High-level Architecture

TigerGraph ML Workbench Community or Enterprise Edition contains two major components:

  • A JupyterLab-based IDE

  • pyTigerGraph, the Python client

Diagram of ML Workbench architecture
Figure 1. High level architecture

ML Workbench Jupyter Plugin

The ML Workbench Jupyter Plugin is a JupyterLab-based development environment with TigerGraph specific utilities and components, such as a server manager and link to GraphStudio. In addition, all Python libraries such as PyTorch Geometric, DGL, and TGML come pre-installed, so you don’t have to worry about setting up the right Python environment.

The plugin is included in the sandbox option for getting started with ML Workbench.


pyTigerGraph is a Python package installed on the computer or server that does the machine learning training. The tgml package provides utilities such as vertex set splitting for training, validation, and testing, as well as graph data loaders for both PyTorch Geometric (PyG) and Deep Graph Library (DGL). As tgml is a Python package, it can be installed anywhere Python is used.