR_SERVER_IP" # e.g., "10.10.0.43" or "192.168.1.100"
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SERVER_PORT="8266" # Default Tdarr server port
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NODE_NAME="YOUR_NODE_NAME" # e.g., "workstation-gpu" or "local-gpu-node"
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MEDIA_PATH="/path/to/your/media" # e.g., "/mnt/media" or "/home/user/Videos"
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CACHE_PATH="/path/to/cache" # e.g., "/mnt/ssd/tdarr-cache"
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echo "🚀 Starting MAPPED Tdarr Node with GPU support using Podman..."
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echo " Media Path: ${MEDIA_PATH}"
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echo " Cache Path: ${CACHE_PATH}"
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# Stop and remove existing container if it exists
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if podman ps -a --format "{{.Names}}" | grep -q "^${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
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podman rm "${CONTAINER_NAME}" 2>/dev/null || true
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fi
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# Start Tdarr node with GPU support
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echo "🎬 Starting Tdarr Node container..."
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podman run -d --name "${CONTAINER_NAME}" \
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--gpus 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="${NODE_NAME}" \
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-e serverIP="${SERVER_IP}" \
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-e serverPort="${SERVER_PORT}" \
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-e inContainer=true \
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-e ffmpegVersion=6 \
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-e logLevel=DEBUG \
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-e NVIDIA_DRIVER_CAPABILITIES=all \
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-e NVIDIA_VISIBLE_DEVICES=all \
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-v "${MEDIA_PATH}:/media" \
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-v "${CACHE_PATH}:/temp" \
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ghcr.io/haveagitgat/tdarr_node:latest
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echo "⏳ Waiting for container to initialize..."
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sleep 5
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# Check container status
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if podman ps --format "{{.Names}}" | grep -q "^${CONTAINER_NAME}$"; then
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echo "✅ Mapped Tdarr Node is running successfully!"
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echo ""
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echo "📊 Container Status:"
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podman ps --filter "name=${CONTAINER_NAME}" --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
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echo ""
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echo "🔍 Testing GPU Access:"
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if podman exec "${CONTAINER_NAME}" nvidia-smi --query-gpu=name --format=csv,noheader,nounits 2>/dev/null; then
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echo "🎉 GPU is accessible in container!"
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else
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echo "⚠️ GPU test failed, but container is running"
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fi
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echo ""
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echo "🌐 Connection Details:"
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echo " Server: ${SERVER_IP}:${SERVER_PORT}"
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echo " Node Name: ${NODE_NAME}"
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echo ""
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echo "🧪 Test NVENC encoding:"
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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"
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echo ""
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echo "📋 Container Management:"
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echo " View logs: podman logs ${CONTAINER_NAME}"
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echo " Stop: podman stop ${CONTAINER_NAME}"
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echo " Remove: podman rm ${CONTAINER_NAME}"
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else
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echo "❌ Failed to start container"
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echo "📋 Checking logs..."
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podman logs "${CONTAINER_NAME}" --tail 10
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exit 1
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fi |