Context Variables
Python's
contextvars module helps manage thread-local state in asynchronous deep learning pipelines. It allows you to create context-specific variables that persist across asynchronous calls, ensuring data integrity in complex workflows.This approach prevents race conditions in parallel processing, maintains separate states for different execution contexts, and enables clean, modular code in asynchronous environments. It's especially useful in Parallel Training scenarios or when handling multiple models concurrently for an AI Server.
import contextvars import asyncio # Create a context variable model_state = contextvars.ContextVar('model_state', default={}) async def update_model(data): state = model_state.get() # Update state with new data state.update(data) model_state.set(state) async def process_batch(batch): await update_model(batch) # More processing... async def main(): batches = [{'batch1': 'data'}, {'batch2': 'data'}] await asyncio.gather(*(process_batch(b) for b in batches)) print(model_state.get()) # For Jupyter notebooks, use this instead of asyncio.run(main()) await main()
