1. Organizations integrating gen AI and LLMs require distributed computing solutions with minimal scheduling overhead.
2. Ray, an open-source Python framework for scaling AI workloads, is increasingly popular for scalable gen AI solutions.
3. Deploying Ray on Google Kubernetes Engine (GKE) with KubeRay provides benefits such as scalability, cost-efficiency, fault tolerance, and portability for production workloads.
The revolution in generative AI and large language models is leading to increased demands on compute infrastructure, requiring distributed computing solutions with minimal scheduling overhead. Ray, an open-source Python framework designed for scaling and distributing AI workloads, has become popular as organizations seek scalable gen AI solutions. Traditional Ray deployments on virtual machines have limitations, prompting the use of Kubernetes and deploying Ray on Google Kubernetes Engine (GKE) with KubeRay.
Running Ray on GKE brings benefits such as scalability, cost-efficiency, fault tolerance, isolation, and portability. Kubernetes orchestrates infrastructure resources using containers, pods, and VMs while Ray distributes data-parallel processes within applications, employing actors and tasks for scheduling. KubeRay introduces cloud-agnostic autoscaling, simplifying infrastructure management.
GKE offers discount-based savings, cost-saving measures like spot nodes, and low startup latency through image streaming. GKE’s declarative YAML-based approach simplifies deployment and fault tolerance with automatic self-healing capabilities. Kubernetes namespaces allow for easy multi-team sharing and trust boundaries, while GKE provides flexibility for managing various workloads.
Running Ray on GKE is a straightforward way to achieve scalability, cost-efficiency, fault tolerance, and isolation in production workloads. The cloud portability and flexibility offered make it an ideal choice for organizations adapting to the evolving generative AI landscape. Getting started with KubeRay on GKE involves following specific instructions and utilizing resources, such as Terraform templates and tutorials, to train and serve AI models effectively.