Provision ephemeral Ray clusters per Flyte task execution via KubeRay.
npx skills add https://github.com/tylertitsworth/skills --skill flyte-kuberay
Please help me install this Agent Skill. Skill: flyte-kuberay Source: https://github.com/tylertitsworth/skills/tree/main/flyte-kuberay Command: npx skills add https://github.com/tylertitsworth/skills --skill flyte-kuberay
Seamlessly run Ray workloads as Flyte tasks by provisioning ephemeral Ray clusters per task execution via the KubeRay operator. The integration enables Flyte users to configure RayJobConfig (head and worker resources, autoscaling, runtime_env) and run Ray Core, Ray Data, Ray Train, or Ray Serve within Flyte workflows while ensuring clusters are created, used, and torn down per task run.
Run a Flyte task that uses the flytekitplugins-ray integration to submit your Ray function to an ephemeral cluster.
High-intent search queries and answers about installing and using this skill.
Run Ray distributed training as Flyte tasks by provisioning ephemeral Ray clusters per task execution. This integration provisions a dedicated cluster for each run and automatically tears it down upon completion.
Configure RayJobConfig to customize head and worker node resources, autoscaling, pod templates, and runtime_env. This setup safely manages per-task lifecycle and resources within a Kubernetes environment.
Flyte supports Ray Core, Ray Data, Ray Train, and Ray Serve within workflows. The integration uses the flytekitplugins-ray package to submit functions to an ephemeral cluster.
Yes, running Ray jobs in Flyte requires a KubeRay operator installed, the flytekitplugins-ray package in the task image, and Flyte's Ray backend plugin enabled.
Yes, you can autoscale Ray worker nodes per task execution in Flyte. RayJobConfig allows specifying autoscaling settings alongside head and worker node configurations.
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