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Resources for Students

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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.​
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​​6 Deep Learning workstations , each of them contains these specs​
  • 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 or equivalent​
  • Software installed: CUDA9.2 with matching cuDNN,  NVidia Docker, Theano, Tensorflow, Caffe2, Pytorch, Python, SSH​
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​To apply for a GPU Server user account
​Apply via: https://wis.ntu.edu.sg/pls/webexe/REGISTER_NTU.REGISTER?EVENT_ID=OA19090318363910

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​
  1.  Open your SSH client. ​
  2. Type the username provided before the @ symbol (eg. user1@xxx.xx.xx.xxx ) 
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