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    ACCELERATING DISTRIBUTED MACHINE LEARNING WITH SMART NICS
    2.
    发明公开
    ACCELERATING DISTRIBUTED MACHINE LEARNING WITH SMART NICS 审中-公开

    公开(公告)号：US20230342599A1

    公开(公告)日：2023-10-26

    申请号：US17727172

    申请日：2022-04-22

    Applicant: VMware, Inc.

    Inventor： Shay Vargaftik ,  Yaniv Ben-Itzhak ,  Alex Markuze ,  Igor Golikov ,  Avishay Yanai

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/0454

    Abstract: Some embodiments provide a method for performing distributed machine learning (ML) across multiple computers. At a smart network interface controller (NIC) of a first computer, the method receives a set of ML parameters from the first computer related to training an ML model. The method compresses the set of ML parameters based on a current state of a connection to a central computer that receives sets of ML parameters from a plurality of the computers. The method sends the compressed set of ML parameters to the central computer for the central computer to process the compressed set of ML parameters along with corresponding sets of ML parameters received from the other computers of the plurality of computers.

    Internal load balancer for tree-based ensemble classifiers
    3.
    发明授权
    Internal load balancer for tree-based ensemble classifiers 有权

    公开(公告)号：US11748668B2

    公开(公告)日：2023-09-05

    申请号：US16923988

    申请日：2020-07-08

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

    IPC: G06N20/20 ,  G06F9/50 ,  G06F18/243 ,  G06F18/214 ,  G06F18/21

    CPC classification number: G06N20/20 ,  G06F9/505 ,  G06F9/5083 ,  G06F18/214 ,  G06F18/2185 ,  G06F18/24323

    Abstract: Techniques for implementing a tree-based ensemble classifier comprising an internal load balancer are provided. In one set of embodiments, the tree-based ensemble classifier can receive a query data instance and select, via the internal load balancer, a subset of its decision trees for processing the query data instance. The tree-based ensemble classifier can then query each decision tree in the selected subset with the query data instance, combine the per-tree classifications generated by the subset trees to generate a subset classification, and determine whether a confidence level associated with the subset classification is sufficiently high. If the answer is yes, the tree-based ensemble classifier can output the subset classification as a final classification result for the query data instance. If the answer is no, the tree-based ensemble classifier can repeat the foregoing steps until a sufficient confidence level is reached or until all of its decision trees have been selected and queried.

    Predictability-driven compression of training data sets
    4.
    发明授权
    Predictability-driven compression of training data sets 有权

    公开(公告)号：US11526785B2

    公开(公告)日：2022-12-13

    申请号：US16908498

    申请日：2020-06-22

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Techniques for performing predictability-driven compression of training data sets used for machine learning (ML) are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of data instances and can train an ML model using the plurality of data instances, the training resulting in a trained version of the ML model. The computer system can further generate prediction metadata for each data instance in the plurality of data instances using the trained version of the ML model and can compute a predictability measure for each data instance based on the prediction metadata, the predictability measure indicating a training value of the data instance. The computer system can then filter one or more data instances from the plurality of data instances based on the computed predictability measures, the filtering resulting in a compressed version of the training data set.

    QUANTIZING TRAINING DATA SETS USING ML MODEL METADATA
    5.
    发明申请
    QUANTIZING TRAINING DATA SETS USING ML MODEL METADATA 有权

    公开(公告)号：US20220012639A1

    公开(公告)日：2022-01-13

    申请号：US16924035

    申请日：2020-07-08

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

    IPC: G06N20/20 ,  G06N5/00

    Abstract: Techniques for quantizing training data sets using machine learning (ML) model metadata are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of features and a plurality of data instances, where each data instance includes a feature value for each of the plurality of features. The computer system can further train a machine learning (ML) model using the training data set, where the training results in a trained version of the ML model, and can extract metadata from the trained version of the ML model pertaining to the plurality of features. The computer system can then quantize the plurality of data instances based on the extracted metadata, the quantizing resulting in a quantized version of the training data set.

