Difference between revisions of "NautilusServer"

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* 2 x [https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080-ti/ GTX 1080ti GPU]
* 2 x [https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080-ti/ GTX 1080ti GPU]
* 1 x [https://www.asus.com/us/Motherboards/ROG-MAXIMUS-IX-HERO/specifications/ ASUS ROG MAXIMUS IX HERO]
* 1 x [https://www.asus.com/us/Motherboards/ROG-MAXIMUS-IX-HERO/specifications/ ASUS ROG MAXIMUS IX HERO]
* 2 x CORSAIR 32GB (2x16) D4 3000Mhz CMU32GX4M2C3000C15
* 2 x CORSAIR 32GB (2x16) D4 3000Mhz CMU32GX4M2C3000C15 (total 64GB)
* 1 x CORSAIR CP-9020094-EU 1000W PSU
* 1 x CORSAIR CP-9020094-EU 1000W PSU
* 1 x [https://en.sharkoon.com/product/1678/M25-W#desc Sharkoon M25-W - Mini tower ATX 5.25"]
* 1 x [https://en.sharkoon.com/product/1678/M25-W#desc Sharkoon M25-W - Mini tower ATX 5.25"]

Revision as of 21:31, 15 August 2017




SSH port: 1542

Access from: ITU or ITU VPN.

VPN Help

SSH help

The SSH command to connect from a Unix environment:

   ssh -X -p 1542 hossein@

Switch meanings:

  • -p 1542: connect on port 1542
  • -X: This allows you to run X applications. Omit it if you will be pure command line. You can also run X applications from Windows but you will need to install an X server on your Windows machine.

Note: to check that X forwarding is working, once you have connected, try running on the server the command:




You could for example run spyder like this. But there can be some latency across the network.

Setting up a deep learning environment

Install anaconda

   export ANACONDA_PATH_PARENT=$HOME/software
   export ANACONDA_INSTALLER=Anaconda3-4.3.1-Linux-x86_64.sh
   mkdir -p ~/tmp
   cd ~/tmp
   wget https://repo.continuum.io/archive/$ANACONDA_INSTALLER
   echo PATH: $PATH
   echo >> ~/.bashrc
   echo export PATH=$ANACONDA_PATH/bin:\$PATH >> ~/.bashrc

Install tensorflow and keras

These will be installed in a conda environment called deep:

   export ENVNAME=deep
   conda create --name $ENVNAME
   source activate $ENVNAME
   conda install theano keras tensorflow tensorflow-gpu opencv pillow spyder matplotlib

To check Keras is working:

   python -c "from keras.models import Sequential;Sequential()"

To check the GPU is working with tensorflow, check this first (it should list two GPUs):


Then make sure the following script runs and finds one CPU and two GPUS: https://bitbucket.org/damienjadeduff/uhem_keras_tf/src/master/sariyer_python3/test_tf_gpu.py

Run it like this:

   python test_tf_gpu.py

Warning: for different versions of Tensorflow, Keras or Theano you may need to use pip to install the version you need in an environment.

Easy file access (Linux)

This can be useful for getting files on and off the server by accessing your remote home directory as if it was on your local computer (mounted on your file system).

On YOUR Linux computer run:

   sudo apt-get install sshfs
   fusermount -u $targ # only necessary to unmount if already tried
   mkdir -p $targ
   sshfs -p 1542 -o workaround=rename YOUR_SERVER_USERNAME@ $targ

Note: if parts of your system hang because the connection to the ssh server gets stale (a common problem), just do:

   killall ssfs

It should resolve most of your problems.

Using the SSD

There is an SSD drive installed. This drive is automatically mounted at:


The drive belongs to user root and group fastdata1. If you cannot access it you need to get an admin (Hossein) to add you to the group with the command:

   sudo usermod -aG fastdata1 YOURUSERNAME

And to add you a folder in there with the right permissions:

   sudo mkdir /media/FASTDATA1/YOURUSERNAME
   sudo chmod 700 /media/FASTDATA1/YOURUSERNAME

More Information

Server Construction

Built by Uzmanlar PC



Kubuntu 16.04.3 LTS

Graphics Drivers

Nvidia 384.59 drivers installed using runfile NVIDIA-Linux-x86_64-384.59.run

Installed using (to keep using the integrated graphics as main display graphics):

   sudo ./NVIDIA-Linux-x86_64-370.28.run --no-opengl-files --no-x-check --disable-nouveau

CUDA Drivers

Installed using