Tuesday Exercise 1.3: Running jobs in the OSG

The goal of this exercise is to have your jobs running on the OSG and map their geographical locations.

Where in the world are my jobs? (Part 2)

In this version of the geolocating exercise, you will be submitting jobs to the OSG from osg-learn.chtc.wisc.edu and hopefully getting back much more interesting results! Due to some differences between the machines in the OSG and our local cluster here at UW-Madison, you will be using a slightly different payload and then performing the geolocation on the results from the submit host.

Hostname fetching code

The following Python script finds the ClassAd of the machine it's running on and finds a network identity that can be used to perform lookups:

#!/bin/env python

import re
import os
import socket

machine_ad_file_name = os.getenv('_CONDOR_MACHINE_AD')
    machine_ad_file = open(machine_ad_file_name, 'r')
    machine_ad = machine_ad_file.read()
except TypeError:
    print socket.getfqdn()

    print re.search(r'GLIDEIN_Gatekeeper = "(.*):\d*/jobmanager-\w*"', machine_ad, re.MULTILINE).group(1)
except AttributeError:
        print re.search(r'GLIDEIN_Gatekeeper = "(\S+) \S+:9619"', machine_ad, re.MULTILINE).group(1)
    except AttributeError:

Gathering network information from the OSG

Now to create submit files and that will run in the OSG!

  1. If not already logged in, ssh into osg-learn.chtc.wisc.edu
  2. Make a new directory for this exercise, tuesday-1.3 and change into it
  3. Save the above Python script to a file and call it ce_hostname.py
  4. Create a submit file that runs ce_hostname.py 100 times and uses the $(Process) macro to write different output and error files
  5. Submit your file and wait for the results

Geolocating machines in the OSG

NOTE: In this section, we are bending a rule about running jobs locally on the submit host because this host is a closed environment only utilized by the class and this exercise is designed for low load. Normally you should NOT run jobs on your submit host.

You will be re-using the Python script from the the last exercise to perform the geolocation except instead of submitting it as a job, you will run it manually. Copy it over from learn.chtc.wisc.edu using scp with the following command:

  1. Log on to learn.chtc.wisc.edu
  2. Copy the file over to osg-learn.chtc.wisc.edu: \
%UCL_PROMPT_SHORT% <strong>scp tuesday-1.1/location.py osg-learn.chtc.wisc.edu:tuesday-1.3/</strong>

location.py can take a text file that contains a list of locations as an argument, which can be done by collating your output files into a single file. The easiest way to do this is to use the cat command from today's exercise 1.2, the * wildcard from last exercise and a new operator >, which can write command output to a file. If your output files are named ce_hostname-0.out...ce_hostname-99.out, your commands would look like this:

%UCL_PROMPT_SHORT% <strong>cat ce_hostname-*.out > hostnames.txt</strong>
%UCL_PROMPT_SHORT% <strong>./location.py hostnames.txt</strong>

Mapping your jobs

As before, you will be using http://www.mapcustomizer.com/ from osg-learn.chtc.wisc.edu to visualize where your jobs have landed in the OSG. Copy and paste the results from the Python script into the bulk creation text box at the bottom of the screen. Where did your jobs end up?

Extra Challenge: Running it all as a DAG

Yesterday, you learned about DAGs and you can take advantage of them to avoid manually running location.py by hand. You could make this exercise into DAG with the hostname fetching submit file followed by a submit file that calls the geolocation code. You'll need to leverage PRE and/or POST scripts to collate the hostname results, which can be done with a little bit of shell scripting! To create a shell script:

  1. Write a file with a .sh extension
  2. Add a bash "shebang" line to the top of the script and lines for each shell command that you'd like to run (imagine it as a terminal in file form). For example, the following shell script would create a directory foo then list its contents:\

mkdir foo ls foo

  1. Mark the script as executable
  2. Run it by hand to verify that it works

What did your results look this time around via DAG? Can you make your two-node DAG even smaller with slight modifications to the shell-script and DAG (Hint: NOOP)?