47 lines
1.5 KiB
YAML
47 lines
1.5 KiB
YAML
services:
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applio-studio:
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build: .
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image: mike-ai/applio-studio:3.6.4
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container_name: mike-ai-applio-studio
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restart: "no"
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# PyTorch DataLoader workers exchange training batches through /dev/shm.
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# Docker's 64 MiB default is far too small and can make failed training
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# runs look successful because of an upstream Applio exit-code bug.
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shm_size: "16gb"
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labels:
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com.mike-ai.applio-worker: applio
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environment:
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NVIDIA_VISIBLE_DEVICES: ${VOICE_GPU_UUID:?set VOICE_GPU_UUID to the RTX 5080 UUID}
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NVIDIA_DRIVER_CAPABILITIES: compute,utility
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HF_HOME: /models/huggingface
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PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
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ports:
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- "127.0.0.1:8011:6969"
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volumes:
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- /data/voice/applio/huggingface:/models/huggingface
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- /data/voice/applio/logs:/app/logs
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- /data/voice/applio/models:/app/rvc/models
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- /data/voice/applio/datasets:/app/assets/datasets
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- /data/voice/applio/config.json:/app/assets/config.json
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healthcheck:
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test: ["CMD-SHELL", "curl -fsS http://127.0.0.1:6969/ >/dev/null"]
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interval: 5s
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timeout: 3s
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start_period: 900s
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retries: 3
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids: ["${VOICE_GPU_UUID:?set VOICE_GPU_UUID to the RTX 5080 UUID}"]
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capabilities: [gpu]
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networks:
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frontend:
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aliases: [applio-studio]
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networks:
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frontend:
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name: mike-ai_frontend
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external: true
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