keras-team/keras
Jan 2026 – Feb 2026
2 Months active
Languages Used
Python
Technical Skills
KerasOpenVINOTensor operationsTensorFlowbackend developmenterror handling
Shipra
PROFILE
Shipra
During two months contributing to keras-team/keras, Shipra expanded the OpenVINO backend by implementing a comprehensive suite of tensor operations, including arithmetic, reshaping, and bitwise functions, using Python and NumPy. She refactored backend code to eliminate intermediate outputs, improving both performance and memory efficiency. Her work introduced robust error handling for dynamic tensor ranks and negative indices, enhancing numerical stability and API consistency. Shipra also standardized tensor stacking and splitting utilities across backends, enabling more flexible model pipelines. These contributions deepened backend capabilities, reduced integration friction, and supported broader deployment of machine learning models on OpenVINO hardware.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
18Total
Bugs
0
Commits
18
Features
3
Lines of code
1,300
Activity Months2
Your Network
103 peopleWork History
February 2026
6 Commits • 2 Features
Feb 1, 2026February 2026: Expanded OpenVINO backend capabilities and strengthened cross-backend tensor utilities, enabling more flexible and reliable deployment pipelines.
6 Commits • 2 Features
Feb 1, 2026February 2026: Expanded OpenVINO backend capabilities and strengthened cross-backend tensor utilities, enabling more flexible and reliable deployment pipelines.
February 2026
January 2026
12 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for keras OpenVINO backend. Expanded the OpenVINO backend with a comprehensive tensor-ops suite and refactors to boost performance and stability, enabling broader deployment of keras models on OpenVINO hardware. Key deliverables include cbrt, hypot/trace, size/swapaxes, kron, argpartition, logaddexp2, ldexp, select, round, divide_no_nan, vstack, ptp, tile, and nansum. Also added robust error handling for dynamic rank and proper handling of negative indices, and refactored to remove intermediate Output objects for better performance. This work increases numerical stability, API consistency, and inference throughput, driving business value for production ML workloads.
January 2026
12 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for keras OpenVINO backend. Expanded the OpenVINO backend with a comprehensive tensor-ops suite and refactors to boost performance and stability, enabling broader deployment of keras models on OpenVINO hardware. Key deliverables include cbrt, hypot/trace, size/swapaxes, kron, argpartition, logaddexp2, ldexp, select, round, divide_no_nan, vstack, ptp, tile, and nansum. Also added robust error handling for dynamic rank and proper handling of negative indices, and refactored to remove intermediate Output objects for better performance. This work increases numerical stability, API consistency, and inference throughput, driving business value for production ML workloads.
Activity
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Quality Metrics
Correctness92.2%
Maintainability80.0%
Architecture82.2%
Performance80.0%
AI Usage32.2%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentJAXKerasMachine LearningNumPyOpenVINOPyTorchPythonTensor operationsTensorFlowbackend developmentdeep learningerror handlingnumerical computingnumerical stability
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