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Turing

This is a GPU server of the Department of Computer Engineering, University of Peradeniya.

You can use this server using the LDAP login for ce.pdn.ac.lk undergraduates (which is used for CO top floor lab, Aiken and Tesla) without sudo access.

Specifications
  • Intel Core i9-13900K CPU
  • NVIDIA GeForce RTX 3090 Ti GPU
  • 32 GB (2x16 GB) DDR5-6000 Memory
  • 4 TB (2x2 TB) M.2 SSD
NVIDIA GeForce RTX 3090 Ti

Ubuntu 22.04

  • Ubuntu 22.04
  • NVIDIA-Driver Version: 530.41.03
  • CUDA Version: 12.1 (You can update this using conda)

Read this google doc .

We store frequently used datasets inside the turing.ce.pdn.ac.lk local storage for easy access. You can access these by the following command: 

ls /storage/datasets/

These datasets are in the read only mode. You can use these datasets as the input for your tasks but you have to output the results to your home directory. The following datasets are stored at the moment.

- THIS LIST WILL BE POPULATED SOON

If you have suggestions for what datasets should be made available on this server, please send a request via email available in https://www.cs.umd.edu/~gihan/contact/ address.


Use https://anaconda.org/anaconda/cudatoolkit

Please request through any CO staff member. Please note that the server admin has no authority to install something unless it is requested by a staff member.

Contact server admin

  1. Send a request to the server admin via the server admin contact page to run this command: 
    sudo singularity config fakeroot --add eXXYYY
  2. Download the sample def file: 
    eyyxxx@turing:~$ wget https://raw.githubusercontent.com/cepdnaclk/singularity-deployment/main/base.def
  3. Build the container: 
    eyyxxx@turing:~$ singularity build --fakeroot base.sif base.def
  4. Run commands using the container: 
    eyyxxx@turing:~$ singularity exec base.sif <your-command>

If you need to learn more, go to: https://github.com/cepdnaclk/singularity-deployment


Ask via email