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Supported machine learning tools, libraries, frameworks, and software specifications

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. You can use software specifications to 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 more information, see Python client or REST API.

Predefined software specifications

You can use popular tools, libraries, and frameworks to train and deploy machine learning models and functions.

This table lists the predefined (base) model types and software specifications.

List of predefined (base) model types and software specifications
Framework** Versions Model Type Default software specification
AutoAI 0.1 NA autoai-kb_rt22.2-py3.10 (deprecated)
autoai-ts_rt22.2-py3.10 (deprecated)
hybrid_0.1
autoai-kb_rt23.1-py3.10
autoai-ts_rt23.1-py3.10
autoai-tsad_rt23.1-py3.10
autoai-tsad_rt22.2-py3.10 (deprecated)
Decision Optimization 20.1 do-docplex_20.1
do-opl_20.1
do-cplex_20.1
do-cpo_20.1
do_20.1
Decision Optimization 22.1 do-docplex_22.1
do-opl_22.1
do-cplex_22.1
do-cpo_22.1
do_22.1
Hybrid/AutoML 0.1 wml-hybrid_0.1 hybrid_0.1
PMML 3.0 to 4.3 pmml_. (or) pmml_..*3.0 - 4.3 pmml-3.0_4.3
PyTorch 1.12 (deprecated) pytorch-onnx_1.12 (deprecated)
pytorch-onnx_rt22.2 (deprecated)
runtime-22.2-py3.10 (deprecated)
pytorch-onnx_rt22.2-py3.10 (deprecated)
pytorch-onnx_rt22.2-py3.10-edt (deprecated)
PyTorch 2.0 pytorch-onnx_2.0
pytorch-onnx_rt23.1
runtime-23.1-py3.10
pytorch-onnx_rt23.1-py3.10
pytorch-onnx_rt23.1-py3.10-edt
pytorch-onnx_rt23.1-py3.10-dist
Python Functions 0.1 NA runtime-22.2-py3.10 (deprecated)
runtime-23.1-py3.10
Python Scripts 0.1 NA runtime-22.2-py3.10 (deprecated)
runtime-23.1-py3.10
Scikit-learn 1.1 scikit-learn_1.1 runtime-22.2-py3.10 (deprecated)
runtime-23.1-py3.10
Spark 3.3 mllib_3.3 spark-mllib_3.3
SPSS 17.1 spss-modeler_17.1 spss-modeler_17.1
SPSS 18.1 spss-modeler_18.1 spss-modeler_18.1
SPSS 18.2 spss-modeler_18.2 spss-modeler_18.2
Tensorflow 2.9 (deprecated) tensorflow_2.9 (deprecated)
tensorflow_rt22.2 (deprecated)
runtime-22.2-py3.10 (deprecated)
tensorflow_rt22.2-py3.10 (deprecated)
Tensorflow 2.12 tensorflow_2.12
tensorflow_rt23.1
runtime-23.1-py3.10
tensorflow_rt23.1-py3.10-dist
tensorflow_rt23.1-py3.10-edt
tensorflow_rt23.1-py3.10
XGBoost 1.6 xgboost_1.6 or scikit-learn_1.1 (see notes) runtime-22.2-py3.10 (deprecated)
runtime-23.1-py3.10

Notes:

  • If a framework version is marked as deprecated, then support for this framework will be removed in a future update.
  • For XGBoost, if model is trained with sklearn wrapper (XGBClassifier or XGBRegressor), use the scikit-learn_1.1 model type.
  • Support for CPLEX 12.9 and CPLEX 12.10 is discontinued.
  • Support is discontinued for software specifications based on Python 3.7, Python 3.8, and Python 3.9.
  • Support is discontinued for software specifications based on the Spark 2.4, Spark 3.0, and Spark 3.2 frameworks.
  • Support is discontinued for PMML with the spark-mllib_2.4 software specification.
  • Support for deployments based on spark-mllib_2.4 was discontinued on 4 May 2022. Use pmml-3.0_4.3 as the default software specification for your PMML models.
  • 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 more information, see 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 are removed on the date of discontinuation.

  • No new deployments of models that are built with discontinued framework versions or software specifications are 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.

Learn more

Parent topic: Frameworks and software specifications

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