PaddlePaddle/GraphNet
Nov 2025 – Nov 2025
1 Month active
Languages Used
Python
Technical Skills
PyTorchbackend developmentdeep learninggraph neural networksgraph processingmachine learning
Zhongyin Li
PROFILE
Zhongyin Li
During November 2025, this developer refactored core modules of the PaddlePaddle/GraphNet repository to align with PyTorch’s stable API, focusing on backend and graph components. By replacing internal C-API calls with stable torch functions such as set_grad_enabled, vector_norm, and gelu, they improved compatibility and future-proofed the codebase. The work centered on API modernization rather than bug fixes, reducing maintenance risk and enabling smoother integration with the PyTorch ecosystem. Utilizing Python and deep learning expertise, the developer demonstrated depth in backend development and graph neural networks, delivering a targeted feature that enhances long-term maintainability and performance of the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
127
Activity Months1
Your Network
59 peopleShared Repositories
25
Work History
November 2025
5 Commits • 1 Features
Nov 1, 2025November 2025: API stabilization for PaddlePaddle/GraphNet by aligning core modules with PyTorch’s stable API. Executed focused refactors in backend and graph components to replace internal C-API calls with stable torch.* equivalents, enhancing compatibility, future-proofing, and performance. No explicit bug fixes recorded this month; the emphasis was on API modernization to reduce maintenance risk and enable seamless PyTorch ecosystem integrations.
5 Commits • 1 Features
Nov 1, 2025November 2025: API stabilization for PaddlePaddle/GraphNet by aligning core modules with PyTorch’s stable API. Executed focused refactors in backend and graph components to replace internal C-API calls with stable torch.* equivalents, enhancing compatibility, future-proofing, and performance. No explicit bug fixes recorded this month; the emphasis was on API modernization to reduce maintenance risk and enable seamless PyTorch ecosystem integrations.
November 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PyTorchbackend developmentdeep learninggraph neural networksgraph processingmachine learning
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