keras-team/keras
Jan 2026 – Feb 2026
2 Months active
Languages Used
Python
Technical Skills
KerasOpenVINOTensor operationsTensorFlowbackend developmenterror handling
Shipra
PROFILE
Shipra
Worked on expanding the keras-team/keras repository’s OpenVINO backend, delivering a comprehensive suite of tensor operations and utilities to improve model deployment and inference performance. Focused on implementing advanced tensor manipulation functions such as cross product, tensordot, and bitwise operations, while also standardizing stacking and splitting utilities across multiple backends. Leveraged Python, NumPy, and TensorFlow to enhance numerical stability, error handling, and API consistency. Refactored core backend logic to optimize memory usage and performance, addressing dynamic rank and negative index handling. This work enabled more flexible, reliable production pipelines for machine learning workloads across diverse hardware environments.
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
110 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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