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name: convert-models description: "Convert PyTorch, TensorFlow, MIL, scikit-learn, XGBoost, LightGBM, and LibSVM models to Core ML with coremltools." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Convert Models

Use this sub-skill when the task is to convert a trained source model or source-model artifact into a Core ML MLModel using coremltools.

Route here for

• Unified converter work with ct.convert(...) for PyTorch, TensorFlow, and MIL source programs.
• Input/output typing with ct.TensorType, ct.ImageType, flexible shapes, output names, and ct.ClassifierConfig.
• Deployment target and model-format choices: minimum_deployment_target, convert_to, compute_precision, compute_units, package_dir, skip_model_load, pass_pipeline, and PyTorch states.
• Classic converter entry points for scikit-learn, XGBoost, LightGBM, and LibSVM when their optional dependencies are installed and compatible.
• Diagnosing conversion-time errors before saving or prediction.

Do not handle here

• Core ML model save/load/predict details, MLModel metadata editing, compiled models, or platform-specific prediction setup: route to model-io-and-prediction.
• Hand-authored MIL graph construction, custom MIL passes, graph debugging utilities, and low-level MIL inspection: route to mil-and-debugging.
• Compression after conversion, post-training quantization, palettization, pruning, or deployment optimization workflows: route to optimize-models. This sub-skill only covers converter-time pass_pipeline choices.

Operating workflow

1. Identify the source family and dependency gate. Use ct.convert for PyTorch, TensorFlow, and MIL; use the classic converter namespace for scikit-learn, XGBoost, LightGBM, and LibSVM.
2. Choose source artifact format and conversion path from workflows.
3. Specify inputs, outputs, classifier metadata, deployment target, model format, precision, compute units, and optional pass pipeline using API reference.
4. Convert in memory and inspect the returned object/spec before saving or predicting. For save/load/predict instructions, route to model-io-and-prediction.
5. If conversion fails, use troubleshooting before changing source-model semantics.

Bundled script

• scripts/convert_torch_toy.py: creates a tiny torch.nn.Module, traces it, converts with an explicit named TensorType, and saves a user-provided .mlpackage. It is a smoke/demo helper and is dependency-gated on both PyTorch and coremltools.

Start with workflows for task recipes and API reference for argument-level constraints.