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.
See Machine Learning samples and examples for links to sample notebooks that demonstrate creating batch deployments using the Watson Machine Learning REST API and Watson Machine Learning Python client library.
Supported machine learning frameworks
|Framework||Features supported in IBM Watson Studio|
Versions: 1.2 (deployment) 1.1, 1.3
|IBM SPSS Modeler
|Predictive Model Markup Language (PMML)
Version 3.0 to 4.3
|Decision Optimization runtime
See Model input and output data adaptation
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:
Managing assets that refer to discontinued software or frameworks
Note these guidelines for when assets that rely on discontinued software specifications or frameworks are handled. In some cases, the migration is seamless. In other cases, your action is required to retrain or redeploy assets.
For details on managing assets that rely on deprecated or discontinued frameworks and software specifications, see Specifying a model type and software specification.
- Support for Python 3.6 is deprecated in favor of Python 3.7. Starting on November 20, 2020, new training based on Python 3.6 will be blocked. Support for Python 3.6 frameworks and related software specifications will be discontinued on Jan 20, 2021 for V4-GA API and on March 1, 2021 for v3 APIs and V4-beta API. For example:
0.22/XGBoost 0.90framework with
Python3.6for Watson Machine Learning is deprecated and will be removed. Use
Scikit-learn 0.23/XGBoost 0.90with
- Software specification ‘ai-function_0.1-py3.6
is deprecated and will be removed. Usedefault_py3.7` software specification instead when saving a Python function.
- To deploy with these Python 3.7-based frameworks you must be using the V4 GA version of Watson Machine Learning APIs and a current, V2 machine learning service instance.
- scikit-learn 0.23
- xgboost 0.90 with python 3.7
- TF 2.1
- PyTorch 1.3
- Spark 2.3 framework for Watson Machine Learning is deprecated and will be removed on December 1, 2020. Use Spark 2.4 instead.
- 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=Truewhen exporting your model.
- 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:
- 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 of the Python version in supported SPSS Modeler runtimes, some models that were built using SPSS Modeler in IBM Watson Studio Cloud prior to this upgrade can no longer be deployed using Watson Machine Learning. If you’re using one of the following 6 nodes in your SPSS Modeler Flow, you must rebuild and redeploy your model(s) with SPSS Modeler and Watson Machine Learning starting on September 1, 2020. The affected models include these nodes:
- XGBoost Tree
- XGBoost Linear
- One-Class SVM
- KDE Modeling
- Gaussian Mixture
If your SPSS model uses any of these SPSS Modeler nodes, take the following action:
- If you’re using the Watson Studio user interface, open the SPSS Modeler flow in Watson Studio, retrain, and save the model to Watson Machine Learning. After saving the model to the project, you can promote it to a deployment space and create a new deployment.
- If you’re using REST APIs or Python Client, retrain the model using the latest SPSS Modeler and save the model to the Watson Machine Learning repository with the model type ‘spss-modeler-18.2.’