Tag: devops

The convergence of Kubernetes and Artificial Intelligence continues to accelerate, driven by the insatiable demand for scalable, manageable, and cost-effective infrastructure to support increasingly complex AI workloads. While the initial integration focused on basic model deployment and serving, the last 3-6 months have witnessed significant advancements in areas like AI workload scheduling, data management, model lifecycle management, and enhanced observability.

Enhanced Kubernetes Scheduling for AI Workloads

Traditional Kubernetes scheduling often falls short when dealing with the specific demands of AI workloads, particularly those involving GPUs and other specialized hardware. Several advancements have addressed these limitations:

* **Kubernetes v1.31: Enhanced GPU Sharing and Scheduling:** Released in August 2024, Kubernetes v1.31 introduced significant improvements to GPU resource management. This includes the ability to dynamically share GPUs amongst containers within a pod, enabling finer-grained resource utilization and reduced costs. Prior to this, GPU allocation was often an all-or-nothing proposition, leading to underutilized hardware. The updated device plugin API now allows for more precise resource definition, allowing operators to specify fractions of GPUs (e.g., 0.5 of a GPU) to specific containers. For example, a team training multiple smaller models can now efficiently share a single high-end GPU, leading to substantial cost savings. The `nvidia.com/gpu.resource` field in the Pod spec is used to define the fractional allocation.

apiVersion: v1
kind: Pod
metadata:
  name: fractional-gpu-pod
spec:
  containers:
  - name: training-container-1
    image: nvcr.io/nvidia/pytorch:24.12-py3
    resources:
      limits:
        nvidia.com/gpu.resource: "0.5"
    command: ["python", "train.py"]
  - name: training-container-2
    image: nvcr.io/nvidia/tensorflow:2.16-py3
    resources:
      limits:
        nvidia.com/gpu.resource: "0.5"
    command: ["python", "train.py"]

* **Volcano v1.10: Improved AI Job Management:** Volcano, a Kubernetes-native batch scheduling system, has seen significant enhancements in its v1.10 release (September 2024). These improvements focus on Gang Scheduling and Pod Grouping for more complex AI workloads like distributed training. The enhanced Gang Scheduling ensures that all pods within a distributed training job are scheduled simultaneously, preventing resource starvation and improving overall training efficiency. Volcano now also supports advanced preemption policies, allowing higher-priority AI jobs to preempt lower-priority ones, optimizing resource utilization based on business criticality. The improved Pod Grouping features simplify the management of complex multi-pod applications common in distributed AI training.

* **KubeRay v2.2: Enhanced Ray Cluster Management:** KubeRay, designed specifically for managing Ray clusters on Kubernetes, released version 2.2 in January 2023. A significant enhancement is the integration of autoscaling based on real-time Ray metrics like CPU utilization and task queue length. This allows Ray clusters to dynamically adjust their size based on the current workload, optimizing resource utilization and minimizing costs. Furthermore, KubeRay v2.2 simplifies the management of distributed Ray applications by providing a declarative API for defining Ray cluster configurations, making it easier to deploy and manage complex AI workloads. The addition of Ray Job submission through Kubernetes resources streamlines the deployment process.

Streamlining AI Data Management on Kubernetes

AI models are only as good as the data they are trained on. Managing large datasets efficiently within Kubernetes is crucial. Recent developments address data access, versioning, and processing:

* **Kubernetes Data Volume Snapshotting with CSI Drivers:** Cloud Native Storage (CNS) providers continue to improve their Container Storage Interface (CSI) drivers, enabling efficient data volume snapshotting directly within Kubernetes. These snapshots can be used for versioning datasets, backing up training data, and creating new datasets for experimentation. For example, using the AWS EBS CSI driver, you can create snapshots of your training data residing on EBS volumes, allowing you to easily revert to previous versions or create copies for different training runs. This eliminates the need for complex external data management solutions and streamlines the data pipeline. The `VolumeSnapshotClass` and `VolumeSnapshot` custom resources are key components.

