AI workload orchestration options

Key Concepts

• AI Orchestration: The process of coordinating and managing distributed AI workloads across a cluster of machines.
• Google Kubernetes Engine (GKE): A managed Kubernetes service on Google Cloud for deploying, managing, and scaling containerized applications.
• Slurm: An open-source workload manager and job scheduler commonly used in High-Performance Computing (HPC) environments.
• Cluster Director: A management plane on Google Cloud that simplifies the deployment and operation of Slurm clusters.
• Vertex AI: A fully managed service on Google Cloud for building, deploying, and scaling AI models.
• Pods: The smallest deployable units in Kubernetes, representing a group of one or more containers.
• Node Pools: Groups of nodes in GKE that share the same configuration, often used to provision specific hardware like accelerators.
• Leader-Worker Set: A Kubernetes extension for coordinating distributed workloads.
• Ray: A framework for building distributed applications, providing a Python-native way to distribute tasks.
• Compute Engine: Google Cloud's Infrastructure as a Service (IaaS) offering for virtual machines.
• High-Performance Computing (HPC): The use of supercomputers and parallel processing techniques to solve complex computational problems.

AI Orchestration on Google Cloud: GKE vs. Slurm

This video explores two primary approaches for scaling AI workloads on Google Cloud beyond a single machine, focusing on managing distributed clusters with hundreds of accelerators and serving thousands of customers. While fully managed services like Vertex AI offer simplicity, this discussion centers on scenarios requiring more control over the environment, specifically using Google Kubernetes Engine (GKE) and Slurm clusters on Compute Engine.

1. The Cloud-Native Approach: Google Kubernetes Engine (GKE)

GKE provides a foundation for managing infrastructure and deploying containerized AI workloads.

• Core Functionality: GKE handles the complexities of managing underlying hardware, including creating node pools with specific accelerators (e.g., GPUs) and ensuring that individual workloads (pods) are scheduled on nodes that meet their hardware requirements.
• Modes of Operation: GKE offers two modes:
  • Standard: Provides more control over cluster configuration.
  • Autopilot: A more managed experience where Google handles cluster operations.
• Job Orchestration Layer: While GKE manages the hardware and individual pods, orchestrating a distributed AI job as a single, cohesive unit often requires an additional software layer.
  • Kubernetes Native Tools: For teams familiar with Kubernetes, its native tools can be used for workload management.
  • Leader-Worker Set: A lightweight extension that can be added to Kubernetes to help coordinate pods for distributed tasks.
  • Ray Framework: For developers preferring a different abstraction or less Kubernetes familiarity, Ray offers a Python-native way to distribute application tasks across the cluster. This allows for a layered system: a scalable hardware foundation from GKE, with a chosen software abstraction (Kubernetes native tools or Ray) for team flexibility.

2. The HPC-Style Approach: Slurm Cluster on Compute Engine

This path is for teams needing a powerful, large-scale Slurm cluster, a common choice in HPC environments.

• Slurm: An open-source workload manager and job scheduler.
• Cluster Director: The recommended method for setting up a Slurm cluster on Google Cloud.
  • Management Plane: Cluster Director acts as a management plane, simplifying the deployment and operation of Slurm clusters.
  • Features: It offers a simple UI, automates configuration based on best practices, and assists with planning and scheduling maintenance to ensure cluster readiness for demanding jobs.
  • Alternative: While it's possible to build a Slurm cluster from scratch on Compute Engine VMs, Cluster Director offers a faster path to a production-ready environment.

Choosing the Right Path

The selection between GKE and Slurm depends on team needs and experience:

• GKE: Recommended for a flexible, cloud-native approach, allowing the use of Kubernetes native tools or frameworks like Ray.
• Slurm with Cluster Director: Ideal for teams requiring a managed Slurm environment that simplifies the management of long-running jobs.

Common Customer Patterns:

• Many customers utilize Slurm for training jobs due to its HPC heritage.
• Inference workloads are often deployed on Kubernetes (GKE).

Both approaches enable the construction of scalable production systems capable of handling advanced AI workloads.

Conclusion

Scaling AI workloads on Google Cloud involves choosing the right orchestration strategy. GKE offers a flexible, cloud-native platform with options for Kubernetes-native tools or frameworks like Ray. For HPC-style management of long-running jobs, Slurm with Cluster Director provides a simplified and powerful solution. The choice hinges on team expertise and specific workload requirements, with many organizations leveraging both for different stages of their AI lifecycle.