Amazon Technologies, Inc.

Rahul Raghavendra Huilgol

Andrea Olgiati

Vikas Kumar

Stefano Stefani

Vladimir Zhukov

Lakshmi Naarayanan Ramakrishnan

Fei Wu

Indu Thangakrishnan

Amol Ashok Lele

Anirudh Subramanian

Ajay Paidi

Akhil Mehra

Can Karakus

Yutong Zhang

Jeffrey John Geevarghese

Luis Alves Pereira Quintela

Denis Davydenko

Christopher Cade Daniel

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

Methods, systems, and computer-readable media for debugging and profiling of machine learning model training are disclosed. A machine learning analysis system receives data associated with training of a machine learning model. The data was collected by a machine learning training cluster. The machine learning analysis system performs analysis of the data associated with the training of the machine learning model. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis. The machine learning analysis system generates one or more alarms describing the one or more conditions associated with the training of the machine learning model.

Debugging and profiling of machine learning model training

Methods, systems, and computer-readable media for GPU code injection to summarize machine learning training data are disclosed. Training of a machine learning model is initiated using a graphics processing unit (GPU) associated with a machine learning training cluster. The training of the machine learning model generates tensor data in a memory of the GPU. The GPU determines a summary of the tensor data according to a reduction operator. The summary is smaller in size than the tensor data and is output by the GPU. A machine learning analysis system performs an analysis of the training of the machine learning model based at least in part on the summary of the tensor data. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis.

GPU code injection to summarize machine learning training data

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Sunnyvale, CA, United States of America

Rahul Raghavendra Huilgol