NVIDIA Open Sources Run:ai Scheduler to Foster Community Collaboration

Today, NVIDIA announced the open-source release of the KAI Scheduler, a Kubernetes-native GPU scheduling solution, now available under the Apache 2.0 license.

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NVIDIA
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Ronen Dar
•9 min read•advanced•
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Overview

NVIDIA has open-sourced the KAI Scheduler, a Kubernetes-native GPU scheduling solution under the Apache 2.0 license, originally developed for the Run:ai platform. This initiative aims to enhance community collaboration and improve AI workload management by addressing challenges such as fluctuating GPU demands and resource allocation.

What You'll Learn

1

How to manage fluctuating GPU demands in AI workloads

2

Why reducing wait times for compute access is crucial for ML engineers

3

How to enforce resource guarantees in shared clusters

4

When to use podgroups for gang scheduling in distributed AI training

Key Questions Answered

How does KAI Scheduler manage fluctuating GPU demands?
KAI Scheduler continuously recalculates fair-share values and adjusts quotas in real time, automatically matching current workload demands. This dynamic approach ensures efficient GPU allocation without manual intervention, addressing the variability in AI workloads that traditional schedulers struggle with.
What strategies does KAI Scheduler use to reduce wait times for compute access?
The scheduler employs gang scheduling, GPU sharing, and a hierarchical queuing system, allowing ML engineers to submit batches of jobs and ensuring they launch as soon as resources are available. This reduces idle time and enhances productivity.
What are the core scheduling entities in KAI Scheduler?
The two main entities are podgroups and queues. Podgroups represent interdependent pods that must be executed together, while queues enforce resource fairness and manage resource allocation based on defined quotas and priorities.
How does KAI Scheduler ensure resource guarantees in shared clusters?
KAI Scheduler enforces resource guarantees by ensuring that AI practitioner teams receive their allocated GPUs while dynamically reallocating idle resources to other workloads. This prevents resource hogging and promotes overall cluster efficiency.

Technologies & Tools

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Orchestration
Kubernetes
KAI Scheduler is a Kubernetes-native solution designed to manage GPU scheduling.
Platform
Run:ai
KAI Scheduler is part of the NVIDIA Run:ai platform, which supports AI workload orchestration.

Key Actionable Insights

1
Implementing KAI Scheduler can significantly enhance GPU resource management in AI workloads.
By utilizing KAI Scheduler, teams can dynamically adjust to fluctuating demands, ensuring that resources are allocated efficiently without manual oversight, which is critical for maintaining productivity in AI projects.
2
Utilize the built-in podgrouper feature to simplify the integration of AI tools and frameworks.
This feature reduces the complexity of manual configurations, allowing teams to connect workloads with tools like Kubeflow and Ray more efficiently, thus accelerating the development process.
3
Adopt the hierarchical queuing system to optimize job submission processes.
This system allows ML engineers to submit jobs in batches, reducing wait times and ensuring that high-priority tasks are executed promptly, which is essential in time-sensitive AI projects.

Common Pitfalls

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Failing to enforce resource guarantees can lead to resource hogging by certain teams.
This often occurs in shared clusters where some users may secure more resources than necessary, leading to underutilization. Implementing KAI Scheduler helps prevent this by dynamically reallocating idle resources.