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How to Run Streamlit Apps and Properly Optimize It

Streamlit has revolutionized the way we build and deploy data-driven web applications. But how do you run Streamlit? How can you optimize your Streamlit apps for maximum performance? In this comprehensive guide, we'll answer these questions and more, providing you with the knowledge and tools you need to master Streamlit.

Understanding Streamlit

What is Streamlit? Streamlit is an open-source Python library that allows developers to create interactive, data-driven web applications with ease. It's designed to help data scientists and engineers streamline their work, turning scripts into shareable web apps in just a few lines of code.

Streamlit's integration with Python, one of the most popular programming languages in the world, makes it a powerful tool for data analysis and visualization. But Streamlit isn't just a Python library—it's also a striking distance keyword tool. This means it can help you identify and optimize keywords that are close to ranking on the first page of search engine results, boosting your website's visibility and traffic.

Getting Started with Streamlit

Running Streamlit is straightforward, regardless of your operating system. Whether you're using Windows, Linux, or macOS, you can get your Streamlit app up and running in no time.

Running Streamlit on Windows

To run Streamlit on Windows, you'll first need to install it. Open your command prompt and enter the following command:

pip install streamlit

Once Streamlit is installed, you can run it by entering the following command:

streamlit run

Replace with the name of your Python script. Streamlit will then launch in your default web browser, displaying your app for you to interact with.

Running Streamlit on a Server

Running Streamlit on a server follows a similar process. After installing Streamlit on your server, use the streamlit run command followed by the path to your Python script. You can then access your Streamlit app via your server's IP address and the port number Streamlit is running on.

Running Streamlit from VS Code

Visual Studio Code (VS Code) is a popular code editor that supports Streamlit. To run Streamlit from VS Code, open your Python script in the editor, then open the terminal and enter the streamlit run command followed by the path to your script.

Creating and Running Your First Streamlit App

Now that you know how to run Streamlit, it's time to create your first Streamlit app. In this section, we'll guide you through the process step by step, from writing your first lines of code to deploying your app on Render.

Writing Your First Streamlit App

A Streamlit app is just a Python script that uses the Streamlit library. Here's a simple example:

import streamlit as st
st.title('Hello, Streamlit!')
st.write('This is my first Streamlit app.')

When you run this script with Streamlit, it will display a web page with the title "Hello, Streamlit!" and the text "This is my first Streamlit app."

Deploying Your Streamlit App on Render

Render is a cloud platform that makes it easy to deploy web apps, including those built with Streamlit. To deploy your Streamlit app on Render, you'll need to create a Dockerfile that tells Render how to build and run your app. Here's an example:

FROM python:3.7
COPY . /app
RUN pip install -r requirements.txt
CMD streamlit run

This Dockerfile starts with a Python 3.7 image, sets the working directory to /app, copies your app's files into the image, installs any required Python packages, and finally runs your Streamlit app.

Once you've created your Dockerfile, you can upload your app to Render and it will automatically build and deploy it for you.

Optimizing Your Streamlit Application

Streamlit apps are designed to be fast and efficient, but as with any software, there's always room for improvement. In this section, we'll explore some techniques for optimizing your Streamlit apps, focusing on improving performance and responsiveness.

Improving App Performance

Streamlit provides powerful caching primitives that can significantly enhance your app's performance. These caching primitives allow your app to remain performant even when loading data from the web, manipulating large datasets, or performing expensive computations.

Streamlit offers two main types of caching: @st.cache_data and @st.cache_resource. The @st.cache_data decorator is used to cache functions that return data, such as dataframe transformations, database queries, or machine learning inference. On the other hand, @st.cache_resource is used to cache functions that return global resources, such as database connections or machine learning models.

Here's an example of how to use these decorators:

def long_function(param1, param2):
    # Perform expensive computation here or
    # fetch data from the web here
    return data
def init_model():
    # Return a global resource here
    return pipeline(

You can also clear all in-memory and on-disk data caches using st.cache_data.clear() and st.cache_resource.clear().

