ML Workbench on TigerGraph Cloud

ML Workbench is offered as a starter kit on TigerGraph Cloud when creating a new cluster.

This page explains how to set up a new cluster with ML Workbench enabled and access the starter kit.

Set up an ML Workbench-enabled cluster

Follow the instructions in Create a Cluster to log in to TigerGraph Cloud and begin setting up a new cluster.

At the bottom of the stage, under Advanced settings, click the switch labeled Enable Machine Learning Workbench.

create cluster mlwb

This limits the starter kits to just one, the Graph Machine Learning in Practice starter kit.

With this option selected, finish creating your cluster and wait for it to be provisioned.

Go to ML Workbench on TigerGraph Cloud

From the Tools dropdown menu on a provisioned cluster, choose Machine Learning Workbench.

mlwb dropdown

You can also view a list of all clusters by going to the Tools menu on the left (or any nine-dot TigerGraph Suite button) and going to ML Workbench. These options only appear with Machine Learning Workbench-enabled clusters running TigerGraph version 3.8.0 or newer.

family of apps
If you access a cluster with ML Workbench through its <domain> domain name, it will not appear in the TigerGraph Suite display. It can still be accessed through the Tools menu.

Click on ML Workbench to see a list of clusters with ML Workbench enabled.

mlwb connect to cluster

Get started with ML Workbench

Included with the ML Workbench is a Jupyter notebook readme to help you get started. It has all necessary ML Workbench notebooks to learn the basics so that you can begin running machine learning applications on TigerGraph Cloud.

mlwb ipynb main