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
- For non-GPU VMs use the Ubuntu 22.04 + Docker image (in CLI tool type
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 \ -e CHOWN_HOME=yes -e CHOWN_HOME_OPTS='-R' \ -v /jupyter:/home/jovyan jupyter/minimal-notebook
To access the server, open this file in a browser: ... or http://127.0.0.1:8888/lab?token=626217ed7607331b9523cb2d55aa72ea4dd044e4020d21e1
127.0.0.1 with the public ip address of your VM, and open the URL in your browser.