Difference between revisions of "NautilusServer"
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These will be installed in a [https://conda.io/docs/intro.html conda] environment called '''deep''': | These will be installed in a [https://conda.io/docs/intro.html conda] environment called '''deep''': | ||
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export ENVNAME=deep | export ENVNAME=deep | ||
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python -c "from keras.models import Sequential;Sequential()" | python -c "from keras.models import Sequential;Sequential()" | ||
− | To check the GPU is working with tensorflow | + | To check the GPU is working with tensorflow, xheck this first (it should list two GPUs): |
− | |||
− | |||
nvidia-smi | nvidia-smi | ||
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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 | 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: | + | Run it like this: |
+ | |||
python test_tf_gpu.py | python test_tf_gpu.py | ||
Revision as of 18:24, 14 August 2017
Contents
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.
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 sudo 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, xheck 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
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
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