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name: core-conversion description: "Convert already-trained scikit-learn-style models with Hummingbird convert and convert_batch, then validate CPU PyTorch prediction parity." disable-model-invocation: true metadata: disco-role: operating license: MIT

Core conversion

Use this sub-skill when the task is to convert an already-trained scikit-learn-style estimator with hummingbird.ml.convert or hummingbird.ml.convert_batch, especially to the CPU PyTorch backend, and quickly verify that Hummingbird predictions match the source estimator.

Read when

• The user asks to "convert sklearn model with Hummingbird", "use hummingbird.ml.convert", or "compile a trained sklearn estimator to PyTorch".
• The user asks for fixed-batch or uneven-row inference with convert_batch.
• The user needs quick prediction parity checks between the original estimator and the converted container.
• The user sees conversion failures such as NotFittedError, MissingBackend, MissingConverter, or a backend requiring test_input.

Do not use as the only source for

• Detailed scikit-learn operator and pipeline coverage; route to ../sklearn-pipelines-and-operators/SKILL.md.
• ONNX output details, ONNX-ML source models, save/load, and container integrity workflows; route to ../onnx-and-model-io/SKILL.md.
• LightGBM, XGBoost, SparkML, Prophet, and optional source-package dependency handling; route to ../optional-source-models/SKILL.md.
• CUDA, TVM, TorchScript deployment tuning, threading, benchmarking, and deep performance work; route to ../advanced-backends-and-performance/SKILL.md.

Operating path

1. Confirm the estimator is trained/fitted and belongs to a supported source family.
2. Pick the smallest backend that satisfies the request: usually "torch"/"pytorch" for CPU parity work.
3. Provide representative test_input when the backend needs tracing ("torch.jit", "torchscript", "onnx", TVM) or when using convert_batch.
4. Convert with convert(...) for ordinary inference or convert_batch(...) for fixed batch-size/remainder workflows.
5. Validate method-level parity (predict, predict_proba, transform, decision_function, or score_samples) on held-out or representative input before replacing the source estimator.

Bundled references and helper

• Conversion workflows covers normal convert, fixed-batch convert_batch, parity checks, and representative examples.
• API reference summarizes signatures, backend aliases, test_input, device, extra_config, and container behavior.
• Troubleshooting maps common exceptions and parity failures to corrective actions and sibling sub-skills.
• Synthetic conversion smoke runs a deterministic tiny scikit-learn conversion check with optional JSON output.