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
Worked on stabilizing the API for the PaddlePaddle/GraphNet repository by aligning its core backend and graph modules with PyTorch’s stable API. Focused on refactoring internal C-API calls, replacing them with stable torch.* equivalents such as set_grad_enabled, vector_norm, norm, pad, and gelu to enhance compatibility and future-proof the codebase. This modernization effort aimed to reduce maintenance risk and enable smoother integration within the PyTorch ecosystem. The work was implemented in Python and leveraged expertise in backend development, deep learning, and graph neural networks, with the primary emphasis on long-term maintainability rather than immediate bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
127
Activity Months1
Your Network
62 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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