Build a production-ready ML dashboard with Streamlit and Docker

Let me show you step-by-step, how to build a production-ready dashboard using Streamlit and Docker.

Hands-on example 👩🏻‍💻🧑🏽‍💻

In this tutorial we will build a web app that plots crypto-currency Open-High-Low-Close data (aka OHLC) every 10 seconds.

 

We will use the following tools:

• Streamlit to build our frontend-app using pure Python, without having to learn any JavaScript 🎉

• Python Poetry to package our Python code professionally 😎, and

• Docker to containerize our web app, and ease the deployment to a production-environment 📦

Where is the data coming from? 📊

This web app does not plot a static dataset, but live data generated by a real-time feature pipeline that

• Ingests raw trades from the Kraken API

• Transforms these raw trades into 10-second OHLC data, and

• Saves these features in a Feature Store.

If you want to learn how to build and deploy the feature pipeline, check this previous blog post with full source-code

 

Without further ado, let’s get to it!

Steps

You can find all the source code in this repository
→ Give it a star ⭐ on Github to support my work 🙏

These are the 4 steps to build a production-ready ML dashboard.

Step 1. Create the project structure

We will use Python Poetry to create our ML project structure. You can install it for free in your system with a one-liner.

$ curl -sSL https://install.python-poetry.org | python3 -

Once installed, go to the command line and type

$ poetry new dashboard --name src

to generate the following folder structure

dashboard
├── README.md
├── src
│   └── __init__.py
├── pyproject.toml
└── tests
    └── __init__.py

Then type

$ cd dashboard && poetry install

to create your virtual environment and install your local package src in editable mode.

Step 2. Build the UI with Streamlit

Install the Streamlit library inside your virtual environment

$ poetry add streamlit

and then add a main.py file to define the web app components.

dashboard
├── README.md
├── main.py
├── src
│   └── __init__.py
├── pyproject.toml
└── tests
    └── __init__.py

At the top of your main.py you need to import

• Of course Streamlit

  import streamlit as st

• Any third-party libraries your code needs, for example

  import pandas as pd

• Local dependencies from your src folder (more on this in the next section)

  from src.backend import get_features

Then, you start building the UI using the Streamlit API. For example, you can add

• a title with st.header

• a markdown block with st.markdown

• a table widget with st.dataframe

• a Bokeh chart with st.bokeh_chart

 

We then add a while True loop, to periodically

• call the get_features() function to fetch the data from the feature store, and

• refresh the widgets’ content

 

Step 3. Extract non UI functionality into separate files

It is best practice to keep your Streamlit app in main.py as lightweight as possible, and define non UI logic in separate .py files. This way your code is modular and easier to test.

In this case, our get_features() function is defined in src/backend.py, which in turns uses the src/feature_store_api.py to handle low-level communication with the feature store.

dashboard
├── README.md
├── main.py
├── poetry.lock
├── pyproject.toml
├── src
│   ├── __init__.py
│   ├── backend.py
│   ├── feature_store_api
│   │   └── ...
│   ├── plot.py
│   └── utils.py
└── tests
    └── __init__.py

At this point, you can run your web app by going to the terminal and typing

$ poetry run streamlit run main.py --server.runOnSave true

Step 4. Dockerize the web app

So far you have a working web app locally. However, to make sure your app will work once deployed in a production environment, like a Kubernetes Cluster, you need to dockerize it.

For that, you need to add a Dockerfile to your repo

dashboard
├── Dockerfile
├── README.md
├── main.py
├── poetry.lock
├── pyproject.toml
├── src
│   └── ...
└── tests
    └── __init__.py

with the following layered instructions

 

From this Dockerfile you can build your Docker image

$ docker build -t ${DOCKER_IMAGE_NAME} .

and run the container locally

$ docker run --env ./.env -p 80:80 ${DOCKER_IMAGE_NAME}

where

• —env ./.env → specifies a file with environment variables, in this case the necessary credentials to communicate with the Hopsworks Feature Store.

• -p 80:80 → maps port 80 on the Docker host to TCP port 80 in the container.

Once you have a containerized app, you are ready for production.

Because this is the magic of Docker. If it works on your laptop. It also works on production.

BOOM!

and before you leave… I have a present for you.

Wanna learn to build real-world ML systems following MLOps best-practices?

 

Building Machine Learning Systems with a Feature Store is an upcoming book by Jim Dowling, where you will learn how to design and implement

• Batch-scoring ML systems,

• Real-Time ML systems, and

• LLM systems

following MLOps best-practices.

🎁 Gift

Get early access to the first chapter, for FREE by using this exclusive promo code
➡️ PlBzch1D24

Get your copy TODAY

Happy learning,

Talk to you next week,

Pau