Automatically Scaling Foundation Models Workflows: Ray with CodeFlare and Red Hat OpenShift

Explore the integration of Ray with CodeFlare and Red Hat OpenShift Data Science Pipelines for automatically scaling end-to-end workflows in foundation model development. Learn how this combination addresses the compute-intensive nature of preprocessing steps, tokenization, and model fine-tuning. Discover how CodeFlare enables parallel task execution using Ray clusters on OpenShift Container Platform, overcoming the limitations of RHODS Pipelines and Tekton in auto-scaling. Examine use cases that demonstrate the benefits of this integrated approach, allowing developers to easily specify workflow DAGs, manage tasks independently, and automatically scale operations. Gain insights into leveraging this toolset for effective parallel execution of foundation model workflows on OpenShift, with a focus on simplifying parameter input and DAG specification in RHODS Pipelines.

Automatically scaling the execution of Foundation Models workflows: Ray with CodeFlare and Red Hat