Principle:Kubeflow Pipelines Task Resource Configuration
| Sources | Kubernetes Resources, KFP SDK |
|---|---|
| Domains | ML_Pipelines, Resource_Management |
| Last Updated | 2026-02-13 |
Overview
A technique for specifying computational resource constraints and execution policies on individual pipeline tasks to control scheduling, caching, and fault tolerance.
Description
Each pipeline task can be configured with resource limits (CPU, memory), caching behavior, and retry policies. These translate to Kubernetes resource requests/limits and pipeline-level execution policies. Resource configuration ensures tasks get adequate compute while preventing resource starvation across the cluster.
Usage
Apply when pipeline tasks need specific resource guarantees (memory-intensive training), when enabling/disabling execution caching, or when adding fault tolerance via retries.
Theoretical Basis
Resource management in distributed systems. Tasks declare resource needs, the scheduler allocates accordingly. Caching avoids redundant computation. Retry policies handle transient failures. All are applied as task-level decorations in a fluent API pattern.