Building a Simple Docker Project with MLflow and Streamlit

Introduction:

This project serves as an educational guide for beginners to set up and run services using Docker and Docker Compose. It focuses on deploying two services:

1. MLflow: An open-source platform to manage the machine learning lifecycle.
2. Streamlit: A simple framework for building interactive web applications for data projects.

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Objectives:

• Introduce beginners to the fundamentals of Docker and Docker Compose.
• Create separate Dockerfiles for MLflow and Streamlit services.
• Launch both services using a docker-compose.yml file.

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Setup:

Prerequisites:

• Docker installed.
• Docker Compose installed.
• Basic knowledge of Python.

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Project Steps:

1. Create a Dockerfile for Each Service:

Dockerfile for MLflow:

FROM python:3.9-slim

# Install the MLflow library
RUN pip install mlflow

# Set the working directory
WORKDIR /app

# Expose the port
EXPOSE 5001

# Default command to run the MLflow server
CMD ["mlflow", "server", "--host", "0.0.0.0", "--port", "5001"]

Dockerfile for Streamlit:

FROM python:3.9-slim

# Install the Streamlit library
RUN pip install streamlit

# Copy the application file
COPY app.py /app/app.py

# Set the working directory
WORKDIR /app

# Expose the port
EXPOSE 8501

# Default command to run the Streamlit app
CMD ["streamlit", "run", "app.py", "--server.address=0.0.0.0", "--server.port=8501"]

2. Prepare the app.py File for Streamlit:

# app.py
import streamlit as st

st.title("Simple Streamlit Application")
st.write("Welcome to the MLflow integration interface!")

3. Write the docker-compose.yml File:

services:
  mlflow:
    build:
      context: .
      dockerfile: Dockerfile.mlflow
    ports:
      - "5001:5001"

  streamlit:
    build:
      context: .
      dockerfile: Dockerfile.streamlit
    ports:
      - "8501:8501"

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Running the Project:

1. Build and Run the Containers:

Run the following command:

docker-compose up --build

2. Access the Services:

• MLflow: http://localhost:5001
• Streamlit: http://localhost:8501

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Educational Takeaways:

1. Dockerfile Basics:

   • Define the base image (e.g., python:3.9-slim).
   • Install required libraries using pip install.
   • Configure ports using EXPOSE.
   • Specify default commands with CMD.

2. Docker Compose Essentials:

   • Combine multiple services into one project.
   • Define port mappings and network connections between containers.

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Future Enhancements:

• Connect MLflow to a database like MySQL.
• Add a third service such as an API interface using FastAPI.
• Enhance the Streamlit interface to enable interaction with MLflow functionalities.

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Conclusion:

This project provides a foundational understanding of deploying a multi-service application using Docker. It offers a stepping stone for beginners to build more complex and integrated projects in the future.