Introduction to Kubernetes and AI

Two transformative technologies that are changing the way we build, deploy, and manage applications today are Kubernetes and Artificial Intelligence. While AI continues to revolutionize industries across the board, the need for a robust and scalable infrastructure was never greater. Enter Kubernetes, the open source container orchestration platform that has become the backbone of modern cloud native applications and modern virtualization.

In this post, we will look at how Kubernetes and AI bring new avenues for improved app development and deployment and enable organizations to leverage the power of machine learning at an unprecedented scale.

Overview of Kubernetes

Kubernetes, often abbreviated as k8s, is an open source container orchestration originating from Borg, a platform originally developed by Google and now maintained by the Cloud Native Computing Foundation. It helps automate the deployment, scaling, and management of containerized applications and has become the de facto standard for container orchestration for modern cloud native applications.

Some of the features that make Kubernetes a preferred choice are:

• It manages the end-to-end lifecycle of containers, ensuring that they are running as expected.
• It can automatically scale applications based on resource usage or custom metrics.
• It ensures high availability by automatically replacing or rescheduling containers that fail.
• The Kubernetes storage system allows you to use storage systems such as local storage, network storage systems, public cloud providers or other cloud native storage solutions.

Kubernetes helps developers focus on writing code and building applications by abstracting away the complexity of managing containerized applications, thus improving developer productivity.

The Rise of Artificial Intelligence

The release of ChatGPT in late 2022 and other generative AI tools was a pivotal point in the public perception of AI’s capabilities. Further, with the introduction of tools like GitHub Co-pilot, which generates code snippets, these AI assistants have revolutionized how we build applications.

Several factors drive this rapid rise:

• Increased computational power: The availability of powerful hardware such as Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs) has significantly accelerated AI development. These can process massive datasets in parallel and also run more sophisticated and resource-intensive AI models.
• Advancement in machine learning: Advancements in deep learning and reinforcement learning techniques and the development of newer algorithms have allowed AI models to tackle complex computational tasks easily. The success of neural networks in image recognition and generation, natural language processing, and other domains has strengthened AI’s rise.
• Availability of Big Data: The proliferation of digital devices and internet services has led to the creation of vast datasets, which provide the fuel for training complex machine learning models. With more data, AI systems can be trained on larger datasets, leading to more accurate and powerful models.

As AI advances, its integration with solutions like Kubernetes is becoming critical for organizations to leverage this transformative power at scale.

The Intersection of Kubernetes and AI

Kubernetes offers a powerful platform to deploy, scale, and manage AI workloads and related workflows. The convergence of AI and Kubernetes is transforming how organizations develop, train, and deploy ML models and AI workloads as it aligns well with the requirements of AI workflows from model training to inference.

How Kubernetes Facilitates AI Development

Kubernetes plays a critical role in enabling AI development by addressing various challenges associated with running machine learning workloads and is an invaluable tool in the AI development lifecycle. Here’s how Kubernetes facilitates AI development.

• Scalability and Resource Management: AI models, especially during training, are resource-intensive and require significant CPU and GPU resources. Kubernetes’ ability to dynamically scale resources allows teams to manage AI models by allocating resources based on workload requirements. Kubernetes also excels at scaling horizontally, allowing organizations to distribute training jobs across multiple nodes.
• GPU and Hardware Management: GPUs are essential for training machine learning models, and Kubernetes supports GPU acceleration through device plugins that facilitate the scheduling and management of GPU resources across nodes. This allows for efficient GPU sharing among different tasks and processes.
• Seamless MLOps and CI/CD Integration: Kubernetes seamlessly integrates with CI/CD tools and facilitates MLOps pipelines. This allows teams to automate the training, testing, and deployment of AI models. By integrating with CI/CD tools, teams can continuously deploy and monitor their AI applications, ensuring faster and more reliable updates and feedback.
• Flexible Storage Options: As AI applications need to deal with large datasets, Kubernetes’ storage abstractions like PersistentVolumes and StorageClasses allow teams to manage and access data. Based on their requirements, teams can configure local or network storage or even enable cloud storage solutions.
• Cloud Agnostic Deployments: Kubernetes’ portability avoids vendor lock-in and allows organizations to run AI workloads across different cloud providers or on-prem systems. This is especially beneficial for companies in a highly regulated space or looking to optimize costs across multiple cloud platforms.

