Asynchronous updates refer to a method of updating model parameters in distributed training where each worker node computes gradients independently and updates the model without waiting for others to finish. This technique is particularly useful in data parallelism, as it allows for faster convergence and better resource utilization by overlapping computation and communication. Asynchronous updates can reduce the overall training time but may introduce challenges like stale gradients, which occur when different workers use outdated model parameters for their calculations.
congrats on reading the definition of Asynchronous updates. now let's actually learn it.