Supported 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 Watson Data API.

Supported software specifications for machine learning frameworks

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

The following 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
Supported platforms
AutoAI 0.1 NA hybrid_0.1
autoai-kb_rt23.1-py3.10 (deprecated)
autoai-ts_rt23.1-py3.10 (deprecated)
autoai-tsad_rt23.1-py3.10(deprecated)
x86, s390x, PPC
AutoAI 0.1 NA hybrid_0.1
autoai-kb_rt24.1-py3.11
autoai-ts_rt24.1-py3.11
autoai-tsad_rt24.1-py3.11
x86, PPC
Decision Optimization 20.1 do-docplex_20.1
do-opl_20.1
do-cplex_20.1
do-cpo_20.1
do_20.1 x86, PPC
Decision Optimization 22.1 do-docplex_22.1
do-opl_22.1
do-cplex_22.1
do-cpo_22.1
do_22.1 x86, PPC
Hybrid/AutoML 0.1 wml-hybrid_0.1 hybrid_0.1 x86, PPC, s390x
PMML 3.0 to 4.3 pmml_. (or) pmml_..*3.0 - 4.3 pmml-3.0_4.3 x86, PPC
PyTorch 2.0 (deprecated) pytorch-onnx_2.0 (deprecated)
pytorch-onnx_rt23.1 (deprecated)
runtime-23.1-py3.10 (deprecated)
pytorch-onnx_rt23.1-py3.10 (deprecated)
pytorch-onnx_rt23.1-py3.10-edt (deprecated)
pytorch-onnx_rt23.1-py3.10-dist (deprecated)
x86, s390x, PPC
PyTorch 2.1 pytorch-onnx_2.1
pytorch-onnx_rt24.1
runtime-24.1-py3.11
pytorch-onnx_rt24.1-py3.11
pytorch-onnx_rt24.1-py3.11-edt
pytorch-onnx_rt24.1-py3.11-dist
x86, PPC
Python Functions NA NA runtime-23.1-py3.10 (deprecated) x86, PPC, s390x
Python Functions NA NA runtime-24.1-py3.11 x86, PPC
Python Functions NA NA runtime-23.1-py3.10-cuda (deprecated)
runtime-24.1-py3.11-cuda
x86
Python Scripts NA NA runtime-23.1-py3.10 (deprecated) x86, s390x, PPC
Python Scripts NA NA runtime-24.1-py3.11 x86, PPC
R Scripts NA NA runtime-23.1-r4.2 (deprecated) x86, PPC
R Scripts NA NA runtime-24.1-r4.3 x86, PPC
R Shiny applications NA NA rstudio-23.1-r4.2 (deprecated)
rstudio-24.1-r4.3
x86, PPC
Scikit-learn 1.1 (deprecated) scikit-learn_1.1 (deprecated) runtime-23.1-py3.10 (deprecated) x86, s390x, PPC
Scikit-learn 1.3 scikit-learn_1.3 runtime-24.1-py3.11 x86, PPC
Spark 3.3 (deprecated) mllib_3.3 (deprecated) spark-mllib_3.3 (deprecated) x86, PPC
Spark 3.4 mllib_3.4 spark-mllib_3.4 x86, PPC
SPSS 17.1 spss-modeler_17.1 (see notes) spss-modeler_17.1 x86
SPSS 18.1 spss-modeler_18.1 (see notes) spss-modeler_18.1 x86
SPSS 18.2 spss-modeler_18.2 (see notes) spss-modeler_18.2 x86
Tensorflow 2.12 (deprecated) tensorflow_2.12 (deprecated)
tensorflow_rt23.1 (deprecated)
runtime-23.1-py3.10 (deprecated)
tensorflow_rt23.1-py3.10-dist (deprecated)
tensorflow_rt23.1-py3.10-edt (deprecated)
tensorflow_rt23.1-py3.10 (deprecated)
x86, s390x, PPC
Tensorflow 2.12 (deprecated) tensorflow_2.12(deprecated)
tensorflow_rt23.1(deprecated)
runtime-23.1-py3.10-cuda(deprecated) x86
Tensorflow 2.14 tensorflow_2.14
tensorflow_rt24.1
runtime-24.1-py3.11
tensorflow_rt24.1-py3.11-dist
tensorflow_rt24.1-py3.11-edt
tensorflow_rt24.1-py3.11
x86, PPC
Tensorflow 2.14 tensorflow_2.14
tensorflow_rt24.1
runtime-24.1-py3.11-cuda x86
XGBoost 1.6 (deprecated) xgboost_1.6 (deprecated) or scikit-learn_1.1 (deprecated)(see notes) runtime-23.1-py3.10 (deprecated) x86, s390x, PPC
XGBoost 2.0 xgboost_2.0 or scikit-learn_1.3 (see notes) runtime-24.1-py3.11 x86, PPC

Important:

  • If a framework version is marked as deprecated, then support for this framework will be removed in a future release.

  • For XGBoost, if model is trained with sklearn wrapper (XGBClassifier or XGBRegressor), use the scikit-learn_1.1 model type in Python 3.10.

  • You can also deploy R Shiny apps (version 0.1). Software specifications available for Shiny apps: rstudio_r4.2 and rstudio-23.1-r4.2 (for x86) and shiny-r3.6 (deprecated, used for x86 and PPC).

  • None of the R Shiny software specifications are FIPS-compliant.

  • For SPSS Modeler 17.1, 18.1 and 18.2, FIPS compliance is supported only for online deployments. Batch deployments for these model types are not FIPS compliant.

  • Beginning Cloud Pak for Data version 4.8.4, you can create deployments that use GPU inferencing with Watson Machine Learning API for the runtime-23.1-py3.10-cuda software specification. This feature is not available in Cloud Pak for Data versions 4.8.3 and earlier.

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.

Parent topic: Frameworks and software specifications