51 lines
1.8 KiB
YAML
51 lines
1.8 KiB
YAML
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version: '3.8'
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services:
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videotranscriber:
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build: .
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container_name: videotranscriber
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ports:
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- "8501:8501"
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volumes:
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# Mount your video files directory (change the left path to your actual videos folder)
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- "${VIDEO_PATH:-./videos}:/app/data/videos"
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# Mount output directory for transcripts and summaries
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- "${OUTPUT_PATH:-./outputs}:/app/data/outputs"
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# Mount cache directory for model caching (optional, improves performance)
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- "${CACHE_PATH:-./cache}:/app/data/cache"
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# Mount a config directory if needed
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- "${CONFIG_PATH:-./config}:/app/config"
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environment:
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# Ollama configuration for host access
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- OLLAMA_API_URL=${OLLAMA_API_URL:-http://host.docker.internal:11434/api}
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# Optional: HuggingFace token for advanced features
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- HF_TOKEN=${HF_TOKEN:-}
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# GPU configuration
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- CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-}
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# Cache settings
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- TRANSFORMERS_CACHE=/app/data/cache/transformers
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- WHISPER_CACHE=/app/data/cache/whisper
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# For GPU access (uncomment if you have NVIDIA GPU and nvidia-docker)
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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: 1
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# capabilities: [gpu]
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restart: unless-stopped
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# For Linux hosts, you might prefer host networking for better Ollama access
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# network_mode: host # Uncomment for Linux hosts
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# Use bridge networking for Windows/Mac with host.docker.internal
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networks:
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- videotranscriber-network
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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networks:
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videotranscriber-network:
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driver: bridge
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