quic/aimet
Oct 2024 – Feb 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
AIMETModel OptimizationONNXPyTorchDebuggingModel Evaluation
Raj Gite
PROFILE
Raj Gite
Worked on the quic/aimet repository to enhance ONNX quantization workflows and improve reliability across model evaluation and training. Introduced a model simplification step using the onnxsim library within Python-based Jupyter notebooks, optimizing models for compatibility and performance prior to quantization. Addressed workflow stability by implementing robust fallback logic and updating documentation for clearer guidance. Fixed critical bugs in ONNX evaluation loops and quantizer group management, ensuring consistent results and reducing maintenance overhead. Demonstrated proficiency in Python development, ONNX Runtime, and backend debugging, focusing on cross-provider consistency and dependable benchmarking for quantization and evaluation pipelines in collaborative environments.
Overall Statistics
Feature vs Bugs
25%Features
Repository Contributions
4Total
Bugs
3
Commits
4
Features
1
Lines of code
165
Activity Months3
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
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary for quic/aimet focusing on key accomplishments in the repo. This month centered on strengthening the integrity of quantizer group management within ONNX Training Extensions and ensuring consistency across training runs.
1 Commits
Feb 1, 2025February 2025 monthly summary for quic/aimet focusing on key accomplishments in the repo. This month centered on strengthening the integrity of quantizer group management within ONNX Training Extensions and ensuring consistency across training runs.
February 2025
December 2024
2 Commits
Dec 1, 2024December 2024: Focused bug fixes in quic/aimet to stabilize evaluation and quantization workflows, with clear cross-provider consistency and reliability improvements.
December 2024
2 Commits
Dec 1, 2024December 2024: Focused bug fixes in quic/aimet to stabilize evaluation and quantization workflows, with clear cross-provider consistency and reliability improvements.
October 2024
1 Commits • 1 Features
Oct 1, 2024Summary for 2024-10 (quic/aimet): This month focused on delivering a targeted workflow enhancement in the ONNX quantization space, with a clear business value in reliability and performance. Key features delivered: introduced a model simplification step using the onnxsim library into several ONNX quantization notebooks to optimize compatibility and potential performance gains before AIMET processing. The change included new code cells to perform the simplification and a robust fallback path if simplification fails. Documentation updates were added to explain the recommended step and how to run it within the notebooks. Major bugs fixed: none reported for this period; the emphasis was on feature delivery and workflow stabilization rather than bug remediation. Overall impact and accomplishments: standardizes a pre-quantization optimization in the quic/aimet workflow, reducing downstream failures, improving model readiness for quantization, and enabling more consistent results across models. Technologies/skills demonstrated: ONNX, onnxsim, Python notebook automation, code cell integration, markdown/documentation updates, and end-to-end workflow refinement for quantization pipelines in a collaborative repo.
1 Commits • 1 Features
Oct 1, 2024Summary for 2024-10 (quic/aimet): This month focused on delivering a targeted workflow enhancement in the ONNX quantization space, with a clear business value in reliability and performance. Key features delivered: introduced a model simplification step using the onnxsim library into several ONNX quantization notebooks to optimize compatibility and potential performance gains before AIMET processing. The change included new code cells to perform the simplification and a robust fallback path if simplification fails. Documentation updates were added to explain the recommended step and how to run it within the notebooks. Major bugs fixed: none reported for this period; the emphasis was on feature delivery and workflow stabilization rather than bug remediation. Overall impact and accomplishments: standardizes a pre-quantization optimization in the quic/aimet workflow, reducing downstream failures, improving model readiness for quantization, and enabling more consistent results across models. Technologies/skills demonstrated: ONNX, onnxsim, Python notebook automation, code cell integration, markdown/documentation updates, and end-to-end workflow refinement for quantization pipelines in a collaborative repo.
October 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability85.0%
Architecture75.0%
Performance65.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AIMETBackend DevelopmentDebuggingModel EvaluationModel OptimizationModel QuantizationONNXONNX RuntimePyTorchPython Development
Repositories Contributed To
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