# 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\. 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](https://ibm.github.io/watson-machine-learning-sdk/) or [REST API](https://cloud.ibm.com/apidocs/machine-learning)\.

## 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\.

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List of predefined (base) model types and software specifications

| Framework\*\*         | Versions     | Model Type                                                                       | Default software specification                                                                                                                                                                                               |
| --------------------- | ------------ | -------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| AutoAI                | 0\.1         | NA                                                                               | autoai\-kb\_rt22\.2\-py3\.10  <br>autoai\-ts\_rt22\.2\-py3\.10  <br>hybrid\_0\.1  <br>autoai\-kb\_rt23\.1\-py3\.10  <br>autoai\-ts\_rt23\.1\-py3\.10  <br>autoai\-tsad\_rt23\.1\-py3\.10  <br>autoai\-tsad\_rt22\.2\-py3\.10 |
| Decision Optimization | 20\.1        | do\-docplex\_20\.1  <br>do\-opl\_20\.1  <br>do\-cplex\_20\.1  <br>do\-cpo\_20\.1 | do\_20\.1                                                                                                                                                                                                                    |
| Decision Optimization | 22\.1        | do\-docplex\_22\.1  <br>do\-opl\_22\.1  <br>do\-cplex\_22\.1  <br>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  <br>pytorch\-onnx\_rt22\.2                                 | runtime\-22\.2\-py3\.10  <br>pytorch\-onnx\_rt22\.2\-py3\.10  <br>pytorch\-onnx\_rt22\.2\-py3\.10\-edt                                                                                                                       |
| PyTorch               | 2\.0         | pytorch\-onnx\_2\.0  <br>pytorch\-onnx\_rt23\.1                                  | runtime\-23\.1\-py3\.10  <br>pytorch\-onnx\_rt23\.1\-py3\.10  <br>pytorch\-onnx\_rt23\.1\-py3\.10\-edt  <br>pytorch\-onnx\_rt23\.1\-py3\.10\-dist                                                                            |
| Python Functions      | 0\.1         | NA                                                                               | runtime\-22\.2\-py3\.10  <br>runtime\-23\.1\-py3\.10                                                                                                                                                                         |
| Python Scripts        | 0\.1         | NA                                                                               | runtime\-22\.2\-py3\.10  <br>runtime\-23\.1\-py3\.10                                                                                                                                                                         |
| Scikit\-learn         | 1\.1         | scikit\-learn\_1\.1                                                              | runtime\-22\.2\-py3\.10  <br>runtime\-23\.1\-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  <br>tensorflow\_rt22\.2                                        | runtime\-22\.2\-py3\.10  <br>tensorflow\_rt22\.2\-py3\.10                                                                                                                                                                    |
| Tensorflow            | 2\.12        | tensorflow\_2\.12  <br>tensorflow\_rt23\.1                                       | runtime\-23\.1\-py3\.10  <br>tensorflow\_rt23\.1\-py3\.10\-dist  <br>tensorflow\_rt23\.1\-py3\.10\-edt  <br>tensorflow\_rt23\.1\-py3\.10                                                                                     |
| XGBoost               | 1\.6         | xgboost\_1\.6 or scikit\-learn\_1\.1 (see notes)                                 | runtime\-22\.2\-py3\.10  <br>runtime\-23\.1\-py3\.10                                                                                                                                                                         |

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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\.
 *  No new deployments of models that are built with discontinued framework versions or software specifications are allowed\.

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## Learn more ##

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 *  To learn more about how to customize software specifications, see [Customizing with third\-party and private Python libraries](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-create-custom-software-spec.html)\.
 *  To learn more about how to use and customize environments, see [Environments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/environments-parent.html)\.
 *  To learn more about how to use software specifications for deployments, see the following Jupyter notebooks:
    
    <!-- <ul> -->
    
     *  [Using REST API and cURL](https://github.com/IBM/watson-machine-learning-samples/tree/master/cloud/notebooks/rest_api/curl/deployments)
     *  [Using the Python client](https://github.com/IBM/watson-machine-learning-samples/tree/master/cloud/notebooks/python_sdk/deployments)
    
    <!-- </ul> -->
    
<!-- </ul> -->

**Parent topic:**[Frameworks and software specifications](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-manage-frame-and-specs.html)

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