Deep Learning Framework Error Files for Richer API Error Reporting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In deep learning frameworks, when errors occur while calling third-party library APIs, developers only receive an error code, lacking detailed error information, which increases development costs and reduces efficiency.
Innovation Solution
A method to generate error reporting content by acquiring error codes and information, creating an error file, and packaging it into the deep learning framework, allowing extraction and reporting of richer error details when errors occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the deep learning framework only displays error codes returned by third-party libraries, then the framework maintains simplicity in error handling, but the developer cannot obtain detailed error information, increasing development cost and reducing development efficiency
Solution Approach 1:
The patent pre-generates error files containing detailed error information for various error codes before runtime. When an error occurs, the framework simply looks up the error code in the pre-generated error file and displays the corresponding detailed information. This preliminary preparation of error information resolves the contradiction by providing comprehensive error details without adding complex real-time processing logic during error handling.
2Reliability
If the deep learning framework extracts and displays detailed error information from error files, then the developer receives comprehensive error details, but the framework requires additional error file processing capabilities
Solution Approach 1:
The patent introduces error files as an intermediary component that stores the mapping between error codes and detailed error information. Instead of implementing complex real-time analysis of error causes, the framework uses these intermediary error files to provide accurate error details. This intermediary approach resolves the contradiction by achieving reliable error reporting through a simple lookup mechanism rather than complex processing.
3Loss of time
If the deep learning framework packages error files into the framework, then error information is readily available during runtime, but the framework size increases
Solution Approach 1:
The patent extracts only the essential error information needed for common error scenarios into compact error files, rather than including comprehensive documentation or complex diagnostic tools. This selective extraction provides sufficient error resolution information quickly while minimizing the increase in framework size. The error files contain only the critical mapping between error codes and human-readable descriptions.
Data Source
AI summary
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.