add named volumes instead of mapped local directory

Caddyfile

@@ -1,22 +0,0 @@
-# Minio Console
-s3.localhost:9001 {
-    handle_path /* {
-        reverse_proxy s3:9001
-    }
-}
-
-# Minio API
-s3.localhost:9000 {
-    handle_path /* {
-        reverse_proxy s3:9000
-    }
-}
-
-mlflow.localhost {
-    basicauth /* {
-	    root JDJhJDEwJEVCNmdaNEg2Ti5iejRMYkF3MFZhZ3VtV3E1SzBWZEZ5Q3VWc0tzOEJwZE9TaFlZdEVkZDhX # root hiccup
-    }
-    handle_path /* {
-        reverse_proxy mlflow:5000
-    }
-}

docker-compose.yml

@@ -1,69 +1,58 @@
-version: '3.2'
+version: "3.9"
 services:
-  caddy:
-    image: caddy:2-alpine
-    container_name: caddy
-    volumes:
-      - ./Caddyfile:/etc/caddy/Caddyfile
-      - /caddy/data:/data
-      - /caddy/config:/config
-    ports:
-      - 80:80
-      - 443:443
-      - 9000:9000
-      - 9001:9001
-    restart: unless-stopped
   s3:
-    restart: always
-    image: minio/minio:latest
+    image: minio/minio:RELEASE.2021-08-25T00-41-18Z.hotfix.55efceab5
+    restart: unless-stopped
     container_name: aws-s3
     ports:
-      - 9000
-      - 9001
+      - "9000:9000"
+      - "9001:9001"
     environment:
       - MINIO_ROOT_USER=${AWS_ACCESS_KEY_ID}
       - MINIO_ROOT_PASSWORD=${AWS_SECRET_ACCESS_KEY}
-    command:
-      server /date --console-address ":9001"
-    volumes:
-      - ./s3:/date
+    command: server /data --console-address ":9001"
     networks:
-      - default
-      - proxy-net
+      - internal
+      - public
+    volumes:
+      - minio_volume:/data
   db:
-      restart: always
-      image: mysql/mysql-server:5.7.28
-      container_name: mlflow_db
-      expose:
-          - "3306"
-      environment:
-          - MYSQL_DATABASE=${MYSQL_DATABASE}
-          - MYSQL_USER=${MYSQL_USER}
-          - MYSQL_PASSWORD=${MYSQL_PASSWORD}
-          - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
-      volumes:
-          - ./dbdata:/var/lib/mysql
-      networks:
-          - default
+    image: mysql/mysql-server:5.7.28
+    restart: unless-stopped
+    container_name: mlflow_db
+    expose:
+      - "3306"
+    environment:
+      - MYSQL_DATABASE=${MYSQL_DATABASE}
+      - MYSQL_USER=${MYSQL_USER}
+      - MYSQL_PASSWORD=${MYSQL_PASSWORD}
+      - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
+    volumes:
+      - db_volume:/var/lib/mysql
+    networks:
+      - internal
   mlflow:
-      restart: always
-      container_name: tracker_mlflow
-      image: tracker_ml
-      build:
-          context: ./mlflow
-          dockerfile: Dockerfile
-      ports:
-          - "5000:5000"
-      environment:
-          - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
-          - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
-          - AWS_DEFAULT_REGION=${AWS_REGION}
-          - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
-      entrypoint: mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://${AWS_BUCKET_NAME}/ -h 0.0.0.0
-      networks:
-          - proxy-net
-          - default
-
+    container_name: tracker_mlflow
+    image: tracker_ml
+    restart: unless-stopped
+    build:
+      context: ./mlflow
+      dockerfile: Dockerfile
+    ports:
+      - "5000:5000"
+    environment:
+      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
+      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
+      - AWS_DEFAULT_REGION=${AWS_REGION}
+      - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
+    networks:
+      - public
+      - internal
+    entrypoint: ./wait-for-it.sh db:3306 -t 90 -- mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://${AWS_BUCKET_NAME}/ -h 0.0.0.0
 networks:
-  default:
-  proxy-net:
+  internal:
+  public:
+    driver: bridge
+volumes:
+  db_volume:
+  minio_volume:

quickstart/mlflow_tracking.py

@@ -5,7 +5,7 @@ from mlflow import mlflow,log_metric, log_param, log_artifacts
 
 if __name__ == "__main__":
     with mlflow.start_run() as run:
-        mlflow.set_tracking_uri('https://mlflow.localhost')
+        mlflow.set_tracking_uri('http://localhost:5000')
         print("Running mlflow_tracking.py")
 
         log_param("param1", randint(0, 100))