This repository provides a comprehensive guide and setup environment for AI development on Ubuntu and Windows platforms. It targets engineers and researchers looking to establish a robust AI experimental environment, covering everything from OS installation and driver setup to deep learning framework configuration and algorithm implementation. The primary benefit is a consolidated, step-by-step approach to complex AI system setup, reducing the common friction points in getting started with deep learning.

How It Works

The project meticulously details the installation and configuration of essential AI software, including CUDA, cuDNN, TensorRT, OpenCV, and major deep learning frameworks like PyTorch and TensorFlow. It emphasizes a structured approach to managing multiple software versions and environments, particularly within Linux (Ubuntu) and Windows, offering solutions for common issues like dual-boot time synchronization and NVIDIA driver conflicts. The documentation is organized by task, from basic system setup to advanced framework usage and model optimization techniques.

Quick Start & Requirements

• Installation: Primarily through following detailed instructions within the README for manual setup of Ubuntu/Windows, drivers, and software. Docker installation is also covered.
• Prerequisites:
  • Hardware: NVIDIA GPU (e.g., RTX 3090), sufficient RAM and storage.
  • OS: Ubuntu 18.04-22.04 or Windows 11.
  • Software: CUDA 9.0-12.1, cuDNN, TensorRT, OpenCV, Python.
• Resources: Setup involves extensive software installation and configuration, potentially taking several hours depending on user familiarity and system specifications.
• Links: The README itself serves as the primary guide.

Highlighted Details

• Extensive coverage of NVIDIA driver installation and CUDA/cuDNN version management across different Ubuntu releases.
• Detailed guides for installing and configuring popular AI frameworks (PyTorch, TensorFlow, Caffe) and tools (Docker, TensorRT, Anaconda).
• Includes sections on AI algorithm implementation (e.g., YOLO V3, Faster R-CNN) and model optimization (quantization, pruning).
• Provides solutions for common development environment issues and system administration tasks on Ubuntu.

Maintenance & Community

The repository is maintained by "aimuch." Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. The content appears to be for educational and informational purposes, and users should verify compatibility for commercial or closed-source use.

Limitations & Caveats

The README is extremely dense and primarily serves as a personal knowledge base, which may require significant effort to parse and adapt. It focuses heavily on specific hardware and Ubuntu versions, and compatibility with other platforms or newer OS versions may require adaptation. The lack of a formal license could pose restrictions on redistribution or commercial use.