ml-deploy/docker-compose.yaml
2025-05-29 21:05:30 -05:00

384 lines
12 KiB
YAML

---
x-airflow-common:
&airflow-common
# In order to add custom dependencies or upgrade provider packages you can use your extended image.
# Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml
# and uncomment the "build" line below, Then run `docker-compose build` to build the images.
#image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.6.0}
build: ./docker/airflow
# build: .
environment:
&airflow-common-env
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow
AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
AIRFLOW__CORE__FERNET_KEY: ''
AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
AIRFLOW__CORE__LOAD_EXAMPLES: 'False'
AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'
AIRFLOW_CONN_RAW_DATA: 'mysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db_raw:${MYSQL_RAW_PORT}/${MYSQL_RAW_DATABASE}'
AIRFLOW_CONN_CLEAN_DATA: 'mysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db_clean:${MYSQL_CLEAN_PORT}/${MYSQL_CLEAN_DATABASE}'
# yamllint disable rule:line-length
# Use simple http server on scheduler for health checks
# See https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/check-health.html#scheduler-health-check-server
# yamllint enable rule:line-length
AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true'
# WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks
# for other purpose (development, test and especially production usage) build/extend Airflow image.
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
volumes:
- ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
- ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
- ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
user: "${AIRFLOW_UID:-50000}:0"
depends_on:
&airflow-common-depends-on
redis:
condition: service_healthy
postgres:
condition: service_healthy
services:
db:
restart: always
image: mysql/mysql-server:5.7.28
container_name: mlflow_db
ports:
- "${MYSQL_PORT}:3306"
networks:
- backend
environment:
- MYSQL_DATABASE=${MYSQL_DATABASE}
- MYSQL_USER=${MYSQL_USER}
- MYSQL_PASSWORD=${MYSQL_PASSWORD}
- MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
volumes:
- ./db_ml_data:/var/lib/mysql
db_raw:
restart: always
image: mysql/mysql-server:5.7.28
container_name: raw_data_db
ports:
- "${MYSQL_RAW_PORT}:3306"
networks:
- backend
environment:
- MYSQL_DATABASE=${MYSQL_RAW_DATABASE}
- MYSQL_USER=${MYSQL_USER}
- MYSQL_PASSWORD=${MYSQL_PASSWORD}
- MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
volumes:
- ./db_raw_data:/var/lib/mysql
db_clean:
restart: always
image: mysql/mysql-server:5.7.28
container_name: clean_data_db
ports:
- "${MYSQL_CLEAN_PORT}:3306"
networks:
- backend
environment:
- MYSQL_DATABASE=${MYSQL_CLEAN_DATABASE}
- MYSQL_USER=${MYSQL_USER}
- MYSQL_PASSWORD=${MYSQL_PASSWORD}
- MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
volumes:
- ./db_clean_data:/var/lib/mysql
fast_api:
build: ./docker/fast_api
ports:
- 8086:8086
container_name: fast_api
depends_on:
- db_raw
- db_clean
networks:
- backend
volumes:
- ./src/back:/opt/code/
command: "uvicorn opt.code.main:app --host 0.0.0.0 --port 8086 --reload"
minio:
container_name: minio
command: server /data --console-address ":${MINIO_CONSOLE_PORT}" --address ':${MINIO_PORT}'
environment:
- MINIO_ROOT_USER=${MINIO_ROOT_USER}
- MINIO_ROOT_PASSWORD=${MINIO_ROOT_PASSWORD}
image: quay.io/minio/minio:latest
ports:
- "${MINIO_PORT}:8081"
- "${MINIO_CONSOLE_PORT}:8082"
networks:
- backend
volumes:
- ./minio_data:/data
restart: unless-stopped
mlflow:
restart: always
build: ./docker/mlflow
image: mlflow
container_name: mlflow
depends_on:
- db
- minio
ports:
- "${MLFLOW_PORT}:8083"
networks:
- backend
environment:
- AWS_ACCESS_KEY_ID=${MINIO_ACCESS_KEY}
- AWS_SECRET_ACCESS_KEY=${MINIO_SECRET_ACCESS_KEY}
- MLFLOW_S3_ENDPOINT_URL=http://minio:${MINIO_PORT}
- MLFLOW_TRACKING_URI=http://mlflow:${MLFLOW_PORT}
- BACKEND_STORE_URI=mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:${MYSQL_PORT}/${MYSQL_DATABASE}
- DEFAULT_ARTIFACT_ROOT=s3://minio:${MINIO_PORT}
- MLFLOW_S3_IGNORE_TLS=true
command: >
mlflow server
--backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:${MYSQL_PORT}/${MYSQL_DATABASE}
--host 0.0.0.0
--port ${MLFLOW_PORT}
--serve-artifacts
--artifacts-destination s3://${MLFLOW_BUCKET_NAME}/
--default-artifact-root s3://${MLFLOW_BUCKET_NAME}/
work_jupyter:
