For runhouse kubetorch infrastructure and model engineering teams
Kubetorch worker failure: Python exception or Kubernetes event?
Keep the remote traceback, pod lifecycle and host timeline together. Evaluate what a diagnosis adds to Kubetorch existing error propagation and recovery.
Free 30-day Team evaluation. No card or automatic subscription. Work email verification required.
Replay a resolved Runhouse Kubetorch workflow incident
Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.
Before uploading, review cloud data handling and local options.
Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.
Try Denpex for 30 days. FREE.
No credit card. No automatic subscription. See whether Denpex helps with an incident you already understand.
Start your free 30-day Team evaluation
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 the first failure, quoted evidence and unresolved questions with an incident your team has already resolved. Begin with this question: Is the Python exception the initiating failure? Only attribute a process, rank, device or recovery state when the submitted evidence establishes it.
Is the Python exception the initiating failure?
Evidence to compare: The remote traceback and worker timestamps propagated to the caller.
A caller can see a wrapper exception after the actual process stopped. Preserve the earliest retained worker evidence instead of diagnosing the wrapper alone.
Did Kubernetes terminate or replace the worker?
Evidence to compare: Approved pod events, exit reason and container restart sequence.
An eviction or host-memory event is different from a model exception. A restart by the orchestration layer does not make every later client error a separate cause.
Did recovery produce a healthy continuation?
Evidence to compare: Retry history, fresh worker logs and the workload output contract.
Existing automatic recovery can succeed. Evaluate residual failures and missing evidence rather than presenting a recovered event as an ongoing outage.
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.
Follow the failure through the cascade

Prepare the hardware escalation

Ask for the evidence that is missing

Keep the investigation in the editor
