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.
This table lists the predefined (base) model types and software specifications.
| Framework | Versions | Model Type | Software specification |
|---|---|---|---|
| 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 |
| PMML | 3.0 to 4.3 | pmml_. (or) pmml_..*3.0 - 4.3 | pmml-3.0_4.3 |
| 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 |
| 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 |
| Python Functions | NA | NA | runtime-24.1-py3.11 |
| Python Scripts | NA | NA | runtime-24.1-py3.11 |
| Scikit-learn | 1.1 | scikit-learn_1.1 | runtime-23.1-py3.10 |
| Scikit-learn | 1.3 | scikit-learn_1.3 | runtime-24.1-py3.11 |
| Spark | 3.3 | mllib_3.3(deprecated) | spark-mllib_3.3(deprecated) |
| Spark | 3.4 | mllib_3.4 | spark-mllib_3.4 |
| 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 | 2.12 | 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 |
| 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 |
| XGBoost | 1.6 | xgboost_1.6 or scikit-learn_1.1 (see notes) | runtime-23.1-py3.10 |
| XGBoost | 2.0 | xgboost_2.0 or scikit-learn_1.3 | runtime-24.1-py3.11 |
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