Jupyter Notebooks

Description

This catalog item starts a JupyterLab environment in which you can work with Jupyter notebooks.

Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

All users in your Collaborative Organisation can login to the workspace and will then be presented with their own notebook environment.

If needed, we can help you to automatically install certain packages when the workspace is created, so that these are available from inside the Jupyter environment immediately. Please contact us for this. However, you can of course also install your own dependencies from with the Notebook.

This workspace can be created with or without nvidia CUDA GPU drivers pre-installed.

To create with GPU pre-installed select Jupyter Notebooks with CUDA when creating the workspace,

GPU workspaces consume more credits. See the cost calculator for an estimate.

A GPU may speed up your code if the (python) libraries that you use are designed to take advantage of GPU acceleration (e.g. Pytorch, Tensorflow, LLMs). Only choose GPU workspaces if you know that this is the case. If you are not sure, contact us.

Creation

Create a storage volume

If desired, first create a storage volume before creating the workspace.

See the Getting started page for more info about how and why to create a storage volume.

Create a workspace

In the Research Cloud portal click the ‘Create a new workspace’ button and follow the steps in the wizzard.

See the workspace creation manual page for more guidance.

Access

You can login to the workspace in your browser, via your ResearchCloud time-based password.

Usage

When logged in you are immediately presented with a JupyterLab environment in which you can work with different notebooks.

Data Transfer Jupyter Notebook

Use this table to find the appropriate data transfer method for your situation:

Data Source Recommended Tool Best For Skill Level
Your PC/laptop JupyterLab Upload button Small files, quick uploads Beginner (GUI)
Yoda/iRODS iBridges All file sizes, CLI access Beginner to Intermediate
Yoda/iRODS iCommands Large datasets, transfer automation, CLI Intermediate (CLI)
SURFdrive, ResearchDrive rclone Cloud storage sync, scheduled transfers Intermediate (CLI)
Your PC/laptop scp Direct one-time transfers Intermediate (CLI)
Your PC/laptop rsync Sync or repeated transfers Intermediate (CLI)
Your PC/laptop (Windows only) MobaXterm Graphical SSH/SFTP client Intermediate
Your PC/laptop Cyberduck Graphical SFTP client Intermediate
GitHub/GitLab git clone Code repositories, version control Intermediate (CLI)
TipUsing the Upload Button

For small files, use the Upload button in the JupyterLab file browser (left sidebar):

  1. Click the Upload Files icon (↑) in the file browser toolbar
  2. Select files from your local computer
  3. Files will be uploaded to your current directory
TipTransferring from OneDrive

OneDrive is currently not directly supported with rclone for UU accounts. To transfer files from OneDrive:

  1. Download (preferably compress/zip) files from OneDrive to your local PC
  2. Transfer from your PC to the workspace using one of the methods above
Note

All command-line tools (iBridges CLI, iCommands, rclone, scp/rsync, Git) must be run from the JupyterLab terminal.

See our data transfer manuals for detailed step-by-step guides.

Installing additional software

The user has admin rights to install additional software on the system from the Jupyter terminal (use sudo <command>).

Tips

Workspace security

Please take a moment to read the security recommendations for VREs.