apiVersion: snapshot.storage.k8s.io/v1
kind: VolumeSnapshot
metadata:
  name: my-training-data-snapshot
spec:
  volumeSnapshotClassName: csi-aws-ebs-snapclass
  source:
    persistentVolumeClaimName: my-training-data-pvc

* **DVC (Data Version Control) Integration with Kubernetes:** DVC, a popular open-source tool for data versioning and pipeline management, has seen increased integration with Kubernetes. Specifically, the ability to use DVC pipelines to process data within Kubernetes pods has become more streamlined. This allows you to define data transformation steps as DVC stages and execute them as Kubernetes Jobs, leveraging the scalability and manageability of Kubernetes for data processing. DVC can then track the lineage of your data, ensuring reproducibility and facilitating collaboration. This integration typically involves configuring DVC to use Kubernetes as a remote execution environment.

* **Alluxio v3.0 for Data Orchestration:** Alluxio, a data orchestration system, released version 3.7 in August 2025, with a stronger focus on Kubernetes integration. Alluxio acts as a data virtualization layer, allowing AI workloads running on Kubernetes to access data stored in various sources (e.g., object storage, HDFS) without requiring data migration. This significantly speeds up data access and reduces storage costs. Alluxio v3.7 features improved metadata management and data caching capabilities, optimizing data access for AI training and inference. Alluxio can be deployed as a Kubernetes StatefulSet and configured to mount data from different sources, providing a unified data access layer.

Model Lifecycle Management and Observability

Managing the entire lifecycle of AI models, from training to deployment and monitoring, is crucial for ensuring model accuracy and reliability. Recent advancements have focused on automating the model lifecycle and enhancing observability:

* **MLflow 3.0 on Kubernetes:** MLflow, a popular open-source platform for managing the ML lifecycle, released version 3.0 in June 2025, with improved support for running MLflow tracking, model registry, and model serving on Kubernetes. Specifically, the MLflow Kubernetes operator now provides a simplified way to deploy and manage MLflow components as Kubernetes resources. This eliminates the need for manual configuration and streamlines the deployment process. Furthermore, MLflow’s autologging capabilities have been enhanced to automatically track training metrics and parameters within Kubernetes jobs, providing comprehensive insights into the model training process. This makes it easy to compare different training runs and identify the best model.

* **Prometheus and Grafana for AI Model Monitoring:** Leveraging Prometheus and Grafana for monitoring AI model performance has become increasingly sophisticated. Custom metrics are being exposed from model serving endpoints (e.g., using Seldon Core or KFServing) to track key performance indicators (KPIs) like latency, throughput, and accuracy. Grafana dashboards can then be created to visualize these metrics in real-time, allowing DevOps teams to quickly identify and address performance issues. Furthermore, anomaly detection algorithms can be integrated with Prometheus to automatically detect deviations from expected model behavior, triggering alerts when model performance degrades.

* **Kubernetes Event-Driven Autoscaling (KEDA) for Model Inference:** KEDA is increasingly being used to autoscale model inference endpoints based on real-time request rates. By scaling inference deployments based on the number of incoming requests, KEDA ensures that models are always available to handle demand while minimizing resource consumption during periods of low traffic. For example, KEDA can be configured to scale a Seldon Core deployment based on the number of requests being received by the model serving endpoint. This dynamic scaling ensures optimal resource utilization and reduces costs.

Case Study: GenAI Workloads with Kubeflow on AWS EKS

A major global financial institution recently migrated its GenAI model training and inference workloads to AWS EKS using Kubeflow. Their primary challenges were:

* **Resource Management:** Efficiently managing and sharing expensive GPU resources across multiple teams.

* **Scalability:** Scaling training jobs to handle massive datasets and scaling inference endpoints to handle peak loads.

* **Observability:** Gaining visibility into model performance and identifying potential issues.

By leveraging Kubernetes v1.31’s enhanced GPU sharing capabilities, they were able to reduce GPU costs by 30%. Kubeflow’s Pipelines component streamlined the model training workflow, while KFServing provided a scalable and manageable platform for model inference. Prometheus and Grafana were used to monitor model performance, allowing them to quickly identify and address performance bottlenecks. The institution reported a 40% reduction in model deployment time and a 25% improvement in model inference latency after migrating to Kubernetes on EKS.

