mozilla/onnxruntime
May 2025 – May 2025
1 Month active
Languages Used
C++CMake
Technical Skills
C++C++ developmentCMakeCUDAData transfer optimizationDeep Learning
Ishwar Raut
PROFILE
Ishwar Raut
Worked on enhancing the TensorRT RTX execution provider in the mozilla/onnxruntime repository, focusing on modularizing the RTX path by removing CUDA execution provider DLL dependencies. Developed RTX-specific allocators and optimized data transfer paths to improve GPU throughput and reliability, while extending support for ONNX operations to execute directly on TensorRT RTX instead of the CPU. Addressed domain check issues to ensure correct RTX operation and updated DLL naming and versioning for clarity and compatibility. Utilized C++, CMake, and CUDA throughout the development process, resulting in simplified deployment, increased inference performance, and reduced CPU load for deep learning workloads.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
514
Activity Months1
Your Network
5 peopleWork History
May 2025
3 Commits • 1 Features
May 1, 2025May 2025: TensorRT RTX Execution Provider enhancements in mozilla/onnxruntime. Consolidated RTX-focused improvements by removing CUDA EP DLL dependencies to enable a more modular RTX path, added RTX-specific allocators and data transfer paths to boost GPU performance and reliability, and extended ONNX ops to execute on TensorRT RTX instead of CPU. Aligned DLL naming/versioning to RTX for clarity and compatibility. Also addressed a domain check issue to ensure correct RTX operation and updated DLL naming to reflect RTX. Business value includes simplified deployment, higher inference throughput, reduced CPU load, and smoother upgrade paths. Technologies demonstrated include TensorRT RTX EP, ONNX Runtime, C++, DLL management, and performance testing.
3 Commits • 1 Features
May 1, 2025May 2025: TensorRT RTX Execution Provider enhancements in mozilla/onnxruntime. Consolidated RTX-focused improvements by removing CUDA EP DLL dependencies to enable a more modular RTX path, added RTX-specific allocators and data transfer paths to boost GPU performance and reliability, and extended ONNX ops to execute on TensorRT RTX instead of CPU. Aligned DLL naming/versioning to RTX for clarity and compatibility. Also addressed a domain check issue to ensure correct RTX operation and updated DLL naming to reflect RTX. Business value includes simplified deployment, higher inference throughput, reduced CPU load, and smoother upgrade paths. Technologies demonstrated include TensorRT RTX EP, ONNX Runtime, C++, DLL management, and performance testing.
May 2025
Activity
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Quality Metrics
Correctness93.4%
Maintainability80.0%
Architecture80.0%
Performance86.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
C++CMake
Technical Skills
C++C++ developmentCMakeCUDAData transfer optimizationDeep LearningGPU programmingLibrary ManagementMachine LearningSoftware DevelopmentTensorRT
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