Intro to workflow automation

By the end of this module, we will:

• Understand what workflow automation is and how it helps reproducibility.
• Review several different ways to execute repetitive tasks on Great Lakes.
• Introduce the idea of job/task geometries to visualize approachs and their advantages and limitations.

Workflow automation helps reproducibility

Data-intensive research entails transforming raw data into more meaningful/valuable results. Often, this involves a series of step-wise transformation tasks. To make this research reproducible by someone else, all the transformations must either be documented so that a human can reproduce them, or automated so a computer can. A reproducible solution has a blend of documentation and automation.

Data-intensive research often involves repeating transformation tasks many times. Also overtime transformations evolve to be more complex, more computationally demanding, or take longer. A workflow describes the key transformation tasks and their relationships to the inputs and outputs.

Workflow automation describes the tools and techniques to systematically assemble these tasks into a executable, repeatable, robust solution. Building an automated workflow appears harder than documenting it to be run manually, but there are many benefits to reproducibility:

• Automation facilitates repetition.
• Automation can simplify manual documentation.
• Automation simplifies validation of your workflow.
• Automation streamlines sharing.
• Automation scales to larger inputs

There are many ways to build an automated workflow. In this module we will consider several ways of executing pleasingly parallel tasks on Great Lakes:

• A serial task loop
• Parallelizing tasks using driver scripts and sbatch files.
• The SLURM Launcher

All these approaches execute the same workflow in different ways. The workflow produces word pangrams. A word pangram is like an anagram that allows repeating letters, e.g. the sequence of letters ACEHMNT can be rearranged to create the pangrams ATTACHMENT, CATCHMENT, ENCHANTMENT, and ENHANCEMENT.

The workflow accepts a text file containing list of letter sequences separated by lines; for each letter sequence it produces a file containing one or more pangrams.

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Pangram: A serial task loop

# Orient on project pangram
cd /nfs/turbo/umms-bioinf-wkshp/workshop/home/$USER
cd project_pangrams
ls -1

pangram_job_launcher
pangram_parallel_sbatch
pangram_serial_loop
README.md

cd pangram_serial_loop
ls -1

find_pangrams.sbat
find_pangrams.sh
letters.txt
pangram.sh
README.md

Let’s consider a few of these files in turn, starting with the README.

README.md

# pangram_serial_loop - Produces pangrams for inputs in letters.txt. Makes one file for each line in letters. - cgates 6/1/2024 - Usage: ./find_pangrams.sh #to run locally or sbatch find_pangrams.sbat # to submit to worker node Files: - find_pangrams.sbat: SLURM batch file; calls find_pangrams.sh - find_pangrams.sh: Loops throught input and calls pangram.sh - letters.txt: list of letter sequences seperated by newlines. - pangram.sh: accepts a single letter sequence and prints all pangrams.

The pangram.sh script is the workhorse of this workflow. You are welcome to look at the implementation, but for our purposes we can treat it as a black box. We’ll run it once to see it in action.

./pangram.sh lovely

lovely
lovey
volley

Consider the input to the workflow:

letters.txt
Ndefglu Hacilno Tdghnou Nailmpt Pbegikn Yacilrt Achnopy Uginoqt Eachkmn Alhyidn

And finally, the script we will launch to execute the workflow:

find_pangrams.sh
#/bin/bash set -eu for letters in $(cat letters.txt); do echo pangrams for: $letters >> /dev/stderr ./pangram.sh $letters > results.${letters}.txt done echo done >> /dev/stderr

Having reviewed the inputs and scripts, we can launch the workflow like as shown below. (This project is called “serial loop” because in this workflow we are looping over the inputs and processing one at a time.)

