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autoresearch-win-rtx is a Windows-based implementation of the autoresearch framework designed to run autonomous AI research loops on consumer NVIDIA RTX GPUs. It adapts the original autoresearch concept to a Windows environment, enabling users to perform iterative machine learning optimization without requiring specialized Linux or data center setups. The system revolves around a small set of core files, including a training script that is continuously modified by an AI agent, along with supporting utilities for data preparation and evaluation. Experiments are executed within a fixed time budget, ensuring consistent benchmarking across iterations and allowing the agent to focus on incremental improvements. The framework is designed to be lightweight and accessible, making it suitable for developers and researchers working on desktop hardware. It also supports modern GPU acceleration features through PyTorch, enabling efficient experimentation even on limited resources.
Features
- Windows-native implementation of autoresearch loops
- Support for NVIDIA RTX GPUs and PyTorch acceleration
- Agent-driven modification of training and optimization logic
- Fixed-time experiment execution for benchmarking
- Lightweight project structure for ease of use
- Designed for consumer desktop hardware environments
Project Samples