By providing the above benefits, Kubernetes simplifies the process of building and deploying AI models and enables organizations to innovate faster and optimize better in the rapidly evolving landscape of artificial intelligence.

Benefits of Using Kubernetes for AI Applications

High availability

Gen AI applications require specialized databases called vector databases for their functioning. These databases must be available whenever a user requests a response; in such cases, Kubernetes allows their deployment and management with high availability.

In addition, Kubernetes can help dynamically adjust to the fluctuating demands without compromising performance and also move workloads to different workers if needed. This is critical in case of node, network, zone, and other failures, as it keeps your pipelines up and running with access to the databases.

Automated operations

As AI applications scale, manual management becomes increasingly challenging. From updating machine learning (ML) models to managing computational resources, automating these tasks is key to maintaining efficiency and avoiding human error. Kubernetes offers automation for deploying, scaling, and managing AI workloads through its built-in features like automatic scaling, self-healing, and rolling updates.

Data access

When we talk about AI in Kubernetes, it is essential to note that models can be enormous in size and complexity. Different inference servers require models to be in specific formats, which can vary depending on the server, hardware, or software being used (e.g., TensorRT, ONNX, or TorchScript). To ensure compatibility, we convert our models accordingly before deployment.

We can store all model formats in one place by using solutions like NFS (Network File System) as a centralized storage solution. This allows us to easily mount the models on any node, based on demand, and serve them using available GPUs.

GPU management with Kubernetes

AI workloads requires significant computational resources, particularly GPUs. Kubernetes offers efficient GPU management through device plugin frameworks, allowing GPUs to be allocated to containerized applications dynamically. This ensures that AI workloads can scale without manually managing GPU resources across different nodes. Additionally, vendor-specific operators like NVIDIA GPU Operator support simplify GPU resource management within a Kubernetes cluster for NVIDIA GPUs.

Use Cases of Kubernetes in AI

Machine Learning Model Training

Training Machine learning models require immense computational power and efficient resource distribution. Kubernetes facilitates model training by distributing workloads across nodes in a cluster, allowing for faster processing of large datasets. With integrations for frameworks like TensorFlow and PyTorch, Kubernetes can scale machine learning pipelines dynamically, making the model training process faster and more efficient.

Retrieval Augmented Processing (RAG)

RAG is an advanced technique that enhances large language models (LLMs) by retrieving and adding relevant data to generate more accurate responses. With Kubernetes, it becomes easy to deploy the dependent vector databases that store embedding required by the model. Existing projects like Postgres allow users to enable vector database support to store embedding for various types, such as images, audio, and text.

AI-Powered Microservices

AI-powered microservices allow businesses to integrate intelligent features—such as recommendation systems or image recognition—into their applications. Kubernetes automates the management and scaling of these microservices, ensuring they remain responsive and available even under high demand. With Kubernetes, AI models can be deployed as independent services, each scaling and functioning autonomously within the larger application architecture.

Key Tools and Frameworks for AI on Kubernetes

There are some Kubernetes native tooling and community-driven platforms available that can speed up the development of AI applications and help you with the end-to-end machine learning lifecycle. Below are some of the popular tools and frameworks to keep in mind.

Kubeflow: Machine Learning Toolkit for Kubernetes

Kubeflow is an open-source platform designed to simplify the deployment of machine learning (ML) workflows on Kubernetes. It integrates seamlessly with popular ML frameworks like TensorFlow and PyTorch, providing components for every stage of the machine learning lifecycle, including model training, tuning, and deployment. With Kubeflow, developers can define and execute end-to-end ML workflows, making AI deployment faster and more efficient on Kubernetes.

