US 12,602,302 B2 · Machine learning model training with hardware error simulation

Abstract

Methods, systems, and apparatus, including computer-readable storage media for handling component error of datacenter infrastructure by training machine learning models to handle errors when the models are deployed. Rather than predict or mitigate errors in the hardware of a datacenter or another site deploying the machine learning model, the model is trained to perform a task with comparable accuracy and efficiency even when some hardware on which the model is deployed fails. Component error can instead be simulated during training to cause the machine learning model deployed on the infrastructure to learn to correct errors caused by the component errors. The model can continue to be trained to compensate for periods in which not all parts of the model are available at inference. Updates to the model can be backpropagated to correct errors for handling instances of component error.

Claims20

Dependent claims

A method for training a machine learning model, comprising:

receiving, by a plurality of hardware components of a computing infrastructure configured to train machine learning models, error frequency data comprising a frequency and duration for component error of the plurality of hardware components;
training, by the plurality of hardware components, a machine learning model over one or more training iterations to generate a trained machine learning model, wherein, during at least one of the one or more the training iterations, error of one or more hardware components of the plurality of hardware components is simulated in accordance with the error frequency data;
and
outputting, by the plurality of hardware components, the trained machine learning model.

1 more element

Dependencies are inferred from the claim text.

Machine learning model training with hardware error simulation

Life of this patent

Machine learning model training with hardware error simulation

Life of this patent

Abstract

Claims20