Managing assets that refer to discontinued software specifications
Copy link to section
During migration, assets that refer to the discontinued software specification are mapped to a comparable-supported default software specification (only in cases where the model type is still supported).
When you create new deployments of the migrated assets, the updated software specification in the asset metadata is used.
Existing deployments of the migrated assets are updated to use the new software specification. If deployment or scoring fails due to framework or library version incompatibilities, follow the instructions in Updating software specifications.
If the problem persists, follow the steps that are listed in Updating a machine learning model.
Migrating assets that refer to discontinued framework versions
Copy link to section
During migration, model types are not be updated. You must manually update this data. For more information, see Updating a machine learning model.
After migration, the existing deployments are removed and new deployments for the deprecated framework are not allowed.
Updating software specifications
Copy link to section
You can update software specifications from the UI or by using the API. For more information, see the following sections:
Score the Python function to generate predictions.
If your Python function fails during scoring, the function is not compatible with the new runtime or software specification version that was used for saving the Python function. In this case, use Option 2.
Option 2: Modify the function code and save it with a compatible runtime or software specification
Copy link to section
Modify the Python function code to make it compatible with the new runtime or software specification version. In some cases, you must update dependent libraries that are installed within the Python function code.
Save the Python function with the new runtime or software specification version.
Some models that were built with SPSS Modeler in IBM watsonx.ai Studio Cloud before 1 September 2020 can no longer be deployed by using watsonx.ai Runtime. This problem is caused by an upgrade of the Python version in supported SPSS Modeler
runtimes. If you're using one of the following six nodes in your SPSS Modeler flow, you must rebuild and redeploy your models with SPSS Modeler and watsonx.ai Runtime:
XGBoost Tree
XGBoost Linear
One-Class SVM
HDBSCAN
KDE Modeling
Gaussian Mixture
To retrain your SPSS Modeler flow, follow these steps:
If you're using the watsonx.ai Studio user interface, open the SPSS Modeler flow in watsonx.ai Studio, retrain, and save the model to watsonx.ai Runtime. After you save the model to the project, you can promote it to a deployment space and
create a new deployment.
If you're using REST API or Python client, retrain the model by using SPSS Modeler and save the model to the watsonx.ai
Runtime repository with the model type spss-modeler-18.2.
About cookies on this siteOur websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising.For more information, please review your cookie preferences options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.