Abstract: An example system includes prediction workers, training workers, and a parameter server. The prediction workers store a local copy of a machine-learned model and run the mode exclusively in serving mode. The training workers store a local copy of a machine-learned model and a local snapshot and run the local copy exclusively in training mode and compare the local model or state to the snapshot after training to send delta updates to the parameter server after training. The parameter server aggregates received delta updates into a master copy of the model, sends the aggregated updates back to training workers and provides two types of updates; a real-time update based on a comparison of the master model with a local snapshot, and a full update. The real-time update occurs at least an order of magnitude more frequently than the full update and includes a subset of the weights in the model.

Abstract: A data compare technique for detecting memory errors on a computer's memory subsystem is disclosed. The technique simulates memory intensive software to determine if memory errors occur when the computer's memory subsystem is subjected to heavy memory usage. The technique copies an extensive test file, performs checksums of the original and copied test files and compares the checksum of the original file to the checksum of each copied file to identify checksum differences. The checksum differences indicate the occurrence of a memory error.