Supported machine learning tools, libraries, frameworks, and software specifications
In IBM Watson Machine Learning, you can use popular tools, libraries, and frameworks to train and deploy machine learning models and functions. The environment for these models and functions is made up of specific hardware and software specifications.
Software specifications define the language and version that you use for a model or function. They enable you to better configure the software that is used for running your models and functions. By using software specifications, you can precisely define the software version to be used and include your own extensions (for example, by using conda .yml files or custom libraries).
You can get a list of available software and hardware specifications and then use their names and IDs for use with your deployment. For details on how to do that, refer to the documentation for Python client or REST API.
Predefined software specifications
This table lists the predefined (base) model types and software specifications.
|PMML||3.0 to 4.3||pmml. (or) pmml..*3.0 - 4.3||pmml-3.0_4.3|
If model is trained with sklearn wrapper
(XGBClassifier or XGBRegressor)
in Python 3.9 use scikit-learn_1.0
- If a framework version is marked as deprecated, then support for this framework will be removed in a future update.
- Support for CPLEX 12.9 and CPLEX 12.10 is discontinued.
- Support is discontinued for software specifications based on Python 3.7 and Python 3.8.
- Support is discontinued for software specifications based on the Spark 2.4 and Spark 3.0 frameworks.
- Support is discontinued for PMML with the
- Existing PMML models that are deployed with software specification
spark-mllib_2.4are deprecated. Support for deployments based on
spark-mllib_2.4was discontinued on 4 May 2022. Use
pmml-3.0_4.3as the default software specification.
- Due to an upgrade of the Python version in supported SPSS Modeler runtimes, some models that were built with SPSS Modeler in IBM Watson Studio Cloud before 1 September 2020 can no longer be deployed in Watson Machine Learning. For details, refer to Retraining an SPSS Modeler flow.
When you have assets that rely on discontinued software specifications or frameworks, in some cases the migration is seamless. In other cases, your action is required to retrain or redeploy assets.
- Existing deployments of models that are built with discontinued framework versions or software specifications will be removed on the date of discontinuation.
- No new deployments of models that are built with discontinued framework versions or software specifications will be allowed.
- If you upgrade from a previous version of Cloud Pak for Data, deployments of models, functions, or scripts that are based on unsupported frameworks are removed. You must re-create the deployments with supported frameworks.
- If you upgrade from a previous version of Cloud Pak for Data and you have models that use unsupported frameworks, you can still access the models. However, you cannot train or score them until you upgrade the model type and software specification, as described in Managing outdated software specifications or frameworks.
For details on default hardware configurations, refer to Compute options for model training and scoring.
For details on customizing software specifications, refer to Creating a custom software specification in a project.
- For details on using and customizing environments, refer to Environments.
For specific deployment examples, refer to sample Jupyter notebooks:
Parent topic: Managing frameworks and software specifications