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30-Minute Quickstart

Step 1: Connect via SSH​

NB: requires you are connected via Hertie campus Wi-Fi or VPN connection. Contact IT service desk if you need VPN access.

From your laptop terminal:

ssh <your-username>@hertie-school.lan@10.1.23.20

Your username depends on your account type:

Account typeUsername formatExample
Master's student<student-id>@hertie-school.lan212345@hertie-school.lan
PhD student<student-id>@hertie-school.lan197968@hertie-school.lan
Staff / faculty<firstname>.<lastname>@hertie-school.lanj.smith@hertie-school.lan

Note: @hertie-school.lan is the canonical internal domain and works for all account types. You can also use your email domain (e.g. @students.hertie-school.org, @phd.hertie-school.org, or @hertie-school.org for staff) — these resolve to @hertie-school.lan automatically.

If you don't have SSH keys set up, you'll be prompted for your usual Hertie Microsoft password.

Step 2: Run First-Time Setup​

Once connected, run:

user-setup

This interactive GUI will:

  • Generate & configure SSH keys (for DS01 & GitHub)
  • Configure VS Code (necessary extensions & auto-SSH)

Then run:

project-init --guided

This interactive GUI will:

  • Create your first project workspace
  • Setup best-practice DS directory structure
  • Auto-build project docs (README.md, requirements.txt, Dockerfile, pyproject.toml)
  • Initialise git
  • Build a custom Docker image1
  • Deploy your first container

Then run:

project-launch --guided

This interactive GUI will:

  • Scan available projects
  • Build an executable Docker image from the project's Dockerfile (or otherwise first define a Dockerfile)
  • Deploy a container instance of the image onto a GPU
  • Either attach the terminal to running container, or start in background

You have successfully deployed a container!

Step 3: Start Working​

After setup completes, you'll be inside a container. Check that GPU is available:

nvidia-smi

You should see GPU information. Now you can:

# Check Python
python --version

# Check PyTorch (or TensorFlow)
python -c "import torch; print(torch.cuda.is_available())"

# Navigate to your workspace (if not already)
cd /workspace

# If you have a remote repo you wish to clone (otherwise project-init configures for you)
git clone your-repo

# Start running scripts in your project directory!

NB: /workspace inside the container is your project directory on the host (~/workspace/<project-name>/). This is a bind mount; your files persist even after retiring the container.

Step 3.5: Attach Terminal/IDE to Running Container​

If you are comfortable with working from the terminal project launch will offer you the option to directly attach your terminal to the deployed container (or add --open flag to the launch command).

If you are more comfortable working in an IDE you will need the following 3 extensions in your IDE (here, presuming VS Code)

  • SSH Remote
  • Dev Containers
  • Container Tools

Once installed: Cmd + Shift + P to open the Command Pallete, and type Dev Containers: Attach to Running Container.... This will open up a new window attached to the running container!

NB: this ^^^ is all walked through by user setup CLI.

Step 4: Exit and Retire Container​

When done with the current job:

# To exit the container from inside an attached terminal:
exit

# If you have a container running in background (terminal not attached):
container retire --guided

That's it. Files saved in /workspace are permanent.

Next Steps​

See Index & Learning Paths for entry points, or jump right in:

I want to...

Further Refs:​

Quick Reference for all commands