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KAI Scheduler - Kubernetes Native Scheduler for Large-Scale AI Workloads
KAI Scheduler is an open source, Kubernetes-native scheduler specifically designed for managing and optimizing AI workloads at large scale, providing efficient resource utilization and improved job throughput.
Go
Added on 2025年6月29日676
Stars71
ForksGo
LanguageProject Introduction
Summary
KAI Scheduler extends the Kubernetes scheduling capabilities with features specifically tailored for large-scale AI/ML workloads, enabling better resource utilization, performance, and manageability for AI clusters.
Problem Solved
Standard Kubernetes schedulers lack the specialized awareness and features needed to effectively manage complex AI workloads, leading to inefficient resource allocation, job starvation, and difficulty in managing distributed training tasks at scale.
Core Features
AI/GPU Aware Scheduling
Optimizes scheduling based on GPU and other accelerator resource requirements, ensuring efficient placement of compute-intensive AI tasks.
Gang Scheduling Support
Supports gang scheduling to ensure all pods of a distributed AI job are scheduled simultaneously, preventing deadlocks.
Job Queueing and Prioritization
Allows configuration of job queues and priorities to manage resource contention and ensure critical workloads are scheduled first.
Tech Stack
Go
Kubernetes API
Container Technologies (Docker, containerd)
Prometheus (for metrics)
Use Cases
KAI Scheduler is ideal for environments where large-scale AI/ML computations are performed on Kubernetes clusters, including:
Large-Scale AI Model Training
Details
Orchestrating hundreds or thousands of distributed deep learning training jobs that require specific GPU configurations and gang scheduling for performance.
User Value
Increases cluster throughput and reduces job completion times by efficiently scheduling high-demand workloads.
Multi-tenant AI Platforms
Details
Managing multi-tenant Kubernetes clusters where different teams or users run diverse AI workloads (training, inference, data processing) with varying resource needs.
User Value
Enables fair resource sharing, prioritization, and isolation between different users or teams on a shared infrastructure.
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