This repository contains files and code snippets related to the Medium article titled "Utilizing SLURM for Fine-Tuning Large Language Models." The article explores the challenges of training large language models, especially when pushing the limits of individual GPUs like the NVIDIA GeForce RTX 3090, and introduces SLURM (Simple Linux Utility for Resource Management) as a solution for distributed learning systems.
📝 Read the Medium Article Here
The dynamic world of deep learning often encounters memory limits with individual GPUs, necessitating solutions like SLURM for distributed training. This repository includes practical examples, SLURM scripts, and code snippets to guide you through the transition from single-GPU setups to efficient distributed systems.
- Scripts: Explore SLURM scripts for job scheduling and resource management.
- Code Snippets: Examples demonstrating challenges faced during single-GPU training and solutions.
- Bash Script: A step-by-step guide to writing your first SLURM job script.
- Monitoring Jobs: Tips on tracking job status, canceling jobs, and navigating output and error files.
For those interested in setting up and fine-tuning Ludwig models and optimizing SLURM for distributed systems, check out our Medium Page for a comprehensive guide with step-by-step instructions and insights.
For in-depth information on SLURM, refer to the official SLURM Workload Manager Documentation: SLURM Documentation
Feel free to explore the repository for hands-on learning and additional resources. We hope this guide enhances your understanding of utilizing SLURM in the world of distributed deep learning. Happy coding!