Get started with ML Workbench Sandbox Image

This page walks you through running the TigerGraph ML Workbench in a Docker container.

To help you get started quickly, we provide a “sandbox” docker image that has the TigerGraph database, GDPS, and ML Workbench pre-installed. The “Cora” dataset used for tutorials is also preloaded into the database.

1. Prerequisites

  • You machine needs to meet the Hardware and Software Requirements.

    • Make sure you are allocating enough CPU and memory to your Docker containers. It is recommended that you allocate at least 8 CPUs and 16 GB of memory to your Docker containers.

  • Docker Desktop is running on your machine.

2. Procedure

  1. Run the following command to pull the Workbench image, bind ports, map a shared data folder, and start a container from the image

    $ docker run -it -p 14022:22 -p 8000:8000 -p 8888:8888 -p 9000:9000 -p 14240:14240 -p 6006:6006  --name tgsandbox --ulimit nofile=1000000:1000000 -v ~/tgsandbox:/home/tigergraph/tgsandbox/save tigergraphml/sandbox

    Here is a breakdown of the options in the docker run command above

    • -it: run the container interactively and allocate a pseudo-TTY. If you’d rather run the container non-interactively, use -d instead. You can use docker logs to retrieve the URL for JupyterLab.

    • -p: map a port of the host (14022) to a port of the container (22). Port 22 is for the SSH server; ports 9000 and 14240 are for the database; port 8000 is for GDPS; port 8888 is for the ML Workbench; port 6006 for Tensorboard.

    • --name: name of the container. The command sets the name to tgsandbox, but you can change this to another name.

    • --ulimit: the limit of number of open file descriptors per process.

    • -v: mount a folder on the host (~/tgsandbox) to a folder in the container (/home/tigergraph/tgsandbox). These two folders are synced and hence allow you to save any file even after the container is removed. However, the folder on the host (~/tgsandbox) has to be writable by the user inside the container. Normally, this can be ensured by making the folder public, e.g., chmod 777 ~/tgsandbox. If you are using Windows, change the path using Windows file system conventions. For example, c:\tgsandbox instead of ~/tgsandbox.

    After the container has started up, JupyterLab will provide you with a URL in your terminal to access the JupyterLab IDE. Visit the URL in your browser.

  2. If you want shell access to your Docker container, run the following command:

    $ ssh -p 14022 tigergraph@localhost

    When prompted for the password for user tigergraph, enter tigergraph.

    Alternatively, if you don’t have access to ssh, you can get shell access through docker exec:

    $ docker exec -it $(docker ps | grep tgsandbox | awk '{print $1}') /bin/sh

  3. If you turn off the sandbox container and start the container again, JupyterLab starts automatically as soon as the container starts. However, JupyterLab generates a new token everytime it restarts. You can retrieve the URL with the token by running docker logs tgsandbox, or click the container in the Docker Desktop UI to retrieve the URL from the latest terminal output.

3. Video walk-through

Here is a video walk-through of running the sandbox image:

4. Next steps