We are thrilled to announce a brand new integration of the Run:ai platform with Ray, unlocking a new level of efficiency and scalability for your workloads. As part of Run:ai’s open agenda, we embrace and support a wide array of ML tools and frameworks, and this collaboration with Ray Cluster further solidifies our commitment. The integration seamlessly fuses Run:ai's robust scheduling and orchestration capabilities with the Ray framework, empowering companies to supercharge their Ray clusters to massive scales while still providing fair sharing of resources between individuals and teams.

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Support for Ray Cluster and Ray Jobs

Run:ai’s integration ensures that all of the existing features of Ray such as its intuitive dashboard remain intact and are complimented by the Run:ai’s platform. This means you can effortlessly manage your Ray clusters while benefiting from the enhanced features and flexibility offered by Run:ai's platform.

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Elastic Workloads: Shrink and Grow as Needed

One of the core advantages of the Run:ai and Ray integration is the ability to embrace elastic workloads. This means your workloads can dynamically adjust in response to varying compute demands, ensuring optimal resource utilization at all times. With the elastic capabilities of Run:ai's platform, you can effortlessly scale your workloads up or down horizontally, maximizing the efficiency of your Ray cluster.

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At Run:ai, our mission is to empower organizations with cutting-edge technologies that streamline AI workflows and unlock the full potential of their compute resources. We remain dedicated to providing an open platform that supports all ML tools and frameworks. With this integration, we strive to deliver a transformative experience, enabling you to harness the power of Ray Cluster within the Run:ai platform.

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Watch a demo of how to supercharge your Ray workloads

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