1. Gang scheduling with RayJob and Kueue ensures that RayClusters are scheduled only when all required resources are available, improving resource efficiency.
2. Gang scheduling is important for use cases like data parallelism in distributed model training, preventing wasted resources and maximizing utilization.
3. Kueue’s dynamic resource provisioning and queueing, along with the ProvisioningRequest API on GKE, allows for efficient gang scheduling with KubeRay, optimizing Ray applications in GKE for better performance and cost-effectiveness.
The Priority Scheduling with RayJob and Kueue guide provides a detailed walk-through of how gang scheduling is utilized to improve resource efficiency by ensuring that RayJobs and RayClusters are only scheduled when all required resources are available. This strategy, known as “gang scheduling,” is particularly valuable for resource-intensive AI/ML workloads, such as data parallelism in distributed model training.
In data parallelism, where data is shard across multiple Pods running the same model, gang scheduling is crucial to prevent partially provisioned clusters from hindering the parameter server’s ability to update hyperparameters. Kueue’s all-or-nothing approach to workload admission ensures that Ray workloads execute only when all necessary resources are available, preventing wasted GPU/TPU cycles and maximizing resource utilization.
Kueue leverages the ProvisioningRequest API in GKE to orchestrate gang scheduling with KubeRay, ensuring that Ray cluster Pods are only scheduled together on newly provisioned nodes when all required resources are available. This efficient approach helps prevent resource wastage and maximizes utilization of hardware accelerators like GPUs and TPUs.
By utilizing gang scheduling with KubeRay and Kueue, users can effectively manage and optimize Ray applications within GKE. Priority scheduling ensures that the most critical AI/ML tasks receive the necessary resources, while gang scheduling prevents wasted time and resources on partially provisioned clusters. These techniques improve the overall performance and cost-effectiveness of Ray applications in the cloud.