optimizing-streamlit-performance

To speed up a slow Streamlit dashboard, you must reduce unnecessary reruns and improve resource usage by implementing proper caching, fragments, forms, and conditional rendering techniques.

Streamlit executes the entire script from top to bottom on every interaction by default, causing reruns. You can control this behavior using fragments and forms to isolate execution and prevent full script reruns.

Optimizing data loading in Streamlit applications requires applying caching strategies to data fetching and model initialization, which prevents reloading large datasets and speeds up application response times.

Yes, you can use fragments and conditional rendering to improve Streamlit performance by isolating specific parts of your application to execute independently, reducing the computational overhead of unnecessary full reruns.

To handle large datasets without making your Streamlit app sluggish, you need to optimize data handling and apply targeted caching techniques to manage computational resources more efficiently.