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公开(公告)号：US11194999B2
公开(公告)日：2021-12-07
申请号：US16102924
申请日：2018-08-14
Inventor： Tianhan Xu , Faen Zhang , Kai Zhou , Qian Wang , Kun Liu , Yuanhao Xiao , Dongze Xu , Jiayuan Sun , Lan Liu
Abstract: An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.
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公开(公告)号：US11640550B2
公开(公告)日：2023-05-02
申请号：US16026976
申请日：2018-07-03
Inventor： Lan Liu , Faen Zhang , Kai Zhou , Qian Wang , Kun Liu , Yuanhao Xiao , Dongze Xu , Tianhan Xu , Jiayuan Sun
Abstract: The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.
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公开(公告)号：US20210389981A1
公开(公告)日：2021-12-16
申请号：US17176870
申请日：2021-02-16
Abstract: An application construction method and apparatus, an electronic device and a storage medium are provided, which are related to the field of artificial intelligence. The application construction method includes: acquiring a service orchestration file of an application; and determining an execution program of the application based on the service orchestration file, wherein the service orchestration file includes at least one of the following contents corresponding to at least one task obtained by disassembling the application: information relating to a format of data transferred between tasks; information relating to syntax transformation of the data transferred between the tasks; information relating to logical processing between the tasks; and information relating to a model that is to be used by the task.
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公开(公告)号：US11762697B2
公开(公告)日：2023-09-19
申请号：US16248560
申请日：2019-01-15
Inventor： Kun Liu , Kai Zhou , Qian Wang , Yuanhao Xiao , Lan Liu , Dongze Xu , Tianhan Xu , Jiangliang Guo , Jin Tang , Faen Zhang , Shiming Yin
IPC: G06N3/08 , G06F9/50 , G06F18/2413 , G06V10/764 , G06V10/82 , G06F18/21
CPC classification number: G06F9/5027 , G06F9/5011 , G06F18/24143 , G06N3/08 , G06V10/764 , G06V10/82 , G06F18/21
Abstract: The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.
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Patent Agency Ranking
公开(公告)号：US20190228303A1
公开(公告)日：2019-07-25
申请号：US16248560
申请日：2019-01-15
Inventor： Kun LIU , Kai Zhou , Qian Wang , Yuanhao Xiao , Lan Liu , Dongze Xu , Tianhan Xu , Jiangliang Guo , Jin Tang , Faen Zhang , Shiming Yin
Abstract: The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.
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6.发明授权公开(公告)号：US11315034B2
公开(公告)日：2022-04-26
申请号：US16118197
申请日：2018-08-30
Inventor： Kai Zhou , Qian Wang , Faen Zhang , Kun Liu , Yuanhao Xiao , Dongze Xu , Tianhan Xu , Jiayuan Sun , Lan Liu
Abstract: A system comprises: a data warehouse, a storage device and a cluster including a plurality of computing nodes; the data warehouse is configured to store task data obtained from the user; at least one computing node in the cluster includes a resource scheduling component, and is configured to perform resource scheduling for the task and determine a computing node executing the task; the computing node executing the task comprises a model training component and/or a prediction component; the model training component is configured to, according to task data, invoke a corresponding type of learning model from the storage device; use sample data and training target included in the task data to train the learning model, to obtain the prediction model corresponding to the task and store the prediction model in the storage device; the prediction component is configured to obtain a prediction result output by the prediction model.
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公开(公告)号：US11055602B2
公开(公告)日：2021-07-06
申请号：US16159317
申请日：2018-10-12
Inventor： Dongze Xu , Faen Zhang , Kai Zhou , Qian Wang , Kun Liu , Yuanhao Xiao , Jiayuan Sun , Lan Liu , Tianhan Xu
IPC: G06N3/02 , G06F3/0483
Abstract: The present disclosure provides a deep learning assignment processing method and apparatus, a device and a storage medium. It is feasible to obtain the deep learning assignment submitted by the user in a predetermined manner, the predetermined manner comprising the web UI manner, then submit the deep learning assignment to the deep learning system so that the deep learning system runs the submitted deep learning assignment. As compared with the prior art, processing such as programming is not needed upon submitting the deep learning assignment in the solutions of the present disclosure, thereby simplifying the user's operations, improving the processing efficiency of the deep learning assignment, and accelerating the user's speed of developing deep learning.
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公开(公告)号：US10264063B2
公开(公告)日：2019-04-16
申请号：US15429386
申请日：2017-02-10
Inventor： Jiaxing Wang , Kai Zhou , Faen Zhang , Qian Wang , Yuanhao Xiao
IPC: G06F15/173 , H04L29/08 , G06F9/50
Abstract: The present disclosure provides a method and apparatus for scheduling a cloud server. A specific implementation mode of the method comprises: monitoring whether current time is in a first pre-set time period; in response to the monitoring that the current time is in the first pre-set time period, scheduling a cloud server in a first cloud server cluster having a running state being an idle state, as a target cloud server, to a second cloud server cluster, so that the target cloud server executes a task obtained by the second cloud server cluster; monitoring whether the current time is in a second pre-set time period; in response to the monitoring that the current time is in the second pre-set time period, rescheduling the target cloud server to the first cloud server cluster, so that the target cloud server executes a task obtained by the first cloud server cluster.
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公开(公告)号：US20190080154A1
公开(公告)日：2019-03-14
申请号：US16102924
申请日：2018-08-14
Inventor： Tianhan Xu , Faen Zhang , Kai Zhou , Qian Wang , Kun Liu , Yuanhao Xiao , Dongze Xu , Jiayuan Sun , Lan Liu
Abstract: An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.
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