International Business Machines Corporation

Gauri Joshi

Parijat Dube

Priya Ashok Nagpurkar

Zehra Noman Sura

Diana Jeanne Arroyo

Stefania Costache

Sanghamitra Dutta

Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model. The estimated runtime can define a period of time beginning at an initiation of the machine learning task and ending at a completion of the machine learning task.

Runtime estimation for machine learning tasks

A method for performing machine learning includes assigning processing jobs to a plurality of model learners, using a central parameter server. The processing jobs includes solving gradients based on a current set of parameters. As the results from the processing job are returned, the set of parameters is iterated. A degree of staleness of the solving of the second gradient is determined based on a difference between the set of parameters when the jobs are assigned and the set of parameters when the jobs are returned. The learning rates used to iterate the parameters based on the solved gradients are proportional to the determined degrees of staleness.

Adaptive learning rate schedule in distributed stochastic gradient descent

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Gauri Joshi