alvis
f8d66aa01f
Drop all four Airflow containers (db, init, webserver, scheduler) from the mlops compose profile, leaving MLflow as the sole mlops service. Remove AIRFLOW_* env vars, config fields, health-check entries, DAG trigger code in admin/bench routes, the airflow_dag_run_id schema column, Airflow nav links and DAG-run links in the admin UI, the two Airflow DAG files (bench_dag.py, sim_dag.py), and all related docs/ADR references. Simulations now run exclusively via the subprocess path. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
129 lines
4.2 KiB
YAML
129 lines
4.2 KiB
YAML
name: oo
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services:
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# ── core profile ──────────────────────────────────────────────────────────
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api:
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build:
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context: ../..
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dockerfile: infra/docker/Dockerfile.api
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profiles: [core, full]
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env_file: ../../.env.local
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environment:
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NODE_ENV: production
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ML_SERVING_URL: "http://ml-serving:8000"
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MLFLOW_URL: "http://mlflow:5000"
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INTERNAL_API_TOKEN: "${INTERNAL_API_TOKEN:-}"
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volumes:
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- /mnt/ssd/dbs/oo:/mnt/ssd/dbs/oo
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ports:
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- "127.0.0.1:3078:3078"
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healthcheck:
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test: ["CMD", "node", "-e", "fetch('http://localhost:3078/health').then(r=>process.exit(r.ok?0:1)).catch(()=>process.exit(1))"]
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interval: 10s
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timeout: 5s
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retries: 5
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web:
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build:
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context: ../..
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dockerfile: infra/docker/Dockerfile.web
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profiles: [core, full]
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env_file: ../../.env.local
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environment:
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NODE_ENV: production
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PORT: "3079"
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HOSTNAME: "0.0.0.0"
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NEXT_PUBLIC_API_URL: "" # Caddy routes /api/* directly to the API in prod
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ports:
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- "127.0.0.1:3079:3079"
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depends_on:
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api:
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condition: service_healthy
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admin:
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build:
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context: ../..
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dockerfile: infra/docker/Dockerfile.admin
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profiles: [core, full]
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env_file: ../../.env.local
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environment:
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NODE_ENV: production
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PORT: "3080"
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HOSTNAME: "0.0.0.0"
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NEXT_PUBLIC_API_URL: ""
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NEXT_PUBLIC_MLFLOW_URL: "/mlflow"
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INTERNAL_API_URL: "http://api:3078"
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ports:
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- "127.0.0.1:3080:3080"
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depends_on:
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api:
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condition: service_healthy
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# ── full profile ──────────────────────────────────────────────────────────
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ml-serving:
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build:
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context: ../..
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dockerfile: infra/docker/Dockerfile.ml
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profiles: [full]
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env_file: ../../.env.local
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environment:
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LITELLM_URL: ${LITELLM_URL:-http://host.docker.internal:4000}
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OLLAMA_URL: ${OLLAMA_URL:-http://host.docker.internal:11434}
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extra_hosts:
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- "host.docker.internal:host-gateway"
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ports:
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- "127.0.0.1:8000:8000"
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://localhost:8000/health',timeout=3).status==200 else 1)"]
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interval: 10s
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timeout: 5s
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retries: 5
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# ── mlops profile — MLflow ────────────────────────────────────────────────
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# Start: docker compose --profile mlops up
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# MLflow UI: http://localhost:5000 or https://o.alogins.net/mlflow
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# ── events profile — NATS JetStream ─────────────────────────────────────
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# Start: docker compose --profile events up
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# NATS monitoring: http://localhost:8222
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# Enable in the API by setting NATS_URL=nats://nats:4222 in .env.local
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nats:
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image: nats:2.10-alpine
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profiles: [events, full]
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command: ["-js", "-sd", "/data", "-m", "8222"]
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volumes:
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- /mnt/ssd/dbs/oo/nats:/data
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ports:
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- "127.0.0.1:4222:4222" # client connections
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- "127.0.0.1:8222:8222" # HTTP monitoring
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healthcheck:
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test: ["CMD", "wget", "--spider", "-q", "http://localhost:8222/healthz"]
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interval: 10s
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timeout: 5s
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retries: 5
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mlflow:
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image: ghcr.io/mlflow/mlflow:v3.11.1
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profiles: [mlops]
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command: >
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mlflow server
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--backend-store-uri sqlite:////mlflow/mlflow.db
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--default-artifact-root /mlflow/artifacts
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--host 0.0.0.0
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--port 5000
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--static-prefix /mlflow
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--allowed-hosts o.alogins.net,localhost
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--cors-allowed-origins https://o.alogins.net
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volumes:
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- /mnt/ssd/dbs/oo/mlflow:/mlflow
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ports:
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- "127.0.0.1:5000:5000"
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://localhost:5000/mlflow/health',timeout=3).status==200 else 1)"]
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interval: 10s
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timeout: 5s
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retries: 5
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