Error in HUMAnN2 Execution: job error "exceeds memory allocation"

I am trying this tutorial

But when I execute HUMAnN2, it is giving me the following error:

Hi @Anas_Jamshed

That specific error means one of two things.
Details Understanding 'exceeds memory allocation' error messages

  1. There is some input or parameter problem that caused the job to spin out and consume an unusual amount of working memory on the cluster node where it ran. That type of memory is unrelated to the available data storage space (“quota”) you may have in your account.

  2. The job really does exceed the computational resources at the public server where you are working. The three usegalaxy.* public servers each have distinct and significant resources but those can be exceeded of course. The solution is to make the job “smaller” somehow (inputs, parameters) or to consider the need for a private server with resources scaled to fit the work.

What to do:

  • Review your inputs. Can you find a data problem? Check format/content versus the tutorial examples, tool form examples, and link out in the tool form help section to author resources, publications, and related.
  • Review your parameter choices. Do those fit the data? Can alternative choices achieve the same goal? Keep in mind that some tools are computationally expensive, always or more so only with certain choices.
  • If both seem Ok, is the data very large? How “large” is defined can vary by the tool and algorithm. Complex work might need careful tuning or specific preparatory steps – not just when used in Galaxy, but anywhere it is used.

All that said, if you are following the tutorial exactly, with the tutorial data, and this comes up … one way to troubleshoot is to import the workflow associated with the tutorial and try running that in a new history. That creates a reference and is a good way to find where things may have gone wrong.

Review that and if you need more help, you can share your history back and we can try to help more here. But try those troubleshooting items first. Training your eye to spot problems is an important skill, almost as important as learning the scientific parts, and will be useful even whether working in Galaxy or not. Errors come up all the time even for experienced bioinformatics scientists :slight_smile:

How to review job inputs, parameters, and full logs directly in the application plus how to ask questions with enough context that others can help effectively → Troubleshooting errors