provide caddyfile basic auth for the mlflow

use fixed minio stable version (minio team separated console and api …

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
+    }
+}

README.md

@@ -4,6 +4,8 @@ If you want to boot up mlflow project with one-liner - this repo is for you.
 
 The only requirement is docker installed on your system and we are going to use Bash on linux/windows.
 
+[![Youtube tutorial](https://img.youtube.com/vi/ma5lA19IJRA/0.jpg)](https://www.youtube.com/watch?v=ma5lA19IJRA)
+
 # Features
  - Setup by one file (.env)
  - Production-ready docker volumes
@@ -24,7 +26,14 @@ Creating tracker_mlflow ... done
 Creating aws-s3         ... done
 ```
 
-3. Create mlflow bucket. You can do it **either using AWS CLI or Python Api**. **You dont need an AWS subscription**
+3. Create mlflow bucket. You can use my bundled script.
+
+Just run 
+```shell 
+bash ./run_create_bucket.sh
+```
+
+You can also do it **either using AWS CLI or Python Api**.
 <details><summary>AWS CLI</summary>
 
 1. [Install AWS cli](https://aws.amazon.com/cli/) **Yes, i know that you dont have an Amazon Web Services Subscription - dont worry! It wont be needed!**

create_bucket.py

@@ -0,0 +1,38 @@
+import os
+
+from minio import Minio
+from minio.error import InvalidResponseError
+
+accessID = os.environ.get('AWS_ACCESS_KEY_ID')
+accessSecret =  os.environ.get('AWS_SECRET_ACCESS_KEY')
+minioUrl =  os.environ.get('MLFLOW_S3_ENDPOINT_URL')
+bucketName =  os.environ.get('AWS_BUCKET_NAME')
+
+if accessID == None:
+    print('[!] AWS_ACCESS_KEY_ID environemnt variable is empty! run \'source .env\' to load it from the .env file')
+    exit(1)
+
+if accessSecret == None:
+    print('[!] AWS_SECRET_ACCESS_KEY environemnt variable is empty! run \'source .env\' to load it from the .env file')
+    exit(1)
+
+if minioUrl == None:
+    print('[!] MLFLOW_S3_ENDPOINT_URL environemnt variable is empty! run \'source .env\' to load it from the .env file')
+    exit(1)
+
+    
+if bucketName == None:
+    print('[!] AWS_BUCKET_NAME environemnt variable is empty! run \'source .env\' to load it from the .env file')
+    exit(1)
+
+minioUrlHostWithPort = minioUrl.split('//')[1]
+print('[*] minio url: ',minioUrlHostWithPort)
+
+s3Client = Minio(
+    minioUrlHostWithPort,
+    access_key=accessID,
+    secret_key=accessSecret,
+    secure=False
+)
+
+s3Client.make_bucket(bucketName)

docker-compose.yml

@@ -1,19 +1,35 @@
 version: '3.2'
 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
     container_name: aws-s3
     ports:
-      - 9000:9000
+      - 9000
+      - 9001
     environment:
-      - MINIO_ACCESS_KEY=${AWS_ACCESS_KEY_ID}
+      - MINIO_ROOT_USER=${AWS_ACCESS_KEY_ID}
-      - MINIO_SECRET_KEY=${AWS_SECRET_ACCESS_KEY}
+      - MINIO_ROOT_PASSWORD=${AWS_SECRET_ACCESS_KEY}
-    command: 
+    command:
-      server /date
+      server /date --console-address ":9001"
-    networks:
-      - A
     volumes:
       - ./s3:/date
+    networks:
+      - default
+      - proxy-net
   db:
       restart: always
       image: mysql/mysql-server:5.7.28
@@ -28,8 +44,9 @@ services:
       volumes:
           - ./dbdata:/var/lib/mysql
       networks:
-            - A
+          - default
   mlflow:
+      restart: always
       container_name: tracker_mlflow
       image: tracker_ml
       build:
@@ -37,14 +54,16 @@ services:
           dockerfile: Dockerfile
       ports:
           - "5000:5000"
-      environment: 
+      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://${AWS_BUCKET_NAME}/ -h 0.0.0.0
       networks:
-          - A
+          - proxy-net
-      entrypoint: ./wait-for-it.sh db:3306 -t 90 -- mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://${AWS_BUCKET_NAME}/ -h 0.0.0.0
+          - default
+
 networks:
-    A:
+  default:
-        driver: bridge
+  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))

run_create_bucket.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+set -o allexport; source .env; set +o allexport
+
+pip3 install Minio
+python3 ./create_bucket.py