dusty-nv/jetson-containers
Aug 2025 – Aug 2025
1 Month active
Languages Used
C++PythonShell
Technical Skills
Build SystemsCMakeCUDAContainerizationDeep LearningJetPack
David Ung
PROFILE
David Ung
During August 2025, Code Layout enabled CUDA 13.0 support and JetPack compatibility for the dusty-nv/jetson-containers repository, focusing on vLLM builds for SM110 hardware. They aligned core dependencies such as CUDA, cuDNN, NCCL, and Triton with JetPack 7.x requirements, implementing targeted patches to ensure reliable builds across evolving hardware. Using C++, Python, and shell scripting, Code Layout consolidated configuration updates and introduced conditional logic for CUDA architectures. This foundational work addressed compatibility challenges and positioned the stack for future deep learning optimizations, demonstrating a strong grasp of build systems, containerization, and system configuration within a complex, hardware-driven environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
412
Activity Months1
Your Network
30 people
Work History
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025: Focused on enabling CUDA 13.0 support and JetPack compatibility for the dusty-nv/jetson-containers stack, with emphasis on enabling vLLM builds for SM110. Aligned core dependencies (CUDA/cuDNN/NCCL/Triton) with JetPack 7.x requirements and implemented targeted patches to ensure compatibility and build reliability. Delivered foundational changes that position the project for broader hardware support and future performance optimizations.
3 Commits • 1 Features
Aug 1, 2025August 2025: Focused on enabling CUDA 13.0 support and JetPack compatibility for the dusty-nv/jetson-containers stack, with emphasis on enabling vLLM builds for SM110. Aligned core dependencies (CUDA/cuDNN/NCCL/Triton) with JetPack 7.x requirements and implemented targeted patches to ensure compatibility and build reliability. Delivered foundational changes that position the project for broader hardware support and future performance optimizations.
August 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
Build SystemsCMakeCUDAContainerizationDeep LearningJetPackLLM OptimizationPackage ManagementShell ScriptingSystem Configuration
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