FIX: Fix parallel execution for mri_robust_template #192
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #192 +/- ##
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Coverage 80.63% 80.64%
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Files 84 84
Lines 6512 6514 +2
Branches 657 657
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+ Hits 5251 5253 +2
Misses 1097 1097
Partials 164 164 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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| outputnode = pe.Node(niu.IdentityInterface(fields=['xforms', 'petref']), name='outputnode') | ||
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| robust_template_threads = min(omp_nthreads, 4) | ||
| robust_template_mem_gb = min(mem_gb, 16) |
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What is the actual memory usage of mri_robust_template? Does it keep usage below 16GB? Note that what you're making here is a claim that the process will consume <=16GB of memory, and nipype will schedule accordingly. There is nothing in nipype that will stop the process from consuming more memory.
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I see - then this will not work as intended, and will depend on the input data. Will need to come up with a smarter way of estimating the memory use of mri_robust_template given the input data, and then ideally simplify the mri_robust_template call e.g. by fixing the reference frame.
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but putting the memory aside, but do you think of the changes regarding the processors? I have experienced that this workflow often not run in parallel despite plenty of resources being available.
This PR addresses issue #191 by capping the PET head motion correction mri_robust_template node to use at most four threads and provided scheduler resource limits to bound each instance to 16 GB, enabling multiple parallel runs.