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Everything you need to know about Discovery HPC

Discovery is a HPC cluster which DBIC users could utilize to run heavy computation. Discovery Overview and pages within provide official generic information about Discovery - what it provides and how to use it. This section provides additional, typically DBIC users specific information and hints.

The "modules"

TODO: brief intro into modules - the system used a collection of available environments.

For the purpose of using DataLad, please use python/3.7-Anaconda-datalad module which you can enable via module load python/3.7-Anaconda-datalad:

[d31548v@discovery7 ~]$ which datalad
/usr/bin/which: no datalad in (/dartfs-hpc/admin/opt/el7/intel/...
[d31548v@discovery7 ~]$ module load python/3.7-Anaconda-datalad 
[d31548v@discovery7 ~]$ which datalad

The Great and Evil ACL

If you drag&drop a file from your Windows OS machine into the mounted POSIX environment, its permissions now will be ACLed and you will not be able to do anything with that file until you use nfs4_setfacl -e filename to interactive remove "ace"s from the file permissions. (TODO: JH example)

POSIXy filesystem(s) for git-annex/DataLad inspired


Password less authentication for SSH

  1. modify ~/.ssh/config to have a section like

    Host discovery7

    User GSSAPIAuthentication yes GSSAPIDelegateCredentials yes

  2. install kerberos client utilities.

On Debian: sudo apt-get install krb5-user

On MacOS with Homebrew: brew install krb5

  1. initialize your kerberos token

where <NETID> is your Dartmouth NetID (like d11191d). Use klist command to see if there is an active token. As you will see from klist output, that token has expiration date, which is 10 hours from the moment you kinited it. You could use kinit -R (or just ssh again) to refresh the ticket. It will be refreshed for up to 30 days.

TODO: make it even more sophisticated (auto-updated)

Now you should be able to just ssh discovery7 (or ndoli -- TODO JH)

Using Jupyter Notebook on Discovery

The following instructions allow you to run a Jupyter Notebook on Discovery from your local web browser using an ssh tunnel.

  • Create a Jupyter password on your discovery account

Log onto discovery: ssh <username>

Create a configuration directory and password for Jupyter Notebook:

[d31548v@discovery7 ~]$ jupyter notebook --generate-config
Writing default config to: /dartfs-hpc/rc/home/k/d31548v/.jupyter/
[d31548v@discovery7 ~]$ jupyter notebook password
Enter password: 
Verify password:
[NotebookPasswordApp] Wrote hashed password to /dartfs-hpc/rc/home/k/d31548v/.jupyter/jupyter_notebook_config.json
[d31548v@discovery7 ~]$
  • Submit a job to Discovery cluster that starts the Jupyter Notebook server on the cluster

Use a text editor to create the new file jupyter_notebook.pbs. Cut and paste the following text into the file and save.

#!/bin/bash -l
#PBS -q default
#PBS -N Jupyter_notebook
#PBS -l walltime=10:00:00
#PBS -l nodes=1:ppn=1
#PBS -l feature=bigmem # to request 8 Gb per core

# get tunneling info
node=$(hostname -s)

# This next command chooses a random port number between 8000 and 8999
port=`echo $(( 8000 + RANDOM % 1000 ))`

# print tunneling instructions jupyter-log
echo -e "
# Command to create ssh tunnel:
ssh -N -f -L ${port}:${node}:${port} ${user}@${cluster}

# Use a Browser on your local machine to go to:
module load python
jupyter-notebook --no-browser --port=${port} --ip=${node}
# keep it up and running
sleep 3600

Now submit the job to the cluster:

[d31548v@discovery7 ~]$ mksub jupyter_notebook.pbs
[d31548v@discovery7 ~]$

Note the number of the submitted job 1310001. This is the unique Job ID for the process running your Jupyter Notebook. You can list all of your jobs using the myjobs command, which will list all of your currently submitted jobs, including this one. For example:

[d31548v@discovery7 ~]$ myjobs

active jobs------------------------

1310001             d31548v    Running     1     9:59:35  Thu Dec  5 16:35:11

1 active job             1 of 2456 processors in use by local jobs (0.04%)
              1 of 124 nodes active      (0.81%)

eligible jobs----------------------

0 eligible jobs   

blocked jobs-----------------------

0 blocked jobs   

Total job:  1

Once the job has been submitted, it will take a minute or two before two new files will appear in your working directory. Use ls to see when they show up:

[d31548v@discovery7 ~]$ ls Jupyter*
Jupyter_notebook.e1310001  Jupyter_notebook.o1310001

If these files don't appear right away, wait a minute or two and try again. The filenames are formatted to have <job_name>.[o,e]<job_ID> where <job_name> is the name you specified in the submission script with the line: #PBS -N Jupyter_notebook, and the <job_ID> is the assigned unique job ID number. The e file contains any errors reported by your job, and the o files contain any text your job prints to standard output. See Scheduling Jobs to learn more about submitting jobs to Discovery.

Once the files appear, view the output file:

[d31548v@discovery7 ~]$ cat Jupyter_notebook.o1310001
------------------------- Prologue ----------------------------
     Started: Thu Dec  5 16:35:20 EST 2019
      Job ID:
     User ID: d31548v
    Group ID: rc-users
    Job Name: Jupyter_notebook
 Resources Req d: walltime=10:00:00,nodes=1:ppn=1,neednodes=1:ppn=1
      Queue Name: default
    Account Name: 
------------------------- Prologue ----------------------------

# Command to create ssh tunnel:
ssh -N -f -L 8254:n12:8254

# Use a Browser on your local machine to go to:

This file contains instructions to connect to your Jupyter notebook from your local desktop or laptop.

Open a terminal on your local machine and paste in the command to create the ssh tunnel:

[andy@MyLaptop ~]$ ssh -N -f -L 8254:n12:8254
[andy@MyLaptop ~]$
  • Open a web browser and start working

    Type localhost:8254 into your browser. You should now be prompted for the password you created at the beginning of these instructions. Congratulations!


The submission script provided here chooses a random port number between 8000 and 8999. This is somewhat arbitrary as any port number above 1024 should be a legal choice for creating an ssh tunnel, as long as that port is not otherwise in use. The relevant line in the script is:

port=`echo $(( 8000 + RANDOM % 1000 ))`

In order to specify a specific port (for example 8888), simply replace this line with


It is possible that two users may request the same port on a given compute node, which should produce an error when you try to submit the job to run the Jupyter Notebook. It is also possible that you have an old ssh tunnel running on your local machine that is using the same port number as the one you are currently requesting, which should cause an error on your local computer. You can avoid this by killing old or stale jobs in order to free up ports.

To kill jobs on the Discovery cluster use qdel. For example to kill the job running the server on Discovery created in this tutorial with job ID 1310001, type:

[d31548v@discovery7 ~]$ qdel 1310001

On your local machine, first find the process id (PID) for the process running the ssh tunnel, for example:

[andy@MyLaptop ~]$ ps -e | grep 8254
1630 ??         0:00.02 ssh -N -f -L 8254:n12:8254

I can now see that the PID for my ssh tunnel is 1630. I can literally kill this job with the kill command:

[andy@MyLaptop ~]$ kill 1630