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name: mil-and-debugging description: "Advanced coremltools MIL Builder, pass pipeline, custom op, typed execution, and debugging workflows for isolating conversion and runtime failures." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

MIL and debugging

Use this sub-skill when the task is about advanced coremltools internals rather than an ordinary framework-to-Core ML recipe:

• Build or inspect MIL Builder programs, functions, input_specs, op names, dtypes, and shapes.
• Control graph pass pipelines, bisect pass-related failures, or set graph pass options.
• Diagnose deployment-target compatibility, typed execution, precision regressions, or backend-specific ML Program versus neural network behavior.
• Register composite or custom operators and understand when custom layers are only viable for the neural network backend.
• Use experimental MLModelInspector, MLModelValidator, MLModelComparator, Torch comparators, or submodel extraction to isolate conversion/runtime failures.

Do not use this sub-skill for routine conversion recipes; route those to the sibling convert-models sub-skill. Route saved-model loading, artifact metadata, prediction, and packaging operations to model-io-and-prediction. Route palettization, pruning, quantization, and compression workflows to optimize-models.

Operating map

1. Classify the failure stage: MIL construction/type inference, graph passes, backend lowering/save, deployment compatibility, or Core ML runtime/prediction.
2. Use API reference for exact import paths and capability boundaries.
3. Use workflows for concrete triage sequences and code skeletons.
4. Use troubleshooting for symptom-to-action mapping, especially native BlobWriter/runtime issues and experimental debugger limitations.
5. For a minimal environment probe, run scripts/mil_smoke.py with --help, then choose --convert-to mlprogram or --convert-to neuralnetwork based on the backend under investigation.

Minimal smoke command

python scripts/mil_smoke.py --convert-to mlprogram --output mil_smoke.mlpackage
python scripts/mil_smoke.py --convert-to neuralnetwork --output mil_smoke.mlmodel

The smoke script intentionally avoids prediction by default because Core ML prediction and intermediate-output retrieval are runtime-dependent and commonly require macOS or configured remote-device support.