mlflow server
      --host 0.0.0.0
      --port 5000
      --serve-artifacts
      --artifacts-destination s3://my-bucket
      --backend-store-uri postgresql://postgres:postgres@backend-store:5432/mlflow
      --gunicorn-opts "--log-level debug"

services:
  tracking-server:
    build:
      context: .
      dockerfile: Dockerfile
    depends_on:
      - backend-store
    ports:
      - "5000:5000"
    environment:
      MLFLOW_S3_ENDPOINT_URL:  http://mlflow-artifacts:9000
      AWS_ACCESS_KEY_ID: AKIAIOSFODNN7EXAMPLE
      AWS_SECRET_ACCESS_KEY: aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
      AWS_DEFAULT_REGION: eu-west-2
    command: >
      mlflow server
      --host 0.0.0.0
      --port 5000
      --serve-artifacts
      --artifacts-destination s3://my-bucket
      --backend-store-uri postgresql://postgres:postgres@backend-store:5432/mlflow
      --gunicorn-opts "--log-level debug"

  nginx-proxy:
    build:
      context: nginx
      dockerfile: Dockerfile
    depends_on:
      - tracking-server
    ports:
      - "80:80"

  backend-store:
    image: postgres:12
    volumes:
      - ./init.sql:/docker-entrypoint-initdb.d/init.sql
      - postgres_data:/var/lib/postgresql/data/
    environment:
      POSTGRES_USER: "postgres"
      POSTGRES_HOST_AUTH_METHOD: "trust"

  mlflow-artifacts:
    image: minio/minio:RELEASE.2021-10-08T23-58-24Z.fips
    environment:
      MINIO_ROOT_USER: AKIAIOSFODNN7EXAMPLE
      MINIO_ROOT_PASSWORD: aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
      MINIO_REGION: eu-west-2
    ports:
      - "9000:9000"
      - "9001:9001"
    entrypoint: sh
    volumes:
      - ".minio:/data"
    command: ['-c', 'mkdir -p /data/my-bucket && minio server /data --console-address ":9001"']

volumes:
  postgres_data:
  minio:

FROM python:3.7-buster

WORKDIR /app

RUN pip install sklearn mlflow psycopg2 boto3

import sys

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
import mlflow
import mlflow.sklearn
from mlflow.models.signature import ModelSignature
from mlflow.types.schema import Schema, ColSpec

def eval_metrics(actual, pred):
    rmse = np.sqrt(mean_squared_error(actual, pred))
    mae = mean_absolute_error(actual, pred)
    r2 = r2_score(actual, pred)
    return rmse, mae, r2


if __name__ == "__main__":
    np.random.seed(40)

    # Read the wine-quality csv file from the URL
    csv_url = (
        "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
    )
    data = pd.read_csv(csv_url, sep=";")

    # Split the data into training and test sets. (0.75, 0.25) split.
    train, test = train_test_split(data)

    # The predicted column is "quality" which is a scalar from [3, 9]
    train_x = train.drop(["quality"], axis=1)
    test_x = test.drop(["quality"], axis=1)
    train_y = train[["quality"]]
    test_y = test[["quality"]]

    alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5

    with mlflow.start_run():
        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
        lr.fit(train_x, train_y)

        predicted_qualities = lr.predict(test_x)

        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

        mlflow.log_param("alpha", alpha)
        mlflow.log_param("l1_ratio", l1_ratio)
        mlflow.log_metric("rmse", rmse)
        mlflow.log_metric("r2", r2)
        mlflow.log_metric("mae", mae)

        input_schema = Schema([
            ColSpec("double", "sepal length (cm)"),
            ColSpec("double", "sepal width (cm)"),
            ColSpec("double", "petal length (cm)"),
            ColSpec("double", "petal width (cm)"),
        ])
        output_schema = Schema([ColSpec("long")])
        signature = ModelSignature(inputs=input_schema, outputs=output_schema)

        mlflow.sklearn.log_model(lr, "model", registered_model_name="ElasticnetWineModel", signature=signature)

CREATE DATABASE mlflow;

FROM nginx
COPY nginx.conf /etc/nginx/nginx.conf
COPY htpasswd /etc/nginx/conf.d/htpasswd

username:jRlgfrjACzG6Y

user  nginx;
worker_processes  auto;

error_log  /var/log/nginx/error.log notice;
pid        /var/run/nginx.pid;


events {
    worker_connections  1024;
}


http {
    include       /etc/nginx/mime.types;
    default_type  application/octet-stream;

    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_user_agent" "$http_x_forwarded_for"';

    access_log  /var/log/nginx/access.log  main;

    sendfile        on;
    #tcp_nopush     on;

    keepalive_timeout  65;

    #gzip  on;

    server {
        location / {
            auth_basic           "closed site";
            auth_basic_user_file conf.d/htpasswd;

            proxy_pass http://tracking-server:5000/;
        }
    }
}