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.
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
andrstudio-23.1-r4.2
(for x86) andshiny-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