公开(公告)号：US20240354218A1

公开(公告)日：2024-10-24

申请号：US18302279

申请日：2023-04-18

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： SRIDHAR BALACHANDRIAH ,  SATISH KUMAR MOPUR ,  LANCE MACKIMME EVANS ,  SHERIN THYIL GEORGE ,  KEVAN REHM

IPC: G06F11/34

CPC classification number: G06F11/3495 ,  G06F11/3433

Abstract: Systems and methods are provided for utilization of optimal data access interface usage in machine learning pipelines. Examples of the systems and methods disclosed herein include identifying data access interfaces comprising at least a first data access interface for a persistent storage distributed across a plurality of storage nodes and at least a second data access interface for an in-memory object store, and receiving, from a compute node, a data operation request as part of a machine learning pipeline. Additionally, performance metrics are obtained for the plurality of access interfaces, and based on a type of data operation request, the data operation is executed using a data access interface selected from the plurality of data access interface based on the performance metrics and providing an object handle to the compute node.

公开(公告)号：US20200050578A1

公开(公告)日：2020-02-13

申请号：US16100076

申请日：2018-08-09

Applicant: Hewlett Packard Enterprise Development LP

Inventor： SATISH KUMAR MOPUR ,  SAIKAT MUKHERJEE ,  GUNALAN PERUMAL VIJAYAN ,  SRIDHAR BALACHANDRIAH ,  ASHUTOSH AGRAWAL ,  KRISHNAPRASAD LINGADAHALLI SHASTRY ,  GREGORY S. BATTAS

IPC: G06F15/18 ,  G06F9/54 ,  H04L29/08

Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.