This repository explores research-based optimization techniques for model training using the Ray library in Python.
The primary goal is to make model training easier, faster, and more scalable by leveraging Ray’s distributed computing capabilities.
Instead of relying on a single core, the workload is divided across multiple cores (and nodes, if available), leading to significant improvements in efficiency.
- Traditional model training → Runs sequentially or with limited parallelization.
- Ray → Distributes training tasks across multiple CPU cores (or GPUs).
- Optimized training → Faster experiments, reduced bottlenecks, and efficient resource utilization.
- Research goal → Evaluate and improve current techniques using Ray’s ecosystem.
- ⚡ Parallel & distributed execution with Ray
- 🖥️ Multi-core utilization for faster training
- 📊 Scalable experiments with large datasets and models
- 🔬 Research-focused: compares baseline vs optimized training approaches
- 🔄 Easily extendable for different ML/DL frameworks
Clone the repository:
git clone https://github.com/TahaGPT/Ray.git
cd Rayray start --head