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. They enable you to better 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 details on how to do that, refer to the documentation for Python client or REST API.
Predefined software specifications
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 | Default Software specification |
|---|---|---|---|
| AutoAI | 0.1 | NA | autoai-kb_rt22.2-py3.10 autoai-ts_rt22.2-py3.10 |
| 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 |
| Hybrid/AutoML | 0.1 | wml-hybrid_0.1 | hybrid_0.1 |
| PMML | 3.0 to 4.3 | pmml_. (or) pmml_..*3.0 - 4.3 | pmml-3.0_4.3 |
| PyTorch | 1.12 | pytorch-onnx_1.12 pytorch-onnx_rt22.2 |
runtime-22.2-py3.10 pytorch-onnx_rt22.2-py3.10 pytorch-onnx_rt22.2-py3.10-edt |
| Python Functions | 0.1 | NA | runtime-22.2-py3.10 |
| Python Scripts | 0.1 | NA | runtime-22.2-py3.10 |
| Scikit-learn | 1.1 | scikit-learn_1.1 | runtime-22.2-py3.10 |
| Spark | 3.3 | mllib_3.3 | spark-mllib_3.3 |
| 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 | 2.9 | tensorflow_2.9 tensorflow_rt22.2 |
runtime-22.2-py3.10 tensorflow_rt22.2-py3.10 |
| XGBoost | 1.6 | xgboost_1.6 or scikit-learn_1.1 (see notes) | runtime-22.2-py3.10 |
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 will be removed on the date of discontinuation.
- No new deployments of models that are built with discontinued framework versions or software specifications will be allowed.
Learn more
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For details on customizing software specifications, refer to Customizing with third-party and private Python libraries.
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For details on using and customizing environments, refer to Environments.
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For specific deployment examples, refer to sample Jupyter notebooks:
Parent topic: Frameworks and software specifications