MLflow is an open-source platform for managing the machine learning lifecycle — experiment tracking, model registry, and reproducible runs. This guide deploys MLflow using Docker Compose with a PostgreSQL backend, S3-compatible artifact storage, basic-auth, and Traefik handling automatic HTTPS, then logs a sample scikit-learn run. By the end, you'll have MLflow recording experiments at your domain over HTTPS.
Prerequisite: An S3-compatible bucket (e.g. Vultr Object Storage) with access key, secret key, region, and endpoint URL.
Set Up the Directory Structure
1. Create the project directory:
$ mkdir -p ~/mlflow
$ cd ~/mlflow
2. Create the environment file:
$ nano .env
DOMAIN=mlflow.example.com
LETSENCRYPT_EMAIL=admin@example.com
POSTGRES_USER=mlflow
POSTGRES_PASSWORD=StrongDatabasePassword123
MLFLOW_AUTH_CONFIG_PATH=/app/basic_auth.ini
MLFLOW_FLASK_SERVER_SECRET_KEY=GENERATED_SECRET_KEY
S3_BUCKET=mlflow-artifacts
S3_ACCESS_KEY=YOUR_ACCESS_KEY
S3_SECRET_KEY=YOUR_SECRET_KEY
S3_REGION=YOUR_REGION
S3_ENDPOINT=https://YOUR_OBJECT_STORAGE_ENDPOINT
3. Create the basic-auth configuration:
$ nano basic_auth.ini
[mlflow]
default_permission = READ
database_uri = sqlite:///basic_auth.db
admin_username = admin
admin_password = ADMIN_PASSWORD
authorization_function = mlflow.server.auth:authenticate_request_basic_auth
4. Create a Dockerfile that adds the auth-server extras and Postgres/S3 clients to the official image:
$ nano Dockerfile
FROM ghcr.io/mlflow/mlflow:v3.10.1
RUN pip install --no-cache-dir psycopg2-binary boto3 'mlflow[auth]'
Deploy with Docker Compose
1. Create the Compose manifest:
$ nano docker-compose.yml
services:
traefik:
image: traefik:v3.6
container_name: traefik
command:
- "--providers.docker=true"
- "--providers.docker.exposedbydefault=false"
- "--entrypoints.web.address=:80"
- "--entrypoints.websecure.address=:443"
- "--entrypoints.web.http.redirections.entrypoint.to=websecure"
- "--entrypoints.web.http.redirections.entrypoint.scheme=https"
- "--certificatesresolvers.letsencrypt.acme.httpchallenge=true"
- "--certificatesresolvers.letsencrypt.acme.httpchallenge.entrypoint=web"
- "--certificatesresolvers.letsencrypt.acme.email=${LETSENCRYPT_EMAIL}"
- "--certificatesresolvers.letsencrypt.acme.storage=/letsencrypt/acme.json"
ports:
- "80:80"
- "443:443"
volumes:
- "/var/run/docker.sock:/var/run/docker.sock:ro"
- "./letsencrypt:/letsencrypt"
restart: unless-stopped
postgres:
image: postgres:15
container_name: mlflow-postgres
environment:
POSTGRES_USER: ${POSTGRES_USER}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
POSTGRES_DB: mlflow
volumes:
- ./postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER}"]
interval: 10s
retries: 5
restart: unless-stopped
mlflow:
build: .
container_name: mlflow
expose:
- "5000"
environment:
AWS_ACCESS_KEY_ID: ${S3_ACCESS_KEY}
AWS_SECRET_ACCESS_KEY: ${S3_SECRET_KEY}
AWS_DEFAULT_REGION: ${S3_REGION}
AWS_S3_FORCE_PATH_STYLE: "true"
MLFLOW_S3_ENDPOINT_URL: ${S3_ENDPOINT}
MLFLOW_AUTH_CONFIG_PATH: ${MLFLOW_AUTH_CONFIG_PATH}
MLFLOW_FLASK_SERVER_SECRET_KEY: ${MLFLOW_FLASK_SERVER_SECRET_KEY}
volumes:
- ./basic_auth.ini:/app/basic_auth.ini:ro
- ./mlflow_auth:/app
command: >
mlflow server
--backend-store-uri "postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@postgres:5432/mlflow"
--default-artifact-root "s3://${S3_BUCKET}/"
--serve-artifacts
--host 0.0.0.0
--port 5000
--allowed-hosts "${DOMAIN},https://${DOMAIN}"
--app-name basic-auth
depends_on:
postgres:
condition: service_healthy
labels:
- "traefik.enable=true"
- "traefik.http.routers.mlflow.rule=Host(`${DOMAIN}`)"
- "traefik.http.routers.mlflow.entrypoints=websecure"
- "traefik.http.routers.mlflow.tls.certresolver=letsencrypt"
restart: unless-stopped
2. Build and start the stack:
$ docker compose up -d --build
3. Verify the services and tail logs:
$ docker compose ps
$ docker compose logs
Sign In and Log a Sample Experiment
1. Open https://mlflow.example.com and authenticate with admin / the password from basic_auth.ini.
2. On the workstation, create a virtualenv and install dependencies:
$ sudo apt install python3-venv -y
$ python3 -m venv mlflow-env
$ source mlflow-env/bin/activate
$ pip install mlflow scikit-learn pandas numpy boto3
3. Save a demo experiment script:
$ nano mlflow_demo.py
import mlflow
import mlflow.sklearn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.datasets import load_diabetes
import pandas as pd
import numpy as np
diabetes = load_diabetes()
X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
y = pd.Series(diabetes.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
mlflow.set_tracking_uri("https://mlflow.example.com")
mlflow.set_experiment("official_demo_experiment")
with mlflow.start_run():
alpha, l1_ratio = 0.5, 0.5
mlflow.log_param("alpha", alpha)
mlflow.log_param("l1_ratio", l1_ratio)
model = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
mlflow.log_metric("rmse", rmse)
mlflow.log_metric("r2", r2)
mlflow.sklearn.log_model(model, "model")
4. Export credentials and run the script:
$ export MLFLOW_TRACKING_USERNAME=admin
$ export MLFLOW_TRACKING_PASSWORD=ADMIN_PASSWORD
$ export AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY
$ export AWS_SECRET_ACCESS_KEY=YOUR_SECRET_KEY
$ export MLFLOW_S3_ENDPOINT_URL=https://YOUR_OBJECT_STORAGE_ENDPOINT
$ python3 mlflow_demo.py
Refresh the MLflow UI — the run appears under official_demo_experiment with logged parameters, metrics, and the persisted model artifact.
Next Steps
MLflow is running with PostgreSQL persistence and S3 artifacts. From here you can:
- Register top runs in the Model Registry for staged promotion
- Wire
mlflow.autolog()into your training code for hands-off tracking - Add team users and per-experiment permissions in the basic-auth database
For the full guide with additional tips, visit the original article on Vultr Docs.
