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:1.0.0

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 127.0.0.1 in the returned address with the remote machine IP address.

Mac OS installer

  1. Download the installer at this link: Download MLWorkbench-1.0.0-MacOSX-x86_64.sh

  2. Open a Terminal window in the folder where the installer was downloaded.

  3. Run the command bash MLWorkbench-1.0.0-MacOSX-x86_64.sh and follow the prompts to install.

The installation prompt asks whether you want to "initialize."

  • If you agree to initialize, it will change your default Python environment to the workbench.

  • If you decline, the ML workbench still functions, but you will need to activate the Python environment before going to the JupyterLab.

  1. Relaunch Terminal for the PATH change to take effect.

  2. Run the command jupyter lab to start. This starts the Jupyter server and opens JupyterLab in your browser. If you would like to access JupyterLab from a remote browser, type jupyter lab --ip=0.0.0.0 --no-browser instead.

JupyterLab only has access to the folder where it is launched. If you need to access files at a different location, launch JupyterLab in that location.

Linux installer

  1. Download the installer at this link: Download MLWorkbench-1.0.0-Linux-x86_64.sh

  2. Open the terminal and navigate to where the installer was downloaded.

  3. Run the command bash MLWorkbench-1.0.0-Linux-x86_64.sh and follow the prompts to install.

The installation prompt asks whether you want to "initialize."

  • If you agree to initialize, it will change your default Python environment to the workbench.

  • If you decline, the ML workbench still functions, but you will need to type the full path of the jupyter executable to start it.

  1. Relaunch the terminal for the PATH change to take effect.

  2. Run the command jupyter lab to start. This starts the Jupyter server and opens JupyterLab in your browser. If you would like to access JupyterLab from a remote browser, type jupyter lab --ip=0.0.0.0 --no-browser instead.

JupyterLab only has access to the folder where it is launched. If you need to access files at a different location, launch JupyterLab in that location.

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.