Suzhou Metabrain Intelligent Technology Co., Ltd.

Inspur Suzhou Intelligent Technology Co., Ltd

Pei Chen

Dekui Wang

Wenxiao Wang

Huixing Liu

Yingjie Kang

Disclosed are a network card communication method and apparatus for an AI training platform, a device, and a medium. The method includes: building a switch network topology on the basis of a spine-leaf network, and configuring a preset number of virtual local area networks for each leaf switch in the switch network topology; virtualizing a physical network card to obtain virtual network cards, allocating the virtual network cards to corresponding job-containers according to a preset allocation rule, and allocating, to each virtual network card in the job-containers, different sub-networks corresponding to the virtual local area networks; and adding a corresponding sub-network communication policy routing rule to a pod where each job-container is located, whereby a virtual network card in the job-container sends training data to the remaining virtual network cards on a basis of the sub-network communication policy routing rule.

Network card communication method and apparatus for AI training platform, device, and medium

The present disclosure provides a configuration method for virtual network interface card resource, including: integrating a plurality of virtual network interface cards in a node where a container group is located, so as to obtain a virtual network interface card set including a plurality of virtual network interface card groups; assigning a target virtual network interface card group for the container group from the virtual network interface card set; analyzing the target virtual network interface card group to obtain address information of target virtual network interface cards in the target virtual network interface card group; and configuring a virtual network interface card resource for the container group according to the address information.

Configuration method and apparatus for virtual network interface card resource, and computer device and medium

A method of resource allocation includes: when it is detected that a target training task in a target development environment is started, triggering loading of a client plugin library. Therefore, the client plugin library may redirect a loading process of a target deep learning framework for the target training task, to hijack a startup process of the deep learning framework, and a target graphics processing unit request is generated during this process to request allocation of graphics processing unit resources for the target training task. Compared to the prior art, this embodiment of the present disclosure starts from a perspective of the deep learning framework, analyzes a loading logic of the deep learning framework when the training task is started, and achieves dynamic graphics processing unit sharing through hijacking the framework.

Resource allocation method and apparatus and artificial intelligence training system

Disclosed is a data set and node cache-based scheduling method, which includes: obtaining storage resource information of each host node; in response to receiving a training task, obtaining operation information of the training task, and according to the operation information and the storage resource information, screening host nodes that satisfy a space required by the training task; in response to no host node satisfying the space required by the training task, scoring each host node according to the storage resource information; according to scoring results, selecting, from among all of the host nodes, a host node to be executed that is used to execute the training task; and obtaining and deleting an obsolete data set cache in the host node to be executed, and executing the training task in the host node to be executed.

Growing community of inventors

Jiangsu, China

Pei Chen