Supported frameworks

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.

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

Supported machine learning frameworks

Framework notes

  • Spark 2.3 framework for Watson Machine Learning is deprecated and will be removed on December 1, 2020. Use Spark 2.4 instead.
  • Due to security vulnerabilities with several TensorFlow versions, Watson Machine Learning has added support for TensorFlow version 1.15 and removed support for all unsecure TensorFlow versions, including 1.14 and 1.13. For details, read the blog announcement. For help changing to a supported runtime, see the TensorFlow compatibility guide.
  • If a framework is marked as deprecated then support for the framework will be removed in a future release.
  • If you trained a model in a GPU Notebook, only TensorFlow is supported as a deployment framework.

SPSS Modeler flows might need retraining

  • Due to an upgrade in the Python version in supported Modeler runtimes, some models which were built using SPSS Modeler Canvas tool in IBM Watson Studio Cloud prior to this upgrade can no longer be deployed using Watson Machine Learning. If you are using one of these 6 nodes in your Modeler Flow, you must rebuild and redeploy your model(s) with Modeler Canvas and Watson Machine Learning, starting on September 1, 2020. The affected models include these nodes:

    • XGBoost Tree
    • XGboost Linear
    • One-Class SVM
    • HDBScan
    • KDE Modeling
    • Gaussian Mixture

If your SPSS model uses any of those modeler nodes, take the following action:

  • If you are using Watson Studio UI, open the modeler flow in Watson Studio Canvas retrain and save the model to Watson Machine Learning. Once the model is saved in the project, it can be promoted to a deployment space and you can create a new deployment.
  • If you are using REST APIs or Python Client, retrain the model using the latest modeler and save the model in Watson Machine Learning repository with the model type ‘spss-modeler-18.2’.
Table 1. Supported machine learning tools, libraries, and frameworks
Framework Features supported in IBM Watson Studio
Spark MLlib
Spark 2.4.6
2.3 (deprecated)
Note: You can no longer associate an Apache Spark Lite service with a project. Apache Spark Lite services will be deleted on June 28, 2019. Read this blog post: Deprecation of Apache Spark (Lite Plan).
Streaming deployments will also be discontinued.
  • Deployment: online and batch.
  • Only classification and regression models are supported.
  • Custom transformers, user-defined functions, and classes are not supported.
scikit-learn
  • scikit-learn 0.22.1 on Anaconda 2019.03 for Python 3.6 Runtime
  • scikit-learn 0.20.3 on Anaconda 2019.03 for Python 3.6 Runtime
  • scikit-learn 0.19 on Anaconda 2019.03 for Python 3.6 Runtime (deprecated)
  • Deployment: online and batch.
  • Core ML (scikit-learn 0.19 and 0.20 only)
  • Only classification and regression models are supported.
  • Models that use custom components cannot be trained using Watson Machine Learning training runs or experiments.
  • Models can contain references to only those packages (and package versions) that are available by default in the Anaconda installer of the Anaconda distribution used.
  • To deploy models with references to user-defined transformers/estimators, see: Using custom components
  • Online deployment and Core ML models use Python 3.6.
XGBoost
  • XGBoost 0.90 in an Anaconda 2019.03 with scikit-learn 0.22.1 for Python 3.6 Runtime
  • XGBoost 0.82 in an Anaconda 2019.03 with scikit-learn 0.20.3 for Python 3.6 Runtime
  • XGBoost 0.80 in an Anaconda 2019.03 environment with scikit-learn 0.19 for Python 3.6 Runtime (deprecated)
  • Deployment: online and batch.
  • Core ML (XGBoost 0.80, 0.82, and 0.90)
  • You can define custom scikit-learn transformers and then add them as a stage in a scikit-learn Pipeline with XGBoostClassifier or XGBRegressor.
  • Probability of prediction is not returned in results.
  • Models can contain references to only those packages (and package versions) that are available by default in the Anaconda installer of corresponding Anaconda Distribution version.
  • To deploy models with references to user-defined transformers/estimators, see: Using custom components
  • Online deployment uses Python 3.6.
  • To use XGBoost versions >= 0.80, you must use the Watson Machine Learning Python client version >= 1.0.338
TensorFlow
  • Version 1.15 in an Anaconda 2019.03 environment
  • Version 2.1 for training only
  • Deployment: online and batch.
  • Only JSON content is supported as the payload for scoring.
  • tf.estimator is not supported.
  • Custom component support:
    • User-defined tensor operations that are defined using the tf.py_func API are supported with online deployment.
    • Custom C++ operations are not supported.
    • Models that use custom components cannot be trained using Watson Machine Learning training runs or experiments.
    • See: Using custom operators
  • Training service supports 3.6
  • Online deployment uses Python 3.6.
Keras
  • Keras 2.2.5 with TensorFlow 1.15 in an Anaconda 2019.03 environment
  • Deployment: online, batch and Core ML.
  • Only JSON content is supported as the payload for scoring.
  • tf.estimator is not supported.
  • User-defined layers with trainable weights are not supported. However, stateless custom operations defined using layers.core.Lambda layers are supported with online deployment.
  • Online deployment uses Python 3.6.
Caffe
Version 1.0 (deprecated)
  • Deployment: online and batch only.
  • Only JSON content is supported as the payload for scoring.
  • User-defined layers and blobs are not supported.
  • Online deployment uses Python 3.6.
PyTorch
Versions: 1.2 (deployment) 1.1, 1.3 (training)
  • Deployment: online and batch.
IBM SPSS Modeler
  • IBM SPSS Modeler 17.1
  • IBM SPSS Modeler 18.1 (deprecated), 18.2
  • Deployment: online and batch.
  • A branch cannot require a connection to an external service (such as references to rules stored in an IBM SPSS Collaboration and Deployment Services repository.)
  • When using IBM SPSS Modeler embedded Python scripting, the only supported way to reference a node is by its unique ID. (See: Finding nodes external link)
  • The following features are not supported:
    • DB data/function
    • Hadoop
    • Analytic Server
    • R/Python extensions
    • Social Network Analysis
    • Entity Analytics
    • Text Analytics Japanese edition
  • If you export a stream file (.str) from the Watson Studio flow editor, it can be imported back into the flow editor (to back up or share a design, for example) and it can be imported into SPSS Modeler. But that stream file cannot itself be deployed to Watson Machine Learning; you must train the stream in the flow editor.
Predictive Model Markup Language (PMML)
Version 3.0 to 4.3
  • Deployment: online only.
  • Supported models: R, SAS, SPSS
Decision Optimization runtime
  • opl (do-opl_12.9, do-opl_12.10)
  • cplex (do-cplex_12.9. do-cplex_12.10 )
  • cpo (do-cpo_12.9, do-cpo_12.10)
  • docplex (do-docplex_12.9, do-docplex_12.10)

Note: GPU-accelerated computing is the employment of a graphics processing unit (GPU) along with a computer processing unit (CPU) in order to facilitate processing-intensive operations such as deep learning experiments. GPU acceleration is limited to training and is only available with the following frameworks:

  • Keras
  • PyTorch
  • Tensorflow
  • Caffe