dspy-async-training

DSPy training throws 'no running event loop' errors because its default parallelizer lacks asynchronous context handling. You must patch the parallelizer to propagate context variables and use a background event loop for thread-safe optimization.

To optimize DSPy modules with async services, apply the async training patch before compiling your module. This introduces a background event loop and context var propagation, ensuring non-blocking integration during optimization.

Context var propagation in DSPy asynchronous training is a thread-safe mechanism that passes execution context across a background event loop. It modifies the parallelizer to maintain proper context handling during non-blocking service integration.

Yes, you can use DSPy optimization in production environments with non-blocking services by applying the async training script pattern. This ensures proper patching and context management, reducing compile-time errors during module integration.

You need to patch the DSPy parallelizer for async training when your module requires access to asynchronous services during optimization. Patching prevents runtime errors by ensuring thread-safe context handling and background event loop integration.