pytorch/executorch
Oct 2024 – Feb 2026
4 Months active
Languages Used
PythonBashYAMLbashpython
Technical Skills
Data ProcessingData SerializationMachine LearningModel EvaluationPython ProgrammingUnit Testing
Tom Allsop
PROFILE
Tom Allsop
Tom Allsop contributed to the pytorch/executorch repository by developing a unified model evaluation framework and enhancing backend infrastructure to support robust machine learning workflows. He implemented the GenericModelEvaluator and MobileNetV2Evaluator in Python, enabling centralized metric collection and improved quantization assessment. Tom expanded CI/CD coverage for ARM backends, introducing automated tests and refining job naming for better traceability using GitHub Actions and YAML. He streamlined installation and environment setup processes with Python scripting, reducing onboarding friction. Additionally, Tom improved build stability by pinning dependencies and updating test scripts, demonstrating depth in backend development, dependency management, and continuous integration for reliable deployment.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
3
Lines of code
544
Activity Months4
Your Network
833 peopleSame Organization
@arm.com498
Work History
February 2026
2 Commits
Feb 1, 2026February 2026 monthly summary for pytorch/executorch focusing on reliability and build stability in the Arm backend. The work delivered under the Arm Backend Build and Test Stability initiative reduced build and test churn and strengthened the CI/test infrastructure by aligning dependencies with the TOSA serializer workflow.
2 Commits
Feb 1, 2026February 2026 monthly summary for pytorch/executorch focusing on reliability and build stability in the Arm backend. The work delivered under the Arm Backend Build and Test Stability initiative reduced build and test churn and strengthened the CI/test infrastructure by aligning dependencies with the TOSA serializer workflow.
February 2026
September 2025
2 Commits • 1 Features
Sep 1, 2025Concise monthly summary for 2025-09 focusing on ExecuTorch work in pytorch/executorch: installation and setup workflow improvements, streamlined onboarding, and CI/ENV updates. No major bugs fixed this month; primary focus was feature delivery and process improvements with measurable business value.
September 2025
2 Commits • 1 Features
Sep 1, 2025Concise monthly summary for 2025-09 focusing on ExecuTorch work in pytorch/executorch: installation and setup workflow improvements, streamlined onboarding, and CI/ENV updates. No major bugs fixed this month; primary focus was feature delivery and process improvements with measurable business value.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for pytorch/executorch: Focused on expanding ARM backend testing coverage on macOS M1 and improving CI/CD clarity. Delivered automated tests for macos-m1-stable runner and tightened job naming for easier diagnosis in CI pipelines.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for pytorch/executorch: Focused on expanding ARM backend testing coverage on macOS M1 and improving CI/CD clarity. Delivered automated tests for macos-m1-stable runner and tightened job naming for easier diagnosis in CI pipelines.
July 2025
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for the pytorch/executorch repository. Delivered a unified evaluation framework and backend enhancements to drive measurable model performance and hardware-aligned evaluation. Implemented GenericModelEvaluator to enable centralized collection of model metrics, expanded evaluation coverage with MobileNetV2Evaluator, and extended the aot_arm_compiler to support additional evaluators and improved calibration dataset handling. These changes solidify quantization and calibration workflows and lay groundwork for consistent performance comparisons across models and accelerators, accelerating deployment readiness.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for the pytorch/executorch repository. Delivered a unified evaluation framework and backend enhancements to drive measurable model performance and hardware-aligned evaluation. Implemented GenericModelEvaluator to enable centralized collection of model metrics, expanded evaluation coverage with MobileNetV2Evaluator, and extended the aot_arm_compiler to support additional evaluators and improved calibration dataset handling. These changes solidify quantization and calibration workflows and lay groundwork for consistent performance comparisons across models and accelerators, accelerating deployment readiness.
Activity
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Quality Metrics
Correctness91.4%
Maintainability91.4%
Architecture91.4%
Performance88.6%
AI Usage45.8%
Skills & Technologies
Programming Languages
BashPythonYAMLbashpython
Technical Skills
CI/CDData ProcessingData SerializationDevOpsEnvironment SetupGitHub ActionsMachine LearningModel EvaluationPythonPython ProgrammingPython package managementPython scriptingScriptingTestingUnit Testing
Repositories Contributed To
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