model_artifacts_format

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Axis model_artifacts_format on sub-layer L8_B_saved_objects (layer l8).

Sub-layer

L8_B_saved_objects

Axis metadata

  • Default: 'pickle'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

joblib – operational

Default sklearn / xgboost serialisation via joblib.

Optimised for numpy-array-heavy estimators (sklearn / xgboost / lightgbm). Smaller and faster than plain pickle for typical sklearn fitted-model graphs.

When to use

Default; broad compatibility across sklearn / xgboost / lightgbm.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: pickle, onnx, pmml

Last reviewed 2026-05-05 by macroforecast author.

onnx – operational

ONNX export (where supported by the family).

Open Neural Network Exchange format. Cross-language deployment (C++ / C# / Java / JS runtimes) and faster inference than the native sklearn pickle. Supported for sklearn / xgboost / lightgbm / pytorch families; raises if the active L4 family lacks an ONNX exporter.

When to use

Cross-language deployment; production inference servers.

When NOT to use

Models without ONNX support (BVAR, DFM, MRF, custom callables).

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

  • ONNX specification. https://onnx.ai/

Related options: joblib, pmml

Last reviewed 2026-05-05 by macroforecast author.

pickle – operational

Plain Python pickle (less efficient than joblib).

Compatibility option for older toolchains or non-sklearn estimators that don’t benefit from joblib’s array optimisation. Larger files but maximally portable across Python versions.

When to use

Compatibility with older toolchains.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: joblib, onnx, pmml

Last reviewed 2026-05-05 by macroforecast author.

pmml – operational

PMML export (PMML-compatible families only).

Predictive Model Markup Language; XML-based exchange format primarily used in enterprise / Java deployments. Supported for linear / tree-family models via sklearn2pmml.

When to use

Enterprise / Java deployment. Selecting pmml on l8.model_artifacts_format activates this branch of the layer’s runtime.

When NOT to use

Modern ML deployment – ONNX is more widely supported.

References

Related options: joblib, onnx

Last reviewed 2026-05-05 by macroforecast author.