Installing ML Workspace on FreeBSD Latest

In this tutorial, we will be installing the ML Workspace on FreeBSD Latest from the official GitHub repository.

Prerequisites

• FreeBSD Latest installed on your machine
• Git, Docker, and Docker Compose installed on your machine

Steps

1. Open a terminal on your FreeBSD machine and clone the ML Workspace repository by running the following command:

   git clone https://github.com/ml-tooling/ml-workspace.git
   

2. Navigate into the cloned repository using the following command:

   cd ml-workspace
   

3. Open the .env file in a text editor of your choice using the following command:

   nano .env
   

4. Configure the environment variables according to your preferences, such as the port number, password, and so on.

5. Exit the text editor by pressing Ctrl + X, followed by Y and Enter.

6. Start the ML Workspace using Docker Compose by running the following command:

   docker-compose up -d
   

7. This will download, build and start the containers. After a few minutes, you can access the ML Workspace through your web browser by entering the IP address of the machine and the port number you specified in the .env file. For example, if you set the port number to 8080, you can access the workspace by entering http://localhost:8080 in your web browser.

8. The ML Workspace should now be up and running, and you can start using it for your Machine Learning tasks.

Conclusion

You have now successfully installed the ML Workspace on FreeBSD Latest. You can start using this powerful toolset to simplify and streamline your Machine Learning tasks.

If you want to self-host in an easy, hands free way, need an external IP address, or simply want your data in your own hands, give IPv6.rs a try!

Alternatively, for the best virtual desktop, try Shells!