For skild ai infrastructure and model engineering teams

Skild S1 training: data preparation or GPU-worker failure?

Compare the first offline worker error with dataset and device context. Keep model behavior evaluation separate from infrastructure failure evidence.

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Why this workflow may fit Skild AI

Skild describes S1 as a robotics model trained on diverse data using NVIDIA AI infrastructure, with in-context learning from demonstrations. A training-worker incident differs from a robot behavior or demonstration outcome.

Read the official Skild AI workflow reference

Evaluation scope: This uses the public S1 account, not assumptions about private cluster configuration. Denpex does not certify robot safety, policy competence or the correctness of learned actions. Evaluate only approved offline incident evidence.

Prepared from public materials. This is a proposed evaluation, not a report of an incident at Skild AI or an endorsement.

Selectable demo logs are shared examples, not logs from Skild AI. Upload a shareable incident from your own workflow to evaluate the fit.

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Replay a resolved Skild AI 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.

training_logs.txt

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Upload or drop a plain-text log

Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.

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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 the training input stage finish correctly? Only assign a process, device or recovery state when the submitted evidence establishes it.

Did the training input stage finish correctly?

Evidence to compare: Dataset preparation result, sample-shape validation and the first loader traceback.

An invalid demonstration can stop a job before GPU execution. Identify the input stage before recommending device or distributed-runtime changes.

Did a model process fail or produce a poor evaluation?

Evidence to compare: Worker exit status, actual runtime exception and separate evaluation metrics.

A completed run with weak task performance is not an infrastructure crash. Preserve the boundary between model quality and executable failure.

Does the device claim have a matched host event?

Evidence to compare: Affected process, device mapping and relevant host timestamps.

A lost-worker message does not identify a bad GPU. Leave missing mappings unresolved and compare host evidence only when it belongs to the same job.

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.