Authors:
J Taylor Childers (ANL HEP)
Tom Uram (ANL ALCF)

Balsam is an interface to a batch system's local scheduler. The each scheduler is abstracted such that Balsam remains scheduler independent.

ARGO is a workflow manager. ARGO can submit jobs to any system on which Balsam is running.

Both ARGO and Balsam are implemented in Python as django apps. This was done because django provides some services by default that are needed such as database handling and web interfaces for monitoring job statuses. django version 1.6 has been used and Python 2.6.6 (with GCC 4.4.7 20120313 (Red Hat 4.4.7-3)).

The communication layer is handled using RabbitMQ and pika. RabbitMQ is a message queue system. Message queues were an easy alternative to writing a custom TCP/IP interface. This requires installing and running a RabbitMQ server. RabbitMQ 3.3.1 is used with Erlang R16B02.

The data transport is handled using GridFTP. This requires installing and running a GridFTP server. Globus version 5.2.0 is used.

Jobs are submitted to ARGO via a RabbitMQ message queue. The messages use the python json serialization format. An example submission is:

example_msg.txt

'''
{
   "preprocess": null,
   "preprocess_args": null,
   "postprocess": null,
   "postprocess_args": null,
   "input_url":"gsiftp://www.gridftpserver.com/path/to/input/files",
   "output_url":"gsiftp://www.gridftpserver.com/path/to/output/files",
   "username": "bob",
   "email_address": "[email protected]",
   "jobs":[
      {
       "executable": "zjetgen90_mpi",
       "executable_args": "alpout.input.0",
       "input_files": ["alpout.input.0","cteq6l1.tbl"],
       "nodes": 1,
       "num_evts": -1,
       "output_files": ["alpout.grid1","alpout.grid2"],
       "postprocess": null,
       "postprocess_args": null,
       "preprocess": null,
       "preprocess_args": null,
       "processes_per_node": 1,
       "scheduler_args": null,
       "wall_minutes": 60,
       "target_site": "argo_cluster"
       },
 
      {
       "executable": "alpgenCombo.sh",
       "executable_args": "zjetgen90_mpi alpout.input.1 alpout.input.2 32",
       "input_files": ["alpout.input.1","alpout.input.2","cteq6l1.tbl","alpout.grid1","alpout.grid2"],
       "nodes": 2,
       "num_evts": -1,
       "output_files": ["alpout.unw","alpout_unw.par","directoryList_before.txt","directoryList_after.txt","alpgen_postsubmit.err","alpgen_postsubmit.out"],
       "postprocess": "alpgen_postsubmit.sh",
       "postprocess_args": "alpout",
       "preprocess": "alpgen_presubmit.sh",
       "preprocess_args": null,
       "processes_per_node": 32,
       "scheduler_args": "--mode=script",
       "wall_minutes": 60,
       "target_site": "vesta"
      }
   ]
}
'''

1. Install virtualenv
2. Create install directory: mkdir /path/to/installation/argobalsam
3. export INST_PATH=/path/to/installation/argobalsam
4. cd $INST_PATH
5. Create virtual environment: virtualenv argobalsam_env
6. Activate virtual environment: . argobalsam_env/bin/activate
7. Install needed software:
   1. pip install django==1.6.2 (necessary for python 2.6, if you have 2.7 you can go to higher django version)
   2. pip install south
   3. pip install pika
   4. pip install MySql
      1. For this I had to install on SLC6 yum install mysql mysql-devel mysql-server
8. Create django project: django-admin.py startproject argobalsam_deploy
9. Grab software from git repo: git clone [email protected]:balsam.git argobalsam_deploy You may have to force git to write to an already existing folder, or checkout into one directory and move the files into the argobalsam_deploy directory.
10. Update the following line of the argobalsam_deploy/argobalsam_deploy/settings.py file to import the site-specific settings that are needed (may need a new one):

    from site_settings.mira_settings import *

11. Move to the argobalsam_deploy directory with the manage.py script inside.
12. Sync your database

     python manage.py syncdb 

13. Create the first migration

     python manage.py schemamigration balsam_core --initial 

14. Apply the first migration

     python manage.py migrate balsam_core --fake 

15. Create the first migration

     python manage.py schemamigration argo_core --initial 

16. Apply the first migration

     python manage.py migrate argo_core --fake 

Git Browser

4.1

• updated vesta balsam delays to 5 min
• making all settings files consistent.
• added code to deal with jobs after a crash, and added print statements
• added restart try of subprocesses in the service loop
• updated vesta settings
• Updated Balsam code to support tukey submit script with manual mpirun call. Mira handles the mpirun call for the user whereas tukey does not.
• added tukey balsam site to argo settings
• updated argo settings to reflect new install directory balsam_production

4.0

• adding condor command files which should have already been added. Fixed bugs with adding alpgen data to finished emails.
• adding logger.exception in the proper places. Fixed small bugs and added JobHold error
• Adding files for the condor scheduler changes to include dagman jobs and condor job files.
• Many updates to accomodate a Condor Scheduler ability to accept condor job files and condor dagman job files. Also includes updates to include more information on emails upon completion.
• fixing missing str() conversion
• fixed error messages where integers needed to be converted to strings
• updating only specific db fields instead of full object

3.2

• re-added JobStatusReceiver

3.1

• adding QueueMessage class to argo
• merging old and new?
• adding longer wait times to balsam on ascinode settings, and an error checking to the job status receiver.

3.0

• major change to ARGO so that each transition takes place in a subprocess and does not block argo from processing other jobs in parallel
• added error catching for job id mismatch