Optimizing App Rendering

The performance of a Streamlit app can also be affected by the complexity of its user interface. If your app becomes unresponsive when resizing the browser window or expanding/collapsing the sidebar, it might be due to the rendering of complex UI elements.

One way to optimize the rendering of your Streamlit app is to simplify its UI. For example, instead of using a large number of sliders or select sliders, consider using other types of input widgets that are less resource-intensive.

Another approach is to optimize the rendering of your plots. If your app includes complex Plotly charts, consider using the use_container_width=True option when calling st.plotly_chart. This option ensures that your charts are rendered at the optimal size for your app's container, improving rendering performance.

Deploying Streamlit Apps Efficiently

When deploying a Streamlit app, it's important to consider the resources required by your app and the capabilities of your deployment platform. For example, if your app performs heavy computations or handles large datasets, you might need to choose a deployment platform with sufficient CPU and memory resources.

One common pitfall when deploying Streamlit apps is not accounting for memory optimization. If your app uses a large amount of memory, it can crash upon deployment or become unresponsive. To avoid this, consider using techniques such as caching, efficient data handling, and memory profiling to reduce your app's memory usage.

In summary, optimizing a Streamlit app involves a combination of improving computational performance, optimizing UI rendering, and deploying your app efficiently. By applying these techniques, you can ensure that your Streamlit apps are fast, responsive, and reliable.

Advanced Features of Streamlit

Streamlit is not just a tool for creating simple web apps. It's a powerful platform that supports a wide range of advanced features, allowing you to build complex, interactive applications with ease. Once you're comfortable with the basics, you can start exploring Streamlit's advanced features. These include:

  • Interactive widgets: Streamlit supports a wide range of interactive widgets, including sliders, checkboxes, radio buttons, and more. These widgets allow your users to interact with your app, controlling its behavior and visualizing data in different ways.

  • Data caching: As we discussed earlier, Streamlit's caching primitives can significantly improve your app's performance. By caching the results of expensive computations, you can make your app faster and more responsive.

  • Session state: Streamlit's session state feature allows you to maintain a state across reruns, enabling more complex interactions. For example, you can use session state to remember the user's input or the current page in a multi-page app.

  • Components: Streamlit components allow you to extend Streamlit's capabilities by integrating with other JavaScript libraries and frameworks. You can use components to create custom widgets, embed interactive visualizations, and more.

By mastering these advanced features, you can take your Streamlit apps to the next level, creating complex, interactive applications that meet the needs of your users.

Additionally, you can create a No Code Data Visualization tool with this awesome Open Source Python Library:

PyGWalker (opens in a new tab) is a Python Library that helps you easily embed a tableau-alternative UI into your own Streamlit app effortlessly.

PyGWalker for Data visualization in Streamlit (opens in a new tab)


Streamlit is a powerful tool for creating data-driven web applications. With its easy-to-use interface and extensive features, it allows developers to quickly turn Python scripts into interactive web apps. Whether you're running Streamlit on your local machine, a server, or VS Code, getting started is straightforward. And with the right optimization techniques, you can ensure that your Streamlit apps are fast, responsive, and reliable. So why wait? Start exploring Streamlit today, and discover how it can transform your data analysis and visualization workflows.

Frequently Asked Questions

  1. What is Streamlit?

    Streamlit is an open-source Python library that allows developers to create interactive, data-driven web applications with ease. It's designed to help data scientists and engineers streamline their work, turning scripts into shareable web apps in just a few lines of code.

  2. How do I run Streamlit on my local machine?

    To run Streamlit on your local machine, you first need to install it using pip. Once installed, you can run Streamlit by entering the command streamlit run in your command prompt, replacing with the name of your Python script.

  3. How can I optimize my Streamlit app?

    You can optimize your Streamlit app by using Streamlit's caching primitives, simplifying your app's user interface, and deploying your app efficiently. These techniques can help improve your app's performance, making it faster and more responsive.