Unlocking AI/ML Power: A Practical Guide to Using GPUs with Kubernetes
Modern Artificial Intelligence (AI) and Machine Learning (ML) models demand immense computational power. Training deep learning models or running complex data simulations on standard CPUs is often slow and inefficient. This is where Graphics Processing Units (GPUs) come in, offering the parallel processing capabilities needed to accelerate these tasks exponentially.
However, simply having GPU-equipped hardware isn’t enough. In today’s cloud-native world, container orchestration platforms like Kubernetes are the standard for deploying and managing applications at scale. The challenge lies in bridging the gap between Kubernetes and this specialized hardware. This guide will walk you through how to effectively manage and schedule GPUs within a Kubernetes cluster to supercharge your AI/ML workloads.
Why GPUs are Essential for Modern AI and Machine Learning
CPUs are designed for sequential, task-based operations, handling a few complex tasks at a time. GPUs, on the other hand, are built with thousands of smaller, more efficient cores designed to handle multiple operations simultaneously.
This architecture of massive parallel processing is perfectly suited for the mathematical operations—primarily matrix and vector calculations—that form the backbone of neural networks and other ML algorithms. By leveraging GPUs, data science and MLOps teams can:
- Drastically reduce model training times from weeks to days, or even hours.
- Handle larger, more complex datasets and models.
- Enable real-time inference for production applications.
- Improve cost-efficiency by completing jobs faster and using resources more effectively.
The Challenge: Making Kubernetes GPU-Aware
By default, Kubernetes excels at managing CPU and memory, but it doesn’t natively understand specialized hardware like GPUs. A standard Kubernetes scheduler sees a node’s resources but has no visibility into the number of GPUs available, their health, or how to assign them to a specific container (Pod).
Without a proper integration layer, you would face significant problems:
- No scheduling awareness: Pods could be scheduled on nodes without GPUs, causing them to fail.
- Resource contention: Multiple Pods might try to access the same GPU, leading to conflicts and crashes.
- Inefficient utilization: GPUs would sit idle because the cluster can’t intelligently assign workloads to them.
The Solution: The Kubernetes Device Plugin Framework
To solve this, Kubernetes introduced the Device Plugin framework. This framework provides a standardized way for hardware vendors to expose their resources to the Kubernetes control plane, specifically the Kubelet (the primary agent that runs on each node).
A device plugin is essentially a “translator” that runs on the node, discovers the specialized hardware (like GPUs), reports its status and availability to the Kubelet, and helps manage its lifecycle.
For NVIDIA GPUs, the industry-standard solution is the NVIDIA Device Plugin for Kubernetes. This plugin automatically:
- Detects the number and type of NVIDIA GPUs on each node.
- Exposes them as a schedulable resource within the Kubernetes cluster.
- Manages GPU health checks.
- Ensures that the necessary GPU device drivers are mounted into the container that requests them.
Getting Started: A Step-by-Step Guide to GPU Configuration
Configuring your Kubernetes cluster to use GPUs involves a few key steps.
1. Node Preparation (Prerequisites):
Before anything else, you must install the appropriate NVIDIA drivers on every node in your cluster that has a GPU. This is a critical prerequisite, as the device plugin and your applications depend on these drivers to communicate with the hardware.
2. Deploying the NVIDIA Device Plugin:
The device plugin is typically deployed as a DaemonSet. A DaemonSet ensures that a copy of the plugin’s Pod runs on every node (or a specified subset of nodes) in the cluster. This is the most reliable way to make sure that every GPU-equipped node is properly configured.
You can deploy the plugin using Helm or by applying a YAML manifest directly:
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.1/nvidia-device-plugin.yml
3. Requesting GPUs in Your Workloads:
Once the device plugin is running, it exposes GPUs as a new resource type: nvidia.com/gpu. To schedule a Pod on a node with a GPU, you simply need to request this resource in your Pod’s manifest file.
Here is a simple example of a Pod manifest requesting one GPU:
apiVersion: v1
kind: Pod
metadata:
name: cuda-test
spec:
containers:
- name: cuda-container
image: nvidia/cuda:11.4.2-base-ubuntu20.04
command: ["/bin/sh", "-c", "sleep 3600"]
resources:
limits:
nvidia.com/gpu: 1 # Requesting 1 GPU
restartPolicy: OnFailure
When you apply this manifest, the Kubernetes scheduler will only place this Pod on a node that has at least one available GPU. The device plugin will then ensure that the GPU is allocated exclusively to this Pod for the duration of its lifecycle.
Advanced GPU Management in Kubernetes
For more sophisticated MLOps environments, basic GPU allocation may not be enough. Kubernetes and the NVIDIA ecosystem offer advanced features for finer-grained control and better utilization.
Multi-Instance GPU (MIG): Modern NVIDIA GPUs (like the A100 series) support MIG, which allows a single physical GPU to be partitioned into multiple, fully isolated GPU instances. Each instance has its own dedicated memory and processing cores. This is ideal for running multiple smaller inference workloads on a single GPU without them interfering with each other. The NVIDIA Device Plugin supports discovering and scheduling these MIG instances.
GPU Time-Slicing: For workloads that don’t need a full, dedicated GPU but require GPU access (like development or light inference), time-slicing allows multiple containers to share a single GPU. The containers run in sequential time slots. While this can introduce some performance overhead, it dramatically increases GPU utilization.
Monitoring and Observability: To effectively manage GPU resources, you need visibility. By deploying tools like the DCGM Exporter (Data Center GPU Manager) alongside Prometheus and Grafana, you can monitor key metrics like GPU utilization, memory usage, temperature, and power draw. This data is invaluable for optimizing workloads, detecting performance bottlenecks, and right-sizing your GPU resources.
Best Practices for Secure and Efficient GPU Usage
Use Node Labels and Taints: Apply labels to your GPU-enabled nodes (e.g.,
accelerator=nvidia-a100). Use Node Taints and Tolerations to ensure that only Pods specifically requesting GPUs are scheduled on these expensive nodes, reserving them for their intended purpose.Implement Resource Quotas: In a multi-tenant environment, use Kubernetes ResourceQuotas and LimitRanges to control how many GPUs a specific namespace or team can consume. This prevents a single project from monopolizing all available GPU resources.
Keep Drivers and Plugins Aligned: Ensure your NVIDIA drivers, the device plugin version, and the CUDA toolkit version used in your containers are compatible. Mismatches are a common source of errors.
Optimize Container Images: Build lean, optimized container images for your AI/ML applications. Including the correct CUDA libraries and dependencies without unnecessary bloat will lead to faster startup times and a smaller security footprint.
Conclusion: Powering the Future of AI with Kubernetes and GPUs
Combining the raw power of GPUs with the scalable orchestration of Kubernetes creates a formidable platform for any AI/ML initiative. By leveraging the device plugin framework, teams can seamlessly integrate specialized hardware into their cloud-native workflows. This unlocks the ability to train models faster, run more complex simulations, and deploy high-performance inference services at scale. As AI continues to evolve, a well-architected Kubernetes and GPU infrastructure will be a cornerstone of innovation and success.
Source: https://collabnix.com/kubernetes-and-gpu-the-complete-guide-to-ai-ml-acceleration-in-2025/
