Extending machine learning workloads

    公开(公告)号:US11822976B2

    公开(公告)日:2023-11-21

    申请号:US17538130

    申请日:2021-11-30

    CPC classification number: G06F9/541 G06N20/00

    Abstract: In one embodiment, a device presents information regarding an upstream machine learning workload and a downstream machine learning workload via a user interface. The device receives, via the user interface, a request to form a combined machine learning workload by connecting the upstream machine learning workload and the downstream machine learning workload. The device identifies, after receiving the request, a node associated with the upstream machine learning workload and a node associated with the downstream machine learning workload. The device forms the combined machine learning workload by configuring the node associated with the upstream machine learning workload to use one or more connector application programming interfaces to send data from the upstream machine learning workload to the node associated with the downstream machine learning workload for consumption.

    EXTENDING MACHINE LEARNING WORKLOADS
    2.
    发明公开

    公开(公告)号:US20230168950A1

    公开(公告)日:2023-06-01

    申请号:US17538130

    申请日:2021-11-30

    CPC classification number: G06F9/541 G06N20/00

    Abstract: In one embodiment, a device presents information regarding an upstream machine learning workload and a downstream machine learning workload via a user interface. The device receives, via the user interface, a request to form a combined machine learning workload by connecting the upstream machine learning workload and the downstream machine learning workload. The device identifies, after receiving the request, a node associated with the upstream machine learning workload and a node associated with the downstream machine learning workload. The device forms the combined machine learning workload by configuring the node associated with the upstream machine learning workload to use one or more connector application programming interfaces to send data from the upstream machine learning workload to the node associated with the downstream machine learning workload for consumption.

    DYNAMIC TOPOLOGY RECONFIGURATION IN FEDERATED LEARNING SYSTEMS

    公开(公告)号:US20230281502A1

    公开(公告)日:2023-09-07

    申请号:US17683886

    申请日:2022-03-01

    CPC classification number: G06N20/00 H04L41/12

    Abstract: In one embodiment, a device provides, to a user interface, data representing a topology of a federated learning system configured across nodes in a computer network. Each node in the topology has an assigned role and is connected to at least one other node via a connector that is dependent on its assigned role. The device receives, via the user interface, a requested change to the topology of the federated learning system. The device selects, based on assigned roles of those nodes affected by the requested change to the topology of the federated learning system, code for execution by those nodes. The device implements the requested change to the topology of the federated learning system in part by sending the code selected by the device to those nodes affected by the requested change.

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