./find_pangrams.sh

pangrams for: Ndefglu
pangrams for: Hacilno
pangrams for: Tdghnou
pangrams for: Nailmpt
pangrams for: Pbegikn
pangrams for: Yacilrt
pangrams for: Achnopy
pangrams for: Uginoqt
pangrams for: Eachkmn
pangrams for: Alhyidn

We see the results files have been added:

ls

find_pangrams.sbat  results.Achnopy.txt  results.Ndefglu.txt
find_pangrams.sh    results.Alhyidn.txt  results.Pbegikn.txt
letters.txt         results.Eachkmn.txt  results.Tdghnou.txt
pangram.sh          results.Hacilno.txt  results.Uginoqt.txt
README.md           results.Nailmpt.txt  results.Yacilrt.txt

cat results.Achnopy.txt

cacophony

Question: Which letter combination generated the most pangrams?

Let’s run it again but instead of using the login-node, we’ll submit this to a worker node using the provided sbat script:

# first clear out the old results
rm results.*
sbatch find_pangrams.sbat 

Submitted batch job 1234567

Use squeue -u $USER to see when the job is finished and then review the outputs. We now see the results files and also the slurm log file:

find_pangrams.sbat   results.Alhyidn.txt  results.Tdghnou.txt
find_pangrams.sh     results.Eachkmn.txt  results.Uginoqt.txt
letters.txt          results.Hacilno.txt  results.Yacilrt.txt
pangram.sh           results.Nailmpt.txt  slurm-1234567.out
README.md            results.Ndefglu.txt
results.Achnopy.txt  results.Pbegikn.txt

This approach is correct, clear, and reproducible; however it’s not ideal. Consider how the tasks are contained within a job:

Job/task geometry of the serial loop approach
Each sbatch request is a job script; a job script may be composed of multiple tasks. Key attributes of a job script are what sub-tasks will I run? how many resources do I need? how long will I need to run? You can represent these graphically by making boxes for each job and their tasks (height = resource request and length = time). In the case above, there are many similar tasks contained in a single job. This diagram is a rough representation of the job/task geometry. This is a useful way of visualizing and comparing approaches; also, the job geometry is critically useful information to the scheduler which is trying to pack everyone’s jobs into the available clusters as neatly/efficiently as possible.

Considering that each of these pangram tasks are completely independent of each other (i.e. pleasingly parallel). We might be able to make better use of our ~16000 CPUs by parallelizing the workflow. The approaches below show two different ways to accomplish this.

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Pangram: Parallel SBATCH

# Orient on project
cd /nfs/turbo/umms-bioinf-wkshp/workshop/home/$USER
cd project_pangrams/pangram_parallel_sbatch
ls -1

letters.txt
make_sbat_scripts.sh
pangram.sh
README.md
run_sbat_scripts.sh

README.md

# pangram_parallel_sbatch - Produces pangrams for letters.txt. Makes one file per line in letters. - cgates 6/1/2024 - Usage: `./make_sbat_scripts.sh` `./run_sbat_scripts.sh` Files: - letters.txt: list of letter sequences seperated by newlines. - pangram.sh: accepts a single letter sequence and prints all pangrams. - make_sbat_scripts.sh : Build sbat scripts based on letters.txt; for each row in letters.txt adds a new sbat file. - run_sbat_scripts.sh : Submit all sbat scripts for cluster execution

Executing make_sbat_scripts creates a new directory and adding a collection of sbat files.

./make_sbat_scripts.sh 
ls sbat_scripts

...
Achnopy.sbat  Nailmpt.sbat  Uginoqt.sbat
Alhyidn.sbat  Ndefglu.sbat  Yacilrt.sbat
Eachkmn.sbat  Pbegikn.sbat
Hacilno.sbat  Tdghnou.sbat

Consider a single sbat file.

sbat_scripts/Achnopy.sbat
#!/bin/bash #SBATCH --job-name=pangram_Achnopy #SBATCH --cpus-per-task=1 #SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --mem-per-cpu=400m #SBATCH --time=00:05:00 #SBATCH --account=bioinf_wkshp_class #SBATCH --partition=standard ./pangram.sh Achnopy > results.Achnopy.txt

