Amazon Technologies, Inc.

Christopher Cade Daniel

Fei Wu

Rahul Raghavendra Huilgol

Indu Thangakrishnan

Akhil Mehra

Yutong Zhang

Ajay Paidi

Can Karakus

Anirudh Subramanian

Luis Alves Pereira Quintela

Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.

Automatic partitioning of machine learning models for training across multiple devices

Growing community of inventors

San Jose, CA, United States of America

Christopher Cade Daniel