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AI/ML Scaling Made Simple: Harnessing Kubernetes


Author: Chris Paap, Sales Leader, Platform9
Bio: Chris is an accomplished IT practitioner with more than two decades of experience in enterprise & cloud infrastructure. As a seasoned Product Manager and Sales Leader, Chris specializes in assisting customers in their journey toward modernization through cloud-native strategies and AI/ML technologies. Over the past nine years, Chris has been at the forefront of diverse initiatives, ranging from Database as a Service (DBaaS) to Kubernetes and Artificial Intelligence/Machine Learning (AI/ML) solutions, consistently driving innovation and progress in the tech industry.

In recent memory, few developments have captured the collective imagination, like Artificial Intelligence (AI). The potential applications are boundless, from revolutionizing education to speeding up the delivery of new medicines. Companies across the spectrum are rapidly integrating AI and Machine Learning (ML) into their operations. The promise of increased efficiency, enhanced customer experiences, data-driven insights, and a competitive edge fuels this adoption. Those who successfully embrace AI/ML are better equipped to thrive in today’s fast-evolving markets.

As organizations embark on their AI journey, they often start with data scientists testing and training models on individual servers or laptops. However, scaling up to handle larger datasets and increased resource demands, such as GPUs and RAM, can be daunting.

Enter Kubernetes, the go-to solution for scaling AI/ML workloads. Kubernetes has emerged as the industry standard for developing large models, offering a robust and flexible orchestration platform. Running AI/ML workloads within Kubernetes allows for seamless integration of various components, such as testing, training, inference, and data preprocessing. Consider a healthcare company that employs AI/ML to analyze medical imaging data for diagnostics. Their complex AI pipeline encompasses data preprocessing, model training, and inference. Without Kubernetes, this pipeline might require separate servers or virtual machines for each step: Data preprocessing on one server, model training on another, and inference on another. Kubernetes streamlines this process, enabling efficient data processing, resource scaling, and reduced operational overhead. The result: faster diagnoses, improved patient care, and potential cost savings through resource optimization.

In today’s computing landscape, Graphics Processing Units (GPUs) play a pivotal role, particularly in AI and ML. GPUs accelerate the training and inference of deep learning models, making them indispensable for data scientists and developers. Kubernetes facilitates horizontal scaling of AI/ML workloads by creating multiple pods. This scalability proves especially valuable when training large models. Administrators can use tools like ReplicaSets or Deployments to define the desired number of replicas, optimizing GPU utilization. Horizontal Pod Autoscaling (HPA) is another critical feature, allowing pods to scale based on GPU utilization thresholds. When GPU usage surpasses predefined limits, HPA automatically adjusts the number of pods to meet demand.

AI/ML workloads are not one-size-fits-all, and resource requirements vary significantly. Kubernetes addresses this with GPU sharing, GPU node pools, and scheduling policies. GPU sharing enables multiple pods to share a single GPU, which is ideal for scenarios with limited GPU resources or cost-efficiency considerations. However, improper configuration can lead to performance bottlenecks. Kubernetes lets you specify GPU resource requirements and limits for pods, ensuring that GPU-intensive workloads land on nodes with the necessary resources. To prioritize workloads and minimize contention, GPU node pools can isolate GPU resources, providing exclusive access to intensive tasks. GPU Scheduling Policies offer fine-tuned control over GPU resource allocation, ensuring efficient and stable scheduling.

While Kubernetes offers powerful capabilities for AI/ML workloads, challenges exist. Finding skilled Kubernetes talent can be a hurdle. Data science teams prefer focusing on model development rather than managing infrastructure. Partnering with experts in Kubernetes implementation and best practices can alleviate these concerns. A proficient partner can augment your team’s expertise, ensuring successful deployment, GPU sharing, and node pool configuration. Moreover, deploying GPU drivers and operators correctly is essential. The NVIDIA GPU Operator simplifies this process, guaranteeing the installation of all required components, including:

  • NVIDIA GPU Driver: Essential for GPU acceleration.
  • NVIDIA Container Toolkit: Enables GPU-aware containers to run effectively.
  • Device Plugin: Advertises GPU resources to Kubernetes for precise resource allocation.
  • Node Feature Discovery: Collects hardware and software feature information, including GPU details.
  • GPU Monitoring: Installs DGM exporter for Prometheus and Grafana integration.

As companies continue embracing AI/ML, the demand for scalable infrastructure grows. Kubernetes has risen as the industry standard for scaling AI/ML workloads, offering flexibility and robust orchestration. It empowers companies to process data efficiently, handle large volumes, and adapt resources to their needs, all while reducing operational overhead. Though challenges exist, partnering with the right experts can address these issues and ensure the successful achievement of your AI/ML goals.

Join us at KubeCon + CloudNativeCon North America this November 6 – 9 in Chicago for more on Kubernetes and the cloud-native ecosystem.