Briefly consider the script to see how these sbat files was constructed. Note the SLURM preamble directives are integrated into the new files using a HereDoc.

make_sbat_scripts.sh

#!/bin/bash set -eu mkdir -p sbat_scripts for letters in $(cat letters.txt); do echo sbat for: $letters >> /dev/stderr cat << HERE_DOC > sbat_scripts/$letters.sbat #!/bin/bash #SBATCH --job-name=pangram_${letters} #SBATCH --cpus-per-task=1 #SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --mem-per-cpu=400m #SBATCH --time=00:05:00 #SBATCH --account=bioinf_wkshp_class #SBATCH --partition=standard ./pangram.sh $letters > results.${letters}.txt HERE_DOC done echo done >> /dev/stderr

We can submit jobs one at a time using sbatch. OR we could build a for loop to automate submission; conveniently run_sbat_scripts.sh has done this for us.

run_sbat_scripts.sh
for sbat in $(ls sbat_scripts/*.sbat); do sbatch $sbat done

Before we execute run_sbat_scripts.sh, you might consider opening a separate window to monitor the SLURM job queue. In this second window, you can execute watch squeue -u $USER to see how the jobs are being scheduled. Hit ctrl-C to exit watch. (More info on watch.)

./run_sbat_scripts.sh 

Submitted batch job 9289496
Submitted batch job 9289497
Submitted batch job 9289498
Submitted batch job 9289499
Submitted batch job 9289500
Submitted batch job 9289501
Submitted batch job 9289502
Submitted batch job 9289503
Submitted batch job 9289504
Submitted batch job 9289505

And in a few short seconds, you see the results and SLURM log files. This approach is correct, more complex than the serial loop, and reproducible. And because the tasks are working in parallel, it’s much faster. Contrast this job/task geometry with the serial loop approach from above:

Job/task geometries: serial loop vs parallel sbatch

This is great. But there’s two to three minor drawbacks to this approach:

• Between the the sbat files, the result files, and the slum log files, it’s created quite a lot more files. They are smallish files, but it’s more output to keep track of.
• When the scheduler is under a heavy load, for very quick jobs (<=60 seconds) it can take longer to schedule a job than it takes to run the job. In these circumstances the serial approach might be faster. (If we scaled up from 10 jobs to 1000 jobs in parallel, we might see this kind of slowdown.)
• Each ARC account has an upper limit on the number of jobs that can be submitted and the number actively running. If your account exceeds this limit the jobs will start to queue up, awaiting a turn at scheduling and execution. That’s not a big problem, except for the fact that you share the account with other users. If you saturate your queue, others will have to wait until your job finishes before starting theirs.

To address these concerns, the Texas Advanced Computing Center built a SLURM tool called launcher detailed below.

---

Pangram: Launcher

# Orient on project
cd /nfs/turbo/umms-bioinf-wkshp/workshop/home/$USER
cd project_pangrams/pangram_launcher
ls -1

launcher.sbat
letters.txt
make_launcher_tasks.sh
pangram.sh
README.md

README.md

# pangram_launcher - Produces pangrams for letters.txt. Makes one file per line in letters. - cgates 6/1/2024 - Usage: `./make_launcher_tasks.sh` `sbatch launcher.sbat` Files: - launcher.sbat: sbatch file to start the launcher. - letters.txt: list of letter sequences seperated by newlines. - make_launcher_tasks.sh: builds a single file for all tasks to be executed by the launcher. - pangram.sh: accepts a single letter sequence and prints all pangrams.

Run make_launcher_tasks.sh and note it creates one new file launcher_tasks.txt.

