Resources for Students

GPU resources

GPU Server with 4 x NVidia Tesla V100.
CUDA is pre-installed on the server.
You will be given access to your own home directory where you can install your own Anaconda (with Python, packages and frameworks).
We currently do not have a particular set of Python or ML framework installed as everyone has different requirements.

16x Deep Learning Workstations, each equipped with

GPU: 4 x NVIDIA GTX 2080 Ti, w/ 11GB VRAM Single Blower;
CPU: 1 x 18-Core Intel Xeon W-2295 (36T, 24.75, 3.00 GHz) processor
Motherboard: Asus C422 SAGE/10G, C422 Chipset, LGA2066, 8DIMMs, up to 256GB DDR4 ECC
Memory: 256 GB (8x 32GB) DDR4-2933 REG ECC DIMM
Software installed: CUDA9.2 with matching cuDNN, NVIDIA Docker, Tensorflow, Caffe2, PyTorch, Python, SSH

6x Deep Learning Workstations, each equipped with

GPU: 4 x NVIDIA GTX 1080 Ti, w/ 11GB GDDR5X
CPU: 1 x Eight-Core Intel Xeon E5-1680v4 (3.40GHz, 20M Cache, 2400 MHz) processor
Motherboard: Asus X99-E WS, X99 Chipset, LGA2011-3, 8DIMMs, up to 256GB DDR4 ECC
Memory: 128 GB (4x 32GB) DDR4-2400 REG ECC DIMM
Software installed: CUDA9.2 with matching cuDNN, NVidia Docker, Tensorflow, Caffe2, Pytorch, Python, SSH

To apply for a GPU Server user account

Login credentials

You will be emailed with the following information to log into the server via SSH:

Server IP address
IP:xxx.xx.xx.xxx

username

password

GPU id

School of Electrical and Electronic Engineering