Online Tutorial Welcome and Introduction
Overview
Teaching: 5 min
Exercises: 0 minQuestions
What should I expect in participating in this Tutorial?
Objectives
Introduce instructors and mentors.
Provide overview of the modules
Spotlight helpful network provided by Slack channel.
DUNE Computing Consortium
The DUNE Computing Consortium works to establish a global computing network that will handle the massive data streams produced by distributing these across the computing grid. Selected consortia members coordinate DUNE computing activities to new members to acquaint them with the specific and DUNE software and resources.
DUNE Computing Consortium Coordinator: Michael Kirby (Brookhave National Laboratory)
Schedule
The May 2023 DUNE computing training spanned two days: Indico site.
Instructional Crew
Organizers:
- Heidi Schellman (Oregon State University /FNAL)
- David DeMuth (Valley City State University)
Module Authors (in order of appearance in the schedule):
- Michael Kirby (FNAL): storage spaces
- Steven Timm (FNAL): data management
- Tom Junk (FNAL): art and LArSoft
- Kenneth Herner (FNAL): grid and batch job submission
Mentors
- Amit Bashyal (ANL)
- Aaron Higuera (Rice University)
- Pengfei Ding (FNAL)
- Barnali Chowdury (ANL)
- Lino Gerlach (BNL)
- Nilay Bostan (University of Iowa)
Support
You must be on the DUNE Collaboration member list and have a valid FNAL or CERN account. See the old Indico Requirement page for more information. Windows users are invited to review the Windows Setup page.
You should join the DUNE Slack instance and look in #computing-training-basics for help with this tutorial
go to https://atwork.dunescience.org/tools/ scroll down to Slack and request an invite. Please do not do this if you are already in DUNE Slack.
Key Points
This tutorial is brought to you by the DUNE Computing Consortium.
The goals are to give you the computing basis to work on DUNE.
Storage Spaces (2024)
Overview
Teaching: 45 min
Exercises: 0 minQuestions
What are the types and roles of DUNE’s data volumes?
What are the commands and tools to handle data?
Objectives
Understanding the data volumes and their properties
Displaying volume information (total size, available size, mount point, device location)
Differentiating the commands to handle data between grid accessible and interactive volumes
This is an updated version of the 2023 training
Introduction
There are four types of storage volumes that you will encounter at Fermilab (or CERN):
- local hard drives
- network attached storage
- large-scale, distributed storage
- Rucio Storage Elements (RSE’s)
Each has its own advantages and limitations, and knowing which one to use when isn’t all straightforward or obvious. But with some amount of foresight, you can avoid some of the common pitfalls that have caught out other users.
Vocabulary
What is POSIX? A volume with POSIX access (Portable Operating System Interface Wikipedia) allow users to directly read, write and modify using standard commands, e.g. using bash scripts, fopen(). In general, volumes mounted directly into the operating system.
What is meant by ‘grid accessible’? Volumes that are grid accessible require specific tool suites to handle data stored there. Grid access to a volume is NOT POSIX access. This will be explained in the following sections.
What is immutable? A file that is immutable means that once it is written to the volume it cannot be modified. It can only be read, moved, or deleted. This property is in general a restriction imposed by the storage volume on which the file is stored. Not a good choice for code or other files you want to change.
Interactive storage volumes (mounted on dunegpvmXX.fnal.gov)
Home area is similar to the user’s local hard drive but network mounted
- access speed to the volume very high, on top of full POSIX access
- network volumes are NOT safe to store certificates and tickets
- important: users have a single home area at FNAL used for all experiments
- not accessible from grid worker nodes
- not for code developement (size of less than 2 GB)
- at Fermilab, need a valid Kerberos ticket in order to access files in your Home area
- periodic snapshots are taken so you can recover deleted files. (/nashome/.snapshot)
Locally mounted volumes are physical disks, mounted directly on the computer
- physically inside the computer node you are remotely accessing
- mounted on the machine through the motherboard (not over network)
- used as temporary storage for infrastructure services (e.g. /var, /tmp,)
- can be used to store certificates and tickets. (These are saved there automatically with owner-read enabled and other permissions disabled.)
- usually very small and should not be used to store data files or for code development
- files on these volumes are not backed up
Network Attached Storage (NAS) element behaves similar to a locally mounted volume.
- functions similar to services such as Dropbox or OneDrive
- fast and stable POSIX access to these volumes
- volumes available only on a limited number of computers or servers
- not available on larger grid computing (FermiGrid, Open Science Grid, etc.)
- /exp/dune/app/….yourdir has periodic snapshots in /exp/dune/app/… yourdir/.snap, but /exp/dune/data do NOT
Grid-accessible storage volumes
At Fermilab, an instance of dCache+Enstore is used for large-scale, distributed storage with capacity for more than 100 PB of storage and O(10000) connections. Whenever possible, these storage elements should be accessed over xrootd (see next section) as the mount points on interactive nodes are slow, unstable, and can cash the node to become unusable. Here are the different dCache volumes:
Persistent dCache: the data in the file is actively available for reads at any time and will not be removed until manually deleted by user There is now a second persistent dCache volume that is dedicated for DUNE Physics groups and managed by the respective physics conveners of those physics group. https://wiki.dunescience.org/wiki/DUNE_Computing/Using_the_Physics_Groups_Persistent_Space_at_Fermilab gives more details on how to get access to these groups. In general if you need to store more than 5TB in persistent dCache you should be working with the Physics Groups areas.
Scratch dCache: large volume shared across all experiments. When a new file is written to scratch space, old files are removed in order to make room for the newer file. removal is based on Least Recently Utilized (LRU) policy.
Tape-backed dCache: disk based storage areas that have their contents mirrored to permanent storage on Enstore tape.
Files are not available for immediate read on disk, but needs to be ‘staged’ from tape first (see video of a tape storage robot).
Resilient dCache: NOTE: DIRECT USAGE is being phased out and if the Rapid Code Distribution function in POMS/jobsub does not work for you, consult with the FIFE team for a solution (handles custom user code for their grid jobs, often in the form of a tarball. Inappropriate to store any other files here (NO DATA OR NTUPLES)).
Rucio Storage Elements: Rucio Storage Elements (or RSEs) are storage elements provided by collaborating institution for official DUNE datasets. Data stored in DUNE RSE’s must be fully cataloged in the [metacat][metacat] catalog and is managed by the DUNE data management team. This is where you find the big official data samples.
Summary on storage spaces
Full documentation: Understanding Storage Volumes
Quota/Space | Retention Policy | Tape Backed? | Retention Lifetime on disk | Use for | Path | Grid Accessible | |
---|---|---|---|---|---|---|---|
Persistent dCache | No/~100 TB/exp | Managed by Experiment | No | Until manually deleted | immutable files w/ long lifetime | /pnfs/dune/persistent | Yes |
Persistent PhysGrp | Yes/~500 TB/exp | Managed by PhysGrp | No | Until manually deleted | immutable files w/ long lifetime | /pnfs/dune/persistent/physicsgroups | Yes |
Scratch dCache | No/no limit | LRU eviction - least recently used file deleted | No | Varies, ~30 days (NOT guaranteed) | immutable files w/ short lifetime | /pnfs/<exp>/scratch | Yes |
Tape backed | dCache No/O(10) PB | LRU eviction (from disk) | Yes | Approx 30 days | Long-term archive | /pnfs/dune/… | Yes |
NAS Data | Yes (~1 TB)/ 32+30 TB total | Managed by Experiment | No | Until manually deleted | Storing final analysis samples | /exp/dune/data | No |
NAS App | Yes (~100 GB)/ ~15 TB total | Managed by Experiment | No | Until manually deleted | Storing and compiling software | /exp/dune/app | No |
Home Area (NFS mount) | Yes (~10 GB) | Centrally Managed by CCD | No | Until manually deleted | Storing global environment scripts (All FNAL Exp) | /nashome/<letter>/<uid> | No |
Rucio | 10 PB | Centrally Managed by DUNE | Yes | Each file has retention policy | Official DUNE Data samples | use rucio/justin to access | Yes |
Monitoring and Usage
Remember that these volumes are not infinite, and monitoring your and the experiment’s usage of these volumes is important to smooth access to data and simulation samples. To see your persistent usage visit here (bottom left):
And to see the total volume usage at Rucio Storage Elements around the world:
Resource DUNE Rucio Storage
Note - do not blindly copy files from personal machines to DUNE systems.
You may have files on your personal machine that contain personal information, licensed software or (god forbid) malware or pornography. Do not transfer any files from your personal machine to DUNE machines unless they are directly related to work on DUNE. You must be fully aware of any file’s contents. We have seen it all and we do not want to.
Commands and tools
This section will teach you the main tools and commands to display storage information and access data.
ifdh
Another useful data handling command you will soon come across is ifdh. This stands for Intensity Frontier Data Handling. It is a tool suite that facilitates selecting the appropriate data transfer method from many possibilities while protecting shared resources from overload. You may see ifdhc, where c refers to client.
Note
ifdh is much more efficient than NSF file access. Please use it and/or xroot when accessing remote files.
Here is an example to copy a file. Refer to the Mission Setup for the setting up the DUNESW_VERSION
.
Note
For now do this in the Apptainer
/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash \
-B /cvmfs,/exp,/nashome,/pnfs/dune,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest
once in the Apptainer
#source ~/dune_presetup_2024.sh
#dune_setup
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
kx509
export ROLE=Analysis
voms-proxy-init -rfc -noregen -voms=dune:/dune/Role=$ROLE -valid 120:00
setup ifdhc
ifdh cp root://fndcadoor.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/physics/full-reconstructed/2023/mc/out1/MC_Winter2023_RITM1592444_reReco/54/05/35/65/NNBarAtm_hA_BR_dune10kt_1x2x6_54053565_607_20220331T192335Z_gen_g4_detsim_reco_65751406_0_20230125T150414Z_reReco.root /dev/null
Note, if the destination for an ifdh cp command is a directory instead of filename with full path, you have to add the “-D” option to the command line.
Resource: idfh commands
Exercise 1
Using the ifdh command, complete the following tasks:
- create a directory in your dCache scratch area (/pnfs/dune/scratch/users/${USER}/) called “DUNE_tutorial_2024”
- copy /exp/dune/app/users/${USER}/my_first_login.txt file to that directory
- copy the my_first_login.txt file from your dCache scratch directory (i.e. DUNE_tutorial_2024) to /dev/null
- remove the directory DUNE_tutorial_2024
- create the directory DUNE_tutorial_2024_data_file
Note, if the destination for an ifdh cp command is a directory instead of filename with full path, you have to add the “-D” option to the command line. Also, for a directory to be deleted, it must be empty.
ifdh mkdir /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024
ifdh cp -D /exp/dune/app/users/${USER}/my_first_login.txt /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024
ifdh cp /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024/my_first_login.txt /dev/null
ifdh rm /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024/my_first_login.txt
ifdh rmdir /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024
ifdh mkdir /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024_data_file
xrootd
The eXtended ROOT daemon is software framework designed for accessing data from various architectures and in a complete scalable way (in size and performance).
XRootD is most suitable for read-only data access. XRootD Man pages
Issue the following command. Please look at the input and output of the command, and recognize that this is a listing of /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024. Try and understand how the translation between a NFS path and an xrootd URI could be done by hand if you needed to do so.
xrdfs root://fndca1.fnal.gov:1094/ ls /pnfs/fnal.gov/usr/dune/scratch/users/${USER}/
Note that you can do
lar -c <input.fcl> <xrootd_uri>
to stream into a larsoft module configured within the fhicl file. As well, it can be implemented in standalone C++ as
TFile * thefile = TFile::Open(<xrootd_uri>)
or PyROOT code as
thefile = ROOT.TFile.Open(<xrootd_uri>)
What is the right xroot path for a file.
If a file is in /pnfs/dune/tape_backed/dunepro/protodune-sp/reco-recalibrated/2021/detector/physics/PDSPProd4/00/00/51/41/np04_raw_run005141_0003_dl9_reco1_18127219_0_20210318T104440Z_reco2_51835174_0_20211231T143346Z.root
the command
pnfs2xrootd /pnfs/dune/tape_backed/dunepro/protodune-sp/reco-recalibrated/2021/detector/physics/PDSPProd4/00/00/51/41/np04_raw_run005141_0003_dl9_reco1_18127219_0_20210318T104440Z_reco2_51835174_0_20211231T143346Z.root
will return the correct xrootd uri:
root://fndca1.fnal.gov:1094//pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/reco-recalibrated/2021/detector/physics/PDSPProd4/00/00/51/41/np04_raw_run005141_0003_dl9_reco1_18127219_0_20210318T104440Z_reco2_51835174_0_20211231T143346Z.root
you can then
root -l <that long root: path>
to open the root file.
This even works if the file is in Europe - which you cannot do with a direct /pnfs!
root -l root://dune.dcache.nikhef.nl:1094/pnfs/nikhef.nl/data/dune/generic/rucio/usertests/b4/49/np04hd_raw_run026420_0000_dataflow0_datawriter_0_20240524T170759_20240604T204802_keepup_hists.root
See the next section on data management for instructions on finding files worldwide.
Note Files in /tape_backed/ may not be immediately accessible, those in /persistent/ and /scratch/ are.
Let’s practice
Exercise 2
Using a combination of
ifdh
andxrootd
commands discussed previously:
- Use
ifdh locateFile <file> root
to find the directory for this filePDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root
- Use
xrdcp
to copy that file to/pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024_data_file
- Using
xrdfs
and thels
option, count the number of files in the same directory asPDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root
Note that redirecting the standard output of a command into the command wc -l
will count the number of lines in the output text. e.g. ls -alrth ~/ | wc -l
ifdh locateFile PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root root
xrdcp root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/01/67/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/scratch/users/${USER}/DUNE_tutorial_2024_data_file/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root
xrdfs root://fndca1.fnal.gov:1094/ ls /pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/01/67/ | wc -l
The df command
To find out what types of volumes are available on a node can be achieved with the command df
. The -h
is for human readable format. It will list a lot of information about each volume (total size, available size, mount point, device location).
df -h
Exercise 3
From the output of the
df -h
command, identify:
- the home area
- the NAS storage spaces
- the different dCache volumes
Quiz
Question 01
Which volumes are directly accessible (POSIX) from grid worker nodes?
- /exp/dune/data
- DUNE CVMFS repository
- /pnfs/dune/scratch
- /pnfs/dune/persistent
- None of the Above
Answer
The correct answer is B - DUNE CVMFS repository.
Comment here
Question 02
Which data volume is the best location for the output of an analysis-user grid job?
- dCache scratch (/pnfs/dune/scratch/users/${USER}/)
- dCache persistent (/pnfs/dune/persistent/users/${USER}/)
- Enstore tape (/pnfs/dune/tape_backed/users/${USER}/)
- user’s home area (`~${USER}`)
- NFS data volume (/dune/data or /dune/app)
Answer
The correct answer is A, dCache scratch (/pnfs/dune/scratch/users/${USER}/).
Comment here
Question 03
You have written a shell script that sets up your environment for both DUNE and another FNAL experiment. Where should you put it?
- DUNE CVMFS repository
- /pnfs/dune/scratch/
- /exp/dune/app/
- Your GPVM home area
- Your laptop home area
Answer
The correct answer is D - Your GPVM home area.
Comment here
Question 04
What is the preferred way of reading a file interactively?
- Read it across the nfs mount on the GPVM
- Download the whole file to /tmp with xrdcp
- Open it for streaming via xrootd
- None of the above
- None of the Above
Answer
The correct answer is C - Open it for streaming via xrootd. Use
pnfs2xrootd
to generate the streaming path.Comment here
Useful links to bookmark
- ifdh commands (redmine)
- Understanding storage volumes (redmine)
- How DUNE storage works: pdf
Key Points
Home directories are centrally managed by Computing Division and meant to store setup scripts, do NOT store certificates here.