Conclusion

The last 6 months have witnessed significant advancements in the integration of Kubernetes and AI. From improved GPU scheduling and data management to enhanced model lifecycle management and observability, these developments are making it easier than ever to build, deploy, and manage AI workloads on Kubernetes. By embracing these new tools and techniques, DevOps engineers and AI practitioners can unlock the full potential of Kubernetes for their AI initiatives, driving innovation and accelerating the development of AI-powered applications. As Kubernetes continues to evolve, we can expect even tighter integration with AI, further simplifying the deployment and management of complex AI workloads.

The intersection of Kubernetes and Artificial Intelligence continues to be a hotbed of innovation, pushing the boundaries of what’s possible in terms of scalability, resource management, and model deployment. We’ll examine advancements in areas like model serving, resource optimization, AI-powered Kubernetes management, and the impact of emerging hardware accelerators.

Enhanced Model Serving with KServe v0.10

Model serving frameworks are crucial for deploying AI models at scale. KServe, a CNCF incubating project, has seen significant improvements with the release of version 0.10 (released in June 2025). This release focuses on enhanced explainability, improved scaling capabilities, and streamlined integration with other Kubernetes-native tools.

* **Explainability Integration:** KServe v0.10 introduces tighter integration with explainability frameworks like Alibi and SHAP. This allows users to seamlessly deploy models with built-in explainability features, facilitating model debugging and compliance. You can now easily configure explainers within the KServe `InferenceService` custom resource definition (CRD).

* **Example:** Defining an `InferenceService` with an Alibi explainer:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: sentiment-analysis
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: gs://your-model-bucket/sentiment-model
    explainer:
      alibi:
        type: AnchorImages
        config:
          instance_selection: top_similarity
          threshold: 0.9

This example demonstrates how to configure an Alibi `AnchorImages` explainer directly within the KServe deployment. This allows you to get explanations for your model predictions directly through the KServe API.

* **Autoscaling Improvements with Knative Eventing:** KServe leverages Knative Serving for autoscaling. v0.10 enhances this by integrating more deeply with Knative Eventing. This enables scaling models based on real-time event streams, making it ideal for scenarios like fraud detection or real-time recommendations where the workload is highly variable. Autoscaling is now more reactive and efficient, reducing latency and improving resource utilization.

* **gRPC Health Checks:** KServe v0.10 introduces gRPC health checks for model servers. This provides more granular and reliable health monitoring compared to traditional HTTP probes. This helps to quickly detect and resolve issues with model deployments, ensuring high availability.

Resource Optimization with Volcano Scheduler Enhancements

AI workloads are notoriously resource-intensive. Efficient scheduling and resource management are vital for optimizing costs and performance. The Volcano scheduler, a Kubernetes-native batch scheduler, has seen notable advancements in Q2/Q3 2025, particularly in the areas of GPU allocation and gang scheduling.

* **Fine-grained GPU Allocation:** Volcano now supports fine-grained GPU allocation based on memory and compute requirements within pods. This allows for better utilization of GPUs, particularly in scenarios where different tasks within the same job have varying GPU demands.

* **Example:** You can specify GPU requirements within the pod definition:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-intensive-task
spec:
  containers:
  - name: training-container
    image: your-training-image
    resources:
      limits:
        nvidia.com/gpu: 1 # Request 1 GPU
      requests:
        nvidia.com/gpu.memory: "8Gi" # Request 8GiB of GPU memory

Volcano will then attempt to schedule the pod onto a node with sufficient available GPU memory.

* **Improved Gang Scheduling with Resource Reservations:** Volcano’s gang scheduling capabilities, essential for distributed training jobs that require all tasks to start simultaneously, have been further refined. New features allow for resource reservations, guaranteeing that all the necessary resources will be available before the job starts, preventing deadlocks and improving job completion rates. This is particularly relevant for frameworks like Ray and Horovod that rely on gang scheduling for optimal performance. Configuration can be done at the Queue level, allowing specific teams to have priority on certain GPU types.

