Skill Guide

Kubernetes orchestration and operator design for GPU workloads

The practice of designing, deploying, and managing containerized AI/ML training and inference workloads that require specialized GPU hardware within a Kubernetes cluster, often through the creation of custom controllers (Operators) to manage their full lifecycle and dependencies.

This skill is highly valued because it enables scalable, reproducible, and cost-efficient AI/ML infrastructure, directly accelerating model development cycles while optimizing expensive GPU resource utilization. Mastering it reduces time-to-market for AI products and can save millions in hardware and cloud compute costs.

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How to Learn Kubernetes orchestration and operator design for GPU workloads

1. Master core Kubernetes primitives: Pods, Deployments, StatefulSets, and Services. Understand the pod lifecycle. 2. Learn the fundamentals of GPU hardware in cloud environments (e.g., NVIDIA A100, T4) and how to request GPU resources via Pod `resource.limits`. 3. Study the Container Runtime Interface (CRI) and the role of the NVIDIA Container Toolkit in making GPUs available to containers.

1. Deploy and manage a GPU-enabled Kubernetes cluster using tools like `kubeadm` or a managed cloud service (e.g., GKE with GPU nodes, EKS). 2. Use the NVIDIA GPU Operator to automate driver management and monitoring. 3. Design and run distributed training jobs using frameworks like PyTorch or TensorFlow with the Kubernetes-native `Job` API, and learn to troubleshoot common issues like pod evictions, GPU memory errors, and network plugin misconfigurations.

1. Master the Kubernetes Operator pattern and the Operator SDK to build a custom controller for a domain-specific workload (e.g., managing a stateful ML model serving pipeline with auto-scaling and canary deployments). 2. Implement advanced scheduling with custom schedulers or plugins (e.g., for bin-packing, gang scheduling, or topology-aware placement) to maximize GPU cluster utilization. 3. Design and integrate monitoring (Prometheus, DCGM) and logging (Fluentd) solutions for GPU workloads to create a production-grade MLOps platform.

Practice Projects

Beginner

Project

Deploy a Single-GPU Model Training Job

Scenario

You need to run a simple CNN image classification training script on a single GPU within a Kubernetes cluster.

How to Execute

1. Write a Dockerfile that installs Python, PyTorch/CUDA, and copies your training script. 2. Push the container image to a registry (e.g., Docker Hub, GCR). 3. Write a Kubernetes `Job` manifest that specifies the container image and requests `1` NVIDIA GPU via `resources.limits["nvidia.com/gpu"]: 1`. 4. Apply the manifest with `kubectl apply -f job.yaml` and monitor logs with `kubectl logs -f `.

Intermediate

Project

Orchestrate a Distributed Training Job

Scenario

You need to scale model training to multiple GPUs across multiple nodes using data parallelism.

How to Execute

1. Refactor your training script to use a distributed training library (e.g., PyTorch's `DistributedDataParallel`). 2. Create a `StatefulSet` manifest to launch multiple worker pods (e.g., 3 replicas), each requesting a GPU. Use an init container to configure the host network. 3. Use a headless service for stable DNS names. 4. Implement a launcher pod (a `Job`) that coordinates the workers, passing their addresses as environment variables. 5. Use `kubectl scale` to adjust the number of workers and test scaling and recovery.

Advanced

Project

Build a Custom Model-Serving Operator

Scenario

Your organization needs to manage hundreds of different ML models (e.g., NLP, computer vision) as first-class Kubernetes resources with auto-scaling, versioning, and traffic splitting.

How to Execute

1. Define a Custom Resource Definition (CRD) for `ModelService` with fields for model URI, framework (TensorFlow, Triton), replicas, and scaling metrics. 2. Use the Operator SDK to scaffold a controller in Go or Python. 3. Implement reconciliation logic in the controller: watch `ModelService` CRs, deploy the appropriate `Deployment` and `Service`, and integrate with a metrics adapter (e.g., KEDA) for scaling based on request latency/QPS. 4. Package the operator and CRDs as a Helm chart or OLM bundle for distribution. 5. Test lifecycle management: creation, update (model version swap), deletion, and failure recovery.

Tools & Frameworks

Core Infrastructure

KubernetesNVIDIA GPU OperatorNVIDIA Container Toolkit (nvidia-docker)

The foundational stack. The GPU Operator automates the deployment of NVIDIA drivers, the device plugin, and the monitoring exporter, which are prerequisites for GPU workloads in K8s.

Operator Development

Operator SDK (Go, Ansible, Helm)Kubebuilderclient-go

Frameworks for building Kubernetes operators. The Operator SDK provides high-level APIs and project scaffolding to rapidly develop, test, and deploy a custom controller for your GPU workload domain.

Workload Orchestration & Scheduling

KueueVolcanoScheduler Plugins (Coscheduling)Kubernetes Job API

Kueue is a Kubernetes-native job queuing system for GPU sharing and fair scheduling. Volcano is a batch system optimized for AI/ML. Coscheduling enables gang scheduling for distributed jobs. The Job API is the standard for finite tasks.

Monitoring & Observability

PrometheusDCGM ExporterGrafanaFluentd

DCGM Exporter exposes GPU telemetry (utilization, temperature, memory) for Prometheus. This data is visualized in Grafana dashboards. Fluentd collects container logs. Essential for performance tuning and cost tracking.

Model Serving & Inference

NVIDIA Triton Inference ServerKServe (formerly KFServing)Seldon Core

Triton is an optimized inference server for multiple frameworks. KServe and Seldon Core are Kubernetes-native platforms for deploying, scaling, and monitoring inference services on GPUs, often used to build the 'serving' layer managed by a custom operator.

Interview Questions

Answer Strategy

The interviewer is testing systematic debugging skills and knowledge of GPU-specific failure modes. Strategy: Follow a logical sequence from cluster to node to pod level. Sample answer: "First, I'd check cluster-level quotas and resource availability using `kubectl describe nodes` to see if sufficient `nvidia.com/gpu` allocatable resources exist. If resources are available, I'd inspect the pod events for scheduling failures or image pull errors. A common issue is insufficient CPU/memory requested by the pod, causing preemption. For distributed jobs, I'd verify network plugin (e.g., Calico, Cilium) health and check if the init container that configures NCCL environment variables is succeeding. Finally, I'd examine the NVIDIA DCGM logs on the node for device-level errors or XID failures."

Answer Strategy

Tests knowledge of cost optimization strategies and familiarity with advanced tooling. Sample answer: "First, I would implement GPU time-slicing or Multi-Instance GPU (MIG) using the NVIDIA Device Plugin to allow multiple smaller workloads (e.g., inference pods) to share a single GPU, improving utilization from maybe 30% to 80%. Second, I would deploy Kueue to implement hierarchical fair-share scheduling and job queuing. This ensures expensive GPU nodes aren't idle waiting for jobs and allows preemptible, lower-priority jobs to be evicted in favor of critical workloads, directly lowering wasted compute costs."