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公开(公告)号：US20210382699A1
公开(公告)日：2021-12-09
申请号：US17445347
申请日：2021-08-18
Inventor： Shuo TIAN , Tao Luo , Zhefeng Ning , Xiang Lan , Dianhai Yu , Yanjun Ma
Abstract: A data processing method, an electronic device and a computer storage medium, related to the field of artificial intelligence such as deep learning and big data, are provided. The method includes: extracting, in submission information of a current test task, a first sub-directory in which a dependent package required for constructing a model is located; and obtaining the dependent package from a local storage module, in a case where the first sub-directory is as same as a second sub-directory in submission information for a historical test task. Thereby, efficiency of acquiring the dependent package can be increased and execution efficiency of a process for constructing models according to the dependent package is further increased.
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公开(公告)号：US11544140B2
公开(公告)日：2023-01-03
申请号：US17211680
申请日：2021-03-24
Inventor： Wei Zhou , Xiang Lan , Weihang Chen , Tao Luo
Abstract: The present application discloses a method and apparatus for generating error reporting content of a deep learning framework, an electronic device and a readable storage medium, which relates to the field of deep learning technologies. An implementation solution adopted by the present application to generate error reporting content of a deep learning framework is: acquiring an error code and error information corresponding to the error code; generating an error file according to the error code and the error information corresponding thereto, and packaging the error file into the deep learning framework; running the deep learning framework, and in response to the deep learning framework receiving an error code returned by a third-party library when an error occurs in calling of a third-party library application programming interface (API), extracting, from the error file, error information corresponding to the received error code; and generating error reporting content according to the error information. The present application can automatically generate error reporting content including richer information.
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公开(公告)号：US20220083417A1
公开(公告)日：2022-03-17
申请号：US17211680
申请日：2021-03-24
Inventor： Wei Zhou , Xiang Lan , Weihang Chen , Tao Luo
Abstract: The present application discloses a method and apparatus for generating error reporting content of a deep learning framework, an electronic device and a readable storage medium, which relates to the field of deep learning technologies. An implementation solution adopted by the present application to generate error reporting content of a deep learning framework is: acquiring an error code and error information corresponding to the error code; generating an error file according to the error code and the error information corresponding thereto, and packaging the error file into the deep learning framework; running the deep learning framework, and in response to the deep learning framework receiving an error code returned by a third-party library when an error occurs in calling of a third-party library application programming interface (API), extracting, from the error file, error information corresponding to the received error code; and generating error reporting content according to the error information. The present application can automatically generate error reporting content including richer information.
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公开(公告)号：US20210209114A1
公开(公告)日：2021-07-08
申请号：US17208707
申请日：2021-03-22
Inventor： Yamei Li , Xiang Lan , Tao Luo , Huihuang Zheng
IPC: G06F16/2455 , G06N3/08 , G06F16/22 , G06F16/2453
Abstract: A method for monitoring operator compatibility within a deep learning framework, a computing system, and a non-transitory computer-readable storage medium are provided. The present disclosure relates to the field of deep learning. The method includes: generating first description information associated with at least one original operator and second description information associated with at least one modified operator; determining differences between the first description information associated with the at least one original operator and the second description information associated with the at least one modified operator; determining whether the differences satisfy a preset rule; and prompting information about incompatibility in response to determining that at least one of the differences does not satisfy the preset rule. The first and second description information are associated with the operator compatibility.
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