build: ./docker/jupyter
volumes:
- ./:/home/jovyan/work
ports:
- 8085:8888
container_name: jupyter
networks:
- backend
command: "jupyter lab --ip=0.0.0.0 --allow-root --NotebookApp.token='' --NotebookApp.password=''"
postgres:
image: postgres:13
environment:
POSTGRES_USER: airflow
POSTGRES_PASSWORD: airflow
POSTGRES_DB: airflow
volumes:
- postgres-db-volume:/var/lib/postgresql/data
healthcheck:
test: ["CMD", "pg_isready", "-U", "airflow"]
interval: 10s
retries: 5
start_period: 5s
restart: always
networks:
- backend
redis:
image: redis:7.2-bookworm
expose:
- 6379
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 10s
timeout: 30s
retries: 50
start_period: 30s
restart: always
networks:
- backend
airflow-webserver:
<<: *airflow-common
command: webserver
ports:
- "8080:8080"
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
networks:
- backend
airflow-scheduler:
<<: *airflow-common
command: scheduler
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:8974/health"]
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
networks:
- backend
airflow-worker:
<<: *airflow-common
command: celery worker
healthcheck:
test:
- "CMD-SHELL"
- 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}" || celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
environment:
<<: *airflow-common-env
# Required to handle warm shutdown of the celery workers properly
# See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation
DUMB_INIT_SETSID: "0"
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
networks:
- backend
airflow-triggerer:
<<: *airflow-common
command: triggerer
healthcheck:
test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
networks:
- backend
airflow-init:
<<: *airflow-common
entrypoint: /bin/bash
# yamllint disable rule:line-length
command:
- -c
- |
if [[ -z "${AIRFLOW_UID}" ]]; then
echo
echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
echo "If you are on Linux, you SHOULD follow the instructions below to set "
echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
echo "For other operating systems you can get rid of the warning with manually created .env file:"
echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user"
echo
fi
one_meg=1048576
mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
disk_available=$$(df / | tail -1 | awk '{print $$4}')
warning_resources="false"
if (( mem_available < 4000 )) ; then
echo
echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m"
echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
echo
warning_resources="true"
fi
if (( cpus_available < 2 )); then
echo
echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m"
echo "At least 2 CPUs recommended. You have $${cpus_available}"
echo
warning_resources="true"
fi
if (( disk_available < one_meg * 10 )); then
echo
echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m"
echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
echo
warning_resources="true"
fi
if [[ $${warning_resources} == "true" ]]; then
echo
echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
echo "Please follow the instructions to increase amount of resources available:"
echo " https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin"
echo
fi
mkdir -p /sources/logs /sources/dags /sources/plugins
chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
exec /entrypoint airflow version
# yamllint enable rule:line-length
environment:
<<: *airflow-common-env
_AIRFLOW_DB_UPGRADE: 'true'
_AIRFLOW_WWW_USER_CREATE: 'true'
_AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
_AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
_PIP_ADDITIONAL_REQUIREMENTS: ''
user: "0:0"
volumes:
- ${AIRFLOW_PROJ_DIR:-.}:/sources
networks:
- backend
airflow-cli:
<<: *airflow-common
profiles:
- debug
environment:
<<: *airflow-common-env
CONNECTION_CHECK_MAX_COUNT: "0"
# Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252
command:
- bash
- -c
- airflow
networks:
- backend
# You can enable flower by adding "--profile flower" option e.g. docker-compose --profile flower up
# or by explicitly targeted on the command line e.g. docker-compose up flower.
# See: https://docs.docker.com/compose/profiles/
flower:
<<: *airflow-common
command: celery flower
profiles:
- flower
ports:
- "5555:5555"
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
networks:
- backend
volumes:
postgres-db-volume:
db_ml_data:
db_raw_data:
db_clean_data:
minio_data:
networks:
backend:
driver: bridge