Containerizing Jobs for OSG - Best Practices or Guides?

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All - One persistent concept I have been pushing to my clients is developing reproducible submission scripts / jobs for computation being done on PSU’s HPC system and elsewhere with the intention of leveraging OSG systems from PSU. There has to be a recipe or checklist that we can share with our users that makes this easier. Does anyone have one handy? Chat GPT-4 belched this out as a start:

Submitting jobs to an Open Science Grid (OSG) involves a set of practices that help optimize the use of resources and ensure efficient processing of tasks. Here are some best practices for submitting jobs to OSG:

  1. Job Size and Duration:
  • Design jobs to be short-lived, typically less than 12 hours, as they are more likely to quickly find available resources.
  • Split larger tasks into multiple smaller jobs if possible.
  1. Efficient Data Management:
  • Minimize the amount of data transferred in and out of jobs.
  • Use staging sites or OSG-provided storage elements for data that will be accessed by multiple jobs.
  • Employ compression techniques to reduce data size.
  1. Resource Requirements Specification:
  • Specify the correct requirements for your job, including memory, disk space, and CPUs. This helps the scheduler find the appropriate resources and increases the efficiency of job execution.
  1. Error Handling and Retries:
  • Implement robust error checking and handling in your scripts to manage failures gracefully.
  • Configure automatic retries for transient errors, but ensure to differentiate between transient and non-transient errors.
  1. Use of Containers:
  • Consider using containers (like Docker or Singularity) to package software dependencies. This increases portability and reproducibility of your jobs across different computing resources.
  1. Logging and Monitoring:
  • Include detailed logging in your job scripts to facilitate debugging and monitoring of job performance.
  • Utilize tools provided by OSG or develop custom monitoring scripts to track job status and resource usage.
  1. Compliance with Policies:
  • Understand and adhere to the usage policies of OSG, including fair-use policies and any specific resource allocation agreements.
  1. Testing:
  • Test your jobs on a small scale before scaling up to many resources to ensure that your job configuration is optimized.
  1. Consultation and Continuous Improvement:
  • Engage with the OSG community for support and consultation. They can offer insights and optimizations specific to your use case.
  • Continuously review and improve your job submission practices based on performance metrics and new technologies or tools available.
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This looks like a pretty good list actually. :joy: thanks ChatGPT!

A few points I might add that are more about creating flexible/robust job submissions, especially when moving between systems are:

  • Make sure that scripts can be flexible in where their files are. Often users come to use with scripts from their computer or a different cluster that have hard-coded paths – once you leave the space of a shared file system, having flexible or relative paths to files is important.
  • (Related to above) Be able to specify key job components via arguments.
  • Have an organization plan – how do you want to organize/track your different inputs and outputs for jobs?
  • Putting it all together: have a clear process for naming input and output files. It’s ideal if the output that’s tied to a particular set of inputs can reflect that in its name or how it’s saved, so you can tell at a glance, what you’ve produced.
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Chuck,
That looks like a list I will use or have used without realizing. The only additional thing I can think of is checkpointing which can be inserted into logging and error handling. However, I am looking to see what others think. Maybe make suggestions for us to then compile a complete list.

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Checkpointing has always been a tough sell to clients. I think because it can be a daunting task to a new user to build in checkpoints.