Companies and developers are turning to Kubernetes for better AI workload management to meet the growing computing needs of AI workloads. Kubernetes enables seamless scaling, easy management of complex systems and increased flexibility. In this article, we explore why Kubernetes could be an ideal solution for managing AI workloads, its capabilities for scaling advanced ML systems, and how Hyperstack enhances Kubernetes for GPU-intensive tasks like parallel training and inference.
Unlike traditional applications that process a single request at a time, AI and ML tasks often need to perform massive parallel computations on large datasets. For example, training a deep neural network involves iterating over large amounts of data and computing multiple gradients, sometimes with billions of parameters. This leads to several challenges:
Let’s see why Kubernetes is the ideal solution for managing AI/ML workloads due to its strong features:
Scalability AI and ML workloads can grow exponentially in size and complexity, demanding infrastructure that adapts dynamically. Kubernetes provides robust container orchestration capabilities across distributed environments, with scaling being one of its key features. This orchestration capability is especially valuable for training deep learning models or running inference jobs across large datasets.
Traditional VM setups require manual scaling, taking hours to provision new servers or adjusting load balancers. Kubernetes, on the other hand, enables automated pod scaling. The underlying infrastructure scaling depends on your cloud provider or on-premises setup. Kubernetes can be configured to scale applications based on real-time metrics like CPU, memory, and custom metrics. For example, Kubernetes can automatically adjust the number of pods running AI models in response to incoming data loads, helping ensure AI tasks have the appropriate computational resources available when needed.
Coming Soon: Hyperstack's on-demand Kubernetes is getting automatic infrastructure scaling soon!
AI/ML workloads often involve multiple processes running simultaneously. For example, a pipeline might consist of data preprocessing, model training, and hyperparameter tuning. These tasks when scaled must be isolated to prevent them from interfering with one another. Kubernetes helps mitigate this problem by isolating workloads within containers. These containers are lightweight and self-contained, running with the necessary libraries, dependencies, and runtime. This ensures that AI and ML tasks do not conflict with one another or impact the stability of the system. Kubernetes also enables logical isolation by grouping workloads into namespaces or distinct virtual clusters, thus providing further compartmentalisation, which is helpful for large teams or complex workflows that run multiple experiments concurrently.
AI workloads often require specialised hardware like Graphics Processing Units (GPUs), which are well-suited for the parallel processing needed by neural networks. However, optimising GPU resource utilisation can be challenging, especially when workloads span multiple nodes or when GPUs are allocated inefficiently.
Kubernetes' built-in support for GPU scheduling addresses this problem. Kubernetes can automatically assign the right type of hardware resources, such as GPUs, to the right container workloads. It optimises resource allocation based on the task requirements, reducing idle GPU power and ensuring that your GPU resources are being used as efficiently as possible.
And, Kubernetes makes it simple to define resource requests and limits for AI workloads, ensuring that these tasks don’t exceed available GPU capacity, and guaranteeing that the infrastructure is used optimally. GPU-enabled nodes allow users to schedule AI tasks without manually configuring or managing individual resources.
AI and ML processes require continuous experimentation, model training and model versioning. Kubernetes integrates seamlessly with Continuous Integration/Continuous Deployment (CI/CD) systems, providing the automation capabilities needed to maintain a regular flow of new updates, model iterations, or retrained models. For instance, teams can integrate Kubernetes with CI/CD pipelines, custom controllers, or operators like Kubeflow. These integrations can monitor for events such as new data uploads or model updates and automatically schedule training jobs.
Although Kubernetes is powerful on its own, many cloud providers focus on making the platform even more accessible and efficient for AI/ML workloads. Hyperstack's on-demand Kubernetes addresses several operational challenges when it comes to AI-focused Kubernetes clusters.
When running AI workloads, one of the primary pain points is the time and effort required to deploy and manage infrastructure. Hyperstack’s On-Demand Kubernetes makes the deployment process remarkably simple. You can deploy a fully configured Kubernetes cluster with just a single command- an easy way to avoid the administrative overhead of setting up everything from scratch.
Each Hyperstack Kubernetes cluster is pre-configured with NVIDIA drivers and is optimised for deep learning and ML applications. These optimisations significantly improve throughput during model training, enable seamless handling of large-scale datasets, and ensure consistency when distributing data across multiple nodes. With GPU-accelerated performance baked in, Hyperstack ensures that AI processes running on Kubernetes can utilise GPU power efficiently, while also providing consistent and reliable performance at scale.
To further improve the scalability of Kubernetes on Hyperstack, we are working on an auto-scaling feature. This will allow your cluster to automatically scale up or down based on demand so you have the right amount of computational power available for your Generative AI workloads.
Hyperstack Kubernetes clusters can be equipped with high-speed networking of up to 350Gbps for low-latency connections that are crucial for distributed AI applications. Whether you’re working with massive data sets or performing intensive parallel processing, Hyperstack’s networking infrastructure provides the fast data throughput required to keep AI workloads running efficiently.
Kubernetes provides scalable, efficient and automated resource management for complex AI/ML tasks, optimising GPU usage and dynamically scaling resources as required. Hyperstack’s On-Demand Kubernetes with NVIDIA optimisations and high-speed networking further improves AI workflows. By streamlining deployments and integrating AI-optimised components, Hyperstack enables you to rapidly set up AI infrastructure for parallel training, inference and beyond.
Currently in Beta testing, Hyperstack's on-demand Kubernetes is accessible through our API guide. Ready to get started? Check out the API Guide below!
Kubernetes automates resource scaling, provides seamless GPU management, and isolates tasks for efficiency, making it a robust choice for managing complex AI/ML workflows. It helps optimise resources and scale workloads dynamically, which is crucial for deep learning models.
Hyperstack provides pre-configured Kubernetes clusters with NVIDIA-optimised drivers and high-speed networking. These optimisations ensure AI tasks run efficiently, accelerating training and inference while maintaining reliable performance at scale across large datasets.
Kubernetes orchestrates AI workloads by automating the deployment, scaling, and management of containerised applications. It allocates resources dynamically, enabling efficient execution of complex AI tasks, such as parallel model training and inference, across distributed environments.
Yes, Kubernetes can effectively manage GPU-accelerated workloads. It supports GPU scheduling, ensuring that AI tasks are allocated the appropriate amount of GPU resources, and optimising performance while minimising idle time for GPUs across nodes in a cluster.
NVIDIA optimisations improve GPU utilisation, ensuring faster computation and efficient handling of AI/ML workloads. These optimisations reduce idle GPU time, ensuring AI tasks get the computational power they need when scaling with Kubernetes.
Hyperstack simplifies Kubernetes deployment by allowing users to launch fully configured clusters with a single API call, reducing the setup time. This eliminates the need for complex manual configurations, making AI infrastructure deployment much more efficient.