How Kubernetes Powers OpenAI’s Infrastructure: A 2018–2023 Evolution

OpenAI, the AI research lab behind technologies like GPT-4, ChatGPT, and DALL·E, relies on Kubernetes to manage the vast infrastructure required to support their large-scale machine learning workloads. Kubernetes is a key part of OpenAI’s ability to scale its operations efficiently across both cloud and bare-metal environments.

OpenAI’s Initial Kubernetes Setup in 2018: 2,500 Nodes

By 2018, Kubernetes had become an integral part of OpenAI’s infrastructure, particularly for their deep learning research. At that time, OpenAI was running multiple Kubernetes clusters, including a 2,500-node cluster on Azure. This large setup involved:

• VM Types: OpenAI combined Azure’s D15v2 (general-purpose VMs) and NC24 (GPU-based VMs). D15v2 provided strong CPU resources for general workloads, while NC24 offered GPU acceleration (via NVIDIA Tesla K80 GPUs) for AI model training.
• Networking and DNS Challenges: Scaling Kubernetes across thousands of nodes required improving the performance of critical components like etcd (for managing cluster state) and kube-dns (for reliable DNS resolution).
• Autoscaling: OpenAI initially built their own autoscaler to manage dynamic scaling but eventually transitioned to the Kubernetes Cluster Autoscaler for better maintainability.

This was a pioneering deployment at the time, with OpenAI leading the way in showing how Kubernetes could support AI research at such a large scale.

Scaling to 7,500 Nodes by 2021

By 2021, OpenAI’s infrastructure had grown significantly, with their largest Kubernetes cluster now handling 7,500 nodes. This scale was necessary to support OpenAI’s largest models like GPT-3, CLIP, and DALL·E, and introduced new engineering challenges:

• IP Addressing: With 200,000 IP addresses in use at any given time, networking efficiency became a critical concern. OpenAI switched from Flannel (a basic networking layer) to more advanced Azure-native networking solutions to improve scalability.
• Custom Scheduling: OpenAI needed to schedule AI workloads efficiently, especially when using GPUs. They developed a coscheduling plugin to handle batch jobs and ensure efficient resource utilization.
• API Server Load: Managing thousands of nodes meant the API servers were under heavy load. OpenAI used EndpointSlices to reduce the strain on API servers, ensuring better performance and responsiveness.
• GPU Health Checks: Ensuring the health of the NVIDIA GPUs was crucial, as these were central to the deep learning models.

This rapid scale-up demonstrated OpenAI’s deep technical expertise in managing Kubernetes for both CPU- and GPU-heavy workloads, proving that Kubernetes could handle AI models at a commercial scale.

OpenAI Infrastructure in 2023

As of 2023, while there haven’t been detailed public updates from OpenAI on their Kubernetes usage, some key details about their infrastructure are known:

• Azure remains the exclusive cloud provider for OpenAI’s workloads. The partnership between Microsoft and OpenAI has led to the creation of the Azure OpenAI Service, which allows businesses to access OpenAI’s models via APIs.
• NVIDIA A100 GPUs are now the backbone of OpenAI’s compute power, with tens of thousands of these GPUs in use. These GPUs are critical for training and running large models efficiently.
• Technologies like Terraform, Kafka, Python, PostgreSQL, and Cosmos DB are integral to their infrastructure, providing the tools needed to manage large-scale deployments and data processing.

Why Kubernetes for AI Workloads?

Several reasons explain why OpenAI continues to rely on Kubernetes:

1. Scalability: Kubernetes allows OpenAI to scale its clusters from a few nodes to thousands, making it ideal for handling varying workload demands.
2. Flexibility: Kubernetes supports both cloud-based and on-premise deployments, allowing OpenAI to mix and match infrastructure depending on the requirements.
3. Tooling: The rich ecosystem of tools, such as Prometheus for monitoring and Fluentd for logging, provides a powerful foundation for managing large-scale systems.

While traditional high-performance computing (HPC) frameworks could also support AI workloads, Kubernetes offers a level of flexibility and ecosystem integration that is hard to match.

Conclusion

Kubernetes has been a key enabler of OpenAI’s infrastructure growth, from its early days of 2,500-node clusters to its current setup supporting some of the most advanced AI models in the world. OpenAI’s journey with Kubernetes demonstrates the platform’s strength in managing both large-scale CPU and GPU workloads, making it a prime choice for AI companies operating at a global scale.

As AI and Kubernetes evolve, it will be exciting to see how OpenAI continues to push the boundaries of infrastructure management. More insights from the OpenAI team could reveal even greater innovations in the near future!

#Kubernetes #OpenAI #AIInfrastructure #DeepLearning #GPUs #Scalability #Azure