provide caddyfile basic auth for the mlflow

Caddyfile

@@ -0,0 +1,22 @@
+# 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

@@ -1,58 +1,69 @@
-version: "3.9"
+version: '3.2'
 services:
-  s3:
-    image: minio/minio:RELEASE.2021-08-25T00-41-18Z.hotfix.55efceab5
+  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
     container_name: aws-s3
     ports:
-      - "9000:9000"
-      - "9001:9001"
+      - 9000
+      - 9001
     environment:
       - MINIO_ROOT_USER=${AWS_ACCESS_KEY_ID}
       - MINIO_ROOT_PASSWORD=${AWS_SECRET_ACCESS_KEY}
-    command: server /data --console-address ":9001"
-    networks:
-      - internal
-      - public
+    command:
+      server /date --console-address ":9001"
     volumes:
-      - minio_volume:/data
+      - ./s3:/date
+    networks:
+      - default
+      - proxy-net
   db:
-    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
+      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
   mlflow:
-    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://${AWS_BUCKET_NAME}/ -h 0.0.0.0
+      restart: always
+      container_name: tracker_mlflow
+      image: tracker_ml
+      build:
+          context: ./mlflow
+          dockerfile: Dockerfile
+      ports:
+          - "5000:5000"
+      environment:
+          - AWS_ACCESS_KEY_ID=${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
+
 networks:
-  internal:
-  public:
-    driver: bridge
-volumes:
-  db_volume:
-  minio_volume:
+  default:
+  proxy-net:

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('http://localhost:5000')
+        mlflow.set_tracking_uri('https://mlflow.localhost')
         print("Running mlflow_tracking.py")
 
         log_param("param1", randint(0, 100))