Petuum Inc.

Aurick Qiao

Eric Xing

Qirong Ho

Wei Dai

Weiren Yu

A system including a master machine and a plurality of worker machines is disclosed. The master machine includes, for example, an API server configured to receive a job description; a resource allocation module configured to determine a number of virtual machines required to perform a job based on the job description; a container scheduling module configured to create a container containing the number of virtual machines required to perform the job, wherein at least two of the virtual machines in the container resides on different worker machines, and wherein each of the virtual machines is configured to run a same application to perform the job.

Operating system for distributed enterprise artificial intelligence programs on data centers and the clouds

A computer system implemented a method for elastic resource management for executing a machine learning (ML) program. The system is configured to create a set of logical executors, assign them across a set of networked physical computation units of a distributed computing system, partition and distribute input data and Work Tasks across the set of logical executors, assign them across the set of networked physical computation units, where the Work Tasks are partitioned into short units of computation (micro-tasks), each calculates a partial update to the ML program's model parameters and each last for less than one second; create a set of logical servers (LSes); partition and distribute globally shared model parameters of the ML program across the set of logical servers; execute partitioned Work Tasks according to a bounded asynchronous parallel standard, where a current Work Task is allowed to execute with stale model parameters without having all the current calculation updates from Work Tasks it depend on, provided the staleness of the model parameters is within a predefined limit.

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Pittsburgh, PA, United States of America

Aurick Qiao