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 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
127.0.0.1:8888/lab?token=. 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