Difference between revisions of "NautilusServer"

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m (Install tensorflow and keras)
m (Easy file access (Linux))
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     fusermount -u $targ # only necessary to unmount if already tried
     fusermount -u $targ # only necessary to unmount if already tried
     mkdir -p $targ
     mkdir -p $targ
     sshfs -o workaround=rename YOUR_SERVER_USERNAME@ssh.itu.edu.tr:/home/YOUR_SERVER_USERNAME $targ
     sshfs -p 1542 -o workaround=rename YOUR_SERVER_USERNAME@ $targ
== Using the SSD ==
== Using the SSD ==

Revision as of 14:07, 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

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

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:

   usermod -aG fastdata1 YOURUSERNAME

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

   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