quic/aimet
Jul 2025 – Mar 2026
7 Months active
Languages Used
RSTPythonShellYAMLJSONreStructuredText
Technical Skills
DocumentationRelease ManagementAIMETCI/CDConfiguration ManagementFull Stack Development
Shivakumar, Shobitha
PROFILE
Shivakumar, Shobitha
Shobitha contributed to the quic/aimet repository by developing and enhancing automated quantization workflows, model evaluation pipelines, and CI/CD infrastructure for machine learning deployment. She implemented regression testing frameworks and quantization evaluation metrics using Python and YAML, enabling robust validation of ONNX and PyTorch models. Her work included optimizing batch normalization folding, introducing mixed-precision support, and stabilizing nightly test environments with Docker and GitHub Actions. By refining configuration management and reporting, Shobitha improved release readiness and traceability, reduced CI flakiness, and ensured accurate model performance tracking. Her engineering demonstrated depth in data validation, workflow automation, and continuous integration practices.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
34Total
Bugs
4
Commits
34
Features
13
Lines of code
20,402
Activity Months7
Your Network
212 peopleSame Organization
@qti.qualcomm.com167
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Work History
March 2026
3 Commits
Mar 1, 2026This month focused on stabilizing the quantization QA path and fortifying the CI/CD pipeline for quic/aimet, delivering reliable PyTorch quantization encodings and a more stable release process that reduces flaky builds and accelerates validation of model optimizations.
3 Commits
Mar 1, 2026This month focused on stabilizing the quantization QA path and fortifying the CI/CD pipeline for quic/aimet, delivering reliable PyTorch quantization encodings and a more stable release process that reduces flaky builds and accelerates validation of model optimizations.
March 2026
February 2026
11 Commits • 2 Features
Feb 1, 2026February 2026 (Month: 2026-02) focused on strengthening test coverage, stabilizing CI, and delivering performance-oriented optimizations in quic/aimet. Key deliverables included enhancements to the AIMET Regression Testing Framework with weekly regression and accuracy validation, QNN compatibility, improved reporting, and a refactor for maintainability; expansions to YOLO BN folding optimizations with reduced sample counts, BN fold fixes for shared modules, and added validation tests; and CI stability improvements that reduced segmentation faults, improved CI pip installs via UV_LINK_MODE symlink, and streamlined workflows. These efforts lowered risk for releases, accelerated feedback loops, and improved reliability for QA and downstream customers. Technologies used included Python-based test automation, regression suites, PyTorch model testing, QNN compatibility, CI/CD pipelines, and debugging memory/performance issues. Business value included faster release readiness, higher confidence in model readiness, and more robust automated testing.
February 2026
11 Commits • 2 Features
Feb 1, 2026February 2026 (Month: 2026-02) focused on strengthening test coverage, stabilizing CI, and delivering performance-oriented optimizations in quic/aimet. Key deliverables included enhancements to the AIMET Regression Testing Framework with weekly regression and accuracy validation, QNN compatibility, improved reporting, and a refactor for maintainability; expansions to YOLO BN folding optimizations with reduced sample counts, BN fold fixes for shared modules, and added validation tests; and CI stability improvements that reduced segmentation faults, improved CI pip installs via UV_LINK_MODE symlink, and streamlined workflows. These efforts lowered risk for releases, accelerated feedback loops, and improved reliability for QA and downstream customers. Technologies used included Python-based test automation, regression suites, PyTorch model testing, QNN compatibility, CI/CD pipelines, and debugging memory/performance issues. Business value included faster release readiness, higher confidence in model readiness, and more robust automated testing.
January 2026
5 Commits • 3 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on delivering business value through robust model evaluation, QA, and release-readiness. Highlight key features delivered, stability improvements, and cross-cutting technical achievements for quic/aimet.
5 Commits • 3 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on delivering business value through robust model evaluation, QA, and release-readiness. Highlight key features delivered, stability improvements, and cross-cutting technical achievements for quic/aimet.
January 2026
December 2025
8 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary for quic/aimet: Delivered end-to-end quantization enhancements and test stability improvements that increase model efficiency, reliability, and release readiness. Key capabilities shipped include Torch Quantsim integration with Torch Nightly workflow, min-max quantization, adaptive rounding, mixed-precision support, and reintroduced padding for data movement ops. ONNX Nightly workflow was stabilized with a modular Python environment, deduped results, timestamped test reports, restored models, and streamlined imports to reduce false positives. Fixed accuracy display formatting to remove double percentage multipliers, ensuring correct metric interpretation. Release notes updated for version 2.21.0 to reflect ONNX and PyTorch improvements. Overall impact: smoother quantization deployment, fewer CI failures, and clearer release communication. Technologies demonstrated: PyTorch quantization, Quantsim, mixed precision, adaround, ONNX nightly tests, virtual environments, and CI workflow optimization.
December 2025
8 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary for quic/aimet: Delivered end-to-end quantization enhancements and test stability improvements that increase model efficiency, reliability, and release readiness. Key capabilities shipped include Torch Quantsim integration with Torch Nightly workflow, min-max quantization, adaptive rounding, mixed-precision support, and reintroduced padding for data movement ops. ONNX Nightly workflow was stabilized with a modular Python environment, deduped results, timestamped test reports, restored models, and streamlined imports to reduce false positives. Fixed accuracy display formatting to remove double percentage multipliers, ensuring correct metric interpretation. Release notes updated for version 2.21.0 to reflect ONNX and PyTorch improvements. Overall impact: smoother quantization deployment, fewer CI failures, and clearer release communication. Technologies demonstrated: PyTorch quantization, Quantsim, mixed precision, adaround, ONNX nightly tests, virtual environments, and CI workflow optimization.
