VRE Lab

Description

This VRE Lab workspace hosts a JupyterLab environment that provides Jupyter notebooks alongside a number of useful applications accessible through the browser. You can also access RStudio via the browser by selecting it when creating your workspace. This catalog item can easily be extended with additional applications to suit your research needs.

VRE Lab workspace is also available with a Desktop interface. Select Ubuntu Desktop as the flavor when creating your workspace and you can access it from the JupyterLab environment.

The Desktop option is particularly useful for GUI applications, 3D visualization tasks, and when combined with GPU workspaces (VRE Lab GPU catalog item), provides GPU-accelerated rendering for 3D analysis and extensive data processing from the desktop environment.

The workspace includes Python environment management with uv.

You can automatically install environments and dependencies for Python, R, Julia, and Conda using the Custom Packages Optional Component provided in this workspace.

If you wish to customise this workspace further by installing system packages or need additional help, you can contact us

CUDA GPU Available

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

To create with GPU pre-installed select VRE lab GPU 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.

Requesting Access to the Catalog Item

Most Utrecht University users will already have access to this catalog item. If you cannot find this catalog item when creating a workspace:

You need to request access to the Utrecht University Catalog Item Collection.
Follow these steps to request access.
Once approved, you’ll be able to create the workspaces.

Optional Components

VRE Lab allows you to customise your workspace by selecting additional optional components during workspace creation. This lets you pre-install tools for your research needs.

During workspace creation, you’ll see an Optional components section where you can check the components you want to install.

Available components:

Open VSCode Server: Web-based integrated development environment (IDE) for coding directly in your browser.
OpenRefine UU: Data cleaning and transformation tool accessible from JupyterLab.
Python tools: Additional tools for managing Python environments (includes uv, poetry, and conda).
Custom Packages UU: Automatically install dependencies for Python, R, Julia, or Conda projects from Git repositories, Zenodo, Dataverse, or DOI links.
RStudioUU: Access an RStudio IDE with a R environment in the same workspace.

How to Use Custom Packages UU

Pre-installing Python, R and Julia packages during workspace creation

Setup

Check “Custom Packages UU” in Optional Components when creating your workspace
In the “Projects to pre-install” field, enter your project identifiers as URLs or DOIs (pointing to repositories) separated by commas
Wait for workspace to build (may take longer with many dependencies)
Projects are cloned to ~/local-share/projects/ and environments are created automatically based on the dependency files

Important

The project you wish to install must contain valid dependency files (environment.yml, requirements.txt, pyproject.toml, etc.) for automatic installation to work.

After workspace creation, select the Jupyter kernel for your project, or activate the environment in the terminal via:

conda activate /usr/local/uu/env/conda/{project-name}

source /usr/local/uu/env/python/{project-name}/bin/activate

For detailed instructions on using and modifying these environments, see the detailed Custom Packages Manual.

Simply check the boxes for the components you need, then continue with workspace creation. You can always install additional software later using sudo rights from the terminal.

Access

Accessing the Workspace

Accessing the Workspace using the yellow Access button

Open your SURF Research Cloud portal.
When you login to your SURF Research Cloud portal, you will see your dashboard. Here, look for your workspace and ensure that it is running. You can access it by simply clicking the yellow “Access” button which will redirect you to a new page.
You can then log in with your institutional credentials (Solis ID for Utrecht University users) to access the workspace.

Since you’re already signed in to access SURF Research Cloud, you may not need to authenticate again.

Accessing the Workspace via the command line (SSH)

If you do not have an ssh key pair set up, you will need to generate one and add the public key to your SURF Research Cloud account. Follow the instructions to set up SSH access..

Open the ‘Terminal’ application.
In your terminal application type: ssh <ResearchCloud username>@<workspace ip address>

The ResearchCloud username is given on the “Profile”-Tab of the ResearchCloud portal page. The ip-address of the workspace is displayed on the (extendable) display tile of the workspace in the main (“Dashboard”) tab.

Make sure you have installed an application that allows you to login using SSH: Git Bash (recommended), alternatively MobaXTerm
Open your ‘Terminal’ application (e.g. Git Bash).
In your terminal application type: ssh <ResearchCloud username>@<workspace ip address>

Note

If you just submitted a new public key or when the workspace is new, you might have to wait for a few minutes for this to work.

Usage

After logging in, you’ll have access to the JupyterLab interface which includes:

Jupyter notebooks for interactive coding
Additional browser-based applications
Integrated terminal access
Desktop interface (if you selected the Ubuntu Desktop flavor) that opens in a new browser tab

Data transfer options

See our data transfer manuals.

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)

You can upload data directly to the workspace using the Upload Files button in the JupyterLab interface.

Transferring from OneDrive

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

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

For larger data transfers, the recommended iBridges client for Yoda and iRODS is preinstalled.

Sudo rights

Sudo rights can be obtained by adding collaboration members to the src_co_admin group in SRAM.

Members with sudo rights can install system-level packages and perform administrative tasks on the workspace.

Tips

This workspace includes uv for Python environment management. See the Python environment manual for best practices and getting started.

Workspace security

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