For replit infrastructure and model engineering teams
Replit code-model run: dataset, training worker or evaluation failure?
Connect a failed code-model experiment to its first useful exception. Separate preprocessing, distributed execution and evaluation before choosing the next investigation.
Free 30-day Team evaluation. No card or automatic subscription. Work email verification required.
Replay a resolved Replit 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 one incident your team has already resolved with the first failure, quoted evidence and remaining questions. Begin with this question: Did data preparation complete before training started? Only assign a process, device or recovery state when the submitted evidence establishes it.
Did data preparation complete before training started?
Evidence to compare: Dataset manifest, preprocessing completion and the first training-worker output.
A failed data job can prevent the trainer from receiving valid input. Establish that handoff before interpreting an idle allocation as a GPU runtime incident.
Did a worker stop before the distributed job timed out?
Evidence to compare: Per-process exception order, launch configuration and matched host kill events.
Compare the earliest retained worker error with the eventual distributed symptom. A final timeout alone does not establish a network fault or a healthy checkpoint.
Did execution fail or did the code benchmark score fall?
Evidence to compare: Evaluation process exit status, model artifact identity and separate benchmark output.
A completed evaluation with an unfavorable score differs from an evaluator crash. Keep infrastructure evidence separate from code-generation quality and dataset leakage.
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
