GPU Mem Genius is an advanced reinforcement learning project aimed at optimizing memory allocation on NVIDIA GPUs, specifically designed to enhance performance and efficiency for large-scale machine learning models. This project leverages reinforcement learning to dynamically manage GPU resources, reducing memory usage, minimizing idle times, and improving overall resource utilization.
The primary goal of GPU Mem Genius is to develop an intelligent agent capable of making real-time adjustments to memory allocation and batch configurations. By using a reinforcement learning (RL) approach, this agent learns to maximize GPU efficiency across various models and frameworks.
- Dynamic Memory Management: Real-time memory allocation adjustments to optimize GPU and CPU utilization.
- Reinforcement Learning-based Optimization: The agent learns optimal configurations autonomously to maximize resource efficiency.
- Extensive Baseline and Benchmark Analysis: Data-driven metrics collection and benchmarking across models.
- Support for Various ML Frameworks: Compatible with PyTorch, TensorFlow, and other popular deep learning frameworks.
- Python 3.8+
- Poetry for dependency management
- NVIDIA GPU with Tesla A100 or similar for testing
- VS Code with Remote - SSH extension for remote development
- Clone the repository:
git clone https://github.com/YourUsername/gpu-mem-genius.git cd gpu-mem-genius