Artifacts for installing the Distributed Workloads stack as part of ODH
Distributed Workloads is a simple, user-friendly abstraction for scaling, queuing and resource management of distributed AI/ML and Python workloads. It consists of three components:
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CodeFlare SDK to define and control remote distributed compute jobs and infrastructure with any Python based environment
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Multi-Cluster Application Dispatcher (MCAD) for management of batch jobs
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Instascale for on-demand scaling of a Kubernetes cluster
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KubeRay for management of remote Ray clusters on Kubernetes for running distributed compute workloads
Intrgration of this stack into the Open Data Hub is owned by the Distributed Workloads Working Group. See this page for further details and how to get in touch.
Follow our quick start guide here to get up and running with Distributed Workflows on Open Data Hub.