Supported machine learning tools, libraries, frameworks, and 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. 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. If you see (F) next to the name of a software specification, this means that you can use this software specification is FIPS 140-2 compliant. For more
information on FIPS-compliance, refer to Services that support FIPS. Note that the s390x platform does not support FIPS, even you use FIPS-compliant software specifications.
| Framework | Versions | Model Type | Default Software specification |
Supported platforms |
|---|---|---|---|---|
| AutoAI | 0.1 | NA | hybrid_0.1 autoai-kb_rt23.1-py3.10 (F) autoai-ts_rt23.1-py3.10 (F) autoai-tsad_rt23.1-py3.10 (F) |
x86 |
| AutoAI | 0.1 | NA | hybrid_0.1 autoai-kb_rt22.1-py3.9 (deprecated) autoai-kb_rt22.2-py3.10 (F) autoai-ts_rt22.1-py3.9 (deprecated) autoai-ts_rt22.2-py3.10 (F) |
x86, PPC |
| AutoAI | 0.1 | NA | hybrid_0.1 autoai-kb_rt22.2-py3.10 (F) autoai-ts_rt22.2-py3.10 (F) |
s390x |
| Decision Optimization | 20.1 | do-docplex_20.1 do-opl_20.1 do-cplex_20.1 do-cpo_20.1 |
do_20.1 (F) | 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 (F) | x86, PPC |
| Hybrid/AutoML | 0.1 | wml-hybrid_0.1 | hybrid_0.1 (F) | x86, PPC |
| PMML | 3.0 to 4.3 | pmml_. (or) pmml_..*3.0 - 4.3 | pmml-3.0_4.3 (F) | x86, PPC |
| PyTorch | 1.10 | pytorch-onnx_1.10 pytorch-onnx_rt22.1 |
runtime-22.1-py3.9 (deprecated) pytorch-onnx_rt22.1-py3.9 (deprecated) pytorch-onnx_rt22.1-py3.9-edt (deprecated) |
x86, PPC, s390x |
| PyTorch | 1.10 | pytorch-onnx_1.10 pytorch-onnx_rt22.1 |
runtime_22.1-py3.9-nnpa | s390x |
| PyTorch | 1.12 | pytorch-onnx_1.12 pytorch-onnx_rt22.2 |
runtime-22.2-py3.10 (F) pytorch-onnx_rt22.2-py3.10 (F) pytorch-onnx_rt22.2-py3.10-edt (F) |
x86, PPC s390x |
| PyTorch | 1.12 | pytorch-onnx_1.12 pytorch-onnx_rt22.2 |
pytorch-onnx_rt22.2-py3.10-dist(x86) (F) | x86 |
| PyTorch | 2.0 | pytorch-onnx_2.0 pytorch-onnx_rt23.1 |
runtime-23.1-py3.10 (F) pytorch-onnx_rt23.1-py3.10 (F) pytorch-onnx_rt23.1-py3.10-edt (F) pytorch-onnx_rt23.1-py3.10-dist (F) |
x86 |
| Python Functions | 0.1 | NA | runtime-22.1-py3.9 (deprecated) | x86, PPC, s390x |
| Python Functions | 0.1 | NA | runtime_22.1-py3.9-nnpa | s390x |
| Python Functions | 0.1 | NA | runtime-22.2-py3.10 (F) | x86, PPC |
| Python Functions | 0.1 | NA | runtime-22.2-py3.10 (F) | s390x |
| Python Functions | 0.1 | NA | runtime-23.1-py3.10 (F) | x86 |
| Python Scripts | 1.0 | NA | runtime-22.1-py3.9 (deprecated) | x86, PPC, s390x |
| Python Scripts | 1.0 | NA | runtime_22.1-py3.9-nnpa | s390x |
| Python Scripts | 1.0 | NA | runtime-22.2-py3.10 (F) | x86, PPC, s390x |
| Python Scripts | 0.1 | NA | runtime-23.1-py3.10 (F) | x86 |
| R Scripts | 1.0 | NA | default_r3.6 (deprecated) | x86, PPC |
| R Scripts | 1.0 | NA | runtime-22.1-r3.6 (deprecated) runtime-22.2-r4.2 (F) runtime-23.1-r4.2 (F) |
x86 |
| Scikit-learn | 1.0 | scikit-learn_1.0 | runtime-22.1-py3.9 (deprecated) | x86, PPC, s390x |
| Scikit-learn | 1.0 | scikit-learn_1.0 | runtime_22.1-py3.9-nnpa | s390x |
| Scikit-learn | 1.1 | scikit-learn_1.1 | runtime-22.2-py3.10 (F) | x86, PPC, s390x |
| Scikit-learn | 1.1 | scikit-learn_1.1 | runtime-23.1-py3.10 (F) | x86 |
| Spark | 3.3 | mllib_3.3 | spark-mllib_3.3 (F) | x86, PPC |
| SPSS | 17.1 | spss-modeler_17.1 (see notes) | spss-modeler_17.1 (F) | x86, PPC |
| SPSS | 18.1 | spss-modeler_18.1 (see notes) | spss-modeler_18.1 (F) | x86, PPC |
| SPSS | 18.2 | spss-modeler_18.2 (see notes) | spss-modeler_18.2 (F) | x86, PPC |
| Tensorflow | 2.7 | tensorflow_2.7 tensorflow_rt22.1 |
runtime-22.1-py3.9 (deprecated) tensorflow_rt22.1-py3.9 (deprecated) |
x86, PPC, s390x |
