Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. In just a few minutes you can build and deploy powerful data apps.
In this blog post, we'll show you how to build a Streamlit Docker image and deploy it publicly in 3 minutes. The deployment will give you a public URL (similar to
xx.modelz.live) for your model that anyone can use without installing anything on their computer.
After that, you can share your model with others and get feedback on it!
It's easy to create a Streamlit app. You can follow the official tutorial. Here is an example:
import streamlit as st import pandas as pd import numpy as np st.title('Uber pickups in NYC')
After creating a Streamlit app, you can build a Streamlit Docker image. We provide a
Dockerfile for CPU deployment and a
Dockerfile.gpu for GPU. You can use them directly.
Dockerfile is very simple:
FROM python:3.9 WORKDIR /workspace COPY . /workspace/ RUN pip install -r /workspace/requirements.txt CMD ["streamlit", "run", "main.py", "--server.port=8501", "--server.address=0.0.0.0","--server.enableCORS=false", "--server.enableXsrfProtection=false"]
You just need to update the
main.py to your own. Then you could run the command to push it to the Docker Hub:
docker build -t docker.io/USER/IMAGE . docker push docker.io/USER/IMAGE
After building the Streamlit Docker image, you can deploy it. You could pick up a VM from any cloud provider, such as AWS, GCP, Azure, etc. Then you could run the command to deploy it:
$ pip install openmodelz $ mdz start <your public ip> $ mdz deploy --image docker.io/USER/IMAGE --port 7860
Then you will get a public URL for your model via
mdz list, such as
xx.modelz.live. OpenModelZ takes care of the rest for you!
In this blog post, we showed you how to build a Streamlit Docker image and deploy it publicly in 3 minutes. We hope that this will help you get started with Streamlit and make your models more accessible to others. If you have any questions or feedback, please let us know in our Discord server!