./make_launcher_tasks.sh 

launcher_tasks.txt

./pangram.sh Ndefglu > results.Ndefglu.txt ./pangram.sh Hacilno > results.Hacilno.txt ./pangram.sh Tdghnou > results.Tdghnou.txt ./pangram.sh Nailmpt > results.Nailmpt.txt ./pangram.sh Pbegikn > results.Pbegikn.txt ./pangram.sh Yacilrt > results.Yacilrt.txt ./pangram.sh Achnopy > results.Achnopy.txt ./pangram.sh Uginoqt > results.Uginoqt.txt ./pangram.sh Eachkmn > results.Eachkmn.txt ./pangram.sh Alhyidn > results.Alhyidn.txt

This might remind you of the serial loop approach, but there’s a twist and to see it you need to consider the launcher.sbat file:

launcher.sbat
#!/bin/bash #SBATCH --account=bioinf_wkshp_class #SBATCH --partition=standard #SBATCH --nodes=1 #SBATCH --ntasks-per-node=5 #SBATCH --cpus-per-task=1 #SBATCH --time=0:30:00 module load launcher export LAUNCHER_JOB_FILE=launcher_tasks.txt paramrun
Some details: The file starts with a basic SLURM preamble. Note that it’s asking for 1 node, and 5 CPUs (5 tasks/node * 1 cpu/task) for 30 minutes. The last three lines establish that this is a launcher job and the tasks to execute live in launcher_tasks.txt.

Given this setup, sbatch will allocate a node with 5 CPUs for 30 minutes. Then the launcher will start looping through the launcher_tasks and as each one completes it will send another one through until all tasks are complete.

Consider running watch squeue -u $USER in another window before you run the sbatch command:

sbatch launcher.sbat

Submitted batch job 9290535

Note that all the tasks are running but they are running “inside”” of the one job. The job should finish in a few seconds. It will produce the familliar results.* files and also a single SLURM log file which is a bit more interesting than the previous log files.

slurm-9290535.out

WARNING (06/09/24 15:38:39): LAUNCHER_WORKDIR variable not set. Using current directory. windowsP is false NOTE (06/09/24 15:38:40): Started dynamic task service on port 9471 Launcher: Setup complete. ------------- SUMMARY --------------- Number of hosts: 1 Working directory: /nfs/turbo/umms-bioinf-wkshp/workshop/home/cgates/project_pangrams/pangram_launcher Processes per host: 5 Total processes: 5 Total jobs: 10 Scheduling method: dynamic ------------------------------------- Launcher: Starting parallel tasks... using /tmp/launcher.9290535.hostlist.GYzdOWsB to get hosts starting job on gl3079 Warning: Permanently added the ED25519 host key for IP address '10.164.8.129' to the list of known hosts. Launcher: Task 0 running job 1 on gl3079.arc-ts.umich.edu (./pangram.sh Ndefglu > results.Ndefglu.txt) Launcher: Job 1 completed in 1 seconds. Launcher: Task 0 running job 2 on gl3079.arc-ts.umich.edu (./pangram.sh Hacilno > results.Hacilno.txt) Launcher: Task 2 running job 3 on gl3079.arc-ts.umich.edu (./pangram.sh Tdghnou > results.Tdghnou.txt) Launcher: Job 3 completed in 1 seconds. ... Launcher: Task 0 done. Exiting. Launcher: Done. Job exited without errors

The launcher solution is correct, clear, and efficient. It is a very nice option if you have many independent tasks that each run quickly (<=60 seconds) and each tasks has a modest compute request (e.g. each task needs a single CPU).

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Geometries and dependencies

Job/task geometries compared

The three job geometries diagrammed above hint that we quietly made a a few simplifying assumptions along the way:

• We assumed that all the tasks were independent (and thus pleasingly parallel).
• We assumed that all the tasks in a workflow were the same transformation applied many different inputs.

Commonly workflows contain several different steps which where the input of one step often depends on the output of the previous.

Also steps in a workflow often have variable resrouce needs and run times:

Moreover, workflows are not always linear; the logical flow of steps may join the outputs of two steps as an input to a third:

The techniques we reviewed above are execllent for smaller, simpler workflows, a more complex, more resource intensive workflow will require either a much more nuanced set of scripts or a more sophisticated approach altogether. SLURM supports these more complex scenarios natively with something called job arrays (see job arrays in links below). In the next module, we introduce the Snakemake workflow automation framework to address these more complicated scenarios.

