For university of virginia rivanna infrastructure and platform teams

University of Virginia Rivanna: Open OnDemand request or GPU POD placement?

Connect the first useful exception with the relevant runtime evidence. Did the GPU POD request include its placement constraint?

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Why this workflow may fit University of Virginia Rivanna

UVA documents Rivanna GPU access through Slurm and Open OnDemand. GPU POD nodes require an additional placement constraint.

Read the official University of Virginia Rivanna workflow reference University of Virginia Rivanna additional implementation context

Evaluation scope: The learning material is a documented workflow, not a permanent hardware inventory. Do not infer that every GPU allocation belongs to the POD or uses identical networking. Use approved redacted evidence. No native integration, automatic repair or current outage is implied.

Prepared from public materials. This is a proposed evaluation, not a report of an incident at University of Virginia Rivanna or an endorsement.

Selectable demo logs are shared examples, not logs from University of Virginia Rivanna. Upload a shareable incident from your own workflow to evaluate the fit.

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Replay a resolved University of Virginia Rivanna workflow incident

Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.

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Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.

What a useful evaluation should establish

Compare one incident your team already resolved with the first failure, cited evidence and remaining questions. Did the GPU POD request include its placement constraint? Only assign a rank, device or safe recovery state when the submitted evidence establishes it.

Did the GPU POD request include its placement constraint?

Evidence to compare: Selected OOD partition, requested GPU type and Slurm placement constraints.

Selecting a GPU does not necessarily select a POD node. Establish the allocation topology before interpreting distributed startup or communication errors as device faults.

Was a GPU assigned to the affected process?

Evidence to compare: Job resource request, actual allocation and process-visible device mapping.

A GPU-capable node does not prove this process owns a device. Establish the allocation before interpreting missing visibility as a driver failure.

Which worker reported the earliest useful exception?

Evidence to compare: Matched process identifiers, per-worker timestamps and first retained exception.

A coordinator timeout may follow a worker exit. Retain the earlier exception and avoid assigning a rank or device unless the submitted identifiers support that mapping.

Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.

Inside Denpex

See the evidence, not just the claim

Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.