Release Notes
TigerGraph ML Workbench 1.3
New features
-
Two new data loader classes:
HGTLoader
andNodePiece
. -
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.
Fixes
-
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 https://act.tigergraphlabs.com for details)
Updated features
-
Fixed KeyError when creating a data loader on a graph where
PrimaryIdAsAttribute
isFalse
-
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)