GPU scheduling.
GPU scheduling allocates scarce and expensive accelerator capacity across competing training and inference workloads, through queues, quotas, priorities and sharing mechanisms.
Where GPU scheduling fits on a long engagement.
GPU utilisation in most organisations is poor. Devices sit idle between jobs, are held by interactive sessions nobody is using, or run workloads consuming a fraction of their memory. Given the hardware cost, utilisation is a direct financial metric rather than an efficiency nicety.
Fair access is the organisational half. Without queues and quotas, capacity goes to whoever grabs it first or shouts loudest, which is neither efficient nor good for a team. Priority classes that let production inference pre-empt exploratory training are the usual shape of a working answer.
What an assigned team does with GPU scheduling.
Interactive development is the hardest case. A specialist needs a GPU for exploration, but holding one for a day while thinking is an expensive idle.
Time limits and automatic reclamation are the mechanisms that work, and putting them in place is platform work scoped under devops as a service.
What we use GPU scheduling for.
- Utilisation measured and improved Idle and underused devices identified, which is directly a cost saving.
- Priorities that protect production Inference able to pre-empt exploratory training rather than queueing behind it.
- Interactive sessions reclaimed Time limits on held devices, so thinking time is not billed as compute.
How GPU scheduling capacity is assigned.
GPU capacity management is assigned inside platform work, with utilisation treated as a cost metric rather than an efficiency detail.
Tell us what your roadmap needs GPU scheduling for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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