Revision as of 19:02, 15 August 2017

Help

Accessing

IP: 160.75.27.83

SSH port: 1542

Access from: ITU or ITU VPN.

VPN Help

• BIDB has instructions for accessing the VPN.
• I have also provided here a script for accessing the ITU VPN from Ubuntu (ituvpn.sh) (tested on Kubuntu 16.04).

SSH help

The SSH command to connect from a Unix environment:

   ssh -X -p 1542 hossein@160.75.27.83

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:

   xeyes

Or:

   dolphin

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=$HOME/software
   export ANACONDA_PATH=$ANACONDA_PATH_PARENT/anaconda3
   export ANACONDA_INSTALLER=Anaconda3-4.3.1-Linux-x86_64.sh

   mkdir -p ~/tmp
   cd ~/tmp
   mkdir -p $ANACONDA_PATH_PARENT
   wget https://repo.continuum.io/archive/$ANACONDA_INSTALLER
   bash $ANACONDA_INSTALLER -b -p $ANACONDA_PATH

   export PATH=$ANACONDA_PATH/bin:$PATH
   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):

   nvidia-smi

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.

You may wish to add to your file ~/.bashrc the following line so that the path to Anaconda is set every time you log in:

   export PATH=THE_PATH_TO_WHICH_YOU_INSTALLED_ANACONDA/bin:$PATH

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
   targ=~/remote/nautilus
   fusermount -u $targ # only necessary to unmount if already tried
   mkdir -p $targ
   sshfs -p 1542 -o workaround=rename YOUR_SERVER_USERNAME@160.75.27.83:/home/YOUR_SERVER_USERNAME $targ

Using the SSD

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

   /media/FASTDATA1

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:

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

More Information

Server Construction

• 1 x INTEL i7-6700K
• 2 x GTX 1080ti GPU
• 1 x ASUS ROG MAXIMUS IX HERO
• 2 x CORSAIR 32GB (2x16) D4 3000Mhz CMU32GX4M2C3000C15
• 1 x CORSAIR CP-9020094-EU 1000W PSU
• 1 x Sharkoon M25-W - Mini tower ATX 5.25"
• 1 x 3TB HDD
• 1 x 256GB SSD

Built by Uzmanlar PC

Software

OS

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

   cuda_8.0.61.2_linux.run