Table of contents

Supported frameworks (Watson Machine Learning)

You can use popular tools, libraries, and frameworks to train and deploy machine learning models and functions using IBM Watson Machine Learning. This topic lists supported versions and features.

View Using Watson Machine Learning in a notebook for links to sample notebooks that demonstrate training and deploying models and functions using supported frameworks.

Supported machine learning frameworks

Framework Versions Online Batch CoreML
Spark 2.4 Yes Yes
Inline payload only
No
PMML 3.0 to 4.3 Yes Programmatic only
Inline payload only
No
Hybrid/AutoAI 0.1
See framework notes for more information
Yes Yes No
SPSS 17.1

18.1
18.2
Yes Yes No
Scikit-learn 0.23
0.22 (deprecated)
0.20 (deprecated)
Yes Yes Yes
See framework notes for more information
XGBoost 0.90 with Python 3.7
0.90 with Python 3.6 (deprecated)
0.82 (deprecated)
Yes Yes Yes
See framework notes for more information
TensorFlow 2.1
1.15 (deprecated)
Training requires Watson Machine Learning Accelerator
Yes Yes No
Keras 2.2.5 (deprecated)
Training requires Watson Machine Learning Accelerator
Yes Yes Yes
PyTorch 1.2 (deprecated)
1.1 (deprecated)
1.3.1
Training requires Watson Machine Learning Accelerator
See framework notes for more information
Yes Yes No
Decision Optimization 12.10 No Yes No
Python function 0.1 yes Programmatic only
Inline payload only
no
Python scripts 1.0 No Yes No
RScript 1.0 No Yes No

Framework notes

  • Support for Python version 3.6 is deprecated in favor of Python version 3.7.
  • If a framework is marked as deprecated then support for the framework will be removed in a future release. For details on migrating a model or function, see Upgrading from a deprecated framework.
  • Discontinued frameworks include:
    • Caffe 1.0
    • Pytorch 1.0
    • Spark 2.3
  • The way you can work with deprecated frameworks depends on whether you upgraded Cloud Pak for Data from a previous version or did a new installation of Cloud Pak for Data 3.5. The difference is:
    • For new installations of Cloud Pak for Data 3.5, only non-deprecated frameworks are supported.
    • If you upgraded from a previous release of Cloud Pak for Data, you can use deprecated frameworks.
  • AutoAI models trained in previous versions of Cloud Pak for Data are deprecated as they are based on Python 3.6. You must retrain the experiments.
  • If you are creating a CoreML deployment for a logistic regression model using Scikit-learn 0.23 with Python 3.7, you must explicitly specify a value to override the default Scikit-learn package with Scikit-learn 0.23 during model training. See this article for details of specifying a package version when you are training a Scikit-learn pipeline.
  • If you are using Pytorch 1.3 with Python 3.7 to train a model, you must explicitly set keep_initializers_as_inputs=True when exporting your model.
  • Scikit-learn 0.22 , Xgboost 0.82, Xgboost 0.90 CoreML deployments with Python 3.6 will not work on ROKS vpcgen2 with Portworx storage cluster.
  • In order for a Scikit-learn or Xgboost model to be converted to CoreML format, it must contain only supported transformer algorithms. Refer to this documentation for lists of supported CoreML transformers for Scikit-learn and Xgboost frameworks:
    • https://apple.github.io/coremltools/generated/coremltools.converters.sklearn.convert.html
    • https://apple.github.io/coremltools/generated/coremltools.converters.xgboost.convert.html