Release Notes

TigerGraph ML Workbench 1.1 (October 2022)

New features

  • TensorFlow support for homogeneous GNNs via the Spektral library

  • Heterogeneous Graph Dataloading support for DGL

  • Support for lists of strings in dataloaders

  • Activator for both Community and Enterprise editions of the ML Workbench (see https://act.tigergraphlabs.com for details)

Updated features

  • Fixed KeyError when creating a data loader on a graph where PrimaryIdAsAttribute is False

  • Error catch if Kafka dataloader doesn’t run in asynchronous mode

  • Schema now refreshes during dataloader instantiation and featurizer attribute addition

  • Connection instantiation time reduced

  • Reinstall query if it is disabled

  • Confirm Kafka topic is created before subscription

  • Streamlined Kafka resource usage

  • Allow multiple consumers on the same data

  • Improved deprecation warnings

TigerGraph ML Workbench 1.0 (August 2022)

New features

Soft launch of TigerGraph ML Workbench on Cloud, an end-to-end Kubeflow-managed cloud platform for training and serving machine learning models.

  • Complete KubeFlow integration

  • Fully-managed infrastructure orchestrated by Kubernetes

  • Connection to TigerGraph Cloud Solutions

  • Cloud-hosted Jupyter Notebooks

  • TensorBoard integration

  • Experiments with AutoML (beta)

Known Issues

  • When creating a new Notebook, the user is prompted for Configurations and Affinity/Tolerations. These have no effect on the notebook.