While asynchronous execution keeps the Python frontend highly responsive by allowing it to continuously enqueue operations without waiting, this responsiveness introduces a risk: if the frontend submits work faster than the backend can process it, the task queue grows unboundedly, leading to excessive memory consumption. To prevent such overflow, it is recommended to insert a synchronization barrier after each minibatch during training. This per-minibatch synchronization forces the frontend to pause briefly while the backend catches up, keeping the two approximately in step and bounding the queue's memory footprint without sacrificing the major throughput advantages of asynchronous execution.