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 Data and AI Common Core 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.
Beginning , all software specification are FIPS compliant. For more information, see Services that support FIPS.
| Framework | Versions | Model Type | Default Software specification |
Supported platforms |
|---|---|---|---|---|
| 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 |
| PMML | 3.0 to 4.3 | pmml_. (or) pmml_..*3.0 - 4.3 | pmml-3.0_4.3 | x86, 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, s390x, PPC |
| Python Functions | NA | NA | runtime-24.1-py3.11 | x86, s390x, PPC |
| Python Functions | NA | NA | runtime-24.1-py3.11-cuda | x86 |
| Python Scripts | NA | NA | runtime-24.1-py3.11 | x86, s390x, PPC |
| R Scripts | NA | NA | runtime-24.1-r4.3 | x86 |
| R Shiny applications | NA | NA | rstudio-24.1-r4.3 | x86, PPC |
| Scikit-learn | 1.3 | scikit-learn_1.3 | runtime-24.1-py3.11 | x86, s390x, 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 | 2.14 | 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, s390x, PPC |
| Tensorflow | 2.14 | tensorflow_2.14 tensorflow_rt24.1 |
runtime-24.1-py3.11-cuda | x86 |
| XGBoost | 2.0 | xgboost_2.0 or scikit-learn_1.3 (see notes) | runtime-24.1-py3.11 | x86, s390x, 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-learnmodel type.
Supported model types and software specifications for hybrid models
The following model types and software specifications are supported for hybrid models:
| Framework | Versions | Model Type | Default Software specification |
Pipeline software specification | Supported platforms |
|---|---|---|---|---|---|
| Hybrid | 0.1 | wml-hybrid_0.1 | hybrid_0.1 | autoai-kb_rt24.1-py3.11 autoai-ts_rt24.1-py3.11 |
x86, PPC, s390x |
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.
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Existing deployments of models that are built with discontinued framework versions or software specifications are removed on the date of discontinuation.
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No new deployments of models that are built with discontinued framework versions or software specifications are allowed.
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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.
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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