Troubleshooting

These checks apply to local inference and the example notebook in the User Guide.

Environment and Dependencies

If an import fails, confirm that SDM and the example dependencies are installed in the Python environment running your script or notebook. The notebook installs SDM, pandas, scikit-learn, and openml in its first code cell.

The notebook explicitly selects CPU and does not require a GPU. For a CUDA configuration, check that torch.cuda.is_available() returns True. If it returns False on an NVIDIA GPU host, check the driver and CUDA-enabled PyTorch installation. See the SDM installation guide and PyTorch installation instructions.

Model Download and Access

If weights fail to download, check connectivity to Hugging Face and the error message. Open the model page to confirm access. If authentication or acceptance of terms is required, complete those steps and configure your Hugging Face login in the environment running SDM.

The first model initialization may download pretrained weights. Subsequent runs can reuse the cache. If storage or cache configuration causes an error, see the Hugging Face cache configuration.

GPU Memory

If inference raises a CUDA out-of-memory error, check the selected model size and the available GPU memory. For Kumo Tabular, review the size parameter in the model reference to select a smaller model. The notebook sets size="small" and runs on CPU, so it does not use GPU memory.

Input Columns and Types

Check that the target has the intended semantic type, that query features exclude the target, and that the selected model supports your input types and prediction task. See table semantics and the model reference.

Report a Library Issue

Search or open an issue in the SDM repository. Include your SDM and PyTorch versions, device details, the error message, and a minimal reproduction. Remove credentials and private data before sharing it.