Network attached storage (NAS) /dune/app is primarily for code development.
The NAS /dune/data is for store ntuples and small datasets.
dCache volumes (tape, resilient, scratch, persistent) offer large storage with various retention lifetime.
The tool suites idfh and XRootD allow for accessing data with appropriate transfer method and in a scalable way.
Data Management (2024 updated for metacat/justin/rucio)
Overview
Teaching: 40 min
Exercises: 0 minQuestions
What are the data management tools and software for DUNE?
Objectives
Learn how to access data from DUNE Data Catalog.
Learn a bit about the JustIN workflow system for submitting batch jobs.
Introduction
DUNE data is stored around the world and the storage elements are not always organized in a way that they can be easily inspected. For this purpose we use the metacat data catalog to describe the data and collections and rucio to determine where replicas of files are. There is also a legacy SAM data access system that can be used for older files.
What is metacat
Metacat is a file catalog - it allows you to search for files that have particular attributes and understand their provenance, including details on all of their processing steps.
You can find extensive documentation on metacat at:
Find a file in metacat
DUNE runs multiple experiments (far detectors, protodune-sp, protodune-dp hd-protodune, vd-protodune, iceberg, coldboxes… ) and produces various kinds of data (mc/detector) and process them through different phases.
To find your data you need to specify at the minimum
core.run_type
(the experiment)core.file_type
(mc or detecor)core.data_tier
(the level of processing raw, full-reconstructed, root-tuple)
and when searching for specific types of data
core.data_stream
(physics, calibration, cosmics)core.runs[any]=<runnumber>
Here is an example of a metacat query that gets you raw files from a recent ‘hd-protodune’ cosmics run.
First get metacat if you have not already done so
setup metacat # if using SL7
spack load metacat # if using Alma9
metacat auth login -m password $USER # use your services password to authenticate
export METACAT_AUTH_SERVER_URL=https://metacat.fnal.gov:8143/auth/dune
export METACAT_SERVER_URL=https://metacat.fnal.gov:9443/dune_meta_prod/app
Note: other means of authentication
Check out the metacat documentation for kx509 and token authentication.
then do queries to find particular sets of files.
metacat query "files from dune:all where core.file_type=detector \
and core.run_type=hd-protodune and core.data_tier=raw \
and core.data_stream=cosmics and core.runs[any]=27296 limit 2"
should give you 2 files:
hd-protodune:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
hd-protodune:np04hd_raw_run027296_0000_dataflow0_datawriter_0_20240619T110330.hdf5
the string before the ‘:’ is the namespace and the string after is the filename.
You can find out more about your file by doing:
metacat file show -m -l hd-protodune:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
which gives you a lot of information:
checksums:
adler32 : 6a191436
created_timestamp : 2024-06-19 11:08:24.398197+00:00
creator : dunepro
fid : 83302138
name : np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
namespace : hd-protodune
retired : False
retired_by : None
retired_timestamp : None
size : 4232017188
updated_by : None
updated_timestamp : 1718795304.398197
metadata:
core.data_stream : cosmics
core.data_tier : raw
core.end_time : 1718795024.0
core.event_count : 35
core.events : [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47, 51, 55, 59, 63, 67, 71, 75, 79, 83, 87, 91, 95, 99, 103, 107, 111, 115, 119, 123, 127, 131, 135, 139]
core.file_content_status: good
core.file_format : hdf5
core.file_type : detector
core.first_event_number: 3
core.last_event_number: 139
core.run_type : hd-protodune
core.runs : [27296]
core.runs_subruns : [2729600001]
core.start_time : 1718795010.0
dune.daq_test : False
retention.class : physics
retention.status : active
children:
hd-protodune-det-reco:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330_reco_stage1_20240621T175057_keepup_hists.root (eywzUgkZRZ6llTsU)
hd-protodune-det-reco:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330_reco_stage1_reco_stage2_20240621T175057_keepup.root (GHSm3owITS20vn69)
look in the glossary to see what those fields mean.
find out how much raw data there is in a run using the summary option
metacat query -s "files from dune:all where core.file_type=detector \
and core.run_type=hd-protodune and core.data_tier=raw \
and core.data_stream=cosmics and core.runs[any]=27296"
Files: 963
Total size: 4092539942264 (4.093 TB)
To look at all the files in that run you need to use XRootD - DO NOT TRY TO COPY 4 TB to your local area!!!*
What is(was) SAM?
Sequential Access with Metadata (SAM) is/was a data handling system developed at Fermilab. It is designed to tracklocations of files and other file metadata. It has been replaced by metacat.
What is Rucio?
Rucio is the next-generation Data Replica service and is part of DUNE’s new Distributed Data Management (DDM) system that is currently in deployment. Rucio has two functions:
- A rule-based system to get files to Rucio Storage Elements around the world and keep them there.
- To return the “nearest” replica of any data file for use either in interactive or batch file use. It is expected that most DUNE users will not be regularly using direct Rucio commands, but other wrapper scripts that calls them indirectly.
As of the date of the June 2024 tutorial:
- The Rucio client is available in CVMFS
- Most DUNE users are now enabled to use it. New users may not automatically be added.
Let’s find a file
If you haven’t already done this earlier in setup
- On sl7 type
setup rucio
- On al9 type
spack load rucio-clients@33.3.0
# may need to update that version #
# first get a kx509 proxy, then
export RUCIO_ACCOUNT=$USER
rucio list-file-replicas hd-protodune:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5 --pfns --protocols=root
returns 3 locations:
root://eospublic.cern.ch:1094//eos/experiment/neutplatform/protodune/dune/hd-protodune/e5/57/np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro//hd-protodune/raw/2024/detector/cosmics/None/00/02/72/96/np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
root://eosctapublic.cern.ch:1094//eos/ctapublic/archive/neutplatform/protodune/rawdata/np04//hd-protodune/raw/2024/detector/cosmics/None/00/02/72/96/np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
which is the locations of the file on disk and tape. We can use this to copy the file to our local disk or access the file via xroot.
Finding files by characteristics using metacat
To list raw data files for a given run:
metacat query "files where core.file_type=detector \
and core.run_type='protodune-sp' and core.data_tier=raw \
and core.data_stream=physics and core.runs[any] in (5141)"
core.run_type
tells you which of the many DAQ’s this came from.core.file_type
tells detector from mccore.data_tier
could be raw, full-reconstructed, root-tuple. Same data different formats.
protodune-sp:np04_raw_run005141_0013_dl7.root
protodune-sp:np04_raw_run005141_0005_dl3.root
protodune-sp:np04_raw_run005141_0003_dl1.root
protodune-sp:np04_raw_run005141_0004_dl7.root
...
protodune-sp:np04_raw_run005141_0009_dl7.root
protodune-sp:np04_raw_run005141_0014_dl11.root
protodune-sp:np04_raw_run005141_0007_dl6.root
protodune-sp:np04_raw_run005141_0011_dl8.root
Note the presence of both a namespace and a filename
What about some files from a reconstructed version?
metacat query "files from dune:all where core.file_type=detector \
and core.run_type='protodune-sp' and core.data_tier=full-reconstructed \
and core.data_stream=physics and core.runs[any] in (5141) and dune.campaign=PDSPProd4 limit 10"
pdsp_det_reco:np04_raw_run005141_0013_dl10_reco1_18127013_0_20210318T104043Z.root
pdsp_det_reco:np04_raw_run005141_0015_dl4_reco1_18126145_0_20210318T101646Z.root
pdsp_det_reco:np04_raw_run005141_0008_dl12_reco1_18127279_0_20210318T104635Z.root
pdsp_det_reco:np04_raw_run005141_0002_dl2_reco1_18126921_0_20210318T103516Z.root
pdsp_det_reco:np04_raw_run005141_0002_dl14_reco1_18126686_0_20210318T102955Z.root
pdsp_det_reco:np04_raw_run005141_0015_dl5_reco1_18126081_0_20210318T122619Z.root
pdsp_det_reco:np04_raw_run005141_0017_dl10_reco1_18126384_0_20210318T102231Z.root
pdsp_det_reco:np04_raw_run005141_0006_dl4_reco1_18127317_0_20210318T104702Z.root
pdsp_det_reco:np04_raw_run005141_0007_dl9_reco1_18126730_0_20210318T102939Z.root
pdsp_det_reco:np04_raw_run005141_0011_dl7_reco1_18127369_0_20210318T104844Z.root
To see the total number of files that match a certain query expression, then add the -s
option to metacat query
.
See the metacat documentation for more information about queries. DataCatalogDocs and check out the glossary of common fields at: MetaCatGlossary
Accessing data for use in your analysis
To access data without copying it, XRootD
is the tool to use. However it will work only if the file is staged to the disk.
You can stream files worldwide if you have a DUNE VO certificate as described in the preparation part of this tutorial.
To learn more about using Rucio and Metacat to run over large data samples go here:
Full Justin/Rucio/Metacat Tutorial
Exercise 1
- Use
metacat
to find a file from a particular experiment/run/processing stage- Use
metacat file show -m namespace:filename
to get metadata for this file. Note that--json
gives the output in json format.
When we are analyzing large numbers of files in a group of batch jobs, we use a metacat dataset to describe the full set of files that we are going to analyze and use the JustIn system to run over that dataset. Each job will then come up and ask metacat and rucio to give it the next file in the list. It will try to find the nearest copy. For instance if you are running at CERN and analyzing this file it will automatically take it from the CERN storage space EOS.
Exercise 2
FIXME Need to make an example of looking at a dataset
Resources:
Quiz
Question 01
What is file metadata?
- Information about how and when a file was made
- Information about what type of data the file contains
- Conditions such as liquid argon temperature while the file was being written
- Both A and B
- All of the above
Answer
The correct answer is D - Both A and B.
Comment here
Question 02
How do we determine a DUNE data file location?
- Do `ls -R` on /pnfs/dune and grep
- Use `rucio list-file-replicas` (namespace:filename) --pnfs --protocols=root
- Ask the data management group
- None of the Above
Answer
The correct answer is B - use
rucio list-file-replicas
(namespace:filename).Comment here
Useful links to bookmark
- Metacat: https://dune.github.io/DataCatalogDocs
- Pre-2024 Official dataset definitions: dune-data.fnal.gov
- UPS reference manual
- UPS documentation (redmine)
- UPS qualifiers: About Qualifiers (redmine)
- mrb reference guide (redmine)
- CVMFS on DUNE wiki: Access files in CVMFS
Key Points
SAM and Rucio are data handling systems used by the DUNE collaboration to retrieve data.
Staging is a necessary step to make sure files are on disk in dCache (as opposed to only on tape).
Xrootd allows user to stream data files.
The old UPS code management system
Overview
Teaching: 40 min
Exercises: 0 minQuestions
How are different software versions handled?
Objectives
Understand the roles of the tools UPS (and Spack)
What is UPS and why do we need it?
Note
UPS is going away and only works on SL7 but we do not yet have a fully functional replacement. You need to be in the Apptainer to use it. UPS is being replaced by a new [spack][Spack Documentation] system for Alma9. We will be adding a Spack tutorial soon but for now, you need to use SL7/UPS to use the full DUNE code stack.
Go back and look at the SL7/Apptainer instructions to get an SL7 container for this section.
An important requirement for making valid physics results is computational reproducibility. You need to be able to repeat the same calculations on the data and MC and get the same answers every time. You may be asked to produce a slightly different version of a plot for example, and the data that goes into it has to be the same every time you run the program.
This requirement is in tension with a rapidly-developing software environment, where many collaborators are constantly improving software and adding new features. We therefore require strict version control; the workflows must be stable and not constantly changing due to updates.
DUNE must provide installed binaries and associated files for every version of the software that anyone could be using. Users must then specify which version they want to run before they run it. All software dependencies must be set up with consistent versions in order for the whole stack to run and run reproducibly.
The Unix Product Setup (UPS) is a tool to handle the software product setup operation.
UPS is set up when you setup DUNE:
Launch the Apptainer and then:
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export DUNESW_VERSION=v09_90_01d00
export DUNESW_QUALIFIER=e26:prof
setup dunesw $DUNESW_VERSION -q $DUNESW_QUALIFIER
dunesw
: product name
$DUNESW_VERSION
version tag
$DUNESW_QUALIFIER
are “qualifiers”. Qualifiers are separated with colons and may be specified in any order. The e26
qualifier refers to a specific version of the gcc compiler suite, and prof
means select the installed product that has been compiled with optimizations turned on. An alternative to prof
is the debug
qualifier. All builds of LArSoft and dunesw are compiled with debug symbols turned on, but the “debug” builds are made with optimizations turned off. Both kinds of software can be debugged, but it is easier to debug the debug builds (code executes in the proper order and variables aren’t optimized away so they can be inspected).
Another specifier of a product install is the “flavor”. This refers to the operating system the program was compiled for. These days we only support SL7, but in the past we used to also support SL6 and various versions of macOS. The flavor is automatically selected when you set up a product using setup (unless you override it which is usually a bad idea). Some product are “unflavored” because they do not contain anything that depends on the operating system. Examples are products that only contain data files or text files.
Setting up a UPS product defines many environment variables. Most products have an environment variable of the form <productname>_DIR
, where <productname>
is the name of the UPS product in all capital letters. This is the top-level directory and can be used when searching for installed source code or fcl files for example. <productname>_FQ_DIR
is the one that specifies a particular qualifier and flavor.
Exercise 3
- show all the versions of dunesw that are currently available by using the
ups list -aK+ dunesw
command- pick one version and substitute that for DUNESW_VERSION and DUNESW_QUALIFIER above and set up dunesw
Many products modify the following search path variables, prepending their pieces when set up. These search paths are needed by art jobs.
PATH
: colon-separated list of directories the shell uses when searching for programs to execute when you type their names at the command line. The command which
tells you which version of a program is found first in the PATH search list. Example:
which lar
will tell you where the lar command you would execute is if you were to type lar
at the command prompt.
The other paths are needed by art for finding plug-in libraries, fcl files, and other components, like gdml files.
CET_PLUGIN_PATH
LD_LIBRARY_PATH
FHICL_FILE_PATH
FW_SEARCH_PATH
Also the PYTHONPATH describes where Python modules will be loaded from.
Try
which root
to see the version of root that dunesw sets up. Try it out!
UPS basic commands
Command | Action |
---|---|
ups list -aK+ dunesw |
List the versions and flavors of dunesw that exist on this node |
ups active |
Displays what has been setup |
ups depend dunesw v09_48_01d00 -q e20:prof |
Displays the dependencies for this version of dunesw |
Exercise 4
- show all the dependencies of dunesw by using “ups depend dunesw $DUNESW_VERSION -q $DUNESW_QUALIFIER”
UPS Documentation Links
Key Points
The Unix Product Setup (UPS) is a tool to ensure consistency between different software versions and reproducibility.
CVMFS distributes software and related files without installing them on the target computer (using a VM, Virtual Machine).
CVMFS distributed file system?
Overview
Teaching: 40 min
Exercises: 0 minQuestions
What is cvmfs
Objectives
Understand the roles of the CVMFS.
CVMFS
What is CVMFS and why do we need it?
DUNE has a need to distribute precompiled code to many different computers that collaborators may use. Installed products are needed for four things:
- Running programs interactively
- Running programs on grid nodes
- Linking programs to installed libraries
- Inspection of source code and data files
Results must be reproducible, so identical code and associated files must be distributed everywhere. DUNE does not own any batch resources – we use CPU time on computers that participating institutions donate to the Open Science Grid. We are not allowed to install our software on these computers and must return them to their original state when our programs finish running so they are ready for the next job from another collaboration.