    Predictability-Driven Compression of Training Data Sets
    6.
    发明申请
    Predictability-Driven Compression of Training Data Sets 有权

    公开(公告)号：US20210397990A1

    公开(公告)日：2021-12-23

    申请号：US16908498

    申请日：2020-06-22

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

    IPC: G06N5/04 ,  G06N20/00

    Abstract: Techniques for performing predictability-driven compression of training data sets used for machine learning (ML) are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of data instances and can train an ML model using the plurality of data instances, the training resulting in a trained version of the ML model. The computer system can further generate prediction metadata for each data instance in the plurality of data instances using the trained version of the ML model and can compute a predictability measure for each data instance based on the prediction metadata, the predictability measure indicating a training value of the data instance. The computer system can then filter one or more data instances from the plurality of data instances based on the computed predictability measures, the filtering resulting in a compressed version of the training data set.

    REINFORCEMENT LEARNING-BASED DYNAMIC AGGREGATION FOR DISTRIBUTED AND FEDERATED LEARNING
    7.
    发明申请
    REINFORCEMENT LEARNING-BASED DYNAMIC AGGREGATION FOR DISTRIBUTED AND FEDERATED LEARNING 有权

    公开(公告)号：US20250111244A1

    公开(公告)日：2025-04-03

    申请号：US18479613

    申请日：2023-10-02

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

    IPC: G06N3/098 ,  G06N3/092

    Abstract: A framework for implementing reinforcement learning (RL)-based dynamic aggregation for distributed learning (DL) and federated learning (FL) is provided. In one set of embodiments, the framework includes an RL agent that interacts with the parameter server and clients of a DL/FL system and periodically receives two inputs from the system while the system is executing a training run: a “state” comprising information regarding the current runtime properties of the system and a “reward” comprising information pertaining to one or more training metrics to be optimized. In response to these inputs, the RL agent generates an “action” comprising information for modifying the parameter server's aggregation function in a manner that maximizes future cumulative rewards expected from the DL/FL system based on the state.

    Importance Sampling with Bandwidth Constraints
    9.
    发明公开
    Importance Sampling with Bandwidth Constraints 审中-公开

    公开(公告)号：US20230162022A1

    公开(公告)日：2023-05-25

    申请号：US17535479

    申请日：2021-11-24

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik ,  Boris Shustin

    IPC: G06N3/08 ,  G06F9/54

    CPC classification number: G06N3/08 ,  G06F9/54

    Abstract: At an iteration k of a training procedure for training a deep neural network (DNN), a first computer system can sample a batch bk of data instances from a training dataset local to that computer system in a manner that mostly conforms to importance sampling probabilities of the data instances, but also applies a “stiffness” factor with respect to data instances appearing in batch bk−1 of a prior iteration k−1. This stiffness factor makes it more likely, or guarantees, that some portion of the data instances in prior batch bk−1—which is present on a second computer system holding the DNN—will be reused in current batch bk. The first computer system can then transmit the new data instances in batch bk to the second computer system and the second computer system can reconstruct batch bk using the received new data instances and its local copy of prior batch bk−1.

    Quantizing training data sets using ML model metadata
    10.
    发明授权
    Quantizing training data sets using ML model metadata 有权

    公开(公告)号：US11645587B2

    公开(公告)日：2023-05-09

    申请号：US16924035

    申请日：2020-07-08

    Applicant: VMware, Inc.

    Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

    IPC: G06N20/20 ,  G06N5/00 ,  G06N5/01

    CPC classification number: G06N20/20 ,  G06N5/01

    Abstract: Techniques for quantizing training data sets using machine learning (ML) model metadata are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of features and a plurality of data instances, where each data instance includes a feature value for each of the plurality of features. The computer system can further train a machine learning (ML) model using the training data set, where the training results in a trained version of the ML model, and can extract metadata from the trained version of the ML model pertaining to the plurality of features. The computer system can then quantize the plurality of data instances based on the extracted metadata, the quantizing resulting in a quantized version of the training data set.

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