numerai-model-upload

To create a portable Numerai model upload pickle, build a self-contained model.pkl with a pure numpy/pandas inference bundle and a predict() function. This ensures the artifact runs in Numerai's numerai_predict container without requiring external repository dependencies.

Numerai model upload import errors occur when the pickle lacks a self-contained predict() function or retains external repo dependencies. Constructing a pure numpy/pandas inference bundle with CPU-only weights resolves these segmentation faults and environment compatibility issues.

Ensuring Python version compatibility for a Numerai model pkl requires building a pure numpy/pandas inference bundle with CPU-only weights. This approach prevents segmentation faults and library conflicts when the artifact executes inside the numerai_predict container.

Yes, you can validate a Numerai model pkl with MCP before deployment. The validation workflow provides guidance for testing the self-contained predict() function, querying the MCP, and executing secure deployment steps for the upload artifact.

To retain correct preprocessing in a self-contained Numerai model pkl, construct a pure numpy/pandas inference bundle that encapsulates all preprocessing logic within the predict() function. This ensures the CPU-only weights execute accurately without external repo dependencies.