PaddlePaddle/GraphNet
Jul 2025 – Jul 2025
1 Month active
Languages Used
C++Python
Technical Skills
Decorator PatternDeep LearningGraph ExtractionModel ExportModel ImplementationPyTorch
JewelRoam
PROFILE
Jewelroam
Worked on the PaddlePaddle/GraphNet repository to deliver core computation graph extraction and model integration features for PyTorch models. Developed a Computation Graph Extraction Toolkit using the decorator pattern in Python, enabling the capture and export of model computations as reusable code and metadata. Integrated VGG16 through a GraphModule implementation, providing detailed tensor metadata such as names, shapes, and data types to support accurate graph replay and deployment. Refactored utilities for loading and replaying tensor data, improving reliability and maintainability. These contributions enhanced reproducibility, streamlined prototyping, and strengthened the research-to-production pipeline for deep learning model analysis and deployment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
3,369
Activity Months1
Your Network
25 peopleShared Repositories
25
Work History
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for PaddlePaddle/GraphNet. Delivered core computation graph extraction and model integration capabilities, enabling reproducible graph-level analysis of PyTorch models and smoother deployment pipelines. Implemented a Computation Graph Extraction Toolkit and VGG16 GraphModule integration, with enhanced tensor metadata, and refactored utilities to load and replay tensor data for improved model extraction reliability. These efforts unlock faster debugging, prototyping, and research-to-production workflows, delivering measurable business value in model interpretability and tooling readiness.
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for PaddlePaddle/GraphNet. Delivered core computation graph extraction and model integration capabilities, enabling reproducible graph-level analysis of PyTorch models and smoother deployment pipelines. Implemented a Computation Graph Extraction Toolkit and VGG16 GraphModule integration, with enhanced tensor metadata, and refactored utilities to load and replay tensor data for improved model extraction reliability. These efforts unlock faster debugging, prototyping, and research-to-production workflows, delivering measurable business value in model interpretability and tooling readiness.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Decorator PatternDeep LearningGraph ExtractionModel ExportModel ImplementationPyTorchRefactoring
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