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name: hummingbird description: "Use Hummingbird to convert trained traditional ML models into tensor backends, validate parity, choose backends, and troubleshoot optional dependencies." disable-model-invocation: true metadata: disco-role: operating license: MIT

Hummingbird repo skill

Use this skill when a task involves the hummingbird-ml Python package: converting trained classical ML models into PyTorch, TorchScript, ONNX, or TVM-style tensor computations; validating prediction parity; saving/loading converted containers; or diagnosing optional model-source/back-end dependencies.

Quick identification

• Import package: hummingbird, hummingbird.ml.
• Distribution: hummingbird-ml.
• Core APIs: hummingbird.ml.convert(...), hummingbird.ml.convert_batch(...), and hummingbird.ml.load(...).
• Typical source models: scikit-learn estimators/pipelines, optional LightGBM/XGBoost/Prophet sklearn-style models, optional SparkML models, and ONNX-ML ModelProto inputs.
• Typical targets: "torch"/"pytorch", "torch.jit"/"torchscript", "onnx", and optional "tvm".

Install and smoke-check

Install the package and extras that match the user's workflow:

python -m pip install hummingbird-ml
python -m pip install 'hummingbird-ml[onnx]'      # ONNX output or ONNX-ML input workflows
python -m pip install 'hummingbird-ml[extra]'     # LightGBM, XGBoost, Prophet source workflows
python -m pip install 'hummingbird-ml[sparkml]'   # SparkML source workflows

Run the bundled environment probe before deeper troubleshooting:

python scripts/check_hummingbird_env.py --json

Read repo provenance when deciding whether this skill matches the current package version or should be refreshed.

Route by task

User task shape	Read next
Build a basic convert(...) call, convert a fitted sklearn estimator to PyTorch, or validate predictions quickly	core conversion
Use convert_batch, handle uneven fixed-batch inference, or recover from NotFittedError, MissingBackend, or MissingConverter in the core path	core conversion
Decide whether a scikit-learn estimator, transformer, Pipeline, ColumnTransformer, or tree strategy is supported	sklearn pipelines and operators
Work with ONNX output, ONNX-ML source models, onnxruntime, onnxmltools, skl2onnx, saved containers, digests, or override_flag	ONNX and model I/O
Convert LightGBM, XGBoost, SparkML, or Prophet models, or diagnose optional-library install/import failures	optional source models
Choose TorchScript, TVM, CUDA/GPU, threading, batching, or benchmark/performance options	advanced backends and performance

Core operating rules

1. Hummingbird converts trained models. If the source estimator must be fitted first, route the user to fit/validate it before calling convert.
2. Prefer backend="torch" for the lowest-friction CPU parity check unless the user explicitly needs ONNX, TorchScript, TVM, or GPU execution.
3. Provide representative test_input when tracing/exporting (torch.jit, ONNX, TVM), when shapes are not obvious, or when source packages such as XGBoost need feature-count inference.
4. Validate parity on the method the downstream code will call: predict, predict_proba, transform, decision_function, or score_samples.
5. Treat CUDA, TVM, SparkML, LightGBM, XGBoost, and Prophet as optional surfaces. Check imports and backend availability before promising runtime behavior.
6. Do not use benchmark-scale scripts as smoke tests. Use bundled smoke/probe helpers and tiny data unless the user explicitly requests a benchmark run.

Shared references and helpers

• API overview summarizes package entry points, backend aliases, constants, containers, and dependency surfaces.
• Cross-cutting troubleshooting covers install/import, backend, optional dependency, parity, and artifact-loading failures.
• Environment checker reports package version, available backend aliases, optional imports, and torch CUDA status without installing anything.

Verification note

This skill was generated with CPU PyTorch and ONNX workflows verified. CUDA, TVM, SparkML, LightGBM, XGBoost, and Prophet are documented as optional surfaces unless the active environment separately proves those dependencies/backends.