* **Integration with Kubeflow:** Volcano’s integration with Kubeflow has been strengthened. Kubeflow pipelines can now seamlessly leverage Volcano for scheduling their individual tasks, resulting in improved resource efficiency and faster pipeline execution. This tight integration simplifies the management of complex AI workflows.

Impact of Hardware Accelerators: AMD Instinct MI300X Support

The increasing demand for AI computing power is driving the adoption of specialized hardware accelerators like GPUs and TPUs. AMD’s Instinct MI300X GPU, released in Q2 2025, is quickly becoming a popular choice for AI workloads due to its high memory bandwidth and compute capabilities. Kubernetes is actively adapting to support these new accelerators.

* **Device Plugins and Node Feature Discovery:** Kubernetes’ device plugin mechanism allows vendors like AMD to seamlessly integrate their hardware into the Kubernetes ecosystem. AMD has released updated device plugins that properly detect and expose the MI300X GPU to pods. Node Feature Discovery (NFD) is crucial for automatically labeling nodes with the capabilities of the MI300X GPU, enabling intelligent scheduling.

* **Container Runtime Support:** Container runtimes like containerd and CRI-O are being updated to support the MI300X GPU. This involves improvements in GPU passthrough and resource isolation.

* **Framework Optimization:** AI frameworks like TensorFlow and PyTorch are also being optimized to take advantage of the MI300X’s unique architecture. This includes using libraries like ROCm (AMD’s open-source software platform for GPU computing) for accelerated training and inference. Kubeflow also supports distributing the training across multiple MI300x GPUs via the MPI operator.

Security Enhancements for AI Workloads

Security is a paramount concern in any Kubernetes environment, and AI workloads are no exception. Recent developments have focused on securing the entire AI lifecycle, from data ingestion to model deployment.

* **Confidential Computing with AMD SEV-SNP:** AMD’s Secure Encrypted Virtualization – Secure Nested Paging (SEV-SNP) technology provides hardware-based memory encryption for VMs. Kubernetes is increasingly integrating with SEV-SNP to protect sensitive AI models and data from unauthorized access. This prevents against memory tampering and injection attacks.

* **Supply Chain Security:** The rise of sophisticated AI models has also increased the risk of supply chain attacks. Tools like Sigstore and Cosign are being used to digitally sign and verify the provenance of AI models and container images, ensuring that they have not been tampered with. Kubernetes policies, such as Kyverno, can then enforce these signatures during deployment.

* **Federated Learning Security:** Federated learning, where models are trained on decentralized data sources, presents unique security challenges. Differential privacy and homomorphic encryption techniques are being integrated into Kubernetes-based federated learning platforms to protect the privacy of the data used for training.

Conclusion

The Kubernetes and AI landscape continues to evolve rapidly. The advancements discussed in this blog post, including enhanced model serving with KServe, resource optimization with Volcano, support for new hardware accelerators like the AMD MI300X, and security enhancements, are empowering organizations to build and deploy AI applications at scale with greater efficiency, reliability, and security. By staying abreast of these developments, DevOps engineers and AI practitioners can unlock the full potential of Kubernetes for their AI workloads and drive innovation in their respective fields. Continuous experimentation and evaluation of these new tools and techniques are essential for staying ahead of the curve in this dynamic space.

🚀 Deploying AI models for inference at scale presents unique challenges. We need high performance, rock-solid reliability, and robust security. Let’s dive into deploying vLLM, a fast and memory-efficient inference library, on a Kubernetes cluster, emphasizing security best practices and practical deployment strategies.

vLLM excels at serving large language models (LLMs) by leveraging features like paged attention, which optimizes memory usage by intelligently managing attention keys and values. This allows for higher throughput and lower latency, crucial for real-time AI applications. Combining vLLM with Kubernetes, provides the scalability, resilience, and management capabilities needed for production environments. We’ll explore how to deploy vLLM securely and efficiently using tools like Helm, Istio, and cert-manager. Security will be paramount, considering potential vulnerabilities in AI models and the infrastructure.

