There are three CS labs located within the Malet Place Engineering Building (MPEB) and one at 169 Euston Road.
All the MPEB computer labs require a code to unlock the door. Please contact the Helpdesk for this code.
Lab121 is fitted with 30 Dell Precision T3600’s with 16GB RAM and Nvidia GeForce GTX 650 video cards
Lab105 has 24 custom PCs with i9 processors, 128 GB RAM and Nvidia GeoForce RTX 3090 graphics cards
Lab406 is a bring your own device lab which has additional displays, desk space and wired and wireless access
Bloomberg Lab is located at the Euston road site and only available to Financial Computing students.
Lab opening times are from 8:00am to 7:00pm weekdays.
All our lab PCs require a Computer Science username and password to login (this is different to your UCL login details and usually resembles your name). New students are given these at the start of the academic year. If you join CS later you will need to contact the Helpdesk to get your account details.
The PCs dual boot into both Linux and Windows operating systems as well as giving access to CS computing services. For further details select the Linux and Windows headings above. The lab PCs regularly reboot on Monday and Thursday evenings between 7.30pm and midnight.
The labs are used to support the CS teaching curriculum and are booked for classes and workshops. To plan your usage open the MPEB Labs Timetable 23-24 to see the current bookings.
To access Linux from within windows use the Thinlinc app to connect to csrw2.cs.ucl.ac.uk. See working from home for more details.
To reboot the PC into Linux right click the Start button and choose Restart. After a short pause select Linux from the boot menu.
To setup python, use the cuda tool kit and tensorflow to access GPU resources see anaconda environments below.
The following apps are installed under windows 10:
- 7-Zip
- Anaconda3 – see below for more info
- Chrome
- Citrix Workspace
- Colmap
- CPUID
- Eclipse
- MS Edge
- FireFox
- Git
- GPU-Z
- ImgBurn
- Open JDK Java
- MATLAB R2022a
- Microsoft Office 365
- Microsoft Silverlight
- Notepad++
- Oracle VM VirtualBox
- PuTTY
- R
- RStudio
- Unity 2019.4.34f1
- Visual Studio 2022
- Visual Studio Code
- VMware
- WinSCP
Anaconda environments
To use anaconda successfully in the labs you need to utilise conda enviroments – these are basically folders which store the python modules you need for a project. Python environments can take a lot of space and slow down logins so there is a folder called c:\myconda where you can install your environments and modules. The environments created in this folder will be local to that PC and are writeable by the creator but only readable by other users. If you need read write access to an environment you will need to create one in c:\myconda with a suitable name. For example, user alovlac creates an environment called c:\myconda\alovlac. User alovlac can install and remove modules into this environment and all others users can only read and activate this environment. Using a unique name will help identify which environment is yours.
If you have project module and large dataset that you want to access from multiple PCs you can request a project store from the helpdesk.
To create an environment use conda create –prefix c:\myconda\environment-name module-name. For example, conda create –prefix c:\myconda\tensforflow tensorflow-gpu.
Once created you must activate the environment with conda activate c:\myconda\environment-name.
For example, to create the tensorflow GPU environment:
- start anaconda prompt via the start menu (don’t use navigator)
- conda create –prefix c:\myconda\tensorflow tensorflow-gpu
- conda activate c:\myconda\tensorflow
to start python interpreter use:
- python
to import the module use:
- import tensorflow as tf
when finished use deactivate
Cuda Toolkit
You can use conda to install the cuda toolkit with:
- conda create –prefix c:\myconda\cuda -c nvidia cuda
This will install the cuda toolkit into c:\myconda\cuda.
For more info on conda and cuda see:
The Linux environment offers the standard packages and libraries that come with Rocky 9 and additional packages such as conda python with tensorflow for GPU coding.
Each lab has a printer and these are named after the printer location. These are:
- cps105
- cps121
- cps405
Printing is free within the CS labs but you are allocated a termly quota. Contact the Helpdesk for more quota or to report printing problems.
The labs are used for both teaching the departments curriculum and also for short courses and workshops. The Linux and Windows environments are configured for CS curriculum teaching. If you require additional software not currently installed please email request@cs.ucl.ac.uk with your requirements at least two weeks prior to your teaching.
Some examples of previous lab usage:
- robotics – purpose built robots for undergraduate teaching
- arduino – workshops aimed at school children in arduino programming
- security – pre-built virtual machines loaded onto lab machines used for curriculum teaching
- python – tensorflow environment with GPU resources for machine learning
All the labs have access to both Eduroam and CS wireless networks. The CS network will give you access to both CS services and UCL central services whereas the Eduroam network will give you access to UCL central services only. To access the CS wireless network you need to register the MAC address of your wireless adapter here.
In addition there are on desk network sockets in 4.06 so you can plug in your laptop and gain ethernet access to the CS student network. You must register the MAC address of your laptops wired ethernet adapter to use these sockets. If you require an ethernet cable please come to room 4.20 MPEB.
There are on desk power sockets for laptop and device charging and all the displays have an additional HDMI connection to allow laptop connectivity. Simply connect your laptop to the on desk HDMI cable and select HDMI 2 as the source port. If your laptop has a USB-c display port you will need a USB-c to HDMI adapter.
To set the source port you will need to access the menu button on the display. This button is located in different positions depending upon the screen.
On the Dell wide screens the menu buttons are located under the right side of the display.
On the LG ultra wide screens the menu button is positioned centrally on the under side of the display.
On the 28 inch Dell screens the menu button is located on the rear of the display.
There are various resources available to facilitate student project work which includes specialist hardware devices, dedicated computers and virtual machines. Hardware requirements for student projects must come from your academic supervisor prior to project commencement.
You can also request a virtual machine to facilitate your studies or project work. See Apply for a VM for more details
The PCs in lab105 are fitted with GPUs (Nvidia 3090s) which are accessible under Linux for compute processing. To utilise these GPUs under Linux you will need to activate Tensorflow in your python environment. To use the GPUs under windows refer to the Windows Tab above. The PCs in this lab boot into Linux by default and can potentially be accessed remotely. However please note that the PCs regularly reboot on Monday and Thursday evenings between 7.30pm and midnight. Please note that these PCs are used by others and may be rebooted at any time. If you wish to run a long compute job a good option would be to run the job overnight, excluding Monday and Thursday evenings, or at the weekend. See Lab GPUs for more info on student GPUs.
To access remotely:
- install use thinlinc to login to csrw2.cs.ucl.ac.uk, open Terminal and ssh to a 105 system listed below
- ssh from your home PC into knuckles.cs.ucl.ac.uk and then ssh into a 105 system. For example, ssh <yourcsusername>@knuckles.cs.ucl.ac.uk
- Once logged into either thinlinc or knuckles desktop use ssh host. For example, ssh canada
The 105 PCs are listed below.
aylesbury barnacle breeze-linux brent bufflehead cackling canada
crested eider gadwall goosander gressingham harlequin kamzi-linux
mallard mandarin pintail pocher ruddy scaup scoter
shelduck shoveler smew wigeon