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name: onnx-and-model-io description: "Use Hummingbird's ONNX backend, ONNX-ML source conversion, and container save/load workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

ONNX and Model I/O

Use this sub-skill when a task mentions ONNX output, ONNX-ML input models, onnxmltools, skl2onnx, onnxruntime, ONNXContainer, saving a Hummingbird model, loading a saved digest, or deciding whether override_flag=True is safe.

Fast route

1. If the user only needs the basic hummingbird.ml.convert(...) or convert_batch(...) call shape, route to ../core-conversion/SKILL.md first and return here for ONNX-specific dependency, tracing, and artifact choices.
2. For ONNX output from a fitted sklearn-style model, pass representative test_input and use backend="onnx"; read references/onnx-workflows.md.
3. For ONNX-ML ModelProto inputs produced by onnxmltools or skl2onnx, check the ONNX-ML recipe and schema limits in references/onnx-workflows.md.
4. For saved Hummingbird artifacts, load with the digest returned by save() whenever possible; use override_flag=True only for trusted artifacts. Read references/model-io.md.
5. When conversion or load fails, diagnose from references/troubleshooting.md before changing backends or bypassing integrity checks.

Bundled runtime helper

Run scripts/onnx_conversion_smoke.py to validate a small ONNX backend conversion without relying on source notebooks or tests:

python scripts/onnx_conversion_smoke.py --json
python scripts/onnx_conversion_smoke.py --onnxml --json
python scripts/onnx_conversion_smoke.py --output hb_onnx_demo --json

The default smoke trains a tiny deterministic sklearn classifier, converts it to Hummingbird's ONNX backend, and asserts label/probability parity. --onnxml adds an ONNX-ML source-model check when onnxmltools/skl2onnx are importable. --output is the only mode that writes an artifact.

What this sub-skill owns

• ONNX target backend requirements and onnxruntime container behavior.
• ONNX-ML input model recipes and test_input behavior.
• constants.ONNX_TARGET_OPSET and constants.ONNX_OUTPUT_MODEL_NAME usage.
• ONNXContainer, TorchContainer, TVMContainer, and generic hummingbird.ml.load save/load semantics.
• Digest verification, bad-digest failures, and trusted override behavior.
• Prediction interface routing by estimator kind: predict, predict_proba, transform, decision_function, and score_samples.

Route elsewhere

• Basic conversion syntax, backend aliases, not-fitted estimators, and convert_batch: ../core-conversion/SKILL.md.
• LightGBM, XGBoost, SparkML, Prophet, and optional source package installation: ../optional-source-models/SKILL.md.
• CUDA acceleration, TVM, batching/performance tuning, and GPU-specific runtime choices: ../advanced-backends-and-performance/SKILL.md.

Minimum safety rules

• Do not treat onnx alone as sufficient for Hummingbird ONNX inference; onnxruntime must be importable for ONNXContainer prediction and loading.
• Do not bypass load integrity checks for untrusted artifacts. Prefer digest=save_return_value; use override_flag=True only after an explicit trust decision.
• Do not promise CUDA, TVM, LightGBM, XGBoost, Prophet, or SparkML behavior from this sub-skill; those paths are optional and routed to their owning sub-skills.