One effective strategy for deploying vLLM on Kubernetes involves containerizing the vLLM inference server and deploying it as a Kubernetes Deployment. We’ll use a Dockerfile to package vLLM with the necessary dependencies and model weights. For example, let’s assume you have a Llama-3-8B model weights stored locally. This strategy ensures a repeatable and reproducible deployment process. Crucially, we’ll use a non-root user for enhanced security within the container.

FROM python:3.11-slim-bookworm
WORKDIR /app
COPY requirements.txt .

RUN pip install --no-cache-dir -r requirements.txt
# Copy model weights (replace with your actual path)

COPY models /app/models
# Create a non-root user

RUN groupadd -r appuser && useradd -r -g appuser appuser

USER appuser
COPY inference_server.py .
EXPOSE 8000
CMD ["python", "inference_server.py"]

In the `inference_server.py`, you load the model and expose an inference endpoint, using FastAPI, for example. Use environment variables for sensitive information such as API keys.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from vllm import LLM, SamplingParams
import os
app = FastAPI()
# Load the model using vLLM (replace with your model path)
model_path = "/app/models/Llama-3-8B"  # Adjust path accordingly
llm = LLM(model=model_path)
# Define the inference request schema
class InferenceRequest(BaseModel):
    prompt: str
    max_tokens: int = 50
    temperature: float = 0.7
# Inference endpoint
@app.post("/generate")
async def generate_text(request: InferenceRequest):
    try:
        sampling_params = SamplingParams(max_tokens=request.max_tokens, temperature=request.temperature)
        result = llm.generate(request.prompt, sampling_params)
        return {"text": result[0].outputs[0].text} #changed to outputs instead of output
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Next, we create a Kubernetes Deployment manifest to define the desired state of our vLLM inference server. This includes the number of replicas, resource limits, and security context. We also create a Service to expose the vLLM deployment. For production, setting resource limits is essential to prevent any single deployment from monopolizing cluster resources.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-inference
spec:
  replicas: 3
  selector:
    matchLabels:
      app: vllm-inference
  template:
    metadata:
      labels:
        app: vllm-inference
    spec:
      securityContext:
        runAsUser: 1000 # User ID of appuser
        runAsGroup: 1000 # Group ID of appuser
        fsGroup: 1000 # File system group ID
      containers:
      - name: vllm-container
        image: your-dockerhub-username/vllm-llama3:latest # Replace with your image
        resources:
          limits:
            cpu: "4"
            memory: "16Gi"
          requests:
            cpu: "2"
            memory: "8Gi"
        ports:
        - containerPort: 8000
---
apiVersion: v1
kind: Service
metadata:
  name: vllm-service
spec:
  selector:
    app: vllm-inference
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer #Or NodePort / ClusterIP

To enhance security, implement network policies to restrict traffic to the vLLM service. Use Istio for service mesh capabilities, including mutual TLS (mTLS) authentication between services. Also, leverage cert-manager to automate the provisioning and management of TLS certificates for secure communication. Ensure that your model weights are encrypted at rest and in transit. Regularly audit your Kubernetes configurations and apply security patches to mitigate vulnerabilities.

Real-world examples include companies utilizing similar setups for serving LLMs in chatbots, content generation tools, and code completion services. These implementations emphasize load balancing across multiple vLLM instances for high availability and performance. Monitoring tools like Prometheus and Grafana are integrated to track key metrics such as latency, throughput, and resource utilization. By following these best practices, you can build a secure, scalable, and resilient AI inference platform with vLLM on Kubernetes.

Conclusion

Deploying vLLM on Kubernetes empowers you to serve LLMs efficiently and securely. By containerizing the inference server, managing deployments with Kubernetes manifests, implementing strong security measures (non-root users, network policies, mTLS), and monitoring performance, you can build a robust AI inference platform. Remember to regularly review and update your security practices to stay ahead of potential threats and ensure the long-term reliability of your AI applications.