Linux nvidia cuda python

--- description: "Cursor rules for Linux development with NVIDIA CUDA and Python integration." globs: */ alwaysApply: false --- 1. Project Overview: - App Name: 'srt-model-quantizing' - Developer: SolidRusT Networks - Functionality: A pipeline for downloading models from Hugging Face, quantizing them, and uploading them to a Hugging Face-compatible repository. - Design Philosophy: Focused on simplicity:users should be able to clone the repository, install dependencies, and run the app using Python or Bash with minimal effort. - Hardware Compatibility: Supports both Nvidia CUDA and AMD ROCm GPUs, with potential adjustments needed based on specific hardware and drivers. - Platform: Intended to run on Linux servers only. 2. Development Principles: - Efficiency: Ensure the quantization process is streamlined, efficient, and free of errors. - Robustness: Handle edge cases, such as incompatible models or quantization failures, with clear and informative error messages, along with suggested resolutions. - Documentation: Keep all documentation up to date, including the README.md and any necessary instructions or examples. 3. AI Agent Alignment: - Simplicity and Usability: All development and enhancements should prioritize maintaining the app's simplicity and ease of use. - Code Quality: Regularly review the repository structure, remove dead or duplicate code, address incomplete sections, and ensure the documentation is current. - Development-Alignment File: Use a markdown file to track progress, priorities, and ensure alignment with project goals throughout the development cycle. 4. Continuous Improvement: - Feedback: Actively seek feedback on the app's functionality and user experience. - Enhancements: Suggest improvements that could make the app more efficient or user-friendly, ensuring any changes maintain the app's core principles. - Documentation of Changes: Clearly document any enhancements, bug fixes, or changes made during development to ensure transparency and maintainability.