| Tensorflow | 2.7 | tensorflow_2.7 tensorflow_rt22.1 |
tensorflow_rt22.1-py3.9-nnpa (deprecated) | s390x |
| Tensorflow | 2.9 | tensorflow_2.9 tensorflow_rt22.2 |
runtime-22.2-py3.10 (F) tensorflow_rt22.2-py3.10 (F) |
x86, PPC |
| Tensorflow | 2.9 | tensorflow_2.9 tensorflow_rt22.2 |
runtime-22.2-py3.10 (F) | s390x |
| Tensorflow | 2.9 | tensorflow_2.9 tensorflow_rt22.2 |
tensorflow_rt22.2-py3.10-dist(x86) (F) tensorflow_rt22.2-py3.10-edt(x86) (F) |
x86 |
| Tensorflow | 2.12 | tensorflow_2.12 tensorflow_rt23.1 |
runtime-23.1-py3.10 (F) tensorflow_rt23.1-py3.10-dist (F) tensorflow_rt23.1-py3.10-edt (F) tensorflow_rt23.1-py3.10 (F) |
x86 |
| XGBoost | 1.5 | xgboost_1.5 or scikit-learn_1.0 (see notes) | runtime-22.1-py3.9 (deprecated) | x86, PPC, s390x |
| XGBoost | 1.5 | xgboost_1.5 or scikit-learn_1.0 (see notes) | runtime_22.1-py3.9-nnpa | s390x |
| XGBoost | 1.6 | xgboost_1.6 or scikit-learn_1.1 (see notes) | runtime-22.2-py3.10 (F) | x86, PPC |
| XGBoost | 1.6 | xgboost_1.6 or scikit-learn_1.1 (see notes) | runtime-22.2-py3.10 (F) | s390x |
| XGBoost | 1.6 | xgboost_1.6 or scikit-learn_1.1 (see notes) | runtime-23.1-py3.10 (F) | x86 |
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):
- in Python 3.9, use the
scikit-learn_1.0model type. - in Python 3.10, use the
scikit-learn_1.1model type.
- in Python 3.9, use the
-
You can also deploy R Shiny apps (version 0.1). Software specifications available for Shiny apps:
rstudio_r4.2andrstudio-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.
Discontinued model types and software specifications
Support for the following model types was discontinued:
| Model types | End of support |
|---|---|
| do-docplex_12.10 do-opl_12.10 do-cplex_12.10 do-cpo_12.10 |
4.0.9 |
| do-docplex_12.9 do-opl_12.9 do-cplex_12.19 do-cpo_12.9 |
4.0.7 |
| mllib_2.4 | 4.0.7 |
| mllib_2.4 (for PMML deployments) |
4.0.8 |
| mllib_3.0 | 4.6 |
| pytorch-onnx_1.3 | 4.0.6 |
| pytorch-onnx_1.7 | 4.0.8 |
| scikit-learn_0.23 | 4.0.8 |
| tensorflow_2.1 | 4.0.6 |
| tensorflow_2.4 | 4.0.8 |
| xgboost_0.90 | 4.0.6 |
| xgboost_1.3 | 4.0.8 |
Support for the following software specifications was discontinued:
| Software specification | End of support |
|---|---|
| autoai-kb_3.3-py3.7 | 4.0.8 |
| autoai-kb_3.4-py3.8 | 4.0.8 |
| autoai-obm_3.0 | 4.6 |
| autoai-obm_3.2 | 4.6 |
| autoai-ts_3.9-py3.8 | 4.0.8 |
| default_py3.7 | 4.0.6 |
| default_py3.7_opence | 4.0.8 |
| default_py3.8 | 4.0.8 |
| do_12.10 | 4.0.9 |
| do_12.9 | 4.0.7 |
| pytorch-onnx_1.3-py3.7 | 4.0.6 |
| pytorch-onnx_1.3-py3.7-edt | 4.0.6 |
| spark-mllib_2.4 | 4.0.7 |
| spark-mllib_2.4 (for PMML deployments) |
4.0.8 |
| spark-mllib_3.0 | 4.5 (PMML model type only) |
| spark-mllib_3.0 | 4.6 |
| spark-mllib_3.2 | 4.7 |
| tensorflow_2.4-py3.7 | 4.0.8 |
| tensorflow_2.4-py3.8 | 4.0.8 |
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.
-
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.
Runtime differences
For various reasons, package versions installed in Watson Machine Learning runtimes and Watson Studio Notebook runtimes can be different, even if they are based on the same software specification.
Differences in release 4.7.0:
| Package | Watson Machine Learning version | Watson Studio Notebook version |
|---|---|---|
py4j |
0.10.9.3 | 0.10.9.2 |
docplex |
2.23.222 | 2.22.213 |
project-lib-py |
2.0.7 | 2.0.9 |
| Package | Watson Machine Learning version | Watson Studio Notebook version |
|---|---|---|
docplex |
2.23.222 | 2.22.213 |
project-lib-py |
2.0.7 | 2.0.9 |
| Package | Watson Machine Learning version | Watson Studio Notebook version |
|---|---|---|
docplex |
2.23.222 | 2.22.213 |
project-lib-py |
2.0.7 | 2.0.9 |
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