Supported software specifications
In IBM watsonx.ai Runtime, 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.
This table lists the predefined (base) model types and software specifications.
IBM Runtime 24.1 is deprecated. Beginning 12 March 2026, you cannot create new deployments with software specifications that are based on the 24.1 runtime. To ensure a seamless experience and to leverage the latest features and improvements, switch to software specifications that are based on IBM Runtime 25.1. Support for IBM Runtime 24.1 in watsonx.ai Runtime will be removed on 16 April 2026.
The deprecated software specifications are marked with D.
| Framework | Versions | Model Type | Software specification |
|---|---|---|---|
| AI service | NA | NA | runtime-25.1-py3.12 |
| AI service | NA | NA | genai-A25-py3.12 |
| AI service | NA | NA | runtime-24.1-py3.11 |
| Decision Optimization | 20.1 | do-docplex_20.1do-opl_20.1do-cplex_20.1do-cpo_20.1 |
do_20.1 |
| Decision Optimization | 22.1 | do-docplex_22.1do-opl_22.1do-cplex_22.1do-cpo_22.1 |
do_22.1 |
| onnx or onnxruntime | 1.16 | onnxruntime_1.16 |
onnxruntime_opset_19 |
| onnx or onnxruntime | 1.17 | onnxruntime_1.17 |
onnxruntime_opset_21 |
| PMML | 3.0 to 4.3 | pmml_. (or) pmml_..*3.0 - 4.3 |
pmml-3.0_4.3 |
| Pytorch | 2.1 | onnxruntime_1.17 |
onnxruntime_opset_21 |
| PyTorch | 2.1 | pytorch-onnx_2.1pytorch-onnx_rt24.1 |
runtime-24.1-py3.11pytorch-onnx_rt24.1-py3.11pytorch-onnx_rt24.1-py3.11-edtpytorch-onnx_rt24.1-py3.11-dist |
| Python Functions | NA | NA | runtime-24.1-py3.11 |
| Python Functions | NA | NA | runtime-25.1-py3.12 |
| Python Scripts | NA | NA | runtime-24.1-py3.11 |
| Python Scripts | NA | NA | runtime-25.1-py3.12 |
| Scikit-learn | 1.3 | scikit-learn_1.3 |
runtime-24.1-py3.11 |
| Scikit-learn | 1.6 | scikit-learn_1.6 |
runtime-25.1-py3.12 |
| Spark | 3.4 | mllib_3.4 |
spark-mllib_3.4 |
| Spark | 3.5 | mllib_3.5 |
spark-mllib_3.5 |
| 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 | 2.14 | tensorflow_2.14tensorflow_rt24.1 |
runtime-24.1-py3.11tensorflow_rt24.1-py3.11-disttensorflow_rt24.1-py3.11-edttensorflow_rt24.1-py3.11 |
| Tensorflow | 2.18 | tensorflow_2.18tensorflow_rt25.1 |
runtime-25.1-py3.12tensorflow_rt25.1-py3.12 |
| XGBoost | 2.0 | xgboost_2.0 or scikit-learn_1.3 |
runtime-24.1-py3.11 |
| XGBoost | 2.1 | xgboost_2.1 or scikit-learn_1.6 |
runtime-25.1-py3.12 |
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 |
|---|---|---|---|---|
| Hybrid | 0.1 | wml-hybrid_0.1 |
hybrid_0.1 |
autoai-kb_rt25.1-py3.12 autoai-ts_rt25.1-py3.12 |
| Hybrid | 0.1 | wml-hybrid_0.1 |
hybrid_0.1 |
autoai-kb_rt24.1-py3.11 autoai-ts_rt24.1-py3.11 |
Supported software specifications for deploying NLP models
Here is the list of supported software specifications for deploying NLP models:
| Software specification | Python version | Supported runtimes |
|---|---|---|
runtime-24.1-py3.11 |
3.11 | AI Function, AI Service |
runtime-25.1-py3.12 |
3.12 | AI Function, AI Service |
Handling assets that rely on discontinued software specifications or frameworks
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 an earlier software version, 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 an earlier software version 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.