Building and training AI models at a large scale isn’t as simple as it sounds. It involves managing huge distributed systems that can run across many servers and GPUs. Microsoft is teaming up with Anyscale to make this process easier on Azure, especially when using tools like PyTorch and Kubernetes.

Why Scaling AI Workloads is Challenging

When AI models grow from small tests to full-blown production systems, it feels like starting from scratch. Microsoft’s Brendan Burns points out that moving from a laptop experiment to a full-scale application is complicated. The main challenge is orchestrating these massive workloads efficiently. Early cloud tools focused more on handling large amounts of data, but today’s AI models need to manage a lot of compute power—both CPUs and GPUs—which can be tricky to set up and run.

Introducing Ray and Its Role in Simplifying AI Development

To address these issues, Microsoft has partnered with Anyscale to bring Ray to Azure Kubernetes Service (AKS). Ray is an open-source toolkit for building distributed Python applications, especially for AI. It lets developers run their existing code on a cluster without rewriting it. Ray handles scheduling tasks across CPUs and GPUs and offers libraries for training and deploying models using familiar tools like PyTorch.

Anyscale’s managed edition of Ray adds extra features, such as faster cluster setup and smarter resource management. All of this runs on top of AKS, which takes care of provisioning and scaling the infrastructure. Since Ray is open source and developed by Anyscale, it’s flexible and can extend its capabilities through its ecosystem. Microsoft already uses AKS for its AI projects, and this partnership makes it easier for users to build scalable AI applications without worrying about the underlying infrastructure.

Getting Started with Ray on Azure

Using Ray on AKS is straightforward, thanks to Microsoft’s sample scripts that automate the deployment process. But it’s good to understand how it works under the hood. You’ll need tools like the Azure CLI, Helm, and Terraform or OpenTofu to set everything up. The process involves creating a Docker container with your PyTorch code, which will be loaded onto AKS nodes for training.

To manually set up a Ray job, you start with a YAML configuration file that specifies how many pods you need, their CPU and GPU resources, and what container to run. You can start small and scale up as needed. During training, you can monitor progress through Ray’s logs and dashboards. For data, Microsoft recommends using Azure Blob Storage, which balances performance and cost well.

Ray isn’t just for AI. It can handle any large-scale Python distributed app. Its model libraries help with training, hyperparameter tuning, and deploying models at scale. You can use it to train models locally, then deploy them in the cloud, saving costs and time.

Using Ray and AKS to Accelerate AI Projects

Getting started is easy with Microsoft’s sample scripts, which streamline deployment. You can also customize your setup by building Docker containers with your PyTorch code. This setup allows you to run training jobs across multiple nodes, utilizing GPU resources to speed things up. You can track training progress with Ray’s tools and evaluate results with its dashboards.

Open source tools like KubeRay help manage Ray clusters on Kubernetes, simplifying configuration and deployment. And because Ray can work with various data types, it’s useful beyond just training AI models. For example, models trained for computer vision can be used to spot defects on products or detect safety violations. Similarly, models for analyzing log data can identify fraud or predict maintenance needs.

Using Azure and Ray, companies can create a flexible, cost-effective environment for training and updating AI models. This approach means you can develop custom models tailored to your specific needs without investing heavily in expensive hardware. Plus, as your data grows or your requirements change, you can easily update your models, keeping your AI systems sharp and responsive.

In short, Microsoft’s partnership with Anyscale on AKS opens up new possibilities for building, training, and deploying AI models at scale. It simplifies complex workflows and makes AI development more accessible, helping businesses innovate faster and more efficiently.

Inspired by

• https://www.infoworld.com/article/4088853/running-managed-ray-on-azure-kubernetes-service.html

Sources

• gmpg.org
• microsoft.com
• github.com
• ray.io
• microsoft.com