November 2025
3 Commits • 3 Features
Nov 1, 2025Month: 2025-11 Key features delivered: - ONNX Nightly Evaluation Improvements: improved accuracy comparisons (fp32 vs AIMET), corrected QDQ ONNX eval path, updated report format to W8A8, expanded sample coverage (imagenette), and enhanced baseline report formatting. - Automatic Mixed-Precision (AMP) Optimization for ONNX (MobileNetV3): introduced AMP for ONNX models, added config support and dedicated AMP runner; enabled MobileNetV3 Large for AMP. - AIMET Dual Export Feature: added capability to produce separate outputs for QDQ validation and AIMET deployment bundles for Qualcomm AI Hub. Major bugs fixed: - Fix export: separated QDQ and AIMET exports to prevent mis-exports and ensure correct bundle generation for deployment pipelines. - Stability improvements in ONNX nightly evaluation workflow; addressed review comments and updated documentation artifacts. Overall impact and accomplishments: - Strengthened evaluation rigor and transparency with enhanced accuracy reporting and standardized formats. - Accelerated deployment readiness by enabling AMP for large ONNX models and providing robust export paths for validation and deployment. - Improved cross-team collaboration and CI readiness through clearer reporting, sample coverage, and export reliability. Technologies/skills demonstrated: - ONNX evaluation workflows, AMP/mixed-precision optimization, dual export pipelines, Python/CI scripting, YAML configuration, report formatting, and version-controlled workflow improvements.
3 Commits • 3 Features
Nov 1, 2025Month: 2025-11 Key features delivered: - ONNX Nightly Evaluation Improvements: improved accuracy comparisons (fp32 vs AIMET), corrected QDQ ONNX eval path, updated report format to W8A8, expanded sample coverage (imagenette), and enhanced baseline report formatting. - Automatic Mixed-Precision (AMP) Optimization for ONNX (MobileNetV3): introduced AMP for ONNX models, added config support and dedicated AMP runner; enabled MobileNetV3 Large for AMP. - AIMET Dual Export Feature: added capability to produce separate outputs for QDQ validation and AIMET deployment bundles for Qualcomm AI Hub. Major bugs fixed: - Fix export: separated QDQ and AIMET exports to prevent mis-exports and ensure correct bundle generation for deployment pipelines. - Stability improvements in ONNX nightly evaluation workflow; addressed review comments and updated documentation artifacts. Overall impact and accomplishments: - Strengthened evaluation rigor and transparency with enhanced accuracy reporting and standardized formats. - Accelerated deployment readiness by enabling AMP for large ONNX models and providing robust export paths for validation and deployment. - Improved cross-team collaboration and CI readiness through clearer reporting, sample coverage, and export reliability. Technologies/skills demonstrated: - ONNX evaluation workflows, AMP/mixed-precision optimization, dual export pipelines, Python/CI scripting, YAML configuration, report formatting, and version-controlled workflow improvements.
November 2025
October 2025
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on delivering automated ONNX regression testing for AIMET with enhanced CI and artifact baselines. Key features delivered include the first release of the AIMET ONNX Regression Framework with AI Hub integration, enabling on-device evaluation and centralized reporting, plus a refactor of the ONNX-Nightly configuration with hierarchical defaults/profiles/model/test-specific settings and an updated CI workflow for artifact-based baselines. Major bugs fixed: none reported this month. Overall impact: improved regression reliability, faster feedback on quantization quality across ONNX models, and better traceability via CI artifacts. Technologies/skills demonstrated: ONNX, AIMET quantization, on-device testing, Qualcomm AI Hub integration, hierarchical config design, artifact-based CI, and documentation updates.
October 2025
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on delivering automated ONNX regression testing for AIMET with enhanced CI and artifact baselines. Key features delivered include the first release of the AIMET ONNX Regression Framework with AI Hub integration, enabling on-device evaluation and centralized reporting, plus a refactor of the ONNX-Nightly configuration with hierarchical defaults/profiles/model/test-specific settings and an updated CI workflow for artifact-based baselines. Major bugs fixed: none reported this month. Overall impact: improved regression reliability, faster feedback on quantization quality across ONNX models, and better traceability via CI artifacts. Technologies/skills demonstrated: ONNX, AIMET quantization, on-device testing, Qualcomm AI Hub integration, hierarchical config design, artifact-based CI, and documentation updates.
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) — Focused on release engineering and documentation for quic/aimet. Delivered Release 2.11.0 documentation and release notes, consolidating new features, fixes, and documentation updates. Updated versioning artifacts (versions.rst and the version file) and added a Known Issues section detailing an accuracy drop in AIMET Keras for certain models. The work enhances release readiness, customer transparency, and traceability of changes.
2 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) — Focused on release engineering and documentation for quic/aimet. Delivered Release 2.11.0 documentation and release notes, consolidating new features, fixes, and documentation updates. Updated versioning artifacts (versions.rst and the version file) and added a Known Issues section detailing an accuracy drop in AIMET Keras for certain models. The work enhances release readiness, customer transparency, and traceability of changes.
July 2025
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Quality Metrics
Correctness88.2%
Maintainability85.2%
Architecture86.2%
Performance85.2%
AI Usage38.2%
Skills & Technologies
Programming Languages
JSONPythonRSTShellYAMLreStructuredText
Technical Skills
AI DeploymentAIMETCI/CDConfiguration ManagementContainerizationContinuous IntegrationData AnalysisData ProcessingDeep LearningDependency ManagementDevOpsDockerDocumentationFull Stack DevelopmentGitHub Actions
Repositories Contributed To
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