- 1 Help
- 1.1 Accessing
- 1.2 Setting up a deep learning environment
- 1.3 Easy file access (Linux)
- 1.4 Using the SSD
- 1.5 Compiling your own CUDA programs
- 1.6 Running programs after you log out
- 1.7 Other gotchas
- 2 More Information
Access from: ITU or ITU VPN.
If you are accessing from off-campus you will need to access via the university VPN.
- 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).
The SSH command to connect from a Unix environment:
ssh -X -p 1542 YOURUSERNAME@<SERVER_IP_ADDRESS>
- -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.
- YOURUSERNAME: You should have been given this along with your password when your account on the server was created.
- SERVER_IP_ADDRESS: Currently this is 188.8.131.52
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.
You can use SSH command to tunnel your port into server port in order to access several applications (e.g. Tensorboard) from your machine. The option that is used for tunneling is:
ssh -p 1542 -L 6006:localhost:6006 alican@<SERVER_IP_ADDRESS>
Here, anybody connecting to port 6006 on your local computer will be directed to port 6006 on the remote computer (the server).
Setting up a deep learning environment
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):
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:
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 mkdir -p $targ fusermount -u $targ # only necessary to unmount if already tried sshfs -p 1542 -o workaround=rename YOUR_SERVER_USERNAME@SERVER_IP_ADDRESS:/home/YOUR_SERVER_USERNAME $targ
Note: if parts of your system hang because the connection to the ssh server gets stale (a common problem), just do:
It should resolve most of your problems.
Using the SSD
There is an SSD drive installed. This drive is automatically mounted at:
The drive belongs to user root and group fastdata1. If you cannot access it you need to get an admin (Hossein) to add you to the group with the command:
sudo usermod -aG fastdata1 YOURUSERNAME
And to add you a folder in there with the right permissions:
sudo mkdir /media/FASTDATA1/YOURUSERNAME sudo chown YOURUSERNAME:YOURUSERNAME /media/FASTDATA1/YOURUSERNAME sudo chmod 700 /media/FASTDATA1/YOURUSERNAME
Compiling your own CUDA programs
To do this, add the following lines to your .bashrc file in your home folder:
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export PATH=/usr/local/cuda/bin:$PATH
Running programs after you log out
Sometimes a process may take a while and if you happen to log out the lack of interactivity may cause that process to give up on you.
To get around this, people use the tool screen or later versions like tmux.
You log in over ssh, then type:
This will put you in a little session-in-a-session. Even if you log out of ssh this tmux session will persist and you can get back to it by again typing tmux.
Once you've finished writing your commands (e.g. python my_python_program_that_takes_3_days.py), and started it running, you can get back to an ordinary shell session by typing:
(that is, a Ctrl-b followed by a d).
Now if you want to get back to your previous session, just run tmux again. Actually, to be sure you connect to the right session, run:
See the session number of the session to which you want to connect (e.g. 0) then to connect to it, run:
tmux attach -t 0
There is a lot more to tmux than that but that's the important part.
For more, see:
Who else is using the computer?
The GPUs are set to slowdown at 93C and shutdown at 96C. Idle temperature should be about 50C. To see the current temperature information in full run:
nvidia-smi -q -d temperature
It should never reach that temperature. If it does that's a big whoops.
I have noticed that the first GPU (GPU 0) gets hotter quicker, presumably due to its physical location. With this information, it might be preferable to use GPU 1 more of the time.
GPU and Memory Allocation
Multiple kernels and users can also run on one GPU. This may mean you don't have enough memory at some point. It is possible to set it so that a GPU can only be accessed by one user at a time. This may be necessary in the future to ensure there is enough memory for those big jobs.
Tensorflow actually seems to claim all the memory on all the GPUs so it might be a nice gentle thing for other users for you to make tensorflow be a bit nicer.
Hiding unused GPUs from your program
One suggestion is to use
to check which GPUs are available first then hide the one that you don't need from your program, using
if you want to use the 1st GPU or
if you want to use the 2nd GPU.
Restricting GPU memory used by your tensorflow program
Another approach when using tensorlow is to only use a certain amount of memory as described in the following answer: https://stackoverflow.com/a/34200194/1616231
Automatic memory claiming
Alternatively you may make tensorflow take memory as it is needed by taking the steps described in the following answer: https://stackoverflow.com/a/37454574/1616231 (though this will ultimately use more memory).
Non-tensorflow apparoaches may have different characteristics, so the first option (hiding GPUs from your program) is the most general.
- 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 (total 64GB)
- 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
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