Nvidia has released the KAI Scheduler as an open-source tool under the Apache 2.0 license. This Kubernetes-native GPU scheduling solution is part of the Nvidia Run:ai platform, specifically designed to enhance the management of artificial intelligence (AI) workloads across GPUs and CPUs.
Key Features of KAI Scheduler
The KAI Scheduler adeptly handles varying GPU demands while minimizing wait times for compute access, providing resource guarantees alongside GPU allocation. It supports the entire AI lifecycle, enabling both small interactive tasks and large-scale training and inference within the same Kubernetes cluster. This flexibility ensures optimal resource distribution and fairness among multiple applications competing for GPU resources.
Dynamic Resource Management
Administrators can dynamically assign GPU resources to different workloads using the KAI Scheduler, which can coexist with existing schedulers in a Kubernetes environment. Its ability to continuously adjust fair-share values and quotas in real time makes it effective in meeting fluctuating workload demands without requiring constant manual adjustments from administrators.
Efficiency in Job Scheduling
For machine learning engineers, KAI Scheduler significantly reduces wait times by implementing techniques such as “gang scheduling,” GPU sharing, and a hierarchical queuing system. This allows users to submit job batches that are executed promptly as resources become available, adhering to priority levels and fairness guidelines.
Optimizing Resource Utilization
Nvidia has integrated several optimization strategies into KAI Scheduler to enhance GPU and CPU resource usage. Utilizing methods like bin packing and consolidation, the scheduler maximizes compute utilization by mitigating resource fragmentation. It efficiently packs smaller tasks into underutilized GPUs and CPUs, thereby combating inefficiencies. Additionally, it reallocates tasks across nodes to prevent fragmentation and distribute workloads evenly, ensuring maximum resource availability.
Addressing Resource Hogging
In environments with shared clusters, KAI Scheduler tackles the issue of resource hogging effectively. It recognizes that some researchers may secure more GPUs than necessary early in the day, potentially leading to underutilized resources. By enforcing resource guarantees, KAI Scheduler promotes overall cluster efficiency and prevents individual teams from monopolizing resources.
Integration with Other Tools
The scheduler comes equipped with a built-in pod grouper that automatically recognizes and integrates with popular tools and frameworks such as Kubeflow, Ray, Argo, and the Training Operator. This feature simplifies configuration processes and accelerates development timelines, making it easier for teams to leverage its capabilities.