CVMFS is a perfect tool for distributing software and related files. It stands for CernVM File System (VM is Virtual Machine). Local caches are provided on each target computer, and files are accessed via the /cvmfs
mount point. DUNE software is in the directory /cvmfs/dune.opensciencegrid.org
, and LArSoft code is in /cvmfs/larsoft.opensciencegrid.org
. These directories are auto-mounted and need to be visible when one executes ls /cvmfs
for the first time. Some software is also in /cvmfs/fermilab.opensciencegrid.org.
CVMFS also provides a de-duplication feature. If a given file is the same in all 100 releases of dunetpc, it is only cached and transmitted once, not independently for every release. So it considerably decreases the size of code that has to be transferred.
When a file is accessed in /cvmfs
, a daemon on the target computer wakes up and determines if the file is in the local cache, and delivers it if it is. If not, the daemon contacts the CVMFS repository server responsible for the directory, and fetches the file into local cache. In this sense, it works a lot like AFS. But it is a read-only filesystem on the target computers, and files must be published on special CVMFS publishing servers. Files may also be cached in a layer between the CVMFS host and the target node in a squid server, which helps facilities with many batch workers reduce the network load in fetching many copies of the same file, possibly over an international connection.
CVMFS also has a feature known as “Stashcache” or “xCache”. Files that are in /cvmfs/dune.osgstorage.org are not actually transmitted in their entirety, only pointers to them are, and then they are fetched from one of several regional cache servers or in the case of DUNE from Fermilab dCache directly. DUNE uses this to distribute photon library files, for instance.
CVMFS is by its nature read-all so code is readable by anyone in the world with a CVMFS client. CVMFS clients are available for download to desktops or laptops. Sensitive code can not be stored in CVMFS.
More information on CVMFS is available here
Exercise 6
cd /cvmfs
and do anls
at top level- What do you see–do you see the four subdirectories (dune.opensciencegrid.org, larsoft.opensciencegrid.org, fermilab.opensciencegrid.org, and dune.osgstorage.org)
- cd dune.osgstorage.org/pnfs/fnal.gov/usr/dune/persistent/stash/PhotonPropagation/LibraryData
Useful links to bookmark
- CVMFS on DUNE wiki: Access files in CVMFS
Key Points
CVMFS distributes software and related files without installing them on the target computer (using a VM, Virtual Machine).
Introduction to art and LArSoft (2024 - Apptainer version)
Overview
Teaching: 95 min
Exercises: 0 minQuestions
Why do we need a complicated software framework? Can’t I just write standalone code?
Objectives
Learn what services the art framework provides.
Learn how the LArSoft tookit is organized and how to use it.
Introduction to art
Art is the framework used for the offline software used to process LArTPC data from the far detector and the ProtoDUNEs. It was chosen not only because of the features it provides, but also because it allows DUNE to use and share algorithms developed for other LArTPC experiments, such as ArgoNeuT, LArIAT, MicroBooNE and ICARUS. The section below describes LArSoft, a shared software toolkit. Art is also used by the NOvA and mu2e experiments. The primary language for art and experiment-specific plug-ins is C++.
The art wiki page is here: https://cdcvs.fnal.gov/redmine/projects/art/wiki. It contains important information on command-line utilities, how to configure an art job, how to define, read in and write out data products, how and when to use art modules, services, and tools.
Art features:
- Defines the event loop
- Manages event data storage memory and prevents unintended overwrites
- Input file interface – allows ganging together input files
- Schedules module execution
- Defines a standard way to store data products in art-formatted ROOT files
- Defines a format for associations between data products (for example, tracks have hits, and associations between tracks and hits can be made via art’s association mechanism.
- Provides a uniform job configuration interface
- Stores job configuration information in art-formatted root files.
- Output file control – lets you define output filenames based on parts of the input filename.
- Message handling
- Random number control
- Exception handling
The configuration storage is particularly useful if you receive a data file from a colleague, or find one in a data repository and you want to know more about how it was produced, with what settings.
Getting set up to try the tools
Log in to a dunegpvm*.fnal.gov
machine and set up your environment (This script is defined in Exercise 5 of https://dune.github.io/computing-training-basics/setup.html)
Note
For now do this in the Apptainer
/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash \
-B /cvmfs,/exp,/nashome,/pnfs/dune,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest
once in the Apptainer
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export DUNESW_VERSION=v09_90_01d00
export DUNESW_QUALIFIER=e26:prof
setup dunesw $DUNESW_VERSION -q $DUNESW_QUALIFIER
setup_fnal_security
# define a sample file
export SAMPLE_FILE=root://fndcadoor.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/physics/full-reconstructed/2023/mc/out1/MC_Winter2023_RITM1592444_reReco/54/05/35/65/NNBarAtm_hA_BR_dune10kt_1x2x6_54053565_607_20220331T192335Z_gen_g4_detsim_reco_65751406_0_20230125T150414Z_reReco.root
The examples below will refer to files in dCache
at Fermilab which can best be accessed via xrootd
.
For those with no access to Fermilab computing resources but with a CERN account:
Copies are stored in /afs/cern.ch/work/t/tjunk/public/jan2023tutorialfiles/
.
The follow-up of this tutorial provides help on how to find data and MC files in storage.
You can list available versions of dunesw
installed in CVMFS
with this command:
ups list -aK+ dunesw
The output is not sorted, although portions of it may look sorted. Do not depend on it being sorted. The string indicating the version is called the version tag (v09_72_01d00 here). The qualifiers are e26 and prof. Qualifiers can be entered in any order and are separated by colons. “e26” corresponds to a specific version of the GNU compiler – v9.3.0. We also compile with clang
– the compiler qualifier for that is “c7”.
“prof” means “compiled with optimizations turned on.” “debug” means “compiled with optimizations turned off”. More information on qualifiers is here.
In addition to the version and qualifiers, UPS
products have “flavors”. This refers to the operating system type and version. Older versions of DUNE software supported SL6
and some versions of macOS. Currently only SL7 and the compatible CentOS 7 are supported. The flavor of a product is automatically selected to match your current operating system when you set up a product. If a product does not have a compatible flavor, you will get an error message. “Unflavored” products are ones that do not depend on the operating-system libraries. They are listed with a flavor of “NULL”.
There is a setup command provided by the operating system – you usually don’t want to use it (at least not when developing DUNE software). If you haven’t yet sourced the setup_dune.sh
script in CVMFS
above but type setup xyz
anyway, you will get the system setup command, which will ask you for the root password. Just control-C
out of it, source the setup_dune.sh
script, and try again.
UPS’s setup command (find out where it lives with this command):
type setup
will not only set up the product you specify (in the instructions above, dunesw), but also all dependent products with corresponding versions so that you get a consistent software environment. You can get a list of everything that’s set up with this command
ups active
It is often useful to pipe the output through grep to find a particular product.
ups active | grep geant4
for example, to see what version of geant4 you have set up.
Art command-line tools
All of these command-line tools have online help. Invoke the help feature with the --help
command-line option. Example:
config_dumper --help
Docmentation on art command-line tools is available on the art wiki page.
config_dumper
Configuration information for a file can be printed with config_dumper.
config_dumper -P <artrootfile>
Try it out:
config_dumper -P $SAMPLE_FILE
The output is an executable fcl
file, sent to stdout. We recommend redirecting the output to a file that you can look at in a text editor:
Try it out:
config_dumper -P $SAMPLE_FILE > tmp.fcl
Your shell may be configured with noclobber
, meaning that if you already have a file called tmp.fcl
, the shell will refuse to overwrite it. Just rm tmp.fcl
and try again.
The -P
option to config_dumper
is needed to tell config_dumper
to print out all processing configuration fcl
parameters. The default behavior of config_dumper
prints out only a subset of the configuration parameters, and is most notably missing art services configuration.
Quiz
Quiz questions from the output of the above run of
config_dumper
:
- What generators were used? What physics processes are simulated in this file?
- What geometry is used? (hint: look for “GDML” or “gdml”)
- What electron lifetime was assumed?
- What is the readout window size?
fhicl-dump
You can parse a FCL
file with fhicl-dump
.
Try it out:
fhicl-dump protoDUNE_refactored_g4_stage2.fcl
See the section below on FCL
files for more information on what you’re looking at.
count_events
Try it out:
count_events $SAMPLE_FILE
product_sizes_dumper
You can get a peek at what’s inside an artROOT file with product_sizes_dumper
.
Try it out:
product_sizes_dumper -f 0 $SAMPLE_FILE
It is also useful to redirect the output of this command to a file so you can look at it with a text editor and search for items of interest. This command lists the sizes of the TBranches
in the Events TTree
in the artROOT file. There is one TBranch
per data product, and the name of the TBranch
is the data product name, an “s” is appended (even if the plural of the data product name doesn’t make sense with just an “s” on the end), an underscore, then the module label that made the data product, an underscore, the instance name, an underscore, and the process name and a period.
Quiz questions, looking at the output from above.
Quiz
Questions:
- What is the name of the data product that takes up the most space in the file?
- What the module label for this data product?
- What is the module instance name for this data product? (This question is tricky. You have to count underscores here).
- How many different modules produced simb::MCTruth data products? What are their module labels?
- How many different modules produced recob::Hit data products? What are their module labels?
You can open up an artROOT file with ROOT
and browse the TTrees
in it with a TBrowser
. Not all TBranches
and leaves can be inspected easily this way, but enough can that it can save a lot of time programming if you just want to know something simple about a file such as whether it contains a particular data product and how many there are.
Try it out
root $SAMPLE_FILE
then at the root
prompt, type:
new TBrowser
This will be faster with VNC
. Navigate to the Events TTree
in the file that is automatically opened, navigate to the TBranch
with the Argon 39 MCTruths (it’s near the bottom), click on the branch icon simb::MCTruths_ar39__SinglesGen.obj
, and click on the NParticles()
leaf (It’s near the bottom. Yes, it has a red exclamation point on it, but go ahead and click on it). How many events are there? How many 39Ar decays are there per event on average?
Art is not constrained to using ROOT
files – some effort has already been underway to use HDF5-formatted files for some purposes.
The art main executable program is a very short stub that interprets command-line options, reads in the configuration document (a FHiCL
file which usually includes other FHiCL
files), and loads shared libraries, initializes software components, and schedules execution of modules. Most code we are interested in is in the form of art plug-ins – modules, services, and tools. The generic executable for invoking art is called art
, but a LArSoft-customized one is called lar
. No additional customization has yet been applied so in fact, the lar
executable has identical functionality to the art
executable.
There is online help:
lar --help
All programs in the art suite have a --help
command-line option.
Most art job invocations take the form
lar -n <nevents> -c fclfile.fcl artrootfile.root
where the input file specification is just on the command line without a command-line option. Explicit examples follow below. The -n <nevents>
is optional – it specifies the number of events to process. If omitted, or if <nevents>
is bigger than the number of events in the input file, the job processes all of the events in the input file. -n <nevents>
is important for the generator stage. There’s also a handy --nskip <nevents_to_skip>
argument if you’d like the job to start processing partway through the input file. You can steer the output with
lar -c fclfile.fcl artrootfile.root -o outputartrootfile.root -T outputhistofile.root
The outputhistofile.root
file contains ROOT
objects that have been declared with the TFileService
service in user-supplied art plug-in code (i.e. your code).
Job configuration with FHiCL
The Fermilab Hierarchical Configuration Language, FHiCL is described here https://cdcvs.fnal.gov/redmine/documents/327.
FHiCL is not a Turing-complete language: you cannot write an executable program in it. It is meant to declare values for named parameters to steer job execution and adjust algorithm parameters (such as the electron lifetime in the simulation and reconstruction). Look at .fcl
files in installed job directories, like $DUNESW_DIR/fcl
for examples. Fcl
files are sought in the directory seach path FHICL_FILE_PATH
when art starts up and when #include
statements are processed. A fully-expanded fcl
file with all the #include statements executed is referred to as a fhicl “document”.
Parameters may be defined more than once. The last instance of a parameter definition wins out over previous ones. This makes for a common idiom in changing one or two parameters in a fhicl document. The generic pattern for making a short fcl file that modifies a parameter is:
#include "fcl_file_that_does_almost_what_I_want.fcl"
block.subblock.parameter: new_value
To see what block and subblock a parameter is in, use fhcl-dump
on the parent fcl file and look for the curly brackets. You can also use
lar -c fclfile.fcl --debug-config tmp.txt --annotate
which is equivalent to fhicl-dump
with the –annotate option and piping the output to tmp.txt.
Entire blocks of parameters can be substituted in using @local
and @table
idioms. See the examples and documentation for guidance on how to use these. Generally they are defined in the PROLOG sections of fcl files. PROLOGs must precede all non-PROLOG definitions and if their symbols are not subsequently used they do not get put in the final job configuration document (that gets stored with the data and thus may bloat it). This is useful if there are many alternate configurations for some module and only one is chosen at a time.
Try it out:
fhicl-dump protoDUNE_refactored_g4_stage2.fcl > tmp.txt
Look for the parameter ModBoxA
. It is one of the Modified Box Model ionization parameters. See what block it is in. Here are the contents of a modified g4 stage 2 fcl file that modifies just that parameter:
#include "protoDUNE_refactored_g4_stage2.fcl"
services.LArG4Parameters.ModBoxA: 7.7E-1
Exercise
Do a similar thing – modify the stage 2 g4 fcl configuration to change the drift field from 486.7 V/cm to 500 V/cm. Hint – you will find the drift field in an array of fields which also has the fields between wire planes listed.
Types of Plug-Ins
Plug-ins each have their own .so library which gets dynamically loaded by art when referenced by name in the fcl configuration.
Producer Modules
A producer module is a software component that writes data products to the event memory. It is characterized by produces<> and consumes<> statements in the class constructor, and art::Event::put()
calls in the produces()
method. A producer must produce the data product collection it says it produces, even if it is empty, or art will throw an exception at runtime. art::Event::put()
transfers ownership of memory (use std::move so as not to copy the data) from the module to the art event memory. Data in the art event memory will be written to the output file unless output commands in the fcl file tell art not to do that. Documentation on output commands can be found in the LArSoft wiki here. Producer modules have methods that are called on begin job, begin run, begin subrun, and on each event, as well as at the end of processing, so you can initialize counters or histograms, and finish up summaries at the end. Source code must be in files of the form: modulename_module.cc
, where modulename
does not have any underscores in it.
Analyzer Modules
Analyzer modules read data products from the event memory and produce histograms or TTrees, or other output. They are typically scheduled after the producer modules have been run. Producer modules have methods that are called on begin job, begin run, begin subrun, and on each event, as well as at the end of processing, so you can initialize counters or histograms, and finish up summaries at the end. Source code must be in files of the form: modulename_module.cc
, where modulename
does not have any underscores in it.
Source Modules
Source modules read data from input files and reformat it as need be, in order to put the data in art event data store. Most jobs use the art-provided RootInput source module which reads in art-formatted ROOT files. RootInput interacts well with the rest of the framework in that it provides lazy reading of TTree branches. When using the RootInput source, data are not actually fetched from the file into memory when the source executes, but only when GetHandle or GetValidHandle or other product get methods are called. This is useful for art jobs that only read a subset of the TBranches in an input file. Code for sources must be in files of the form: modulename_source.cc
, where modulename
does not have any underscores in it.
Monte Carlo generator jobs use the input source called EmptyEvent.
Services
These are singleton classes that are globally visible within an art job. They can be FHiCL configured like modules, and they can schedule methods to be called on begin job, begin run, begin event, etc. They are meant to help supply configuration parameters like the drift velocity, or more complicated things like geometry functions, to modules that need them. Please do not use services as a back door for storing event data outside of the art event store. Source code must be in files of the form: servicename_service.cc
, where servicename does not have any underscores in it.
