This file is byte-identical to the first copy the registry indexed, one of 5 indexed copies. Same content hash, so the same checks give both the same grade.
mkl-debug-ml-training from 00200200/maintainer-skills-lab is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
Investigate training failures, NaNs, missing gradients, misleading losses, and non-reproducible runs in PyTorch, Lightning, or TensorFlow/Keras. Use for a concrete training bug or regression, not an open-ended architecture or hyperparameter search.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Debug a training run
Turn the reported symptom into an observable failure on a bounded input. Preserve the user's framework, backend, and intended training semantics. A CPU or eager-mode reproduction can help isolate a bug, but does not establish what happens on the original accelerator, compiled graph, or distributed setup.
## Establish the failing boundary
Record the command, revision, Python and framework versions, imported package paths, device, dtype, precision, and execution mode. For a resume or reproducibility issue, include the data order, seed handling, checkpoint, optimizer, scheduler, and gradient-scaler state that affect the failing step. Classify dependency/import failures separately from a reproduced training bug.
Reduce to a fixed batch while retaining the relevant shapes, label semantics, masks, and value ranges. Inspect the tensors actually entering the loss. Compare their shapes to that loss's contract: class indices, probability targets, and scalar regression targets have different requirements. Do not apply a blanket reshape or squeeze to silence an error.
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The line at the top of this page pins the label git:20261007.b098ce2, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_gdjfeh4uxtj6lzdj
GET https://markdownregistry.com/api/v1/resolve?ref=00200200/maintainer-skills-lab/mkl-debug-ml-training
GET https://markdownregistry.com/api/v1/blob/c3d3be2ff119989370eaa8acacd5758e82860e5856db9ca533073f548e9fde17
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-architect-editor/SKILL.md · Two-model pipeline routing high-level design to an Architect model, and mechanical search/replace edits to an Editor…
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-bisect-regression/SKILL.md · Pinpoint regressions using automated git bisect test scripts with zero prompt token waste.
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-bump-version/SKILL.md · Evaluate conventional commits since latest tag, determine next SemVer, and surgically update version files.
00200200/maintainer-skills-lab · providers/claude/.claude/skills/mkl-humanize/SKILL.md · Edit stiff or AI-sounding prose into natural writing while preserving meaning, facts, citations, and the author's point…
