NautilusServer

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Revision as of 11:30, 2 July 2018 by Damienjadeduff (talk | contribs) (→‎SSH help)

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Help

Accessing

Access from: ITU or ITU VPN.

VPN Help

SSH help

The SSH command to connect from a Unix environment: 

   ssh -X -p 1542 YOURUSERNAME@<SERVER_IP_ADDRESS>

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.
  • 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 160.75.27.83

Note: to check that X forwarding is working, once you have connected, try running on the server the command: 

   xeyes

Or: 

   dolphin

Or: 

   eog

You could for example run spyder like this. But there can be some latency across the network.

Port Tunnelling

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: 

-L <SERVER_PORT>:localhost:<YOUR_PORT>

For example: 

 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

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
   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: 

   killall ssfs

It should resolve most of your problems.

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: 

   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: 

   tmux

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: 

   Ctrl-b d

(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: 

   tmux list-sessions

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:

https://robots.thoughtbot.com/a-tmux-crash-course

Other gotchas

Who else is using the computer?

Try running 

    who

Or 

   nvidia-smi

Or 

   top

Temperature

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 

   nvidia-smi

to check which GPUs are available first then hide the one that you don't need from your program, using 

   CUDA_VISIBLE_DEVICES=0 yourprogram

if you want to use the 1st GPU or 

   CUDA_VISIBLE_DEVICES=1 yourprogram

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.

More Information

Server Construction

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
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