NVIDIA KAI Scheduler is now natively integrated with KubeRay, bringing the same scheduling engine that powers high‑demand and high-scale environments in NVIDIA…
Overview
The article discusses the integration of the NVIDIA KAI Scheduler with Ray, enabling advanced scheduling features like gang scheduling, workload prioritization, and autoscaling in Ray clusters. It provides a hands-on guide to setting up the KAI Scheduler and demonstrates how to manage training and inference workloads efficiently.
What You'll Learn
1
How to set up KAI Scheduler queues for managing workloads in Ray
2
How to implement gang scheduling for distributed Ray workloads
3
How to prioritize inference jobs over training jobs using KAI Scheduler
Prerequisites & Requirements
- Basic understanding of Kubernetes and Ray
- NVIDIA GPU Operator installed
- KubeRay Operator configured to use KAI Scheduler
Key Questions Answered
What is gang scheduling and why is it important for Ray workloads?
Gang scheduling ensures that all workers and actors in distributed Ray workloads start together, preventing partial allocations that can stall training or inference pipelines. This coordination is crucial for maintaining efficiency in high-demand environments.
How does KAI Scheduler handle workload prioritization?
KAI Scheduler allows high-priority inference jobs to preempt lower-priority batch training jobs when resources are limited. This feature keeps applications responsive and optimizes resource utilization without manual intervention.
What are the steps to submit a training job with KAI Scheduler?
To submit a training job, set up KAI Scheduler queues, define a Ray cluster with the necessary configurations, and apply the workload using the appropriate Kubernetes manifest. Ensure to label the job for KAI scheduling.
What are the benefits of hierarchical queuing in KAI Scheduler?
Hierarchical queuing allows for organized resource distribution among different project teams, enabling higher-priority queues to borrow idle resources from lower-priority ones. This dynamic resource sharing enhances overall efficiency.
Technologies & Tools
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Orchestration
Kubernetes
Used to manage containerized applications and deploy Ray clusters.
Scheduling
Nvidia Kai Scheduler
Provides advanced scheduling capabilities for managing workloads in Ray.
Distributed Computing
Ray
Framework used for building and running distributed applications.
Model Serving
Vllm
Used for deploying high-priority inference services.
Key Actionable Insights
1Implementing gang scheduling can significantly enhance the efficiency of distributed workloads in Ray by ensuring that all components start simultaneously.This is particularly important in environments where resource contention is common, as it prevents wasted resources and stalled processes.
2Utilizing workload prioritization allows for smoother operation of applications, especially when handling mixed workloads of training and inference.By allowing inference jobs to preempt training jobs, you can maintain responsiveness in user-facing applications, which is crucial for real-time data processing.
3Setting up hierarchical queues can improve resource management across teams, ensuring that higher-priority tasks receive the necessary resources without manual oversight.This approach is beneficial in larger organizations where multiple teams may compete for limited resources.
Common Pitfalls
1
Failing to configure KAI Scheduler queues properly can lead to inefficient resource allocation.
Without a well-defined queue structure, workloads may not be prioritized correctly, resulting in delays and resource contention.
2
Neglecting to set the priority class for jobs can cause lower-priority tasks to monopolize resources.
This oversight can lead to unresponsive applications, especially in scenarios where timely inference is critical.
Related Concepts
Kubernetes Resource Management
Ray Workload Optimization
Nvidia GPU Utilization Strategies