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https://github.com/devnen/Kitten-TTS-Server.git
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devnen
49 lines
1.5 KiB
YAML
49 lines
1.5 KiB
YAML
version: '3.8'
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services:
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kitten-tts-server:
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build:
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args:
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# Can be nvidia or cpu; Default is Nvidia
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- RUNTIME=nvidia
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context: .
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dockerfile: Dockerfile
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ports:
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- "${PORT:-8005}:8005"
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volumes:
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# Mount local config file for persistence
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- ./config.yaml:/app/config.yaml
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# Mount local directories for persistent app data
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- ./outputs:/app/outputs
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- ./logs:/app/logs
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# Named volume for Hugging Face model cache to persist across container rebuilds
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- hf_cache:/app/hf_cache
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# --- GPU Support (NVIDIA) ---
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# The 'deploy' key is the modern way to request GPU resources.
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# If you get a 'CDI device injection failed' error, comment out the 'deploy' section
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# and uncomment the 'runtime: nvidia' line below.
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# Method 1: Modern Docker Compose (Recommended)
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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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# Method 2: Legacy Docker Compose (for older setups)
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# runtime: nvidia
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restart: unless-stopped
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environment:
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# Enable faster Hugging Face downloads inside the container
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- HF_HUB_ENABLE_HF_TRANSFER=1
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# Make NVIDIA GPUs visible and specify capabilities for PyTorch
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility
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# Define the named volume for the Hugging Face cache
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volumes:
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hf_cache: |