Tools
Tools are FHiCL-configurable software components that are not singletons, like services. They are meant to be swappable by FHiCL parameters which tell art which .so libraries to load up, configure, and call from user code. See the Art Wiki Page for more information on tools and other plug-ins.
You can use cetskelgen to make empty skeletons of art plug-ins. See the art wiki for documentation, or use
cetskelgen --help
for instructions on how to invoke it.
Ordering of Plug-in Execution
The constructors for each plug-in are called at job-start time, after the shared object libraries are loaded by the image activater after their names have been discovered from the fcl configuration. Producer, analyzer and service plug-ins have BeginJob, BeginRun, BeginSubRun, EndSubRun, EndRun, EndJob methods where they can do things like book histograms, write out summary information, or clean up memory.
When processing data, the input source always gets executed first, and it defines the run, subrun and event number of the trigger record being processed. The producers and filters in trigger_paths then get executed for each event. The analyzers and filters in end_paths then get executed. Analyzers cannot be added to trigger_paths, and producers cannot be added to end_paths. This ordering ensures that data products are all produced by the time they are needed to be analyzed. But it also forces high memory usage for the same reason.
Services and tools are visible to other plug-ins at any stage of processing. They are loaded dynamically from names in the fcl configurations, so a common error is to use in code a service that hasn’t been mentioned in the job configuration. You will get an error asking you to configure the service, even if it is just an empty configuration with the service name and no parameters set.
Non-Plug-In Code
You are welcome to write standard C++ code – classes and C-style functions are no problem. In fact, to enhance the portability of code, the art team encourages the separation of algorithm code into non-framework-specific source files, and to call these functions or class methods from the art plug-ins. Typically, source files for standalone algorithm code have the extension .cxx while art plug-ins have .cc extensions. Most directories have a CMakeLists.txt file which has instructions for building the plug-ins, each of which is built into a .so library, and all other code gets built and put in a separate .so library.
Retrieving Data Products
In a producer or analyzer module, data products can be retrieved from the art event store with getHandle()
or getValidHandle()
calls, or more rarely getManyByType
or other calls. The arguments to these calls specify the module label and the instance of the data product. A typical TBranch
name in the Events tree in an artROOT file is
simb::MCParticles_largeant__G4Stage1.
here, simb::MCParticle
is the name of the class that defines the data product. The “s” after the data product name is added by art – you have no choice in this even if the plural of your noun ought not to just add an “s”. The underscore separates the data product name from the module name, “largeant”. Another underscore separates the module name and the instance name, which in this example is the empty string – there are two underscores together there. The last string is the process name and usually is not needed to be specified in data product retrieval. You can find the TBranch
names by browsing an artroot file with ROOT
and using a TBrowser
, or by using product_sizes_dumper -f 0
.
Art documentation
There is a mailing list – art-users@fnal.gov
where users can ask questions and get help.
There is a workbook for art available at https://art.fnal.gov/art-workbook/ Look for the “versions” link in the menu on the left for the actual document. It is a few years old and is missing some pieces like how to write a producer module, but it does answer some questions. I recommend keeping a copy of it on your computer and using it to search for answers.
There was an art/LArSoft course in 2015. While it, too is a few years old, the examples are quite good and it serves as a useful reference.
Gallery
Gallery is a lightweight tool that lets users read art-formatted root files and make plots without having to write and build art modules. It works well with interpreted and compiled ROOT macros, and is thus ideally suited for data exploration and fast turnaround of making plots. It lacks the ability to use art services, however, though some LArSoft services have been split into services and service providers. The service provider code is intended to be able to run outside of the art framework and linked into separate programs.
Gallery also lacks the ability to write data products to an output file. You are of course free to open and write files of your own devising in your gallery programs. There are example gallery ROOT scripts in duneexamples/duneexamples/GalleryScripts. They are only in the git repository but do not get installed in the UPS product.
More documentation: https://art.fnal.gov/gallery/
LArSoft
Introductory Documentation
LArSoft’s home page: larsoft.org
The LArSoft wiki is here: larsoft-wiki.
Software structure
The LArSoft toolkit is a set of software components that simulate and reconstruct LArTPC data, and also it provides tools for accessing raw data from the experiments. LArSoft contains an interface to GEANT4 (art does not list GEANT4 as a dependency) and the GENIE generator. It contains geometry tools that are adapted for wire-based LArTPC detectors.
A recent graph of the UPS products in a full stack starting with dunesw is available here (dunesw). You can see the LArSoft pieces under dunesw, as well as GEANT4, GENIE, ROOT, and a few others.
LArSoft Data Products
A very good introduction to data products such as raw digits, calibrated waveforms, hits and tracks, that are created and used by LArSoft modules and usable by analyzers was given by Tingjun Yang at the 2019 ProtoDUNE analysis workshop (larsoft-data-products).
There are a number of data product dumper fcl files. A non-exhaustive list of useful examples is given below:
dump_mctruth.fcl
dump_mcparticles.fcl
dump_simenergydeposits.fcl
dump_simchannels.fcl
dump_simphotons.fcl
dump_rawdigits.fcl
dump_wires.fcl
dump_hits.fcl
dump_clusters.fcl
dump_tracks.fcl
dump_pfparticles.fcl
eventdump.fcl
dump_lartpcdetector_channelmap.fcl
dump_lartpcdetector_geometry.fcl
Some of these may require some configuration of input module labels so they can find the data products of interest.
Some of these may require some configuration of input module labels so they can find the data products of interest. Try one of these yourself:
lar -n 1 -c dump_mctruth.fcl $SAMPLE_FILE
This command will make a file called DumpMCTruth.log
which you can open in a text editor. Reminder: MCTruth
are particles made by the generator(s), and MCParticles are those made by GEANT4, except for those owned by the MCTruth
data products. Due to the showering nature of LArTPCs, there are usually many more MCParticles than MCTruths.
Examples and current workflows
The page with instructions on how to find and look at ProtoDUNE data has links to standard fcl configurations for simulating and reconstructing ProtoDUNE data: https://wiki.dunescience.org/wiki/Look_at_ProtoDUNE_SP_data.
Try it yourself! The workflow for ProtoDUNE-SP MC is given in the Simulation Task Force web page.
Running on a dunegpvm machine at Fermilab
export USER=`whoami`
mkdir -p /exp/dune/data/users/$USER/tutorialtest
cd /exp/dune/data/users/$USER/tutorialtest
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export DUNESW_VERSION=v09_90_01d00
export DUNESW_QUALIFIER=e26:prof
setup dunesw $DUNESW_VERSION -q $DUNESW_QUALIFIER
TMPDIR=/tmp lar -n 1 -c mcc12_gen_protoDune_beam_cosmics_p1GeV.fcl -o gen.root
lar -n 1 -c protoDUNE_refactored_g4_stage1.fcl gen.root -o g4_stage1.root
lar -n 1 -c protoDUNE_refactored_g4_stage2_sce_datadriven.fcl g4_stage1.root -o g4_stage2.root
lar -n 1 -c protoDUNE_refactored_detsim_stage1.fcl g4_stage2.root -o detsim_stage1.root
lar -n 1 -c protoDUNE_refactored_detsim_stage2.fcl detsim_stage1.root -o detsim_stage2.root
lar -n 1 -c protoDUNE_refactored_reco_35ms_sce_datadriven_stage1.fcl detsim_stage2.root -o reco_stage1.root
lar -c eventdump.fcl reco_stage1.root >& eventdump_output.txt
config_dumper -P reco_stage1.root >& config_output.txt
product_sizes_dumper -f 0 reco_stage1.root >& productsizes.txt
Note added November 22, 2023: The construct “TMPDIR=/tmp lar …” defines the environment variable TMPDIR only for the duration of the subsequent command on the line. This is needed for the tutorial example because the mcc12 gen stage copies a 2.9 GB file (see below – it’s the one we had to copy over to CERN) to /var/tmp using ifdh’s default temporary location. But the dunegpvm machines as of November 2023 seem to rarely have 2.9 GB of space in /var/tmp and you get a “no space left on device” error. The newer prod4 versions of the fcls point to a newer version of the beam particle generator that can stream this file using XRootD instead of copying it with ifdh. But the streaming flag is turned off by default in the prod4 fcl for the version of dunesw used in this tutorial, and so this is the minimal solution. Note for the next iteration: the Prod4 fcls are here: https://wiki.dunescience.org/wiki/ProtoDUNE-SP_Production_IV
Running at CERN
This example puts all files in a subdirectory of your home directory. There is an input file for the ProtoDUNE-SP beamline simulation that is copied over and you need to point the generation job at it. The above sequence of commands will work at CERN if you have a Fermilab grid proxy, but not everyone signed up for the tutorial can get one of these yet, so we copied the necessary file over and adjusted a fcl file to point at it. It also runs faster with the local copy of the input file than the above workflow which copies it.
The apptainer command is slightly different as the mounts are different. Here we assume you are logged into an lxplus node running Alma9.
Note
CERN Apptainer variant
/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --she
ll=/bin/bash \
-B /cvmfs,/afs,/opt,/run/user,/etc/hostname,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest
Make a fcl file:
#include "mcc12_gen_protoDune_beam_cosmics_p1GeV.fcl"
physics.producers.generator.FileName: "/afs/cern.ch/work/t/tjunk/public/may2023tutorialfiles/H4_v34b_1GeV_-27.7_10M_1.root"
cd ~
mkdir 2024Tutorial
cd 2024Tutorial
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export DUNESW_VERSION=v09_90_01
export LARSOFT_VERSION=${DUNESW_VERSION}
export DUNESW_QUALIFIER=e26:prof
setup dunesw $DUNESW_VERSION -q $DUNESW_QUALIFIER
#cat > tmpgen.fcl << EOF
##include "mcc12_gen_protoDune_beam_cosmics_p1GeV.fcl"
#physics.producers.generator.FileName: "/afs/cern.ch/work/t/tjunk/public/may2023tutorialfiles/H4_v34b_1GeV_-27.7_10M_1.root"
#EOF
lar -n 1 -c tmpgen.fcl -o gen.root
lar -n 1 -c protoDUNE_refactored_g4_stage1.fcl gen.root -o g4_stage1.root
lar -n 1 -c protoDUNE_refactored_g4_stage2_sce_datadriven.fcl g4_stage1.root -o g4_stage2.root
lar -n 1 -c protoDUNE_refactored_detsim_stage1.fcl g4_stage2.root -o detsim_stage1.root
lar -n 1 -c protoDUNE_refactored_detsim_stage2.fcl detsim_stage1.root -o detsim_stage2.root
lar -n 1 -c protoDUNE_refactored_reco_35ms_sce_datadriven_stage1.fcl detsim_stage2.root -o reco_stage1.root
lar -c eventdump.fcl reco_stage1.root >& eventdump_output.txt
config_dumper -P reco_stage1.root >& config_output.txt
product_sizes_dumper -f 0 reco_stage1.root >& productsizes.txt
You can also browse the root files with a TBrowser or run other dumper fcl files on them. The dump example commands above redirect their outputs to text files which you can edit with a text editor or run grep on to look for things.
You can run the event display with
lar -c evd_protoDUNE.fcl reco_stage1.root
but it will run very slowly over a tunneled X connection. A VNC session will be much faster. Tips: select the “Reconstructed” radio button at the bottom and click on “Unzoom Interest” on the left to see the reconstructed objects in the three views.
DUNE software documentation and how-to’s
The following legacy wiki page provides information on how to check out, build, and contribute to dune-specific larsoft plug-in code.
https://cdcvs.fnal.gov/redmine/projects/dunetpc/wiki
The follow-up part of this tutorial gives hands-on exercises for doing these things.
Contributing to LArSoft
The LArSoft git repositories are hosted on GitHub and use a pull-request model. LArSoft’s github link is https://github.com/larsoft. DUNE repositories, such as the dunesw stack, protoduneana and garsoft are also on GitHub but at the moment (not for long however), allow users to push code.
To work with pull requests, see the documentation at this link: https://larsoft.github.io/LArSoftWiki/Developing_With_LArSoft
There are bi-weekly LArSoft coordination meetings https://indico.fnal.gov/category/405/ at which stakeholders, managers, and users discuss upcoming releases, plans, and new features to be added to LArSoft.
##w Useful tip: check out an inspection copy of larsoft
A good old-fashioned grep -r
or a find command can be effective if you are looking for an example of how to call something but I do not know where such an example might live. The copies of LArSoft source in CVMFS lack the CMakeLists.txt files and if that’s what you’re looking for to find examples, it’s good to have a copy checked out. Here’s a script that checks out all the LArSoft source and DUNE LArSoft code but does not compile it. Warning: it deletes a directory called “inspect” in your app area. Make sure /exp/dune/app/users/<yourusername>
exists first:
Note
Remember the Apptainer!
#!/bin/bash
USERNAME=`whoami`
export DUNESW_VERSION=v09_90_01
export LARSOFT_VERSION=${DUNESW_VERSION}
export DUNESW_QUALIFIER=e26:prof
export COMPILER=e26
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
cd /exp/dune/app/users/${USERNAME}
rm -rf inspect
mkdir inspect
cd inspect
setup larsoft ${LARSOFT_VERSION} -q debug:${COMPILER}
mrb newDev
source /exp/dune/app/users/${USERNAME}/inspect/localProducts_larsoft_${LARSOFT_VERSION}_debug_${COMPILER}/setup
cd srcs
mrb g larsoft_suite
mrb g larsoftobj_suite
mrb g larutils
mrb g larbatch
mrb g dune_suite
mrb g -d dune_raw_data dune-raw-data
Putting it to use: A very common workflow in developing software is to look for an example of how to do something similar to what you want to do. Let’s say you want to find some examples of how to use FindManyP
– it’s an art method for retrieving associations between data products, and the art documentation isn’t as good as the examples for learning how to use it. You can use a recursive grep through your checked-out version, or you can even look through the installed source in CVMFS. This example looks through the duneprototype product’s source files for FindManyP
:
cd $DUNEPROTOTYPES_DIR/source/duneprototypes
grep -r -i findmanyp *
It is good to use the -i
option to grep which tells it to ignore the difference between uppercase and lowercase string matches, in case you misremembered the case of what you are looking for. The list of matches is quite long – you may want to pipe the output of that grep into another grep
grep -r -i findmanyp * | grep recob::Hit
The checked-out versions of the software have the advantage of providing some files that don’t get installed in CVMFS, notably CMakeLists.txt files and the UPS product_deps files, which you may want to examine when looking for examples of how to do things.
GArSoft
GArSoft is another art-based software package, designed to simulate the ND-GAr near detector. Many components were copied from LArSoft and modified for the pixel-based TPC with an ECAL. You can find installed versions in CVMFS with the following command:
ups list -aK+ garsoft
and you can check out the source and build it by following the instructions on the GArSoft wiki.
Quiz
Question 01
Enter Question here
- .
- .
- .
- .
- None of the Above
Answer
The correct answer is .
Comment here
Key Points
Art provides the tools physicists in a large collaboration need in order to contribute software to a large, shared effort without getting in each others’ way.
Art helps us keep track of our data and job configuration, reducing the chances of producing mystery data that no one knows where it came from.
LArSoft is a set of simulation and reconstruction tools shared among the liquid-argon TPC collaborations.
Multi Repository Build (mrb) system (2024)
Overview
Teaching: 10 min
Exercises: 0 minQuestions
How are different software versions handled?
Objectives
Understand the roles of the tool mrb
mrb
What is mrb and why do we need it?
Early on, the LArSoft team chose git and cmake as the software version manager and the build language, respectively, to keep up with industry standards and to take advantage of their new features. When we clone a git repository to a local copy and check out the code, we end up building it all. We would like LArSoft and DUNE code to be more modular, or at least the builds should reflect some of the inherent modularity of the code.
