Beijing Baidu Netcom Science and Technology Co., Ltd.

International Business Machines Corporation

Lan Zhe Liu

Qian Wang

Kun Liu

Faen Zhang

Kai Zhou

Dongze Xu

Yuanhao Xiao

Tianhan Xu

Jiayuan Sun

Guang Han Sui

Wei Ge

Jiangliang Guo

Jin Tang

Shiming Yin

Li Jun Bj Zhu

Le Yao

Juan Yang

Zhang Li Ping

Er Tao Zhao

The present invention relates to a method, system and computer program product for task failover in an unstable environment, wherein the unstable environment includes a plurality of reclaimable nodes. According to the method, it is monitored if any node of the plurality of reclaimable nodes is to be reclaimed. Whether a task on any node of the plurality of reclaimable nodes is recoverable is determined. Responsive to the task being recoverable, data of the recoverable task is stored. Responsive to a node being reclaimed and the task on the reclaimed node being recoverable, at least one associated task of at least one associated node of the reclaimed node is notified to wait.

Task failover

A block of data intended for a set of receiving computer systems comprising a first system and a second system is divided into a set of equal-size portions. A first portion of the set of portions is transmitted from a first file server storing the block of data to the first system. The first portion is relayed from the first file server to a second file server concurrently with the transmitting. The first portion of the set of portions is transmitted from the second file server to the second system.

Pipelined file server based data transmission

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.

Method and apparatus for scheduling resource for deep learning framework

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.

Method and apparatus for updating deep learning model

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.

Intelligent big data system, and method and apparatus for providing intelligent big data service

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.

Integrated facial recognition method and system

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.

Deep learning assignment processing method and apparatus, device and storage medium

Methods that can protect sensitive data are provided. One method includes determining, by a processor, whether a user is focusing on an obscured set of sensitive data displayed on a computing device, clarifying the obscurity of a portion of the displayed set of sensitive data in response to determining that the user focusing on the portion of the displayed set of sensitive data, and maintaining the obscurity of one or more other portions of the displayed set of sensitive data in response to determining that the user not focusing on the one or more other portions of the displayed set of sensitive data. Apparatus and computer program products that can include, perform, and/or implement the methods are also provided.

Growing community of inventors

Bei Jing, China

Lan Zhe Liu