Release Notes

TigerGraph ML Workbench 1.3

New features

  • Two new data loader classes: HGTLoader and NodePiece.

  • Callback functions to all data loader factories: users can write functions to process the batch in a background thread before it is passed into the training loop.

  • A delimiter parameter to all data loader factories: users can choose what delimiter they want to separate attributes they are loading from the graph.

Updated features

  • Template query support in the featurizer (requires TigerGraph Database version 3.9+).

  • Data splitters to automatically perform a schema change if needed to add attributes to the database.

  • Support for multi-edges in the upsertEdges() function.

  • Better error messaging.


  • Fixed SSL certificate handling when custom certificate is used.

  • Removed ANSI escape characters from output of .gsql() calls.

  • Improved the logic for shuffling vertices in dataloaders when filter_by attribute was used.

  • Improved the batching algorithm in dataloaders so that the output batches have a consistent batch size.

  • Changed getVertexCount() endpoint to more scalable solution.

TigerGraph ML Workbench 1.1 (September 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 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.