Ideally, we would like to only have to recompile a fraction of the software stack when we make a change. The granularity of the build in LArSoft and other art-based projects is the repository. So LArSoft and DUNE have divided code up into multiple repositories (DUNE ought to divide more than it has, but there are a few repositories already with different purposes). Sometimes one needs to modify code in multiple repositories at the same time for a particular project. This is where mrb comes in.
mrb stands for “multi-repository build”. mrb has features for cloning git repositories, setting up build and local products environments, building code, and checking for consistency (i.e. there are not two modules with the same name or two fcl files with the same name). mrb builds UPS products – when it installs the built code into the localProducts directory, it also makes the necessasry UPS table files and .version directories. mrb also has a tool for making a tarball of a build product for distribution to the grid. The software build example later in this tutorial exercises some of the features of mrb.
Command | Action |
---|---|
mrb --help |
prints list of all commands with brief descriptions |
mrb \<command\> --help |
displays help for that command |
mrb gitCheckout |
clone a repository into working area |
mrbsetenv |
set up build environment |
mrb build -jN |
builds local code with N cores |
mrb b -jN |
same as above |
mrb install -jN |
installs local code with N cores |
mrb i -jN |
same as above (this will do a build also) |
mrbslp |
set up all products in localProducts… |
mrb z |
get rid of everything in build area |
Link to the mrb reference guide
Exercise 1
There is no exercise 5. mrb example exercises will be covered in a later session as any useful exercise with mrb takes more than 30 minutes on its own. Everyone gets 100% credit for this exercise!
Key Points
The multi-repository build (mrb) tool allows code modification in multiple repositories, which is relevant for a large project like LArSoft with different cases (end user and developers) demanding consistency between the builds.
Expert in the Room - LArSoft How to modify a module - in progress
Overview
Teaching: 15 min
Exercises: 0 minQuestions
How do I check out, modify, and build DUNE code?
Objectives
How to use mrb.
Set up your environment.
Download source code from DUNE’s git repository.
Build it.
Run an example program.
Modify the job configuration for the program.
Modify the example module to make a custom histogram.
Test the modified module.
Stretch goal – run the debugger.
getting set up
You will need three login sessions. These have different environments set up.
- Session #1 For editing code (and searching for code)
- Session #2 For building (compiling) the software
- Session #3 For running the programs
Session 1
Start up session #1, editing code, on one of the dunegpvm*.fnal.gov interactive nodes. These scripts have also been tested on the lxplus.cern.ch interactive nodes.
Note Remember the Apptainer!
see below for special Apptainers for CERN and build machines.
Create two scripts in your home directory:
newDev2024Tutorial.sh
should have these contents:
#!/bin/bash
export DUNESW_VERSION=v09_90_01d00
export PROTODUNEANA_VERSION=$DUNESW_VERSION
DUNESW_QUALIFIER=e26:prof
DIRECTORY=2024tutorial
USERNAME=`whoami`
export WORKDIR=/exp/dune/app/users/${USERNAME}
if [ ! -d "$WORKDIR" ]; then
export WORKDIR=`echo ~`
fi
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
cd ${WORKDIR}
touch ${DIRECTORY}
rm -rf ${DIRECTORY}
mkdir ${DIRECTORY}
cd ${DIRECTORY}
mrb newDev -q ${DUNESW_QUALIFIER}
source ${WORKDIR}/${DIRECTORY}/localProducts*/setup
mkdir work
cd srcs
mrb g -t ${PROTODUNEANA_VERSION} protoduneana
cd ${MRB_BUILDDIR}
mrbsetenv
mrb i -j16
and setup2024Tutorial.sh
should have these contents:
DIRECTORY=2024tutorial
USERNAME=`whoami`
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export WORKDIR=/exp/dune/app/users/${USERNAME}
if [ ! -d "$WORKDIR" ]; then
export WORKDIR=`echo ~`
fi
cd $WORKDIR/$DIRECTORY
source localProducts*/setup
cd work
setup dunesw $DUNESW_VERSION -q $DUNESW_QUALIFIER
mrbslp
Execute this command to make the first script executable.
chmod +x newDev2024Tutorial.sh
It is not necessary to chmod the setup script. Problems writing to your home directory? Check to see if your Kerberos ticket has been forwarded.
klist
Session 2
Start up session #2 by logging in to one of the build nodes,
dunebuild01.fnal.gov
or dunebuild02.fnal.gov
. They have 16 cores
apiece and the dunegpvm’s have only four, so builds run much faster
on them. If all tutorial users log on to the same one and try
building all at once, the build nodes may become very slow or run
out of memory. The lxplus
nodes are generally big enough to build
sufficiently quickly. The Fermilab build nodes should not be used
to run programs (people need them to build code!)
Note you need a modified container on the build machines and at CERN as they don’t mount /pnfs
This is done to prevent people from running interactive jobs on the dedicated build machines.
FNAL build machines
# remove /pnfs/ for build machines
/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash \
-B /cvmfs,/exp,/nashome,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest
CERN
/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash\
-B /cvmfs,/afs,/opt,/run/user,/etc/hostname,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest
Download source code and build it
On the build node, execute the newDev
script:
./newDev2024Tutorial.sh
Note that this script will delete the directory planned to store the source code and built code, and make a new directory, in order to start clean. Be careful not to execute this script then if you’ve worked on the code some, as this script will wipe it out and start fresh.
This build script will take a few minutes to check code out and compile it.
The mrb g
command does a git clone
of the specified repository with an optional tag and destination name. More information is available here and here.
Some comments on the build command
mrb i -j16
The -j16
says how many concurrent processes to run. Set the number to no more than the number of cores on the computer you’re running it on. A dunegpvm machine has four cores, and the two build nodes each have 16. Running more concurrent processes on a computer with a limited number of cores won’t make the build finish any faster, but you may run out of memory. The dunegpvms do not have enough memory to run 16 instances of the C++ compiler at a time, and you may see the word killed
in your error messages if you ask to run many more concurrent compile processes than the interactive computer can handle.
You can find the number of cores a machine has with
cat /proc/cpuinfo
The mrb
system builds code in a directory distinct from the source code. Source code is in $MRB_SOURCE
and built code is in $MRB_BUILDDIR
. If the build succeeds (no error messages, and compiler warnings are treated as errors, and these will stop the build, forcing you to fix the problem), then the built artifacts are put in $MRB_TOP/localProducts*
. mrbslp directs ups to search in $MRB_TOP/localProducts*
first for software and necessary components like fcl
files. It is good to separate the build directory from the install directory as a failed build will not prevent you from running the program from the last successful build. But you have to look at the error messages from the build step before running a program. If you edited source code, made a mistake, built it unsuccessfully, then running the program may run successfully with the last version which compiled. You may be wondering why your code changes are having no effect. You can look in $MRB_TOP/localProducts*
to see if new code has been added (look for the “lib” directory under the architecture-specific directory of your product).
Because you ran the newDev2024Tutorial.sh
script instead of sourcing it, the environment it
set up within it is not retained in the login session you ran it from. You will need to set up your environment again.
You will need to do this when you log in anyway, so it is good to have
that setup script. In session #2, type this:
source setup2024Tutorial.sh
cd $MRB_BUILDDIR
mrbsetenv
The shell command “source” instructs the command interpreter (bash) to read commands from the file setup2024Tutorial.sh
as if they were typed at the terminal. This way, environment variables set up by the script stay set up.
Do the following in session #1, the source editing session:
source setup2024Tutorial.sh
cd $MRB_SOURCE
mrbslp
Run your program
YouTube Lecture Part 2: Start up the session for running programs – log in to a dunegpvm
interactive
computer for session #3
source setup2024Tutorial.sh
mrbslp
setup_fnal_security
We need to locate an input file. Here are some tips for finding input data:
https://wiki.dunescience.org/wiki/Look_at_ProtoDUNE_SP_data
Data and MC files are typically on tape, but can be cached on disk so you don’t have to wait possibly a long time for the file to be staged in. Check to see if a sample file is in dCache or only on tape:
cache_state.py PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
Get the xrootd
URL:
samweb get-file-access-url --schema=root PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
which should print the following URL:
root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
Now run the program with the input file accessed by that URL:
lar -c analyzer_job.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
CERN Users without access to Fermilab’s dCache
: – example input files for this tutorial have been copied to /afs/cern.ch/work/t/tjunk/public/2024tutorialfiles/
.
After running the program, you should have an output file tutorial_hist.root
. Note – please do not
store large rootfiles in /exp/dune/app
! The disk is rather small, and we’d like to
save it for applications, not data. But this file ought to be quite small.
Open it in root
root tutorial_hist.root
and look at the histograms and trees with a TBrowser
. It is empty!
Adjust the program’s job configuration
In Session #1, the code editing session,
cd ${MRB_SOURCE}/protoduneana/protoduneana/TutorialExamples/
See that analyzer_job.fcl
includes clustercounter.fcl
. The module_type
line in that fcl
file defines the name of the module to run, and
ClusterCounter_module.cc
just prints out a message in its analyze() method
just prints out a line to stdout for each event, without making any
histograms or trees.
Aside on module labels and types: A module label is used to identify
which modules to run in which order in a trigger path in an art job, and also
to label the output data products. The “module type” is the name of the source
file: moduletype_module.cc
is the filename of the source code for a module
with class name moduletype. The build system preserves this and makes a shared object (.so
)
library that art loads when it sees a particular module_type in the configuration document.
The reason there are two names here is so you
can run a module multiple times in a job, usually with different inputs. Underscores
are not allowed in module types or module labels because they are used in
contexts that separate fields with underscores.
Let’s do something more interesting than ClusterCounter_module’s print statement.
Let’s first experiment with the configuration to see if we can get some output. In Session #3 (the running session),
fhicl-dump analyzer_job.fcl > tmp.txt
and open tmp.txt in a text editor. You will see what blocks in there
contain the fcl parameters you need to adjust.
Make a new fcl file in the work directory
called myana.fcl
with these contents:
#include "analyzer_job.fcl"
physics.analyzers.clusterana.module_type: "ClusterCounter3"
Try running it:
lar -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
but you will get error messages about “product not found”.
Inspection of ClusterCounter3_module.cc
in Session #1 shows that it is
looking for input clusters. Let’s see if we have any in the input file,
but with a different module label for the input data.
Look at the contents of the input file:
product_sizes_dumper root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root | grep -i cluster
There are clusters with module label “pandora” but not
lineclusterdc
which you can find in the tmp.txt file above. Now edit myana.fcl
to say
#include "analyzer_job.fcl"
physics.analyzers.clusterana.module_type: "ClusterCounter3"
physics.analyzers.clusterana.ClusterModuleLabel: "pandora"
and run it again:
lar -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
Lots of information on job configuration via FHiCL is available at this link
Editing the example module and building it
YouTube Lecture Part 3: Now in session #1, edit ${MRB_SOURCE}/protoduneana/protoduneana/TutorialExamples/ClusterCounter3_module.cc
Add
#include "TH1F.h"
to the section with includes.
Add a private data member
TH1F *fTutorialHisto;
to the class. Create the histogram in the beginJob()
method:
fTutorialHisto = tfs->make<TH1F>("TutorialHisto","NClus",100,0,500);
Fill the histo in the analyze()
method, after the loop over clusters:
fTutorialHisto->Fill(fNClusters);
Go to session #2 and build it. The current working directory should be the build directory:
make install -j16
Note – this is the quicker way to re-build a product. The -j16
says to use 16 parallel processes,
which matches the number of cores on a build node. The command
mrb i -j16
first does a cmake step – it looks through all the CMakeLists.txt
files and processes them,
making makefiles. If you didn’t edit a CMakeLists.txt
file or add new modules or fcl files
or other code, a simple make can save you some time in running the single-threaded cmake
step.
Rerun your program in session #3 (the run session)
lar -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
Open the output file in a TBrowser:
root tutorial_hist.root
and browse it to see your new histogram. You can also run on some data.
lar -c myana.fcl -T dataoutputfile.root root://fndca1.fnal.gov/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2020/detector/physics/PDSPProd4/00/00/53/87/np04_raw_run005387_0041_dl7_reco1_13832298_0_20201109T215042Z.root
The -T dataoutputfile.root
changes the output filename for the TTrees
and
histograms to dataoutputfile.root
so it doesn’t clobber the one you made
for the MC output.
This iteration of course is rather slow – rebuilding and running on files in dCache
. Far better,
if you are just changing histogram binning, for example, is to use the output TTree.
TTree::MakeClass
is a very useful way to make a script that reads in the TBranches
of a TTree
on
a file. The workflow in this tutorial is also useful in case you decide to add more content
to the example TTree
.
Run your program in the debugger
YouTube Lecture Part 4: In session #3 (the running session)
setup forge_tools
ddt `which lar` -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root
Click the “Run” button in the window that pops up. The which lar
is needed because ddt cannot find
executables in your path – you have to specify their locations explicitly.
In session #1, look at ClusterCounter3_module.cc
in a text editor that lets you know what the line numbers are.
Find the line number that fills your new histogram. In the debugger window, select the “Breakpoints”
tab in the bottom window, and usethe right-mouse button (sorry mac users – you may need to get an external
mouse if you are using VNC. XQuartz
emulates a three-button mouse I believe). Make sure the “line”
radio button is selected, and type ClusterCounter3_module.cc
for the filename. Set the breakpoint
line at the line you want, for the histogram filling or some other place you find interesting. Click
Okay, and “Yes” to the dialog box that says ddt doesn’t know about the source code yet but will try to
find it when it is loaded.
Click the right green arrow to start the program. Watch the program in the Input/Output section. When the breakpoint is hit, you can browse the stack, inspect values (sometimes – it is better when compiled with debug), set more breakpoints, etc.
You will need Session #1 to search for code that ddt cannot find. Shared object libraries contain information about the location of the source code when it was compiled. So debugging something you just compiled usually results in a shared object that knows the location of the source, but installed code in CVMFS points to locations on the Jenkins build nodes.
Looking for source code:
Your environment has lots of variables pointing at installed code. Look for variables like
PROTODUNEANA_DIR
which points to a directory in CVMFS
.
ls $PROTODUNEANA_DIR/source
or $LARDATAOBJ_DIR/include
are good examples of places to look for code, for example.
Checking out and committing code to the git repository
For protoduneana and dunesw, this wiki page is quite good. LArSoft uses GitHub with a pull-request model. See
https://cdcvs.fnal.gov/redmine/projects/larsoft/wiki/Developing_With_LArSoft
https://cdcvs.fnal.gov/redmine/projects/larsoft/wiki/Working_with_GitHub
Common errors and recovery
Version mismatch between source code and installed products
When you perform an mrbsetenv or a mrbslp, sometimes you get a version mismatch. The most common reason for this is that you have set up an older version of the dependent products. Dunesw
depends on protoduneana
, which depends on dunecore
, which depends on larsoft
, which depends on art, ROOT, GEANT4, and many other products. This picture shows the software dependency tree for dunesw v09_72_01_d00. If the source code is newer than the installed products, the versions may mismatch. You can check out an older version of the source code (see the example above) with
mrb g -t <tag> repository
Alternatively, if you have already checked out some code, you can switch to a different tag using your local clone of the git repository.
cd $MRB_SOURCE/<product>
git checkout <tag>
Try mrbsetenv
again after checking out a consistent version.
Telling what version is the right one
The versions of dependent products for a product you’re building from source are listed in the file $MRB_SOURCE/<product>/ups/product_deps
`.
