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Operator deployment method, device and electronic equipment in a deep learning framework

A deep learning and operator technology, applied in machine learning, software deployment, computing, etc., can solve the problems of lack of flexibility, low efficiency, and high cost of use

Active Publication Date: 2022-05-27
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In this way, it will inevitably lead to technical problems such as extremely high cost for users, strong dependence on other three-party libraries (such as library pybind11), lack of flexibility, and low efficiency.

Method used

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  • Operator deployment method, device and electronic equipment in a deep learning framework
  • Operator deployment method, device and electronic equipment in a deep learning framework
  • Operator deployment method, device and electronic equipment in a deep learning framework

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Embodiment Construction

[0031] Exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

[0032] The following briefly describes the technical fields involved in the solution of the present application:

[0033] Data processing (Data Processing), including the collection, storage, retrieval, processing, change and transmission of data, etc., aims to extract and deduce from a large amount of data that may be disorganized and incomprehensible for some ...

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Abstract

The present application discloses a deployment method, device and electronic equipment of operators in a deep learning framework, and relates to the technical field of artificial intelligence such as deep learning. The solution is: obtain the source file of the operator; compile the source file of the operator to form the dynamic link library of the operator; generate the interface file transferred from the dynamic link library of the operator; The link library and the interface file generate an installable library file; installing the installable library file to the target programming language library can completely hide all the operator installation processes, greatly reducing the user's learning cost and improving the depth Efficiency, flexibility and reliability in the deployment process of operators in the learning framework.

Description

technical field [0001] The embodiments of the present application generally relate to the technical field of data processing, and more specifically relate to the technical field of artificial intelligence such as deep learning. Background technique [0002] At present, the mainstream deep learning framework contains a wealth of tensor calculation processing units, collectively referred to as operators. The rich operator library provides powerful basic tools for quickly building deep learning models, but the operator libraries in the deep learning framework are generally Completeness cannot be achieved, especially in the field of cutting-edge academic research, there are often situations where the current operator library cannot satisfy specific logical computing operations. Therefore, major deep learning frameworks provide users with a mechanism for customizing operators. [0003] The custom operator is mainly implemented by the C++ language (The C++ Programming Language / c ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F8/41
CPCG06F8/447G06F9/44521G06F8/61G06F8/36G06N20/00G06N3/105G06N3/04G06F8/31G06F8/54G06F8/41G06N3/10
Inventor 张留杰蓝翔郑辉煌刘红雨周威马艳军于佃海王海峰
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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