公开(公告)号：US20190012575A1

公开(公告)日：2019-01-10

申请号：US16026828

申请日：2018-07-03

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Yuanhao XIAO ,  Faen ZHANG ,  Kai ZHOU ,  Qian WANG ,  Kun LIU ,  Dongze XU ,  Tianhan XU ,  Jiayuan SUN ,  Lan LIU

IPC: G06K9/62 ,  G06F15/18 ,  G06N5/04

Abstract: The present disclosure discloses a method, apparatus and system for updating a deep learning model. A specific embodiment of the method includes: receiving a new training data set sent by a client, the new training data set being detected by the client in a preset path; training a preset deep learning model based on the new training data set to obtain a trained prediction model; and updating the preset deep learning model to the prediction model so that the prediction model is used to perform a data prediction operation online. This embodiment realizes the docking with the training data set of the user and improves the update efficiency of the deep learning model.

公开(公告)号：US20190228303A1

公开(公告)日：2019-07-25

申请号：US16248560

申请日：2019-01-15

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

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

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.

公开(公告)号：US20190114527A1

公开(公告)日：2019-04-18

申请号：US16159317

申请日：2018-10-12

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

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.

公开(公告)号：US20190087383A1

公开(公告)日：2019-03-21

申请号：US16118197

申请日：2018-08-30

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Kai ZHOU ,  Qian WANG ,  Faen ZHANG ,  Kun LIU ,  Yuanhao XIAO ,  Dongze XU ,  Tianhan XU ,  Jiayuan SUN ,  Lan LIU

IPC: G06F15/18 ,  G06F9/48 ,  G06F17/30 ,  G06F9/54 ,  G06K9/62

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.

公开(公告)号：US20190012576A1

公开(公告)日：2019-01-10

申请号：US16026976

申请日：2018-07-03

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Lan LIU ,  Faen ZHANG ,  Kai ZHOU ,  Qian WANG ,  Kun LIU ,  Yuanhao XIAO ,  Dongze XU ,  Tianhan XU ,  Jiayuan SUN

IPC: G06K9/62 ,  G06F15/18

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.