quic/aimet
Mar 2025 – Mar 2025
1 Month active
Languages Used
DockerfilePythonShellYAML
Technical Skills
Build EngineeringCI/CDCode ComplianceDependency ManagementGitGitHub Actions
quic-bhushans
PROFILE
Quic-bhushans
Worked on the quic/aimet repository to deliver architectural enhancements focused on scalable quantization and process automation. Developed GraphQuantization passes using Python to selectively disable quantizers within LayerNorm and RMSNorm sub-graphs, improving quantization efficiency and output accuracy. Introduced a GitHub Actions CI/CD workflow to synchronize internal commits with the public repository, accelerating validation cycles. Streamlined build engineering by removing the blosc dependency from Dockerfiles for ONNX and TensorFlow models, reducing build complexity and image size. Addressed code compliance by adding comprehensive copyright notices to test files, ensuring proper licensing. Emphasized automation, model optimization, and regulatory compliance throughout the development process.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
1,579
Activity Months1
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
March 2025
5 Commits • 4 Features
Mar 1, 2025March 2025 performance summary for quic/aimet focused on delivering architectural enhancements and process improvements with clear business value. Key GraphQuantization Passes were implemented to selectively disable quantizers inside specific sub-graphs (LayerNorm and RMSNorm), improving quantization efficiency and final-output accuracy. A CI/CD automation workflow was introduced to synchronize internal commits to the public repository for testing, accelerating validation of changes. Build processes were streamlined by removing the blosc dependency from Dockerfiles across ONNX and TensorFlow model images, reducing build complexity and image size. Licensing compliance was addressed by adding comprehensive copyright notices to tests. No major bug fixes were required this month; the emphasis was on scalable quantization improvements, automation, and compliance with regulatory requirements.
5 Commits • 4 Features
Mar 1, 2025March 2025 performance summary for quic/aimet focused on delivering architectural enhancements and process improvements with clear business value. Key GraphQuantization Passes were implemented to selectively disable quantizers inside specific sub-graphs (LayerNorm and RMSNorm), improving quantization efficiency and final-output accuracy. A CI/CD automation workflow was introduced to synchronize internal commits to the public repository for testing, accelerating validation of changes. Build processes were streamlined by removing the blosc dependency from Dockerfiles across ONNX and TensorFlow model images, reducing build complexity and image size. Licensing compliance was addressed by adding comprehensive copyright notices to tests. No major bug fixes were required this month; the emphasis was on scalable quantization improvements, automation, and compliance with regulatory requirements.
March 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture92.0%
Performance88.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfilePythonShellYAML
Technical Skills
Build EngineeringCI/CDCode ComplianceDependency ManagementGitGitHub ActionsGraph OptimizationGraph ProcessingLicensingModel OptimizationModel QuantizationONNX RuntimePythonQuantizationSoftware Design Patterns
Repositories Contributed To
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