v1.0.1

[1.0.1] - 2022-07-12

Changed:

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

Version 1.0

[1.0] - 2022-07-11

Release of pyTigerGraph version 1.0, in conjunction with version 1.0 of the TigerGraph Machine Learning Workbench.

Added:

• Kafka authentication support for ML Workbench enterprise users.
• Custom query support for Featurizer, allowing developers to generate their own graph-based features as well as use our built-in Graph Data Science algorithms.

Changed:

• Additional testing of GDS functionality
• More demos and tutorials for TigerGraph ML Workbench, found here.
• Various bug fixes.

Version 0.9.2

[0.9.2]

Changed:

• Authentication with TigerGraph Cloud instances. Added gsqlSecret to replace username and password parameters when connecting to a TigerGraph Cloud instance provisioned after July 5th, 2022.

Version 0.9.1

[0.9.1] - 2022-06-21

Added new parameter, tgCloud for when connecting to a TigerGraph Cloud instance. Set to True if using a new TigerGraph Cloud

Changed

• Deprecated gcp parameter, as tgCloud supercedes this. Existing code will be compatible.

Version 0.9

[0.9] - 2022-05-16

We are excited to announce the pyTigerGraph v0.9 release! This release adds many new features for graph machine learning and graph data science, a refactoring of core code, and more robust testing. Additionally, we have officially “graduated” it to an official TigerGraph product. This means brand-new documentation, a new GitHub repository, and future feature enhancements. While becoming an official product, we are committed to keeping pyTigerGraph true to its roots as an open-source project. Check out the contributing page and GitHub issues if you want to help with pyTigerGraph’s development.

Changed

• Feature: Include Graph Data Science Capability

  • Many new capabilities added for graph data science and graph machine learning. Highlights include data loaders for training Graph Neural Networks in DGL and PyTorch Geometric, a "featurizer" to generate graph-based features for machine learning, and utilities to support those activities.

• Documentation: We have moved the documentation to the official TigerGraph Documentation site and updated many of the contents with type hints and more descriptive parameter explanations.

• Testing: There is now well-defined testing for every function in the package. A more defined testing framework is coming soon.

• Code Structure: A major refactor of the codebase was performed. No breaking changes were made to accomplish this.