quic/aimet
Nov 2024 – Dec 2024
2 Months active
Languages Used
PythonrstRSTnumpyonnxtorch
Technical Skills
Build ProcessClean CodeCode RefactoringDeep Learning InferenceDocumentationModel Optimization
Geunho Lee
PROFILE
Geunho Lee
Worked on the quic/aimet repository to enhance quantization workflows and improve packaging, documentation, and code maintainability. Delivered end-to-end QuantSim examples using PyTorch and ONNX, updating them to MobileNetV2 with clear data loading and transfer learning steps, enabling streamlined quantized model deployment. Addressed packaging issues by ensuring JavaScript and XML assets were included in distributions, and resolved documentation build warnings by correcting cross-references for reliable navigation. Refactored code to remove dead imports and clarified advanced ONNX features through detailed guides and API references. Emphasized clean code, technical writing, and robust testing to support developer onboarding and future maintenance.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
3
Commits
7
Features
3
Lines of code
1,042
Activity Months2
Your Network
232 peopleShared Repositories
47
Khobare, AbhijitMember
Mukherjee, AnanyaMember
Kumar, AshvinMember
Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
Work History
December 2024
3 Commits • 1 Features
Dec 1, 2024Month: 2024-12 — Focused on delivering end-to-end Quantization workflow improvements in quic/aimet and stabilizing documentation build. Key features delivered include MobileNetV2-based QuantSim code examples for PyTorch and ONNX with clear data loading, transfer learning/fine-tuning steps, and a complete quantization workflow (encodings, evaluation, and export). Major bugs fixed include documentation build cross-reference warnings resolved by correcting internal links. Overall impact: accelerated path to quantized model deployment, improved developer onboarding, and reduced support overhead. Technologies demonstrated: PyTorch, ONNX, QuantSim, end-to-end quantization workflow, transfer learning, data handling, and Sphinx/docs tooling.
3 Commits • 1 Features
Dec 1, 2024Month: 2024-12 — Focused on delivering end-to-end Quantization workflow improvements in quic/aimet and stabilizing documentation build. Key features delivered include MobileNetV2-based QuantSim code examples for PyTorch and ONNX with clear data loading, transfer learning/fine-tuning steps, and a complete quantization workflow (encodings, evaluation, and export). Major bugs fixed include documentation build cross-reference warnings resolved by correcting internal links. Overall impact: accelerated path to quantized model deployment, improved developer onboarding, and reduced support overhead. Technologies demonstrated: PyTorch, ONNX, QuantSim, end-to-end quantization workflow, transfer learning, data handling, and Sphinx/docs tooling.
December 2024
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for quic/aimet. Focused on stabilizing packaging, refining quantization workflows, documenting feature improvements, and removing dead code to improve maintainability and developer velocity. The changes delivered business value by reducing release risks, clarifying advanced features for inference optimization, and simplifying future development work.
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for quic/aimet. Focused on stabilizing packaging, refining quantization workflows, documenting feature improvements, and removing dead code to improve maintainability and developer velocity. The changes delivered business value by reducing release risks, clarifying advanced features for inference optimization, and simplifying future development work.
Activity
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Quality Metrics
Correctness94.4%
Maintainability91.4%
Architecture91.4%
Performance88.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRSTnumpyonnxrsttorch
Technical Skills
Build ProcessClean CodeCode RefactoringData LoadingDeep Learning InferenceDocumentationMachine LearningModel ConversionModel EvaluationModel OptimizationONNXONNX QuantizationPackagingPyTorchPython
Repositories Contributed To
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