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Chamber supports two workload classes that determine how workloads access GPU capacity: Reserved and Elastic.

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Overview

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Reserved Workloads

Reserved workloads use capacity from the team’s reservation. They are guaranteed resources and cannot be preempted. Use reserved for:
  • Long-running training that can’t be interrupted
  • Workloads with delivery commitments or deadlines
  • ML pipelines that must complete reliably

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Elastic Workloads

Elastic workloads use idle capacity. They maximize utilization but can be preempted when reserved workloads need resources. Use elastic for:
  • Experiments and exploratory work
  • Development and debugging
  • Batch workloads that can checkpoint and resume
Design elastic workloads to checkpoint periodically so they can resume if preempted.

Scheduling

How Chamber schedules workloads