NautilusServer
Contents
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.
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
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
