The batch prediction job for the XGBoost pipeline fails (model server never becomes ready). Manually tested deploying to an endpoint and got this error:
ERROR:root:Failed to load model: Could not load the model: /tmp/model/0001/model.joblib. __new__() argument after ** must be a mapping, not numpy.ndarray. (Error code: 0)
This appears to be a common issue with scikit-learn - loading the model only works if the exact same library versions are used in training and prediction
Workaround
Use the serving container image (europe-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest) for the XGBoost training component. In pipelines/kfp_components/xgboost/train.py and pipelines/kfp_components/xgboost/predict.py:
The batch prediction job for the XGBoost pipeline fails (model server never becomes ready). Manually tested deploying to an endpoint and got this error:
This appears to be a common issue with scikit-learn - loading the model only works if the exact same library versions are used in training and prediction
Workaround
Use the serving container image (
europe-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest) for the XGBoost training component. Inpipelines/kfp_components/xgboost/train.pyandpipelines/kfp_components/xgboost/predict.py:base_imageto the serving container imagepackages_to_install