tacc_stats Documentation {#mainpage}

Developers and Maintainers

R. Todd Evans (mailto:[email protected]) Bill Barth (mailto:[email protected])

Original Developer

John Hammond

Description

The tacc_stats package provides the tools to monitor resource usage of HPC systems at multiple levels of resolution.

The package is split into an autotools-based monitor subpackage and a Python setuptools-based tacc_stats subpackage. monitor performs the online data collection and transmission in a production environment while tacc_stats performs the data curation and analysis in an offline environment.

Building and installing the tacc_stats-2.3.5-1.el7.x86_64.rpm package with the taccstats.spec file will build and install a systemd service taccstats. This service launches a daemon with an overhead of 3% on a single core when configured to sample at a frequency of 1Hz. It is typically configured to sample at 5 minute intervals, with samples taken at the start and end of every job as well. The TACC Stats daemon, tacc_statsd, is controlled by the taccstats service and sends the data directly to a RabbitMQ server over the administrative ethernet network. RabbitMQ must be installed and running on the server in order for the data to be received.

Installing the tacc_stats module will setup a Django-based web application along with tools for extracting the data from the RabbitMQ server and feeding them into a PostgreSQL database.

Code Access

To get access to the tacc_stats source code clone this repository:

git clone https://github.com/TACC/tacc_stats

Installation

monitor subpackage

First ensure the RabbitMQ library and header file are installed on the build and compute nodes

librabbitmq-devel-0.5.2-1.el6.x86_64

./configure; make; make install will then successfully build the tacc_statsd executable for many systems. If Xeon Phi coprocessors are present on your system they can be monitored with the --enable-mic flag. Additionally the configuration options, --disable-infiniband, --disable-lustre, --disable-hardware will disable infiniband, Lustre Filesystem, and Hardware Counter monitoring which are all enabled by default. Disabling RabbitMQ will result in a legacy build of tacc_statsd that relies on the shared filesystem to transmit data. This mode is not recommended and currently used for testing purposes only. If libraries or header files are not found than add their paths to the include and library paths with the CPPFLAGS and/or LDFLAGS vars as is standard in autoconf based installations.

There will be a configuration file, /etc/taccstats/taccstats.conf, after installation. This file contains the fields

server localhost

queue default

port 5672

freq 600

server should be set to the hostname or IP hosting the RabbitMQ server, queue to the system/cluster name that is being monitored, port to the RabbitMQ port (5672 is default), and freq to the desired sampling frequency in seconds. The file and settings can be reloaded into a running tacc_statsd daemon with a SIGHUP signal.

An RPM can be built for deployment using the taccstats.spec file. The most straightforward approach to build this is to setup your rpmbuild directory then run

rpmbuild -bb taccstats.spec

The taccstats.spec file seds the taccstats.conf file to the correct server and queue. These can be changed by modifying these two lines

sed -i 's/localhost/stats.frontera.tacc.utexas.edu/' src/taccstats.conf

sed -i 's/default/frontera/' src/taccstats.conf

tacc_statsd can be started, stopped, and restarted using systemctl start taccstats, systemctl stop taccstats, and systemctl restart taccstats.

In order to notify tacc_stats of a job beginning, echo the job id into /var/run/TACC_jobid on each node where the job is running. It order to notify it of a job ending echo - into /var/run/TACC_jobid on each node where the job is running. This can be accomplished in the job scheduler prolog and epilog for example.

Job Scheduler Configuration

In order for tacc_stats to correcly label records with JOBIDs it is required that the job scheduler prolog and epilog contain the lines

echo $JOBID > jobid_file

and

echo - > jobid_file

To perform the pickling of this data it is also necessary to generate an accounting file that contains the following information in the following format

JobID|User|Account|Start|End|Submit|Partition|Timelimit|JobName|State|NNodes|ReqCPUS|NodeList

for example,

1837137|rtevans|project140208|2018-08-01T18:18:51|2018-08-02T11:44:51|2018-07-29T08:05:43|normal|1-00:00:00|jobname|COMPLETED|8|104|c420-[024,073],c421-[051-052,063-064,092-093]

If using SLURM the sacct_gen.py script that will be installed with the tacc_stats subpackage may be used. This script generates a file for each date with the name format year-month-day.txt, e.g. 2018-11-01.txt.

tacc_stats subpackage

To install TACC Stats on the machine where data will be processed, analyzed, and the webserver hosted follow these steps:

1. Download the package and setup the Python3 virtual environment. TACC Stats is Python3 dependent.

$ virtualenv machinename --system-site-packages
$ cd machinename; source bin/activate
$ git clone https://github.com/TACC/tacc_stats

tacc_stats is a pure Python package. Dependencies should be automatically downloaded and installed when installed via pip. The package must first be configured however in the tacc_stats.ini file.
2. The initialization file, tacc_stats.ini, controls all the configuration options and has the following content and descriptions

## Basic configuration options - modify these
# machine       = unique name of machine/queue
# server        = database and rmq server hostname
# data_dir      = where data is stored
[DEFAULT]
machine         = ls5
data_dir        = /hpc/tacc_stats_site/%(machine)s
server          = tacc-stats02.tacc.utexas.edu

