Frameworks and software specifications in Watson Machine Learning

Overview of software specifications

Software specifications define the programming language and version that you use for a building a model or a function. You can use software specifications to configure the software that is used for running your models and functions. You can also define the software version to be used and include your own extensions. For example, you can use conda .yml files or custom libraries.

Supported frameworks and software specifications

You can use predefined tools, libraries, and frameworks to train and deploy your machine learning models and functions. Examples of supported frameworks include Scikit-learn, Tensorflow, and more.

For more information, see Supported deployment frameworks and software specifications.

Lifecycle of supported frameworks and software specifications

Frameworks and software specifications that are supported when you install or upgrade to the latest version of Cloud Pak for Data go through deprecation, constriction, and discontinuation phases in future releases of Cloud Pak for Data, described as follows:

1. Deprecated: The runtime environment corresponding to the software specification is supported when you install or upgrade to the latest version of Cloud Pak for Data, but will be removed in an upcoming release.

   For example, the software specification runtime-22.2-py3.10 is deprectaed in Cloud Pak for Data version 4.8.4. In this case, if you install or upgrade to a new Watson Machine Learning service instance, you can create or deploy machine learning models that use runtime-22.2-py3.10. However, the support for runtime-22.2-py3.10 will be discontinued in an upcoming release, so you must upgrade to the latest software specification to make sure that your deployments are supported.

2. Constricted: The runtime environment corresponding to the software specification is supported when you upgrade to the latest version of Cloud Pak for Data. However, the runtime environment is not supported when you install the latest version of Cloud Pak for Data.

   For example, the software specification runtime-22.1-py3.9 is constricted in Cloud Pak for Data version 4.8.0. In this case, if you install a new Watson Machine Learning service instance, you cannot create or deploy machine learning models that use runtime-22.1-py3.9. However, if you upgrade from Cloud Pak for Data version 4.7.0 to version 4.8.0, the existing models that you created and deployed with 4.7.0 are supported. You can also create new deployments based on models that use constricted software specifications during an upgrade.

3. Discontinued: The runtime environment corresponding to the software specification is not supported when you install or upgrade to the latest version of Cloud Pak for Data.

   For example, the software specification tensorflow_rt22.1-py3.9-nnpa is discontinued for Cloud Pak for Data version 4.8.0. In this case, if you install or upgrade to a new Watson Machine Learning service instance, you cannot create or deploy machine learning models that use tensorflow_rt22.1-py3.9-nnpa.

Managing outdated frameworks and software specifications

Update software specifications and frameworks in your models when they become outdated. Sometimes, you can seamlessly update your assets. In other cases, you must retrain or redeploy your assets.

For more information, see Managing outdated software specifications or frameworks.

Parent topic: Deploying assets with Watson Machine Learning