Skypilot

Training and fine-tuning Large Language Models (LLMs) requires significant computational resources and careful experiment tracking. While many focus on the modeling aspects, efficiently managing compute resources and experiment tracking is equally important for successful ML projects. This guide demonstrates how to leverage SkyPilot and MLflow - two powerful open-source tools - to orchestrate LLM fine-tuning jobs effectively. An open-source stack for LLM fine-tuning Modern LLM fine-tuning workflows involve multiple moving parts: Resource orchestration across different cloud providers Environment setup and dependency management Experiment tracking and monitoring Distributed training coordination System metrics collection Using SkyPilot for resource orchestration and MLflow for experiment tracking provides an easy-to-use and fully open-source stack for managing these complexities. ...

Introduction - Solving cloud resources and reproducibility for LLMs A few of weeks ago, I wrote a post about the challenges of training large ML models, in particular: the need for more computing power and the complexity of managing cloud resources; the difficulty of keeping track of ML experiments and reproducing results. There I proposed a solution to these problems by using SkyPilot and DVC to manage cloud resources and track experiments, respectively. ...

Introduction One of the things that makes machine learning hard is that you have to run a lot of experiments. You have to try different models, different data sets, different hyperparameters, different features. And each experiment can take a long time to run, especially if you’re working on deep learning problems. You can’t just run them on your laptop or desktop. You need more computing power, and you need it fast. ...