AutoDQM parses DQM histograms and identifies outliers by various statistical tests for further analysis by the user. Its output can be easily parsed by eye on an AutoPlotter-based html page which is automatically generated when you submit a query from the AutoDQM GUI. Full documentation for AutoDQM can be found on our wiki.

AutoDQM_ML ========

1. Features
2. Setting Up AutoDQM for Development
3. Using AutoDQM Offline
4. Environment Variables

AutoDQM.py

• [x] Outputs histograms that clearly highlight outliers
• [x] Creates a .txt file along with each .pdf with relevant information on it
• [x] Allows user to easily change input
• [x] Seeks and accurately finds outliers

index.php

• [x] Previews input in a readable way
• [x] Gives a clear indication of the status of a user's query

plots.php

• [x] Dynamically displays text files below AutoPlotter toolbar
• [x] Unique url's for sharing plots pages with the data and reference data set names

This shows how to set up AutoDQM to be served from a machine on CERN OpenStack. This was written based on a fresh CC7 VM on CERN OpenStack.

You'll need a CERN User/Host certificate authorized with the CMS VO. CMS VO authorization can take ~8 hours so bear that in mind. Certificates can be aquired either from https://cern.ch/ca or, on a CC7 machine, by using auto-enrollment https://ca.cern.ch/ca/Help/?kbid=024000.

Install docker according to https://docs.docker.com/install/ and docker-compose through pip because CC7 has an old versions in it's repositories. Enable+start the docker service, and be sure to add your user to the docker group.

sudo yum-config-manager \
    --add-repo \
    https://download.docker.com/linux/centos/docker-ce.repo
sudo yum install docker-ce -y
sudo yum install python3-pip -y
sudo pip3 install --upgrade pip 
sudo pip3 install docker-compose

sudo gpasswd -a [user] docker
sudo systemctl enable --now docker

You may need to relog into your account before the group settings take effect.

Store your CERN certificate into docker secrets. You may need to extract your cert from PKCS12 format:

openssl pkcs12 -in cern-cert.p12 -out cern-cert.public.pem -clcerts -nokeys
openssl pkcs12 -in cern-cert.p12 -out cern-cert.private.key -nocerts -nodes

docker swarm init
docker secret create cmsvo-cert.pem cern-cert.public.pem
docker secret create cmsvo-cert.key cern-cert.private.key 

Then initialize a docker swarm, build the autodqm image with docker-compose, and deploy the image as a docker stack

docker-compose build
docker stack deploy --compose-file=./docker-compose.yml autodqm

To view AutoDQM, first your browser proxy will need to be set to listen to a port. Insturctions to do this can be found here.

After setting the proxy on your browser, using your local terminal (not ssh-ed into anything), forward your lxplus connection:

ssh <cmsusr>@lxplus.cern.ch -ND <port>

Note: Any port number will work so long as you match this forwarded port number to the port number in the browser network settings.

You can now view AutoDQM at <VM name>.cern.ch:8083/dqm/autodqm/. If you would like to make your instance of AutoDQM public, open port 8083 to http traffic on your firewall. For example, on CC7:

sudo firewall-cmd --permanent --add-port=8083/tcp
sudo firewall-cmd --reload

After making changes to configuration or source code, rebuild and redeploy the newly built image:

docker-compose build
docker stack rm autodqm
docker stack deploy --compose-file=./docker-compose.yml autodqm

If you're using a CC7 image, you may want to disable autoupdate:

sudo systemctl stop yum-autoupdate.service
sudo systemctl disable yum-autoupdate.service

The runoffline/run-offline.py script can retrieve run data files and process them without needing a web server. Run runoffline/run-offline.py --help for all the options.

run-offline.py requires some packages (listed in runoffline/environment.yml) to run. This environment can be created using conda. If you don't already have a conda installation, you can run:

curl -O -L https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b

Then to activate conda:

source ~/.bashrc 

To create the environment, go into the runoffline directory, then run:

conda env create -f environment.yml

The conda environment can then be activated with

conda activate autodqm 

run-offline.py requires some environment variables to be set in order to run. setenvvar.sh has all the required environment variables for running the script. It assumes that you cloned AutoDQM into your /root/ directory and that your cert and key lives in /root/.globus directory. If that is not the case, you can edit the setenvvar.sh file to match your setup. To set the environment variables, run:

source setenvvar.sh 

You don't need to make the directories defined by ADQM_OUT, ADQM_TMP, ADQM_DB prior to running run-offline.py as these will be created the first time you run the script if they do not exist.

Now inside runoffline directory, you can use run-offline.py to process data with AutoDQM!

Example command:

./run-offline.py Offline RPC Run2022 SingleMuon 355443 355135

This analyzes RPC plots, using Run2022 series, SingleMuon sample (both data and reference), comparing run 355443 (data) and run 355135 (reference).

Descriptions of the Environment Variables ---------------------

• ADQM_CONFIG location of the configuration file to use
• ADQM_DB location to store downloaded root files from offline DQM
• ADQM_TMP location to store generated temporary pdfs, pngs, etc
• ADQM_OUT location to store the result of AutoDQM
• ADQM_PLUGINS location of thep plugins folder
• ADQM_SSLCERT location of CMS VO authorized public key certificate to use in querying offline DQM
• ADQM_SSLKEY location of CMS VO authorized private ky to use in querying offline DQM
• ADQM_CACERT location of a CERN Grid CA certificate chain, if needed