NVIDIA AI toolkit

The NVIDIA AI toolkit offers a range of tools to run GPU-accelerated AI workloads within Kubernetes clusters efficiently. These tools support the deployment, scaling, and management of AI models, ensuring seamless integration with Kubernetes environments. Below are some key tools provided by NVIDIA.

• NVIDIA GPU Operator: The GPU Operator automates the provisioning, management, and monitoring of NVIDIA GPUs in Kubernetes clusters. It handles the installation of GPU drivers, container runtimes, device plugins, and monitoring agents, making it easy to deploy and scale GPU-accelerated AI workloads without manual intervention.
• NVIDIA Container Toolkit: This toolkit provides the necessary components (drivers, runtime libraries) to enable GPU-accelerated containers in Kubernetes environments. It allows containers to access the host’s GPUs, ensuring seamless execution of AI and data science workloads that depend on GPU resources.
• NVIDIA Triton Inference Server: NVIDIA Triton Inference Server is designed to serve models from multiple AI frameworks, such as TensorFlow, PyTorch, and ONNX. It optimizes model inference by providing features like dynamic batching, model versioning, and multi-model serving, helping to improve efficiency and throughput in production environments.
• Multi-Instance GPU (MIG) Support in Kubernetes: MIG enables a single GPU to be partitioned into multiple independent GPU instances, allowing multiple workloads to run concurrently. Kubernetes can treat each partition as a separate GPU, which allows for better resource utilization by enabling fine-grained allocation of GPU resources.
• NVIDIA DCGM Exporter: Part of the NVIDIA Data Center GPU Manager (DCGM), this tool exports GPU health and performance metrics to monitoring systems like Prometheus. It enables real-time monitoring of GPU usage, temperature, power, and errors, helping operators ensure GPU resources perform optimally across Kubernetes clusters.
• NVIDIA Device Plugin for Kubernetes
  This plugin allows Kubernetes to detect and schedule GPU resources for AI workloads automatically. It simplifies managing GPU resources across nodes, ensuring efficient allocation and scaling of GPU-powered applications within the cluster.
• NVIDIA GPU Feature Discovery (GFD)
  GFD enables Kubernetes to detect and report the specific GPU features available on a node, such as GPU model, memory capacity, and MIG support. This information allows Kubernetes to schedule workloads more intelligently, ensuring that jobs are assigned to nodes with the appropriate GPU capabilities.

HuggingFace

Hugging Face is a leader in natural language processing, offering a vast repository of pre-trained models, datasets, and inference engines. It has become a buzzing community where users collaborate and share modes and data. The Hugging Face Inference API and transformers library are widely used for translation, text summarization, and sentiment analysis tasks. Hugging Face models can be easily deployed on Kubernetes for real-time inference. It is also an important resource, offering open-source artifacts and model checkpoints that can be fine-tuned and deployed on Kubernetes, enabling rapid AI model development and deployment.

Challenges of Using Kubernetes in AI

While Kubernetes offers several advantages for deploying and managing AI workloads, it also has its fair share of challenges. Deploying AI capabilities on Kubernetes means dealing with infrastructure and the complexity of integrating models, frameworks, and data. Organizations implementing AI workflows on Kubernetes encounter numerous security, data management, and other operational complexity challenges.

