Optimizing Streamlit for Production ML/DL Gen AI Projects: Enhancing Performance with RQ
Streamlit has gained popularity for its ease of use in creating ML/DL Gen AI project interfaces. However, as projects scale, long-running tasks can impact performance. This blog explores how to combine Streamlit with RQ to offload tasks to a background queue, ensuring a smoother user experience.
Challenges with Streamlit:
- Performance Bottlenecks: Long-running tasks can lead to unresponsive interfaces.
- Scalability Issues: Handling large datasets or multiple users concurrently can strain Streamlit.
- Resource Management: Streamlit’s synchronous nature can be resource-intensive for complex tasks.
Introducing RQ and Background Task Execution:
RQ (Redis Queue) is a Python library for task queueing with Redis. It enables background task execution, ensuring Streamlit remains responsive.
Key Concepts of RQ:
- Queue: Holds tasks to be executed. RQ manages task execution order.
- Worker: Processes that execute tasks. Each worker listens to specific queues.
Combining Streamlit with RQ
Folder Structure:
project/
├── streamlit_app.py
├── rq_worker.py
└── tasks.py
Setting Up Redis Server:
- Install and run Redis server locally or use a cloud-based service.
- Note the Redis server’s host and port for configuration.
Configuring RQ:
# rq_worker.py
import os
from rq import Worker, Queue, Connection
listen = ['high', 'default', 'low']
redis_url = os.getenv('REDIS_URL', 'redis://localhost:6379')
conn = Connection(redis_url)
if __name__ == '__main__':
with Connection(conn):
worker = Worker(map(Queue, listen))
worker.work()
Offloading Long-Running Tasks:
# streamlit_app.py
import streamlit as st
from rq import Queue
from redis import Redis
from tasks import long_running_task
# Connect to Redis server
redis_conn = Redis()
queue = Queue('default', connection=redis_conn)
def run_task():
job = queue.enqueue(long_running_task)
st.write('Task enqueued!')
st.write('Task ID:', job.id)
if st.button('Start Long Task'):
run_task()
Writing Task Function:
# tasks.py
def long_running_task():
# Your long-running task here
pass
Running the Application:
- Run the RQ worker for the ‘default’ queue using
rq worker
command. - Run the Streamlit application using
streamlit run streamlit_app.py
command.
Conclusion
Combining Streamlit with RQ can significantly improve the performance and scalability of your ML/DL Gen AI projects. By offloading long-running tasks to a background queue, you can ensure a smoother user experience and efficient task processing. This integration allows Streamlit to focus on providing a responsive user interface while RQ handles the heavy lifting in the background.
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