Sometimes you may want to know what the version number is of a product way down on the dependency tree so you can check out its source and edit it. Set up the product in a separate login session:
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
setup <product> $DUNESW_VERSION -q $DUNESW_QUALIFIER
ups active
It usually is a good idea to pipe the output through grep to find a particular product version. You can get dependency information with
ups depend <product> $DUNESW_VERSION -q $DUNESW_QUALIFIER
Note: not all dependencies of dependent products are listed by this command. If a product is already listed, it sometimes is not listed a second time, even if two products in the tree depend on it. Some products are listed multiple times.
There is a script in duneutil called dependency_tree.sh
which makes graphical displays of dependency trees.
Inconsistent build directory
The directory $MRB_BUILD contains copies of built code before it gets installed to localProducts. If you change versions of the source or delete things, sometimes the build directory will have clutter in it that has to be removed.
mrb z
will delete the contents of $MRB_BUILDDIR
and you will have to type mrbsetenv
again.
mrb zd
will also delete the contents of localProducts. This can be useful if you are removing code and want to make sure the installed version also has it gone.
Inconsistent environment
When you use UPS’s setup command, a lot of variables get defined. For each product, a variable called <product>_DIR
is defined, which points to the location of the version and flavor of the product. UPS has a command “unsetup” which often succeeds in undoing what setup does, but it is not perfect. It is possible to get a polluted environment in which inconsistent versions of packages are set up and it is too hard to repair it one product at a time. Logging out and logging back in again, and setting up the session is often the best way to start fresh.
The setup command is the wrong one
If you have not sourced the DUNE software setup script
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
you will find that the setup command that is used instead is one provided by the operating system and it requires root privilege to execute and setup will ask you for the root password. Rather than typing that if you get in this situation, ctrl-c, source the setup_dune.sh script and try again.
Compiler and linker warnings and errors
Common messages from the g++
compiler are undeclared variables, uninitialized variables, mismatched parentheses or brackets, missing semicolons, checking unsigned variables to see if they are positive (yes, that’s a warning!) and other things. mrb is set up to tell g++
and clang to treat warnings as errors, so they will stop the build and you will have to fix them. Often undeclared variables or methods that aren’t members of a class messages result from having forgotten to include the appropriate include file.
The linker has fewer ways to fail than the compiler. Usually the error message is “Undefined symbol”. The compiler does not emit this message, so you always know this is in the link step. If you have an undefined symbol, one of three things may have gone wrong. 1) You may have mistyped it (usually this gets caught by the compiler because names are defined in header files). More likely, 2) You introduced a new dependency without updating the CMakeLists.txt
file. Look in the CMakeLists.txt
file that steers the building of the source code that has the problem. Look at other CMakeLists.txt
files in other directories for examples of how to refer to libraries. ` MODULE_LIBRARIES are linked with modules in the
ART_MAKE blocks, and
LIB_LIBRARIES` are linked when building non-module libraries (free-floating source code, for algorithms). 3) You are writing new code and just haven’t gotten around to finishing writing something you called.
Out of disk quota
Do not store data files on the app disk! Sometimes the app disk fills up nonetheless, and there is a quota of 100 GB per user on it. If you need more than that for several builds, you have some options. 1) Use /exp/dune/data/users/<username>
. You have a 400 GB quota on this volume. They are slower than the app disk and can get even slower if many users are accessing them simultaneously or transferring large amounts of data to or ofrm them. 3) Clean up some space on app. You may want to tar up an old release and store the tarball on the data volume or in dCache
for later use.
Runtime errors
Segmentation faults: These do not throw errors that art can catch. They terminate the program immediately. Use the debugger to find out where they happened and why.
Exceptions that are caught. The ddt
debugger has in its menu a set of standard breakpoints. You can instruct the debugger to stop any time an exception is thrown. A common exception is a vector accessed past its size using at()
, but often these are hard to track down because they could be anywhere. Start your program with the debugger, but it is often a good idea to turn off the break-on-exception feature until after the geometry has been read in. Some of the XML parsing code throws a lot of exceptions that are later caught as part of its normal mode of operation, and if you hit a breakpoint on each of these and push the “go” button with your mouse each time, you could be there all day. Wait until the initialization is over, press “pause” and then turn on the breakpoints by exception.
If you miss, start the debugging session over again. Starting the session over is also a useful technique when you want to know what happened before a known error condition occurs. You may find yourself asking “how did it get in that condition? Set a breakpoint that’s earlier in the execution and restart the session. Keep backing up – it’s kind of like running the program in reverse, but it’s very slow. Sometimes it’s the only way.
Print statements are also quite useful for rare error conditions. If a piece of code fails infrequently, based on the input data, sometimes a breakpoint is not very useful because most of the time it’s fine and you need to catch the program in the act of misbehaving. Putting in a low-tech print statement, sometimes with a uniquely-identifying string so you can grep the output, can let you put some logic in there to print only when things have gone bad, or even if you print on each iteration, you can just look at the last bit of printout before a crash.
No authentication/permission
You will almost always need to have a valid Kerberos ticket in your session. Accessing your home directory on the Fermilab machines requires it. Find your tickets with the command
klist
By default, they last for 25 hours or so (a bit more than a day). You can refresh them for another 25 hours (up to one week’s worth of refreshes are allowed) with
kinit -R
If you have a valid ticket on one machine and want to refresh tickets on another, you can
k5push <nodename>
The safest way to get a new ticket to a machine is to kinit on your local computer (like your laptop) and log in again,
making sure to forward all tickets. In a pinch, you can run kinit on a dunegpvm and enter your Kerberos password, but this is discouraged as bad actors can (and have!) installed keyloggers on shared systems, and have stolen passwords. DO NOT KEEP PRIVATE, PERSONAL INFORMATION ON FERMILAB COMPUTERS! Things like bank account numbers, passwords, and social security numbers are definitely not to be stored on public, shared computers. Running kinit -R
on a shared machine is fine.
You will need a grid proxy to submit jobs and access data in dCache
via xrootd
or ifdh
.
setup_fnal_security
will use your valid Kerberos ticket to generate the necessary certificates and proxies.
Link to art/LArSoft tutorial May 2021
https://wiki.dunescience.org/wiki/Presentation_of_LArSoft_May_2021
Key Points
DUNE’s software stack is built out of a tree of UPS products.
You don’t have to build all of the software to make modifications – you can check out and build one or more products to achieve your goals.
You can set up pre-built CVMFS versions of products you aren’t developing, and UPS will check version consistency, though it is up to you to request the right versions.
mrb is the tool DUNE uses to check out software from multiple repositories and build it in a single test release.
mrb uses git and cmake, though aspects of both are exposed to the user.
Grid Job Submission and Common Errors
Overview
Teaching: 65 min
Exercises: 0 minQuestions
How to submit grid jobs?
Objectives
Submit a basic batchjob and understand what’s happening behind the scenes
Monitor the job and look at its outputs
Review best practices for submitting jobs (including what NOT to do)
Extension; submit a small job with POMS
Note:
This section describes basic job submission. Large scale submission of jobs to read DUNE data files are described in the next section.
Session Video
This session will be captured on video a placed here after the workshop for asynchronous study. The video from the two day version of this training in May 2022 is provided here as a reference.
Live Notes
Participants are encouraged to monitor and utilize the Livedoc for May. 2023 to ask questions and learn. For reference, the Livedoc from Jan. 2023 is provided.
Temporary Instructor Note:
The May 2023 training event was cloned from the May 2022, both two day events.
This lesson (07-grid-job-submission.md) was imported from the Jan. 2023 lesson which was a one half day version of the training.
Quiz blocks are added at the bottom of this page, and invite your review, modify, review, and additional comments.
The official timetable for this training event is on the Indico site.
Notes on changes in the 2023/2024 versions
The past few months have seen significant changes in how DUNE (as well as other FNAL experiments) submits jobs and interacts with storage elements. While every effort was made to preserve backward compatibility a few things will be slightly different (and some are easier!) than what’s been shown at previous versions. Therefore even if you’ve attended this tutorial multiple times in past and know the difference between copying and streaming, tokens vs. proxies, and know your schedds from your shadows, you are encouraged to attend this session. Here is a partial list of significant changes:
- The jobsub_client product generally used for job submission has been replaced by the jobsub_lite product, which is very similar to jobsub_client except there is no server on the other side (i.e. there is more direct HTCondor interaction). You no longer need to set up the jobsub_client product as part of your software setup; it is installed via RPM now on all DUNE interactive machines. See this Wiki page for some differences between jobsub_lite and legacy jobsub.
- As of May 2024 you cannot submit batch jobs from SL7 containers but many submission scripts only run on SL7. You need to record the submission command from SL7 and the open a separate window running Alma9 and execute that command.
- Authentication via tokens instead of proxies is now rolling out and is now the primary authentication method. Please note that not only are tokens used for job submission now, they are also used for storage element access.
- It is no longer possible to write to certain directories from grid jobs as analysis users, namely the persistent area. Read access to the full /pnfs tree is still available. Bulk copies of job outputs from scratch to persistent have to be done outside of grid jobs.
- Multiple
--tar_file_name
options are now supported (and will be unpacked) if you need things in multiple tarballs. - The
-f
behavior with and without dropbox:// in front is slightly different from legacy jobsub; see the documentation for details. - jobsub_lite will probably not work directly from lxplus at the moment, though work is underway to make it possible to submit batch jobs to non-FNAL schedulers.
Submit a job
**Note that job submission requires FNAL account or access to another HTCondor submission point conneected to the Fermilab pool (currently BNL or RAL).
First, log in to a dunegpvm
machine . Then you will need to set up the job submission tools (jobsub
). If you set up dunesw
it will be included, but if not, you need to do
mkdir -p /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_may2023 # if you have not done this before
mkdir -p /pnfs/dune/scratch/users/${USER}/may2023tutorial
Having done that, let us submit a prepared script:
jobsub_submit -G dune --mail_always -N 1 --memory=1000MB --disk=1GB --cpu=1 --expected-lifetime=1h --singularity-image /cvmfs/singularity.opensciencegrid.org/fermilab/fnal-wn-sl7:latest --append_condor_requirements='(TARGET.HAS_Singularity==true&&TARGET.HAS_CVMFS_dune_opensciencegrid_org==true&&TARGET.HAS_CVMFS_larsoft_opensciencegrid_org==true&&TARGET.CVMFS_dune_opensciencegrid_org_REVISION>=1105)' -e GFAL_PLUGIN_DIR=/usr/lib64/gfal2-plugins -e GFAL_CONFIG_DIR=/etc/gfal2.d file:///dune/app/users/kherner/submission_test_singularity.sh
If all goes well you should see something like this:
Attempting to get token from https://htvaultprod.fnal.gov:8200 ... succeeded
Storing bearer token in /tmp/bt_token_dune_Analysis_11469
Transferring files to web sandbox...
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_21_205736_877318d7-d14a-4c5a-b2fc-7486f1e54fa2/submission_test_singularity.sh [DONE] after 2s
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_21_205736_877318d7-d14a-4c5a-b2fc-7486f1e54fa2/simple.cmd [DONE] after 5s
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_21_205736_877318d7-d14a-4c5a-b2fc-7486f1e54fa2/simple.sh [DONE] after 5s
Submitting job(s).
1 job(s) submitted to cluster 710165.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
Use job id 710165.0@jobsub05.fnal.gov to retrieve output
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
Quiz
- What is your job ID?
Note if you have not submitted a DUNE batch job within the past 30 days: you may be asked to (re-)authenticate. If so you will see the following:
Attempting OIDC authentication with https://htvaultprod.fnal.gov:8200
Complete the authentication at:
https://cilogon.org/device/?user_code=ABC-D1E-FGH
No web open command defined, please copy/paste the above to any web browser
Waiting for response in web browser
The user code will be different of course. In this particular case, you do want to follow the instructions and copy and paste the link into your browser (can be any browser). There is a time limit on it so its best to do it right away. Always choose Fermilab as the identity provider in the menu, even if your home institution is listed. After you hit log on, you’ll get a message saying you approved the access request, and then after a short delay (may be several seconds) in the terminal you will see
Saving credkey to /nashome/u/username/.config/htgettoken/credkey-dune-default
Saving refresh token ... done
Attempting to get token from https://htvaultprod.fnal.gov:8200 ... succeeded
Storing bearer token in /tmp/bt_token_dune_Analysis_somenumber.othernumber
Storing condor credentials for dune
Submitting job(s)
.
1 job(s) submitted to cluster 57110235.
Now, let’s look at some of these options in more detail.
--mail_always
sends mail after the job completes whether it was successful for not. To disable all emails, use--mail_never
.-N
controls the number of identical jobs submitted with each cluster. Also called the process ID, the number ranges from 0 to N-1 and forms the part of the job ID number after the period, e.g. 12345678.N.--memory, --disk, --cpu, --expected-lifetime
request this much memory, disk, number of cpus, and max run time. Jobs that exceed the requested amounts will go into a held state. Defaults are 2000 MB, 10 GB, 1, and 8h, respectively. Note that jobs are charged against the DUNE FermiGrid quota according to the greater of memory/2000 MB and number of CPUs, with fractional values possible. For example, a 3000 MB request is charged 1.5 “slots”, and 4000 MB would be charged 2. You are charged for the amount requested, not what is actually used, so you should not request any more than you actually need (your jobs will also take longer to start the more resources you request). Note also that jobs that run offsite do NOT count against the FermiGrid quota. In general, aim for memory and run time requests that will cover 90-95% of your jobs and use the autorelease feature to deal with the remainder.-l
(or--lines=
) allows you to pass additional arbitrary HTCondor-styleclassad
variables into the job. In this case, we’re specifying exactly whatSingularity
image we want to use in the job. It will be automatically set up for us when the job starts. Any other valid HTCondorclassad
is possible. In practice you don’t have to do much beyond theSingularity
image. Here, pay particular attention to the quotes and backslashes.--append_condor_requirements
allows you to pass additionalHTCondor-style
requirements to your job. This helps ensure that your jobs don’t start on a worker node that might be missing something you need (a corrupt or out of dateCVMFS
repository, for example). Some checks run at startup for a variety ofCVMFS
repositories. Here, we check that Singularity invocation is working and that theCVMFS
repos we need ( [dune.opensciencegrid.org][dune-openscience-grid-org] and [larsoft.opensciencegrid.org][larsoft-openscience-grid-org] ) are in working order. Optionally you can also place version requirements on CVMFS repos (as we did here as an example), useful in case you want to use software that was published very recently and may not have rolled out everywhere yet.-e VAR=VAL
will set the environment variable VAR to the value VAL inside the job. You can pass this option multiple times for each variable you want to set. You can also just do-e VAR
and that will set VAR inside the job to be whatever value it’s set to in your current environment (make sure it’s actually set though!) One thing to note here as of May 2023 is that these two gfal variables may need to be set as shown to prevent problems with output copyback at a few sites. It is safe to set these variable to the values shown here in all jobs at all sites, since the locations exist in the default container (assuming you’re using that).
Job Output
This particular test writes a file to /pnfs/dune/scratch/users/<username>/job_output_<id number>.log
.
Verify that the file exists and is non-zero size after the job completes.
You can delete it after that; it just prints out some information about the environment.
Manipulating submitted jobs
If you want to remove existing jobs, you can do
jobsub_rm -G dune --jobid=12345678.9@jobsub0N.fnal.gov
to remove all jobs in a given submission (i.e. if you used -N <some number greater than 1>) you can do
jobsub_rm -G dune --jobid=12345678@jobsub0N.fnal.gov
To remove all of your jobs, you can do
jobsub_rm -G dune --user=username
If you want to manipulate only a certain subset of jobs, you can use a HTCondor-style constraint. For example, if I want to remove only held jobs asking for more than say 8 GB of memory that went held because they went over their request, I could do something like
jobsub_rm -G dune --constraint='Owner=="username"&&JobStatus==5&&RequestMemory>=8000&&(HoldReasonCode==34||(HoldReasonCode==26&&HoldReasonSubCode==1))'
To hold jobs, it’s the same procedure as jobsub_rm
; just replace that with jobsub_hold
. To release a held job (which will restart from the beginning), it’s the same commands as above, only use jobsub_release
in place of rm or hold.
if you get tired of typing -G dune
all the time, you can set the JOBSUB_GROUP environment variable to dune, and then omit the -G option.