• Security Risks and Compliance: When organizations deal with multiple AI workloads using different GenAI stacks within the same cluster, they have to deal with the complexity of multi-tenancy. In such situations, data protection and stringent network and RBAC policies are critical for isolating the workloads. For industries bound by regulations such as HIPAA, PCI-DSS, etc., compliance adds another layer of complexity, necessitating comprehensive audit logging and admission controllers for policy enforcement.
• Handling Large-Scale Data: Handling large-scale data directly impacts the performance and efficiency of the model. Organizations must carefully orchestrate storage solutions that can handle this scale. Strategies must be in place to minimize data transfer latency and sophisticated scheduling strategies for optimal performance. Storage architecture must be chosen to balance performance and cost considerations.
• Complexity of Tooling: Integrating AI with Kubernetes introduces a lot of tooling complexity, from orchestrating ML pipelines to selecting the right tools that integrate with other steps like data preparation, model training, and deployment. Model serving poses another challenge, which requires specialized tools for efficient deployment and scaling. The complexity increases further when you integrate multiple AI frameworks, each requiring its operator yet having a shared monitoring solution.

Portworx’s Role in Enhancing AI on Kubernetes

Data drift, duplication, and data silos can create inconsistencies that compromise AI accuracy and reliability, leading to issues like model hallucinations. Developers also struggle with limited data access across environments, which hinders agility and can result in data loss from infrastructure failures. Additionally, token-based pricing models and inefficiencies in GPU utilization drive up costs, limiting scalability.

A comprehensive container data management solution like Portworx addresses these challenges by unifying data management on Kubernetes. It ensures a common, consistent dataset for teams by synchronizing data across clusters to reduce drift and duplication. By building container data management into developer platforms, developers can also gain self-service access to the data they need, with built-in safeguards against failures and the flexibility to move data seamlessly across environments.

A unified container data management solution can localize essential data directly to accelerate compute, minimizing ingress and egress fees, reducing dependence on token-based pricing, and enabling efficient GPU access. This results in lower costs and improved performance.

Trends in Kubernetes and AI

The collaborative power of Kubernetes and AI will only evolve with emerging technologies and changing business requirements. Use cases, such as processing AI workloads at the edge and implementing sophisticated MLOps workflows, will redefine the trends in Kubernetes and AI.

Edge Computing and AI with Kubernetes

Lightweight Kubernetes distributions like K3s and MicroK8s optimized for edge environments are revolutionizing AI deployment. Organizations are using these platforms to run AI inference workloads directly on edge devices, utilizing tools like NVIDIA’s edge AI platform KubeEdge for seamless orchestration. Tools like these enable automated management of edge devices, while features like node taints and tolerations ensure proper workload placement across edge locations. This architecture significantly reduces latency while addressing data sovereignty requirements.

MLOps Integration

Traditional CI/CD pipelines have evolved into sophisticated MLOps pipelines that can manage Kubernetes’s entire ML life cycle. Modern MLOps implementations leverage tools like Kubeflow for orchestrating end-to-end ML pipelines. Organizations implement GitOps practices in their MLOps pipelines to manage ML model deployments, version code, and models. Features stores need to be created to manage ML features at scale. Lastly, specialized monitoring plugins and solutions are integrated with tools like Prometheus and Grafana to track the health of both the model and infrastructure.

AIOps to intelligently manage and troubleshoot IT Operations

By introducing intelligent automation and predictive analytics, AIOps is transforming Kubernetes’ operations. Organizations are implementing advanced analytics tools that intelligently identify potential issues before they occur. Such tools leverage machine learning to establish baseline behavior for Kubernetes clusters and detect anomalies in resource usage patterns and workload performance. Advanced AIOps implementation has intelligent chatbots for automated incident categorization and routing. Such features help reduce the MTTR and enable proactive infrastructure management.

Conclusion

The convergence of AI and Kubernetes is a much-needed intersection of container orchestration and intelligent computing that is reshaping the way we build and ship applications. Throughout this article, we looked at how Kubernetes provides the infrastructure necessary for AI workloads and enables everything from handling GPU resources at scale to distributed training and model serving.

AI workloads demand high-performance, scalable, and reliable storage solutions that can handle massive datasets. Portworx addresses unique challenges of running data rich AI workloads on Kubernetes. It provides automated storage operations that simplify data management and enable cross-cluster data mobility for distributed AI workloads.

Contact us to learn how Portwox can help accelerate your AI journey.