This text gives a couple of practical hints to get you started using the DAS-5 quickly. It is intended for people with little to no experience using compute clusters.

First of all, and this is the most important point in this text: read the usage policy and make sure you understand every word of it: http://www.cs.vu.nl/das5/usage.shtml

The DAS-5 consists of multiple cluster sites, the largest one is located at the VU, which you can reach using by the hostname fs0.das5.cs.vu.nl. The firewall requires that your IP is whitelisted, which means you will be able to access the DAS from the eScience Center office, but not directly when you are somewhere else. To use the DAS from anywhere you can use eduVPN.

When you login in it means you are logged into the headnode, this node should not be used for any computational work. The cluster uses a reservation system, if you want to use any node that is not the head node, you must use the reservation system to gain access to a compute node. The reserveration system on DAS-5 is called Slurm, you can see all running jobs on the cluster using squeue and cancel any of your running jobs with scancel <jobid>.

The files in your home directory /home/username/ will be backed up automatically, if you accidently delete an important file you can email the maintainer and kindly request him to put back an old version of the file. If you have to store large data sets put them under /var/scratch/username/, the scratch space is not backed up.

You can use the command module to gain access to a large set of preinstalled software. Use module list to see what modules are currently loaded and module avail to see all available modules. You can load or unload modules with the 'module load' and module unload. You may want to add some of the modules you frequently use to your bashrc. Note that all that these modules do is add or remove stuff from your PATH and LD_LIBRARY_PATH environment variables. If you need software that is not preinstalled, you can install it into your home directory. For installing Python packages, you have to use Anaconda or pip install --user.

If you want an interactive login on any of the compute nodes through the reservation system, you could use: srun -N 1 --pty bash. The srun command is used to run a program on a compute node, -N specifies the number of nodes, --pty specifies this is an interactive job, bash is the name of the program being launched. This reservation is only cancelled when you logout of the interactive session, please observe the rules regarding reservation lengths.

To access the nodes you've reserved quickly it's a good idea to generate an ssh key and add your own public key to your 'authorized_keys' file. This will allow you to ssh to nodes you have reserved without password prompts.

To reserve a node with a particular GPU you have to specify to srun what kind of node you want. I have the following alias in my bashrc, because I use it all the time:
alias gpurun="srun -N 1 -C TitanX --gres=gpu:1"
If you prefix any command with gpurun the command will be executed on one of the compute nodes with an Nvidia GTX Titan X GPU in them. You can also type gpurun --pty bash to get an interactive login on such a node.

Running Jupyter Notebooks on DAS-5 nodes

If you have a Jupyter notebook that needs a powerfull GPU it can be useful to run the notebook not on your laptop, but on a GPU-equipped DAS-5 node instead.

It can be a bit tricky to get this to work though. That is why I wrote this text to guide you through all the required steps.

First of all, you need to install jupyter into your DAS-5 account. I recommend using miniconda, but any Python environment works. If you are using the native Python 2 installation on the DAS don't forget to add the --user option to the following pip command. You can install Jupyter using: pip install jupyter.

Now comes the tricky bit, we are going to connect to the headnode of the DAS5 and reserve a node through the reservation system and start a notebook server on that node. You can use the following alias for that, I suggest storing it in your .bashrc file:
alias notebook-server="srun -N 1 -C TitanX --gres=gpu:1 bash -c 'hostname; XDG_RUNTIME_DIR= jupyter notebook --ip=* --no-browser'"

The first part of the command is similar to the gpurun alias explained above. If you do not require a GPU in your node, please remove the -C TitanX --gres=gpu:1 part. Now let's take a look at what the rest of this command is doing.

On the node that we reserve through srun we execute the following bash command:
hostname; XDG_RUNTIME_DIR= jupyter notebook --ip=* --no-browser'
This is actually two commands, the first only prints the name of the host, which is important because you'll need to connect to that node later. The second command starts with unsetting the environment variable XDG_RUNTIME_DIR.

On the DAS, we normally do not have access to the default directory pointed to by the environment variable XDG_RUNTIME_DIR. The Jupyter notebook server wants to use this directory for storing temporary files, if XDG_RUNTIME_DIR is not set it will just use /tmp or something for which it does have permission to access.

The notebook server that we start would normally only listen to connections from localhost, which is the node on which the notebook server is running. That is why we pass the --ip=* option, to configure the notebook server to listen to incoming connections from the headnode. Be warned that this is actually highly insecure and should only be used within trusted environments with strict access control, like the DAS-5 system.

We also need the --no-browser no browser option, because we do not want to run the browser on the DAS node.

Now that we have a running Jupyter notebook server, there are 2 different approaches to connect to our notebook server:

  1. run your browser locally and setup a socks proxy to forward your http traffic to the headnode of the DAS
  2. starting a browser on the headnode of the DAS and use X-forwarding to access that browser

Approach 1 is very much recommended, but if you can't get it to work, you can defer to option 2.

Using a SOCKS proxy

In this step, we will create an ssh tunnel that we will use to forward our http traffic, effectively turning the headnode of the DAS into your private proxy server. Make sure you that you can connect to the headnode of the DAS, for example using a VPN. The following command is rather handy, you might want to save it in your bashrc:
alias dasproxy="ssh -fNq -D 8080 <username>@fs0.das5.cs.vu.nl"
Do not forget to replace <username> with your own username on the DAS.

Option -f stands for background mode, which means the process started with this command will keep running in the background, -N means there is no command to be executed on the remote host, and -q stands for quiet mode, meaning that most output will be surpressed.

After executing the above ssh command, start your local browser and configure your browser to use the proxyserver. Manually configure the proxy as a "Socks v5" proxy with the address 'localhost' and port 8080.

After changing this setting navigate to the page http://node0XX:8888/, where node0XX should be replaced with the hostname of the node you are running the notebook server on. Now in the browser open your notebook and get started using notebooks on a remote server!

Using X-Forwarding

Using another terminal, create an ssh -X connection to the headnode of the DAS-5. Note that, it is very important that you use ssh -X for the whole chain of connections to node, including the one used to connect to the headnode of the DAS and any number of intermediate servers you are using. This also requires that you have an X server on your local machine, if you are running Windows I recommend installing VirtualBox with a Linux GuestOS.

On the headnode type firefox http://node0XX:8888/, where node0XX should be replaced with the hostname of the node you are running the notebook server on. Now in the browser open your notebook and get started using notebooks on a remote server!