ML Workbench Standalone Installation

If you already have an existing TigerGraph solution locally, you can try out ML Workbench with your own database using a Docker image and a Mac OS or Linux installer.

Docker image

Docker must be installed and running on your machine.

In the console, run this command:

docker run -it -p 8888:8888 --name mlworkbench -v ~/mlworkbench:/home/tigergraph/save tigergraphml/mlworkbench:1.4.0

This command prints the link to the JupyterLab workbench in a format similar to Use this link in your browser to access the workbench, which is a customized version of JupyterLab.

If the Docker container is running remotely, open port 8888 on the remote machine to allow the connection. Then replace in the returned address with the remote machine IP address.

Next steps

After installation, the next step is to Activate ML Workbench.

You can then go to our Tutorials and Sample Data section. Follow the instructions, use our tutorials and download our latest notebook examples and data sets there to practice using the ML Workbench.