Example
In the example below, pressing the “Clear All” button will clear all cache_resource caches. i.e. Clears cached global resources from all functions decorated with @st.cache_resource.
import streamlit as st
from transformers import BertModel
@st.cache_resource
def get_database_session(url):
# Create a database session object that points to the URL.
return session
@st.cache_resource
def get_model(model_type):
# Create a model of the specified type.
return BertModel.from_pretrained(model_type)
if st.button("Clear All"):
# Clears all st.cache_resource caches:
st.cache_resource.clear()
Using Streamlit commands in cached functions
Static elements
Since version 1.16.0, cached functions can contain Streamlit commands! For example, you can do this:
from transformers import pipeline
@st.cache_resource
def load_model():
model = pipeline("sentiment-analysis")
st.success("Loaded NLP model from Hugging Face!") # 👈 Show a success message
return model
As we know, Streamlit only runs this function if it hasn’t been cached before. On this first run, the st.success message will appear in the app. But what happens on subsequent runs? It still shows up! Streamlit realizes that there is an st. command inside the cached function, saves it during the first run, and replays it on subsequent runs. Replaying static elements works for both caching decorators.
You can also use this functionality to cache entire parts of your UI:
@st.cache_resource
def load_model():
st.header("Data analysis")
model = torchvision.models.resnet50(weights=ResNet50_Weights.DEFAULT)
st.success("Loaded model!")
st.write("Turning on evaluation mode...")
model.eval()
st.write("Here's the model:")
return model
Input widgets
You can also use interactive input widgets like st.slider or st.text_input in cached functions. Widget replay is an experimental feature at the moment. To enable it, you need to set the experimental_allow_widgets parameter:
@st.cache_resource(experimental_allow_widgets=True) # 👈 Set the parameter
def load_model():
pretrained = st.checkbox("Use pre-trained model:") # 👈 Add a checkbox
model = torchvision.models.resnet50(weights=ResNet50_Weights.DEFAULT, pretrained=pretrained)
return model
Streamlit treats the checkbox like an additional input parameter to the cached function. If you uncheck it, Streamlit will see if it has already cached the function for this checkbox state. If yes, it will return the cached value. If not, it will rerun the function using the new slider value.
Using widgets in cached functions is extremely powerful because it lets you cache entire parts of your app. But it can be dangerous! Since Streamlit treats the widget value as an additional input parameter, it can easily lead to excessive memory usage. Imagine your cached function has five sliders and returns a 100 MB DataFrame. Then we’ll add 100 MB to the cache for every permutation of these five slider values – even if the sliders do not influence the returned data! These additions can make your cache explode very quickly. Please be aware of this limitation if you use widgets in cached functions. We recommend using this feature only for isolated parts of your UI where the widgets directly influence the cached return value.