1 - Fleet-Level Intelligent Scheduling for AI Workloads
Open Cluster Management (OCM) provides comprehensive solutions for managing AI workloads across multiple clusters through intelligent scheduling, efficient model loading, and advanced placement capabilities. This page lists the key AI integrations and features that help orchestrate AI workloads in multi-cluster environments.
How OCM Helps AI Workloads on Fleet
AI and machine learning workloads have unique requirements when running at scale across multiple clusters. OCM addresses these challenges through several key capabilities:
Intelligent Workload Placement
OCM enables dynamic placement of AI workloads based on cluster capabilities and real-time conditions. You can schedule training or inference jobs to clusters with available GPU resources, specific hardware accelerators, or optimal network connectivity. The placement engine considers cluster capacity, resource quotas, and custom scoring criteria to make intelligent scheduling decisions.
Resource Management and Queueing
AI workloads often require significant computational resources and benefit from queue-based scheduling. OCM integrates with workload queueing systems to manage resource allocation across your cluster fleet, ensuring fair sharing of expensive resources like GPUs while preventing resource contention and enabling priority-based execution.
Data Locality and Fast Model Loading
Model serving and training workloads require efficient access to large datasets and model files. OCM provides data caching and acceleration capabilities to reduce model loading time, minimize data transfer costs, and improve inference latency. This is particularly important in edge computing scenarios where models need to be deployed close to data sources.
Privacy-Preserving Distributed Training
For organizations with data distributed across multiple locations or regulatory boundaries, OCM enables federated learning patterns where training occurs locally on each cluster, and only model updates are shared. This preserves data privacy and compliance while enabling collaborative model training across the fleet.
Workload Monitoring and Lifecycle Management
OCM provides unified visibility into AI workload execution across all clusters. You can track job completion status, monitor resource utilization, and manage the lifecycle of batch training jobs or long-running inference services from a central control plane.
AI Integrations and Features
OCM achieves these capabilities through a set of integrations and addons specifically designed for AI workloads. The following sections detail each integration and how it contributes to the overall AI workload management solution.
MultiKueue Integration
The MultiKueue integration enables intelligent scheduling of AI workloads across multiple clusters. It leverages Kueue, a Kubernetes-native job queueing system, to manage and schedule batch workloads efficiently in a multi-cluster setup.
Key Features:
- Queue-based workload management across clusters
- Resource quota enforcement
- Priority-based scheduling
- Fair sharing of cluster resources
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Fluid Integration
The Fluid integration provides fast model loading capabilities for AI model serving by leveraging data caching and acceleration. Fluid is an open-source Kubernetes-native distributed dataset orchestrator and accelerator for data-intensive applications.
Key Features:
- Accelerated data access for AI models
- Distributed caching of model data
- Support for various storage backends
- Reduced model loading time for inference workloads
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ManifestWork for Multi-Cluster Jobs
OCM’s ManifestWork API provides a workload completion feature that enables easy execution of jobs across multiple
clusters. This feature allows you to track the completion status of workloads deployed to managed clusters.
Key Features:
- Deploy and track jobs across multiple clusters
- Monitor workload completion status
- Automatic status aggregation from managed clusters
- Simplified multi-cluster batch job management
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Dynamic Scoring Framework
The dynamic scoring addon enables intelligent workload scheduling based on real-time metrics and custom scoring algorithms. This framework allows you to implement sophisticated placement decisions based on cluster conditions, resource availability, and custom business logic.
Key Features:
- Metric-based cluster scoring
- Custom scoring algorithms
- Dynamic placement decisions
- Real-time resource awareness
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Federated Learning Controller
The federated learning controller enables secure model training across multiple clusters while preserving data privacy. This addon implements federated learning patterns where data remains in its original location, and only model updates are shared across clusters.
Key Features:
- Privacy-preserving model training
- Distributed learning across clusters
- Data locality and security
- Aggregation of model updates
Learn More:
Getting Started
To begin using these AI workload capabilities:
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Ensure you have a working OCM environment with at least one managed cluster. See the Getting Started Guide for initial setup.
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Install the specific addons you need from the addon-contrib repository.
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Configure placement policies to target the appropriate clusters for your AI workloads.
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Deploy your AI workloads using the appropriate integration method.
Use Cases
These AI integrations enable various multi-cluster AI scenarios:
- Distributed Training: Train large models across multiple clusters with data locality
- Batch Inference: Run inference jobs at scale across your fleet
- Model Serving: Deploy and serve models with optimized data loading
- Resource Optimization: Dynamically place workloads based on GPU availability and cluster metrics
- Privacy-Preserving ML: Train models on distributed data without centralizing sensitive information
- Edge AI: Deploy and manage AI workloads at the edge with efficient model distribution and local inference