Submit a job using the tarball containing custom code
First off, a very important point: for running analysis jobs, you may not actually need to pass an input tarball, especially if you are just using code from the base release and you don’t actually modify any of it. In that case, it is much more efficient to use everything from the release and refrain from using a tarball. All you need to do is set up any required software from CVMFS (e.g. dunetpc and/or protoduneana), and you are ready to go. If you’re just modifying a fcl file, for example, but no code, it’s actually more efficient to copy just the fcl(s) your changing to the scratch directory within the job, and edit them as part of your job script (copies of a fcl file in the current working directory have priority over others by default).
Sometimes, though, we need to run some custom code that isn’t in a release. We need a way to efficiently get code into jobs without overwhelming our data transfer systems. We have to make a few minor changes to the scripts you made in the previous tutorial section, generate a tarball, and invoke the proper jobsub options to get that into your job. There are many ways of doing this but by far the best is to use the Rapid Code Distribution Service (RCDS), as shown in our example.
If you have finished up the LArSoft follow-up and want to use your own code for this next attempt, feel free to tar it up (you don’t need anything besides the localProducts* and work directories) and use your own tar ball in lieu of the one in this example. You will have to change the last line with your own submit file instead of the pre-made one.
First, we should make a tarball. Here is what we can do (assuming you are starting from /dune/app/users/username/):
cp /dune/app/users/kherner/setupmay2023tutorial-grid.sh /dune/app/users/${USER}/
cp /dune/app/users/kherner/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup-grid /dune/app/users/${USER}/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup-grid
Before we continue, let’s examine these files a bit. We will source the first one in our job script, and it will set up the environment for us.
#!/bin/bash
DIRECTORY=may2023tutorial
# we cannot rely on "whoami" in a grid job. We have no idea what the local username will be.
# Use the GRID_USER environment variable instead (set automatically by jobsub).
USERNAME=${GRID_USER}
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export WORKDIR=${_CONDOR_JOB_IWD} # if we use the RCDS the our tarball will be placed in $INPUT_TAR_DIR_LOCAL.
if [ ! -d "$WORKDIR" ]; then
export WORKDIR=`echo .`
fi
source ${INPUT_TAR_DIR_LOCAL}/${DIRECTORY}/localProducts*/setup-grid
mrbslp
Now let’s look at the difference between the setup-grid script and the plain setup script. Assuming you are currently in the /dune/app/users/username directory:
diff may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup-grid
< setenv MRB_TOP "/dune/app/users/<username>/may2023tutorial"
< setenv MRB_TOP_BUILD "/dune/app/users/<username>/may2023tutorial"
< setenv MRB_SOURCE "/dune/app/users/<username>/may2023tutorial/srcs"
< setenv MRB_INSTALL "/dune/app/users/<username>/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof"
---
> setenv MRB_TOP "${INPUT_TAR_DIR_LOCAL}/may2023tutorial"
> setenv MRB_TOP_BUILD "${INPUT_TAR_DIR_LOCAL}/may2023tutorial"
> setenv MRB_SOURCE "${INPUT_TAR_DIR_LOCAL}/may2023tutorial/srcs"
> setenv MRB_INSTALL "${INPUT_TAR_DIR_LOCAL}/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof"
As you can see, we have switched from the hard-coded directories to directories defined by environment variables; the INPUT_TAR_DIR_LOCAL
variable will be set for us (see below).
Now, let’s actually create our tar file. Again assuming you are in /dune/app/users/kherner/may2023tutorial/
:
tar --exclude '.git' -czf may2023tutorial.tar.gz may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof may2023tutorial/work setupmay2023tutorial-grid.sh
Note how we have excluded the contents of “.git” directories in the various packages, since we don’t need any of that in our jobs. It turns out that the .git directory can sometimes account for a substantial fraction of a package’s size on disk!
Then submit another job (in the following we keep the same submit file as above):
jobsub_submit -G dune --mail_always -N 1 --memory=2500MB --disk=2GB --expected-lifetime=3h --cpu=1 --tar_file_name=dropbox:///dune/app/users/<username>/may2023tutorial.tar.gz --singularity-image /cvmfs/singularity.opensciencegrid.org/fermilab/fnal-wn-sl7:latest --append_condor_requirements='(TARGET.HAS_Singularity==true&&TARGET.HAS_CVMFS_dune_opensciencegrid_org==true&&TARGET.HAS_CVMFS_larsoft_opensciencegrid_org==true&&TARGET.CVMFS_dune_opensciencegrid_org_REVISION>=1105&&TARGET.HAS_CVMFS_fifeuser1_opensciencegrid_org==true&&TARGET.HAS_CVMFS_fifeuser2_opensciencegrid_org==true&&TARGET.HAS_CVMFS_fifeuser3_opensciencegrid_org==true&&TARGET.HAS_CVMFS_fifeuser4_opensciencegrid_org==true)' -e GFAL_PLUGIN_DIR=/usr/lib64/gfal2-plugins -e GFAL_CONFIG_DIR=/etc/gfal2.d file:///dune/app/users/kherner/run_may2023tutorial.sh
You’ll see this is very similar to the previous case, but there are some new options:
--tar_file_name=dropbox://
automatically copies and untars the given tarball into a directory on the worker node, accessed via the INPUT_TAR_DIR_LOCAL environment variable in the job. The value of INPUT_TAR_DIR_LOCAL is by default $CONDOR_DIR_INPUT/name_of_tar_file_without_extension, so if you have a tar file named e.g. may2023tutorial.tar.gz, it would be $CONDOR_DIR_INPUT/may2023tutorial.- Notice that the
--append_condor_requirements
line is longer now, because we also check for the fifeuser[1-4]. opensciencegrid.org CVMFS repositories.
The submission output will look something like this:
Attempting to get token from https://htvaultprod.fnal.gov:8200 ... succeeded
Storing bearer token in /tmp/bt_token_dune_Analysis_11469
Using bearer token located at /tmp/bt_token_dune_Analysis_11469 to authenticate to RCDS
Checking to see if uploaded file is published on RCDS
Could not locate uploaded file on RCDS. Will retry in 30 seconds.
Could not locate uploaded file on RCDS. Will retry in 30 seconds.
Could not locate uploaded file on RCDS. Will retry in 30 seconds.
Found uploaded file on RCDS.
Transferring files to web sandbox...
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_24_224713_9669e535-daf9-496f-8332-c6ec8a4238d9/run_may2023tutorial.sh [DONE] after 0s
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_24_224713_9669e535-daf9-496f-8332-c6ec8a4238d9/simple.cmd [DONE] after 0s
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_24_224713_9669e535-daf9-496f-8332-c6ec8a4238d9/simple.sh [DONE] after 0s
Submitting job(s).
1 job(s) submitted to cluster 62007523.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
Use job id 62007523.0@jobsub01.fnal.gov to retrieve output
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
Note that the job submission will pause while it uploads the tarball to RCDS, and then it continues normally.
Now, there’s a very small gotcha when using the RCDS, and that is when your job runs, the files in the unzipped tarball are actually placed in your work area as symlinks from the CVMFS version of the file (which is what you want since the whole point is not to have N different copies of everything). The catch is that if your job script expected to be able to edit one or more of those files within the job, it won’t work because the link is to a read-only area. Fortunately there’s a very simple trick you can do in your script before trying to edit any such files:
cp ${INPUT_TAR_DIR_LOCAL}/file_I_want_to_edit mytmpfile # do a cp, not mv
rm ${INPUT_TAR_DIR_LOCAL}file_I_want_to_edit # This really just removes the link
mv mytmpfile file_I_want_to_edit # now it's available as an editable regular file.
You certainly don’t want to do this for every file, but for a handful of small text files this is perfectly acceptable and the overall benefits of copying in code via the RCDS far outweigh this small cost. This can get a little complicated when trying to do it for things several directories down, so it’s easiest to have such files in the top level of your tar file.
Monitor your jobs
For all links below, log in with your FNAL Services credentials (FNAL email, not Kerberos password).
-
What DUNE is doing overall:
https://fifemon.fnal.gov/monitor/d/000000053/experiment-batch-details?orgId=1&var-experiment=dune -
What’s going on with only your jobs:
Remember to change the url with your own username and adjust the time range to cover the region of interest. https://fifemon.fnal.gov/monitor/d/000000116/user-batch-details?orgId=1&var-cluster=fifebatch&var-user=kherner -
Why your jobs are held:
Remember to choose your username in the upper left.
https://fifemon.fnal.gov/monitor/d/000000146/why-are-my-jobs-held?orgId=1
View the stdout/stderr of our jobs
Here’s the link for the history page of the example job: link.
Feel free to sub in the link for your own jobs.
Once there, click “View Sandbox files (job logs)”. In general you want the .out and .err files for stdout and stderr. The .cmd file can sometimes be useful to see exactly what got passed in to your job.
Kibana can also provide a lot of information.
You can also download the job logs from the command line with jobsub_fetchlog:
jobsub_fetchlog --jobid=12345678.0@jobsub0N.fnal.gov --unzipdir=some_appropriately_named_directory
That will download them as a tarball and unzip it into the directory specified by the –unzipdir option. Of course replace 12345678.0@jobsub0N.fnal.gov with your own job ID.
Quiz
Download the log of your last submission via jobsub_fetchlog or look it up on the monitoring pages. Then answer the following questions (all should be available in the .out or .err files):
- On what site did your job run?
- How much memory did it use?
- Did it exit abnormally? If so, what was the exit code?
Review of best practices in grid jobs (and a bit on the interactive machines)
- When creating a new workflow or making changes to an existing one, ALWAYS test with a single job first. Then go up to 10, etc. Don’t submit thousands of jobs immediately and expect things to work.
- ALWAYS be sure to prestage your input datasets before launching large sets of jobs. This may become less necesaary in the future as we move to distributed storage locations.
- Use RCDS; do not copy tarballs from places like scratch dCache. There’s a finite amount of transfer bandwidth available from each dCache pool. If you absolutely cannot use RCDS for a given file, it’s better to put it in resilient (but be sure to remove it when you’re done!). The same goes for copying files from within your own job script: if you have a large number of jobs looking for a same file, get it from resilient. Remove the copy when no longer needed. Files in resilient dCache that go unaccessed for 45 days are automatically removed.
- Be careful about placing your output files. **NEVER place more than a few thousand files into any one directory inside dCache. That goes for all type of dCache (scratch, persistent, resilient, etc). Subdirectories also count against the total for these purposes, so don’t put too many subdirectories at any one level.
- AVOID commands like
ifdh ls /some/path
inside grid jobs unless it is absolutely necessary. That is an expensive operation and can cause a lot of pain for many users, especially when a directory has large number of files in it. Remote listings take much, much longer than the corresponding op on a machine where the directory is mounted via NFS. If you just need to verify a directory exists, there are much better ways than ifdh ls, for example the gfal-stat command. Note also that ifdh cp will now, by default, create an output directory if it does not exist (so be careful that you’ve specified your output string properly). - Use xrootd when opening files interactively; this is much more stable than simply doing
root /pnfs/dune/... (and in general, do NOT do that...)
- NEVER copy job outputs to a directory in resilient dCache. Remember that they are replicated by a factor of 20! Any such files are subject to deletion without warning.
- NEVER do hadd on files in
/pnfs
areas unless you’re usingxrootd
. I.e. do NOT do hadd out.root/pnfs/dune/file1 /pnfs/dune/file2 ...
This can cause severe performance degradations. - Generally aim for output file sizes of 1 GB or greater. dCache is really not a fan of small files. You may need to process multiple input files to get to that size (and we generally encourage that anyway!)
- Very short jobs (measured in minutes) are quite inefficient, especially if you have an input tarball. In general you want to run for at least a few hours, and 8-12 is probably ideal (and of course longer jobs are allowed). Again you may need to process multiple input files, depending on what you’re doing, or run multiple workflow stages in the same job. See the POMS section of the tutorial for more details.
Side note: Some people will pass file lists to their jobs instead of using a SAM dataset. We do not recommend that for two reasons: 1) Lists do not protect you from cases where files fall out of cache at the location(s) in your list. When that happens your jobs sit idle waiting for the files to be fetched from tape, which kills your efficiency and blocks resources for others. 2) You miss out on cases where there might be a local copy of the file at the site you’re running on, or at least at closer one to your list. So you may end up unecessarily streaming across oceans, whereas using SAM (or later Rucio) will find you closer, local copies when they exist.
Another important side note: If you are used to using other programs for your work such as project.py (which is NOT officially supported by DUNE or the Fermilab Scientific Computing Division), there is a helpful tool called Project-py that you can use to convert existing xml into POMS configs, so you don’t need to start from scratch! Then you can just switch to using POMS from that point forward. As a reminder, if you use unsupported tools, you are own your own and will receive NO SUPPORT WHATSOEVER. You are still responsible for making sure that your jobs satisfy Fermilab’s policy for job efficiency: https://cd-docdb.fnal.gov/cgi-bin/sso/RetrieveFile?docid=7045&filename=FIFE_User_activity_mitigation_policy_20200625.pdf&version=1
The cost of getting it wrong: a cautionary tale
Earlier in May 2023 there was a fairly significant disruption to FNAL dCache, which resulted in at least five different tickets across four different experiments complaining of poor performance (resulting in jobs going held of exceeding time), timeouts, or other storage-related failures. It’s unclear exactly how many jobs were affcted but it was likely in the many thousands. The root cause was a DUNE user running ifdh ls $OUTDIR 0
to check the existence of a given directory. That command, though it only spits out the directory name, was indeed doing a full internal listing of the contents of $OUTDIR. Normally that’s not the end of the world (see the comment in the best practices section), but this directory had over 100,000 files in it! The user was writing all job outputs into the same directory from what we could tell.
Since the workflow was causing a systemwide disruption we immediately held all of the user’s jobs and blocked new submissions until the workflow was re-engineered. Fortunately dCache performance recovered very quickly after that. The user’s jobs are running again and they are also much more CPU-efficient than they were before the changes.
The bottom line: one single user not following best practices can disrupt the entire system if they get enough jobs running. EVERYONE is responsible for following best practices. Getting it wrong affects not only you, but your collaborators!
A word on the DUNE Global Pool
DUNE has also created a a global glideinWMS pool similar to the CMS Global Pool that is intended to serve as a single point through which multiple job submission systems (e.g. HTCondor schedulers at sites outside of Fermilab) can have access to the same resources. Jobs using the global pool still run in the exactly the same way as those that don’t. We plan to move more and more work over to the global pool in 2023 and priority access to the FermiGrid quota will eventually be given to jobs submitted to the global pool. To switch to the global pool with jobsub, it’s simply a matter of adding --global-pool dune
as an option to your submission command. The only practical difference is that your jobs will come back with IDs of the form NNNNNNN.N@dunegpschedd0X.fnal.gov instead of NNNNNNN.N@jobsub0X.fnal.gov. Again, everything else is identical, so feel free to test it out.
Making subsets of sam definitions
Running across very large number of files puts you at risk of system issues. It is often much nicer to run over several smaller subsets. Many official samweb definitions are large data collections defined only by their properties and not really suitable for a single job.
There are two ways of reducing their size.
