Release Notes

[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


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

[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.

We are committed to keeping pyTigerGraph true to its roots as an open-source project. Check out the Contributing page and our GitHub Issues page if you want to help with pyTigerGraph’s development.

pyTigerGraph 0.9 was released on May 16th, 2022.

New Features

Graph Data Science Capability

Many new capabilities added for graph data science and graph machine learning.

  • Data loaders for training Graph Neural Networks in DGL and PyTorch Geometric

  • A "featurizer" to generate graph-based features

  • Metric trackers for precision, recall, and accuracy

  • Vertex and edge splitters for generation of train/validation/testing splits.

Other Changes


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.


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.