tracel-ai/burn
Apr 2026 – May 2026
2 Months active
Languages Used
Rust
Technical Skills
Autodiff ImplementationDocumentationGPU ProgrammingTensor OperationsUnit TestingBackend Development
polina
PROFILE
Polina
Developed advanced tensor manipulation features for the tracel-ai/burn repository, focusing on native scatter_nd and gather_nd operations with full autodiff support for assign, add, min, max, and mul. Leveraged Rust for backend development, integrating CubeCL and preparing bindings for Candle and tch to broaden hardware compatibility. Enhanced gradient tracking and model training reliability by addressing edge cases such as empty and duplicate indices, supported by comprehensive unit testing and expanded documentation. Prioritized code maintainability through rigorous test coverage, CI hygiene, and documentation updates, ensuring robust and portable tensor operations for dynamic computational workflows in machine learning applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
3,051
Activity Months2
Your Network
91 peopleWork History
May 2026
1 Commits • 1 Features
May 1, 2026Month: 2026-05. Focused on enhancing gradient support for dynamic tensor computations in tracel-ai/burn. Primary deliverable: Autodiff support for scatter_nd operations (min, max, mul), enabling correct gradient tracking for these indices and shapes, improving model training reliability for use cases involving scatter_nd. Also updated documentation and added tests to validate behavior with duplicate indices, reducing risk of silent correctness errors and easing integration for downstream teams.
1 Commits • 1 Features
May 1, 2026Month: 2026-05. Focused on enhancing gradient support for dynamic tensor computations in tracel-ai/burn. Primary deliverable: Autodiff support for scatter_nd operations (min, max, mul), enabling correct gradient tracking for these indices and shapes, improving model training reliability for use cases involving scatter_nd. Also updated documentation and added tests to validate behavior with duplicate indices, reducing risk of silent correctness errors and easing integration for downstream teams.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tracel-ai/burn: Key feature delivered: - Native scatter_nd and gather_nd ops with autodiff support for assign and add. Implemented API, wired components, added ndarray tests, and updated docs. Backend support included a CubeCL implementation; framework bindings for Candle and tch prepared. Major bugs fixed: - Resolved test failures and edge-case handling (e.g., empty indices) to improve test stability; cleaned up tests and refined documentation to prevent regressions. Overall impact and accomplishments: - Expanded core tensor manipulation capabilities with differentiable scatter/gather indexing, enabling more complex model architectures and workflows. - Potential performance and portability gains from native ops and CubeCL backend, reducing Python-side overhead and enabling broader hardware support. - Strengthened code quality and maintainability through comprehensive tests, CI hygiene, and documentation updates. Technologies/skills demonstrated: - Native backend development, autodiff integration, and API design for tensor indexing ops. - Cross-backend support exploration (CubeCL, Candle, tch) and framework bindings preparation. - Test coverage expansion, edge-case handling, rustfmt-driven code quality, and thorough documentation. Top 3-5 achievements: - Implemented native scatter_nd/gather_nd with autodiff for assign/add (commit fdf345b319fdb9b64324eef9adecff7a06884e29). - Wired API, added ndarray tests, and introduced CubeCL backend support; prepared bindings for Candle and tch. - Expanded autodiff coverage and refined tests and docs to ensure reliability and clarity; updated project book. - Improved test stability by addressing edge cases (e.g., empty indices) and cleaning up test suite.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tracel-ai/burn: Key feature delivered: - Native scatter_nd and gather_nd ops with autodiff support for assign and add. Implemented API, wired components, added ndarray tests, and updated docs. Backend support included a CubeCL implementation; framework bindings for Candle and tch prepared. Major bugs fixed: - Resolved test failures and edge-case handling (e.g., empty indices) to improve test stability; cleaned up tests and refined documentation to prevent regressions. Overall impact and accomplishments: - Expanded core tensor manipulation capabilities with differentiable scatter/gather indexing, enabling more complex model architectures and workflows. - Potential performance and portability gains from native ops and CubeCL backend, reducing Python-side overhead and enabling broader hardware support. - Strengthened code quality and maintainability through comprehensive tests, CI hygiene, and documentation updates. Technologies/skills demonstrated: - Native backend development, autodiff integration, and API design for tensor indexing ops. - Cross-backend support exploration (CubeCL, Candle, tch) and framework bindings preparation. - Test coverage expansion, edge-case handling, rustfmt-driven code quality, and thorough documentation. Top 3-5 achievements: - Implemented native scatter_nd/gather_nd with autodiff for assign/add (commit fdf345b319fdb9b64324eef9adecff7a06884e29). - Wired API, added ndarray tests, and introduced CubeCL backend support; prepared bindings for Candle and tch. - Expanded autodiff coverage and refined tests and docs to ensure reliability and clarity; updated project book. - Improved test stability by addressing edge cases (e.g., empty indices) and cleaning up test suite.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Rust
Technical Skills
Autodiff ImplementationBackend DevelopmentDocumentationGPU ProgrammingTensor OperationsTestingUnit Testing
Repositories Contributed To
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