Jupyter Labs with Cudo Compute


  • Create a project and add an SSH key
  • Optionally download CLI tool
  • Choose a VM
  • For GPU VMs use the Ubuntu 22.04 + Nvidia drivers + Docker image (in CLI tool type -image ubuntu-nvidia-docker)
  • For non-GPU VMs use the Ubuntu 22.04 + Docker image (in CLI tool type -image ubuntu-2204-docker)

Jupyter in a Docker container

The fastest way to get started with Jupyter is to use the official Jupyter Docker image. This image contains JupyterLab as well as all of its dependencies. You can find the Docker image on Docker Hub and the source on GitHub.

There are several Jupyter images to choose from, starting with the most minimal:

jupyter/base-notebook includes conda and mamba (a faster alternative to conda) but no other scientific Python packages.

jupyter/minimal-notebook adds TeX Live, git, vi, nano, tz data and unzip

jupyter/r-notebook everything in minimal plus the R interpreter, IRKernel plus additional packges

jupyter/scipy-notebook everything in minimal, plus packages including scipy, scikit-learn, pandas, matplotlib

jupyter/tensorflow-notebook everything in scipy-notebook plus TensorFlow machine learning framework

jupyter/datascience-notebook everything in scipy-notebook and r-notebook plus additional packages for data science

Let's start the minimal notebook, SSH into your VM and run the following command:

docker run \
-it --rm -p 8888:8888 \
--user root \
-v /jupyter:/home/jovyan jupyter/minimal-notebook

This yields:

 To access the server, open this file in a browser:

Replace with the public ip address of your VM, and open the URL in your browser.