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Run:ai’s GPU behavior is controlled at three different layers. A workload manifest declares the GPU fraction, device count, per-workload portion limit, and GPU memory it needs. Cluster-wide scheduling policy decides how projects share capacity, how queues are ordered, and when training work may be preempted. The dashboard and scheduler logs show whether that policy is producing the expected allocations.
The documented workload fields are not a complete reference for scheduler fairness or GPU-sharing policy. For a reliable configuration, match every setting to the Run:ai release deployed in your cluster, then validate the result with project allocations, quotas, pending-workload counts, wait times, and node-level resource fit.
What the documented settings actually control
The clearest distinction is between a request and a policy. A request travels with one workload and tells Run:ai what resources that workload must receive. A policy applies across projects and workloads and determines who receives resources when demand exceeds capacity. The v2.19 Workload YAML Reference documents the request fields; it does not publish a complete catalog of scheduler keys, defaults, or fairness switches.
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|---|---|---|
| Workload request | How much GPU, GPU memory, CPU, and CPU memory one workload asks for | Workload YAML and the workload’s detail view |
| Cluster scheduling policy | How projects compete, how queues are ordered, and whether eligible work can be preempted | The release-matched administrator configuration and observed scheduling behavior |
| Operations and diagnostics | Whether allocations match quota or fair-share expectations and why work is pending | Dashboard Analysis, quota views, pending queues, and scheduler logs |
Do not infer a cluster-wide fairness rule merely because a manifest contains gpu, gpuDevices, gpuLimit, or gpuMemory. Those fields describe resource requirements and limits for the workload.
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Workload GPU fields in Run:ai v2.19
Use the field that matches the resource you intend to reserve. The same definitions apply to the documented workspace, training, distributed, and inference workload types.
| Field | Meaning | Range or condition | Scheduling implication |
|---|---|---|---|
gpu |
Fraction of one GPU allocated to the workload | Use for a fractional GPU request | Requests a share of a device; it does not define project fairness |
gpuDevices |
Number of GPU devices allocated | Supports a device count greater than one for backward compatibility | Requests whole GPU devices rather than a fractional portion |
gpuLimit |
Portion limit associated with a fractional GPU request | 0 through 1 | Caps the portion that the workload may consume; it is not a cluster-wide sharing policy |
gpuMemory |
Amount of GPU memory allocated to the workload | The requested amount must be available and guaranteeable | The workload is not scheduled until Run:ai can guarantee that memory |
Fractional GPU requests with gpu
In v2.19, gpu represents a GPU fraction. This is the appropriate field when a workload needs less than a whole device, but the fraction alone does not explain how competing projects will be treated. A small request can still wait if no node can satisfy the request, if the project has exhausted its available allocation, or if the cluster policy gives another queued workload priority.
Whole-device requests with gpuDevices
gpuDevices specifies the number of GPU devices to allocate. The reference retains support for a count greater than one for backward compatibility. Use this field when the application requires discrete devices, such as a distributed job that cannot operate on a fractional slice. A device count is a workload requirement, not a statement that the project should receive a particular share of the cluster.
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The documented range for gpuLimit is 0 to 1. The field is described as the portion limit when a fractional GPU is requested. Keep the limit and the requested fraction conceptually separate: the request says what the workload seeks, while the limit constrains the portion it may use. Neither value establishes how the scheduler divides capacity between projects.
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Memory guarantees with gpuMemory
A workload that requests gpuMemory remains unscheduled until the system can guarantee the requested amount. This explains a common queue symptom: a cluster may show apparently free GPU compute while the workload waits because available memory is fragmented, already reserved, or located on nodes that cannot host the complete request. Check both GPU memory and device placement before changing a fairness setting.
How to tell a request problem from a policy problem
Start with the cluster state, then compare the workload’s request with what the scheduler can place. The following sequence avoids changing policy when the real issue is an oversized or unplaceable request.
- Open the Run:ai Dashboard Analysis view and determine whether the cluster is fully loaded.
- For a pending workload, record requested CPU, CPU memory, GPU, and GPU memory.
- Check whether any node has enough free resources to host the entire request. A request that fits only by combining resources across nodes may remain pending if the workload requires one node.
- Compare the project’s current allocation with its quota and, when the cluster is fully loaded, with its expected fair-share portion.
- Check pending-workload count and wait time. A growing queue with adequate node capacity points toward ordering, quota, or policy; a request that fits nowhere points toward resource shape or fragmentation.
This procedure follows the operational guidance in the v2.18 Dashboard Analysis documentation. It is a diagnostic method, not a benchmark or a guarantee of a particular scheduling algorithm.
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Queues, quotas, and fairness in the dashboard
When the cluster is fully loaded
Review each active project’s allocated GPU and CPU counts. Compare those allocations with the project’s fair-share portion. A project receiving less than its expected portion may simply be behind higher-priority work, may have requests that do not fit the remaining nodes, or may be constrained by quota. The comparison is meaningful only when there is genuine contention; when capacity is idle, projects can temporarily receive more or less than a target share without indicating unfairness.