## RabbitMQ Configuration
# RMQ_SERVER    = RMQ server
# RMQ_QUEUE     = RMQ server
[RMQ]
rmq_server      = %(server)s
rmq_queue       = %(machine)s

## Configuration for Web Portal Support
[PORTAL]
acct_path       = %(data_dir)s/accounting
pickles_dir     = %(data_dir)s/pickles
archive_dir     = %(data_dir)s/archive
host_name_ext   = %(machine)s.tacc.utexas.edu
batch_system    = SLURM

Set these paths as needed. The accounting_path will contain an accounting file for each date, e.g. 2018-11-01.txt. The raw stats data will be stored in the archive_dir and processed stats data in the pickles_dir. machine should match the system name used in the RabbitMQ server QUEUE field and is the RabbitMQ QUEUE that the monitoring agent sends the data too. This is the only field that needs to match settings in the monitor subpackage. host_name_ext is the extension required to each compute node hostname in order to build a FQDN. This will match to directory names created in the archive_dir. 3. Install tacc_stats

$ pip install -e tacc_stats/

4. Start the RabbitMQ server reader in the background, e.g.

$ nohup listend.py > /tmp/listend.log

Raw stats files will now be generated in the archive_dir. 5. A PostgreSQL database must be setup on the host. To do this, after installation of PostgreSQL and the tacc_stats Python module

$ sudo su - postgres
$ psql
# CREATE DATABASE machinename_db;
# CREATE USER taccstats WITH PASSWORD 'taccstats';
# ALTER ROLE taccstats SET client_encoding TO 'utf8';
# ALTER ROLE taccstats SET default_transaction_isolation TO 'read committed';
# ALTER ROLE taccstats SET timezone TO 'UTC';
# ALTER DATABASE machinename_db OWNER TO taccstats;
# GRANT ALL PRIVILEGES ON DATABASE machinename_db TO taccstats;
# \q

then

$ python manage.py migrate

This will generate a table named machinename_db in your database.

6. Setup cron jobs to process raw data and ingest into database. Add the following to your cron file

*/15 * * * * source /home/rtevans/testing/bin/activate; job_pickles.py; update_db.py > /tmp/ls5_update.log 2>&1

7. Next configure the Apache server (make sure it is installed and the mod_wsgi Apache module is installed) A sample configuration file, /etc/httpd/conf.d/ls5.conf, looks like

LoadModule wsgi_module /stats/stampede2/lib/python3.7/site-packages/mod_wsgi/server/mod_wsgi-py37.cpython-37m-x86_64-linux-gnu.so
WSGISocketPrefix run/wsgi

<VirtualHost *:80>

ServerAdmin [email protected]
ServerName stats.webserver.tacc.utexas.edu
ServerAlias stats.webserver.tacc.utexas.edu

WSGIDaemonProcess s2-stats python-home=/stats/stampede2 python-path=/stats/stampede2/tacc_stats:/stats/stampede2/lib/python3.7/site-packages user=rtevans
WSGIProcessGroup s2-stats
WSGIScriptAlias / /tacc_stats/site/tacc_stats_site/wsgi.py process-group=s2-stats
WSGIApplicationGroup %{GLOBAL}

<Directory /stats/stampede2/tacc_stats/tacc_stats/site/tacc_stats_site>
<Files wsgi.py>
Require all granted
</Files>
</Directory>
</VirtualHost>

8. Start up Apache

Running job_pickles.py

job_pickles.py can be run manually by:

$ ./job_pickles.py [start_date] [end_date] [-dir directory] [-jobids id0 id1 ... idn]

where the 4 optional arguments have the following meaning

• start_date : the start of the date range, e.g. "2013-09-25" (default is today)
• end_date : the end of the date range, e.g. "2013-09-26" (default is start_date)
• -dir : the directory to store pickled dictionaries (default is set in tacc_stats.ini)
• -jobids : individual jobids to pickle (default is all jobs)

No arguments results in all jobs from the previous day getting pickled and stored in the pickles_dir defined in tacc_stats.ini. On Stampede argumentless job_pickles.py is run every 24 hours as a cron job set-up by the user.

Pickled data format: generated job_pickles.py

Pickled stats data will be placed in the directory specified by pickles_dir. The pickled data is contained in a nested python dictionary with the following key layers:

job       : 1st key Job ID
 host     : 2nd key Host node used by Job ID
  type    : 3rd key TYPE specified in tacc_stats
   device : 4th key device belonging to type

For example, to access Job ID 101's stats data on host c560-901 for TYPE intel_snb for device cpu number 0 from within a python script:

pickle_file = open('101','r')
jobid = pickle.load(pickle_file)
pickle_file.close()
jobid['c560-901']['intel_snb']['0']

The value accessed by this key is a 2D array, with rows corresponding to record times and columns to specific counters for the device. To view the names for each counter add

jobid.get_schema('intel_snb')

or for a short version

jobid.get_schema('intel_snb').desc

Copyright

(C) 2011 University of Texas at Austin

License

This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA