Release Notes
[1.0] - 2022-07-11
Release of pyTigerGraph version 1.0, in conjunction with version 1.0 of the TigerGraph Machine Learning Workbench.
Added:
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Kafka authentication support for ML Workbench enterprise users.
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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:
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Additional testing of GDS functionality
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More demos and tutorials for TigerGraph ML Workbench, found here.
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Various bug fixes.
[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.
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Data loaders for training Graph Neural Networks in DGL and PyTorch Geometric
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A "featurizer" to generate graph-based features
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Metric trackers for precision, recall, and accuracy
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Vertex and edge splitters for generation of train/validation/testing splits.
Other Changes
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.