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Pro tips

Automating workflows is a learning process. Here’s a few ideas to consider along the way:

1. Do not try to automate something that you cannot do by hand.

2. Make it right. Make it clear. Make it efficient. (In that order.)

3. Build a README for each workflow. Consider including:

   • Your name/email
   • The date
   • How to install the workflow
   • How to run the workflow
   • Any necessary context/constraints that would help your future collaborator reproduce your results.

4. Automate the workflow with the data you have. Don’t generalize a workflow too soon. You might see that a workflow could be parameterized/extended to apply to new types of data. Feel that excitement, note the opportunity in the README, and trust that you will make that change when you need to.

5. Instead of developing the whole workflow end to end, consider an iterative and incremental approach.
   

Break workflow development into steps:

1. do part of the workflow for one sample and verify correctness as you add steps. (For a large dataset consider subsetting/downsampling your inputs so you can iterate quicker.)
2. run a single sample end to end
3. scale to a few samples and check those outputs; tune resource allocations
4. run the whole batch

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Exercise: Project Railfence

This project is focused around a transformation that can encode or decode a specific kind of encryption called a rail fence cipher. The details of this encryption are interesting and I encourage you to check out the link, but for the purposes of this exercise it’s ok to treat it as magic/black-box transformation.

Review the project directory here:

cd /nfs/turbo/umms-bioinf-wkshp/workshop/home/$USER
cd project_railfence
ls -1

codes.txt
railfence_decode.py
railfence_encode.py
README.md

The railfence_decode.py script accepts two arguments separated by a comma:

• a number (a positive integer)
• a quoted string (the cipher text)

It returns the decoded clear text. You can run the railfence_decode.py script like so:

# need to load python once in the session
module load python

./railfence_decode.py 3,"wrivdetceaedsoee-lea ne  crf o!"

we are discovered-flee at once!

There is a list of encrypted codes in the codes.txt file:

codes.txt
2,"onttyt uoaesmtigta o antd yhn"d o r oatmt oehn htyucno ob ad 3,"e hM craifi.nar.mk trgt aei la.Mk tefcet I htodr)aii.kte e in(t e" 4,"aefie aamth klwth ayhvuoetwro htdto et ow au" 5,"rrnmpce eaadeeap"odatt rn rhniniieictlocs vnaa 3,"t DwieaRAM"r EE 3,"lsie"m!

Your task:

• Consider the three automation approaches outlined in the lessons above: serial loop, parallel batch, and launcher.
• Choose one approach, and, using the patterns above as a template, create script(s) that will decrypt the codes in code.txt.
• If you complete the exercise with a one approach, repeat the exercise with a different approach.

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Key ideas

• Achieving reproducible research requires a blend of documentation and automation.
• Be kind to your future self; they will thank you for the README you left them.
• Automation helps reproducibility:
  • Automation shrinks your README.
  • Automation simplifies validation of your workflow.
  • Automation enables repetition.
  • Automation streamlines sharing.
  • Automation scales to larger inputs
• Job/task geometries help visualize how different approaches are executed. (It also hints at the n-dimensional game of Tetris the job-scheduler is playing to pack everyones jobs as neatly as possible.
• The SLURM launcher allows you to gather many parallel tasks into a main job, in effect creating a transient sub-cluster within the main HPC.
• For a more complex transformation, ascript can be either simple or resource efficient - choose one. Consider more robust solutions (e.g. Snakemake) as necessary.

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Links and references

• UM ITS docs on launcher

• UM ARC docs on job arrays

• SLURM docs on job arrays

• UM ARC miVideo on advanced SLURM techniques (including launcher, job arrays, and more)

• For more examples of pangrams in action, checkout:

  • The New York Times Spelling Bee Puzzle
  • Spelling Bee puzzle solver

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