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When work is queued despite apparent capacity
Inspect the pending workload’s complete resource request and test node fit. GPU compute shown as free is not sufficient if the requested GPU memory cannot be guaranteed, if CPU or system memory is unavailable on the same node, or if the requested device count cannot be placed together. Check the project’s current allocation and quota before treating the queue as a scheduler defect.
Quota versus allocation
The quota dashboard distinguishes quota from allocation. Quota is the amount of resources reserved for a project’s use; allocation is what the project is using now. The dashboard also shows pending workloads. Use all three values together: a project can have unused quota while its particular request is unplaceable, or it can have a long queue because its allocation is already consuming the resources available under policy.
| Observation | What to compare | Likely next check |
|---|---|---|
| Fully loaded cluster | Project GPU/CPU allocation versus fair-share portion | Priority, quota, and whether another project is temporarily ahead |
| Queued workload with idle-looking GPUs | Requested GPU memory, CPU, CPU memory, and device count versus one node’s free resources | Node fit and memory guarantee |
| Quota appears available | Quota versus current allocation and pending requests | Whether the request shape can be placed under the remaining capacity |
| Queue is growing | Pending count and wait time over time | Project contention, policy ordering, and preemption behavior |
Preemption and training queues
Run:ai’s administrator guidance states that training workloads can be preempted, queued, and resumed automatically according to predefined policies. This behavior is different from interactive capacity that an operator expects to remain continuously available. When investigating an interrupted training job, check whether it was preempted under the applicable policy before treating the event as a failure.
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Track pending queue size and wait time rather than relying on a single snapshot. A short queue can hide a rapidly increasing wait time, while a large queue may clear quickly if jobs are resumable and capacity returns. Record workload type alongside these metrics so interactive and training workloads are not interpreted as if they had identical scheduling expectations.
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Scheduler diagnostics and verbosity
When dashboard evidence does not explain a scheduling decision, increase scheduler logging temporarily. The Run:ai Diagnostics documentation describes editing RunaiConfig and adding runai-scheduler arguments under the scheduler section, with verbosity set to 6 in its example.
Use the exact RunaiConfig structure and argument syntax documented for the release you run; the diagnostics page is a troubleshooting guide, not a universal fairness configuration recipe. Capture logs while reproducing the queue or allocation decision, then return verbosity to the normal level after collecting evidence.
The official warning is explicit: “Verbose scheduler logs consume a significant amount of disk space.” Check available disk capacity before enabling level 6, set a short observation window, and make sure log rotation or retention will not be overwhelmed. Raising verbosity does not enable GPU sharing, alter quotas, or make a project fairer; it only provides more evidence about the scheduler’s decisions.
Version alignment before changing configuration
The available documentation spans three release contexts:
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- Workload field definitions are from v2.19.
- Queue, quota, and fair-share investigation guidance is from v2.18.
- Scheduler diagnostic instructions are on the latest documentation page.
These pages should not be treated as one versioned configuration reference. Before applying a scheduler change, identify the controller and dashboard versions installed in the cluster and open the matching administrator documentation. Confirm the exact key names, defaults, accepted values, and upgrade behavior there. The published material above does not establish a complete, version-specific catalog for enabling GPU sharing or enforcing fairness, so do not invent missing keys from workload YAML fields.
A safe administrator workflow
- Inventory the deployment. Record the Run:ai release, Kubernetes context, controller configuration source, and the workload API version used by your manifests.
- Classify the symptom. Decide whether the issue is an invalid request, a node-fit problem, a quota or allocation condition, a queue-ordering question, or a possible preemption event.
- Inspect requests. Verify
gpuversusgpuDevices, thegpuLimitvalue, and anygpuMemoryguarantee. Check CPU and system-memory requirements at the same time. - Inspect policy outcomes. In Dashboard Analysis, compare active project allocations with fair-share portions when the cluster is full. In the quota view, compare reserved quota, current allocation, and pending workloads.
- Check placement. Confirm that one node can satisfy the complete request, including GPU memory and device count.
- Observe the queue. Record pending count and wait time, and note whether the workload is interactive or training.
- Collect targeted logs. If the decision remains unclear, use the release-matched
RunaiConfigscheduler diagnostics procedure, raise verbosity only for the required interval, and monitor disk usage. - Change one layer at a time. Correct the workload request before altering policy. After any policy change, repeat the same allocation, quota, pending-count, wait-time, and node-fit checks.
Common configuration mistakes
Assuming a fractional request creates fairness
A fractional gpu value controls one workload’s request. It does not guarantee that every project receives an equal percentage of the cluster. Fairness must be evaluated through project allocations and the scheduler policy documented for the deployed release.
Using GPU compute as a proxy for GPU memory
Free compute capacity does not prove that a gpuMemory request can be guaranteed. Always inspect memory availability and node placement together.
Reading quota as current usage
Quota is reserved capacity; allocation is current use. Confusing the two can lead to unnecessary policy changes or an incorrect conclusion that a project is over-consuming.
Turning on verbose logs permanently
Verbosity level 6 is an investigation aid. Because verbose scheduler logs consume significant disk space, use a bounded capture period and restore the normal logging level afterward.
Mixing release documentation
A v2.19 workload field definition, a v2.18 dashboard explanation, and a latest diagnostics page answer different questions. Verify every configuration key against the version actually deployed before copying it into production.
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