99 lines
3.8 KiB
YAML
99 lines
3.8 KiB
YAML
version: '3'
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services:
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# Service to download a model file.
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downloader:
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build:
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context: ./
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dockerfile: compile.Dockerfile
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# platform: "linux/amd64"
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# The command extracts the model filename from MODEL_URL and downloads it if it doesn't exist.
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command: /bin/sh -c "SD_MODEL_FILE=$(basename ${MODEL_URL}); if [ ! -f /converted_models/*.bin ]; then wget -O /converted_models/$SD_MODEL_FILE ${MODEL_URL}; python /sd.cpp/models/convert.py --out_type q4_0 --out_file /converted_models/$SD_MODEL_FILE; fi"
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# Mount a local directory to store the downloaded model.
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volumes:
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- ./models:/converted_models
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# Service to wait for the downloader service to finish downloading the model.
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wait-for-downloader:
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image: busybox
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# The command waits until the model file (specified in MODEL_URL) exists.
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command: /bin/sh -c "SD_MODEL_FILE=$(basename ${MODEL_URL}); echo 'Waiting for downloader to finish'; while [ ! -f /models/*.bin ]; do sleep 1; done; echo 'Model downloaded and converted!'"
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# Specifies that this service should start after the downloader service has started.
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depends_on:
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downloader:
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condition: service_started
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# Mount the same local directory to check for the downloaded model.
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volumes:
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- ./models:/models
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# Service to run the SD web application.
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sd:
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build:
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context: ./
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dockerfile: inference.Dockerfile
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# Mount the directory that contains the downloaded model.
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volumes:
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- ./models:/models
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- ./output/:/serving/output
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command: /bin/bash -c "python -m uvicorn main:app --proxy-headers --host 0.0.0.0 --port 8000"
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# platform: "linux/amd64"
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environment:
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# Specify the path to the model for the web application.
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BASE_URL: http://0.0.0.0:8000
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MODEL_NAME: "v1-5-pruned-emaonly-ggml-model-q5_0.bin"
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MODEL_DIR: "/models"
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SD_PATH: "/sd"
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PYTHONUNBUFFERED: 1
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ports:
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- 8000:8000
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# Health check configuration
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healthcheck:
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test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:8000"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 30s
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# Restart policy configuration
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restart: on-failure
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# Specifies that this service should start only after wait-for-downloader has completed successfully.
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depends_on:
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wait-for-downloader:
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condition: service_completed_successfully
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# Connect this service to two networks: inference_net and traefik_public.
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networks:
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- inference_net
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- traefik_public
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# Service for Traefik, a modern HTTP reverse proxy and load balancer.
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traefik:
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image: traefik:v2.5
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command:
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# Enable the Traefik API dashboard without TLS (not recommended for production).
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- "--api.insecure=true"
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# Enable Traefik to use Docker as a provider.
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- "--providers.docker=true"
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# Do not expose services by default. Explicitly specify in each service if it should be exposed.
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- "--providers.docker.exposedbydefault=false"
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# Specify the default entry point on port 80.
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- "--entrypoints.web.address=:80"
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ports:
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# Map port 80 in the container to port 80 on the host.
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- "80:80"
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# Map port 8080 in the container (Traefik's dashboard) to port 8080 on the host.
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- "8080:8080"
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# Mount the Docker socket to allow Traefik to listen to Docker's API.
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volumes:
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- /var/run/docker.sock:/var/run/docker.sock
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# Connect this service to the traefik_public network.
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networks:
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- traefik_public
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# Define networks used in this docker-compose file.
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networks:
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# Network for the llm service (used for inference).
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inference_net:
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# Public-facing network that Traefik uses. Marked as external to indicate it may be defined outside this file.
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traefik_public:
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external: true
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