51 lines
1.5 KiB
YAML
51 lines
1.5 KiB
YAML
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# docker-compose.tuning.paddle.yml - Ray Tune with PaddleOCR GPU
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# Usage:
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# docker compose -f docker-compose.tuning.paddle.yml up -d paddle-ocr-gpu
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# docker compose -f docker-compose.tuning.paddle.yml run raytune --service paddle --samples 64
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# docker compose -f docker-compose.tuning.paddle.yml down
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services:
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raytune:
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image: seryus.ddns.net/unir/raytune:latest
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command: ["--service", "paddle", "--host", "paddle-ocr-gpu", "--port", "8000", "--samples", "64"]
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volumes:
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- ./results:/app/results:rw
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environment:
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- PYTHONUNBUFFERED=1
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depends_on:
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paddle-ocr-gpu:
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condition: service_healthy
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paddle-ocr-gpu:
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image: seryus.ddns.net/unir/paddle-ocr-gpu:latest
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container_name: paddle-ocr-gpu-tuning
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ports:
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- "8002:8000"
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volumes:
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- ./dataset:/app/dataset:ro
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- ./debugset:/app/debugset:rw
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- paddlex-cache:/root/.paddlex
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environment:
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- PYTHONUNBUFFERED=1
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- CUDA_VISIBLE_DEVICES=0
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- PADDLE_DET_MODEL=PP-OCRv5_mobile_det
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- PADDLE_REC_MODEL=PP-OCRv5_mobile_rec
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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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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/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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volumes:
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paddlex-cache:
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name: paddlex-model-cache
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