Optimizing Performance of Spaces Applications
Last updated July 1, 2024
Introduction: Optimizing the performance of your Hugging Face Spaces applications ensures that users have a smooth and efficient experience. This guide covers essential techniques to enhance the performance of both Gradio and Streamlit applications.
Steps:
- Efficient Data Handling
- Use Efficient Data Structures:
- Opt for data structures that are optimized for speed and memory usage, such as NumPy arrays for numerical data.
- Limit Data Loading:
- Load data on-demand rather than all at once to reduce memory usage and speed up initialization times.
- Optimize Model Loading and Inference Lazy Loading Load models only when needed
model = None def load_model(): global model if model is None: model = pipeline('sentiment-analysis') return model Cache Results
- Process multiple inputs in a single batch to reduce inference time:
import streamlit as st from functools import lru_cache @lru_cache (maxsize=32) def cached_model_prediction(text): model = load_model() return model(text)
4.Optimize Front-End Performance
- Minimize Load Times:
- Reduce the size of static assets and optimize their loading order.
- Use Asynchronous Updates:
- In Gradio, use async functions to handle user inputs without blocking the UI:
import gradio as gr import asyncio async def async_function(text): # Your async processing here return text iface = gr.Interface(fn=async_function, inputs="text", outputs="text") iface.launch()