You can create new dataset definitions; say mydataset
has 10000 entries and you want to split it on to groups of 2000:
samweb create-definition $USER-mydataset-part0 “defname:mydataset limit 2000 offset 0”
samweb create-definition $USER-mydataset-part1 “defname:mydataset limit 2000 offset 2000"
samweb create-definition $USER-mydataset-part2 “defname:mydataset limit 2000 offset 4000”
samweb create-definition $USER-mydataset-part3 “defname:mydataset limit 2000 offset 6000"
samweb create-definition $USER-mydataset-part4 “defname:mydataset limit 2000 offset 8000”
Your name needs to be in there, unless it is already in the dataset name, but make certain you don’t miss a few at the end…
Alternatively you can use the syntax with stride 5 offset 0
to take every 5th file:
samweb create-definition $USER-mydataset-part0 “defname:mydataset limit 2000 with stride 5 offset 0”
samweb create-definition $USER-mydataset-part1 “defname:mydataset limit 2000 with stride 5 offset 1"
samweb create-definition $USER-mydataset-part2 “defname:mydataset limit 2000 with stride 5 offset 2”
samweb create-definition $USER-mydataset-part3 “defname:mydataset limit 2000 with stride 5 offset 3"
samweb create-definition $USER-mydataset-part4 “defname:mydataset limit 2000 with stride 5 offset 4”
More on samweb can be found here.
Verify Your Learning:
Question 01
What are the differences in environment between Fermilab worker nodes and those at other sites (assuming the site supports Singularity)?
- Fermilab workers have additional libraries available.
- Worker nodes at other sites have additional libraries installed.
- No difference.
Answer
The correct answer is C - No difference.
Comment:
Question 02
After setting up a new workflow or preparing to run one that has not been exercised in a while, what is an that has not been exercised in a while, what is an appropriate number of test jobs to initially submit?
- 1.
- 10.
- 100.
- As many as needed.
Answer
The correct answer is A - 1.
Comment:
Question 03
project.py is supported by the Fermilab Scientific Computing Division
- True.
- False.
Answer
The correct answer is B - False.
Comment:
Question 04
What is generally the best way to read in a .root file for analysis within a grid job?
- Open with an xrootd URI (root://).
- Copy the entire file at the beginning of the job.
- Both A and B.
Answer
The correct answer is A - Open with an xrootd URI (root://).
Comment:
Question 05
What is the best way to specify your desired operating system and environment in a grid job?
- Use the --OS option in jobsub.
- Do not specify any OS, but control it with the SingularityImage classad.
- Don’t specify anything. The grid does it
- None of the Above
Answer
The correct answer is B - Do not specify any OS, but control it with the SingularityImage classad.
Comment:
Question 06
What is the best way to copy custom code into a grid job?
- Use the RCDS (i.e. --tar_file_name=dropbox://foo/bar/) and stage the file in via CVMFS.
- Copy a tarball to /pnfs/dune/scratch/users/username.
- Copy a tarball to /pnfs/dune/persistent/users/username.
- Copy a tarball to /pnfs/dune/resilient/users/username.
- None of the Above
Answer
The correct answer is A - Use the RCDS (i.e. –tar_file_name=dropbox://foo/bar/) and stage the file in via CVMFS.
Comment:
Further Reading
Some more background material on these topics (including some examples of why certain things are bad) is in these links:
December 2022 jobsub_lite demo and information session
January 2023 additional experiment feedback session on jobsub_lite
Wiki page listing differences between jobsub_lite and legacy jobsub
DUNE Computing Tutorial:Advanced topics and best practices
2021 Intensity Frontier Summer School
The Glidein-based Workflow Management System
Key Points
When in doubt, ask! Understand that policies and procedures that seem annoying, overly complicated, or unnecessary (especially when compared to running an interactive test) are there to ensure efficient operation and scalability. They are also often the result of someone breaking something in the past, or of simpler approaches not scaling well.
Send test jobs after creating new workflows or making changes to existing ones. If things don’t work, don’t blindly resubmit and expect things to magically work the next time.
Only copy what you need in input tar files. In particular, avoid copying log files, .git directories, temporary files, etc. from interactive areas.
Take care to follow best practices when setting up input and output file locations.
Always, always, always prestage input datasets. No exceptions.
Submit grid jobs with JustIn
Overview
Teaching: 20 min
Exercises: 0 minQuestions
How to submit realistic grid jobs with JustIn
Objectives
Demonstrate use of JustIn for job submission with more complicated setups.
PLEASE USE THE NEW JUSTIN SYSTEM INSTEAD OF POMS
The JustIn Tutorial is currently in docdb at: JustIn Tutorial
The JustIn system is describe in detail at:
Note More documentation coming soon
Key Points
Always, always, always prestage input datasets. No exceptions.
Code-makeover - Submit with POMS
Overview
Teaching: 20 min
Exercises: 0 minQuestions
How to submit realistic grid jobs with POMS?
Objectives
Demonstrate use of POMS for job submission with more complicated setups.
Session Video
This session will be captured on video a placed here after the workshop for asynchronous study.
Live Notes
Participants are encouraged to monitor and utilize the Livedoc for May. 2023 to ask questions and learn. For reference, the Livedoc from Jan. 2023 is provided.
Getting set up to use POMS
If you haven’t used POMS in a while (several days), or it is your first time, you will need to be sure you upload a proxy and token to the server for use on your behalf. The easiest way to do that is with the upload_file
command in the fife_utils package. If you don’t already have fife_utils set up, just do
. /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
setup fife_utils
Then simply run the upload command:
upload_file --experiment=dune --poms_role=analysis --proxy --vaulttoken
You’ll see something like this:
updating credmon on jobsub01.fnal.gov
updating credmon on jobsub02.fnal.gov
updating credmon on jobsub04.fnal.gov
updating credmon on jobsub05.fnal.gov
updating credmon on jobsubdevgpvm01.fnal.gov
updating credmon on dunegpschedd01.fnal.gov
updating credmon on dunegpschedd02.fnal.gov
Fetching options from https://fifebatch.fnal.gov/cigetcertopts.txt
Checking if /tmp/x509up_voms_dune_Analysis_UID has at least 1.0 hours left
120.00 hours remaining, enough to reuse
Checking if myproxy.fnal.gov has at least 503.0 hours left
253.67 hours remaining in MyProxy, not enough
Fetching list of IdPs from https://cilogon.org/include/ecpidps.txt
Requesting authorization from SP https://ecp.cilogon.org/secure/getcert/
Making unauthorized request to IdP https://ecp-prod.fnal.gov/idp-krb/profile/SAML2/SOAP/ECP
Making kerberized request to IdP https://ecp-prod.fnal.gov/idp-krb/profile/SAML2/SOAP/ECP
Sending response to Assertion Consumer https://ecp.cilogon.org/Shibboleth.sso/SAML2/ECP
Requesting certificate from SP https://ecp.cilogon.org/secure/getcert/
Converting PKCS12 certificate to PEM
Generating proxy for storage
Storing proxy in /tmp/x509up_voms_dune_Analysis_UID
subject : /DC=org/DC=cilogon/C=US/O=Fermi National Accelerator Laboratory/OU=People/CN=John Blutarsky/CN=UID:bluto/CN=2468866293
issuer : /DC=org/DC=cilogon/C=US/O=Fermi National Accelerator Laboratory/OU=People/CN=John Blutarsky/CN=UID:bluto
Your proxy is valid until: Thu Jun 1 11:54:04 2023
Generating proxy for MyProxy
Storing proxy in MyProxy server myproxy.fnal.gov
uploaded: /tmp/vt_dune_Analysis_username to POMS server
uploaded: /tmp/x509up_voms_dune_Analysis_username to POMS server
Of course your username and UID will be different. Note that you may be prompted to do the CILogon authentication as discussed in the previous lesson. If so, go ahead and do that, and the workflow should resume.
Submit with POMS: more realistic workflows
This lesson extends from earlier work: Grid Job Submission and Common Errors
POMS is the recommended way of submitting large workflows. It offers several advantages over other systems, such as
- Fully configurable. Any executables can be run, not necessarily only lar or art
- Automatic monitoring and campaign management options
- Multi-stage workflow dependencies, automatic dataset creation between stages
- Automated recovery options
At its core, in POMS one makes a “campaign”, which has one or more “stages”. In our first example there was only a single stage.
For analysis use: main POMS page
An example campaign.
Of course in reality you may need to run multiple steps of a workflow in a single job, or run multiple stages in separate jobs, or modify things from a default release, or iterate over multiple config sets, or any number of other arbitrarily complicated ideas. You also have to consider things like SAM metadata for your files. In this lesson we’ll look at some of these more realistic cases and show how they can be set up with POMS campaigns.
Submit work with a modified .fcl file
Suppose you want to run something with a minor modification to a fcl file. Why bother with downloading and building a custom release and shipping that to your jobs when you don’t have any new code or libraries? A much simpler way is to make a local copy of the .fcl file in job and then edit the local copy as needed.
https://pomsgpvm01.fnal.gov/poms/campaign_stage_info/dune/analysis?campaign_stage_id=14110
Run a workflow with multiple executable sections (and chained inputs/outputs)
In this example we run a reco2 step followed by a michelremoving step in the same job. The output of reco2 serves as input to michelremoving. Here we only do one input file per job to preserve the 1:1 correspondence between reco1 and reco2 files (nice in this case but not necessary all the time). We see there are multiple executable stages and multiple job_output sections since we save both outputs and actually write them to different directories. REMEMBER: never put more than a few thousand files in any one directory in dCache. If you have a large campaign with many jobs, you may need to split your outputs appropriately with mutliple directories. The only exception to that is if you’re copying to a dropbox location for transfer to a tape-backed area, since those will be automatically cleaned up after transfer.
You’ll see we also retained the fcl editing bit and a metadata extractor for the reco2 file.
https://pomsgpvm01.fnal.gov/poms/campaign_overview/dune/analysis?campaign_id=6280
Create a multi-stage workflow
Sometimes you may want to break separate components into multiple stages. We have the same components as the mutiple executable example but we split the reco2 and michelremoving since the michelremoving files are very small and we want to merge many to one (not we set n_files_per_job much higher in the michel stage). We could of course do things the same way as before but would have to manually merge later. In particular note the [stage_X] sections of the POMS config file. For each stage section you have have separate overrides and still use the same config file. We also use the add_to_dataset option for job outputs and give it the _poms_task
special keyword; POMS will take care of automatically generating a SMA dataset consisting of the reco2 outputs, which will serve as the input dataset to the next stage.
Iterate over multiple datasets or list items
Note that Datasets do not have to be actual SAM datasets in POMS; they can really be any value or even a list of values (though they could also be a list of SAM datasets depending on what you’re doing). Here we set up a list and cycle through it. Each time you submit, POMS automatically increments the list position until you reach the end of the list. Then the “dataset” variable passed to e.g. fife_launch will take the value from the list.
https://pomsgpvm01.fnal.gov/poms/campaign_stage_info/dune/analysis?campaign_stage_id=14113
Important note about this campaign: since it is an example I’ve sort of hard-coded the path to particular inputs here (the global.inputfile value) to illustrate how you can use the value from the list. In general you should NOT hard-code inputs!
Note 2 about using list of SAM datasets: if the values in your list consist of all or part of an actual dataset name, and you want to run over a dataset, you have to use the --dataset
option in jobsub_submit to get it to understand that you’re really using a dataset. The practical way to do that is to also have e.g. -Osubmit.dataset=', '%(dataset)s']
as an additional override. See the draining dataset example below. You’d do the same thing if you had a lsist of datasets.
Aside: a word on n_files_per_job and SAM consumer limits
It is possible to use the n_files_per_job
option (in some of the examples) as a way to automatically calculate a number of jobs to submit to process your dataset (at least for workflows where you have an input dataset). In general unless your workflow absolutely cannot handle more than one input file per job (or it takes a very long time, say 24h, to process a single file), you should expect to process multiple input files. Only processing one input file per job is often extremely inefficient (you have to pay the job startup overhead for every single file), puts more load on all the various servers, and can often lead to very small output files, which are not great for dCache. By using
n_files_per_job the system will compute the number of parallel jobs for you (basically N files in dataset / n_files_per_job with some rounding). Note that n_files_per_job should generally be driven by the desired job length (generally several hours is a good aim) and/or the desired output file size (generally aim for ~ 1 GB or greater output files sizes, subject to reasonable constraints on job run time). When doing that, be sure to set the SAM consumer limit on your job to be at least n_files_per_job, and it’s usually a good idea to set it a bit higher so that if you have any job failures early on, other jobs can pick up the slack. For example if you did n_files_per_job of 3, maybe you’d set the SAM consumer limit to be e.g. 5 (again subject to run time and output file size concerns).
Draining dataset
If you have very large datasets, or datasets that change over time (such as in keepup processing), you may want to submit only a part of it at once. Of course then you need to keep trakc of what has already been submitted from the main dataset each time. Fortunately POMS provides such functionality with the draining and drainingn dataset types. Our example uses the latter, which will carve out a dataset of at most n files at a time from the larger dataset, keeping track of which files have already been submitted to ensure no duplication between different submissions. We will use the same MC example dataset as we have been using but will now submit it in several pieces.
https://pomsgpvm01.fnal.gov/poms/campaign_stage_info/dune/analysis?campaign_stage_id=14108
There is an important difference with this campaign relative to the previous one with a list as mentioned above. Take particular note of how the submit.dataset override is included. That is what generates the –dataset option to be added to the jobsub_submit command.
Still more complicated setups
These are just some basic examples to get you started that cover ~90% of standard use cases. Many other setups are possible! Look at the POMS documentation for some other interesting cases, and reach out to the experts for guidance. We want to help! Here we’ll just point out a couple of other cases:
“multiparam” type (interating over all combinations of multiple lists): https://cdcvs.fnal.gov/redmine/projects/prod_mgmt_db/wiki/POMS_User_Documentation#Campaign-Stage-Datasets-and-Split-types
Key Points
Always, always, always prestage input datasets. No exceptions.
Expert in the Room Grid and Batch System
Overview
Teaching: 20 min
Exercises: 0 minQuestions
How to become a grid and batch yoda master?
Objectives
Learn common job failures and how to avoid them in the future
Get tips and tricks to debug on your own
Session Video
This session will be captured on video a placed here after the workshop for asynchronous study.
Live Notes
Participants are encouraged to monitor and utilize the Livedoc for May. 2023 to ask questions and learn. For reference, the Livedoc from Jan. 2023 is provided.
Debug Session
This session of Expert in the Room is a Q&A. You bring content!
Write on the live doc your current error(s) and experts will provide guidance to participants in a live debug session.
On top of learning why a job failed, we hope it will give you the tools to solve future issues by yourself.
Debugging is an art.
Confusion is the sweat of learning.
Key Points
Debugging requires a methodical and inquisitive mindset, gained through experience and good bookkeeping (write down how to you solved past issues!)
Closing Remarks
Overview
Teaching: 10 min
Exercises: 0 minQuestions
Are you fortified with enough information to start your event analysis?
Objectives
Reflect on the days of learning.
Closing Remarks
Two Days of Training
The instruction in this two day version of the DUNE computing workshop was provided by several experienced physicists and is based on years of experience.
The secure access to Fermilab computing systems and a familiarity with data storage are key components.
Data management and event processing tools were described and modeled.
Art and LArSoft were introduced, with an examples of workflows so analyzers can know how to make their own module.
Protocols for efficient job submission and monitoring has been demonstrated.
We are thankful for the instructor’s hard work, and for the numerous participants who joined.
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Next Steps
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Point a colleague to the material.
Long Term Support
You made some excellent connections with computing experts and invite your continued dialog.
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See also the GitHub FAQ site for DUNE Computing.
Key Points
The DUNE Computing Consortium has presented this workshop so as to broaden the use of software tools used for analysis.