Vchitect/VBench
Jan 2025 – Jan 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
Build ProcessBuild SystemsDependency ManagementPackage ManagementPyTorchPython Development
NattapolChan
PROFILE
Nattapolchan
Nattapol Chanpaisit worked on the Vchitect/VBench repository, focusing on improving installation reliability and compatibility for users with modern PyTorch and CUDA environments. He addressed a device placement issue in PatchEmbed to ensure seamless operation with PyTorch 2.x, removing restrictive version constraints that previously caused incompatibilities. Using Python and leveraging skills in build systems and dependency management, he enhanced the packaging process by adding explicit CUDA and PyTorch requirement checks and updating dependencies such as timm. These targeted improvements reduced setup errors and deployment friction, resulting in a more robust and user-friendly installation experience for the VBench package.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
106
Activity Months1
Your Network
8 people
Work History
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 – VBench (Vchitect) focused on improving compatibility and installation reliability to expand user adoption and reduce support friction. Key deliverables include PyTorch 2.x compatibility fix for PatchEmbed device placement and packaging improvements that clearly specify CUDA/PyTorch requirements, updated timm dependency, and a release bump to 0.1.5. These changes reduce setup errors, improve CUDA runtime compatibility, and streamline deployment for users with modern PyTorch/CUDA stacks.
4 Commits • 1 Features
Jan 1, 2025January 2025 – VBench (Vchitect) focused on improving compatibility and installation reliability to expand user adoption and reduce support friction. Key deliverables include PyTorch 2.x compatibility fix for PatchEmbed device placement and packaging improvements that clearly specify CUDA/PyTorch requirements, updated timm dependency, and a release bump to 0.1.5. These changes reduce setup errors, improve CUDA runtime compatibility, and streamline deployment for users with modern PyTorch/CUDA stacks.
January 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture75.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Build ProcessBuild SystemsDependency ManagementPackage ManagementPyTorchPython DevelopmentSoftware Compatibility
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