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* refactor: Change some module resource labels Based on previous testing * feat: Add resource profiles and change base reqs - Also makes recombination run by default * refactor: Change panaroo run to high * refactor: Increase time for high * docs: Add basic resource profiles documentation * refactor: Increase reqs for some modules - Decided after Entirococcus testing * docs: Update resource requirement documentation * refactor: Run all ann tools in large config * docs: Bolden sentence * fix: Change descriptions for profiles
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params { | ||
config_profile_name = 'Large profile' | ||
config_profile_description = 'Profile for >1000 sample datasets with subsetting enabled.' | ||
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use_ppanggolin = true | ||
use_fasttree = true | ||
enable_subsetting = true | ||
skip_profile_creation = true | ||
} | ||
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process { | ||
withLabel:process_medium { | ||
cpus = { check_max( 18 * task.attempt, 'cpus' ) } | ||
memory = { check_max( 36.GB * task.attempt, 'memory' ) } | ||
time = { check_max( 8.h * task.attempt, 'time' ) } | ||
} | ||
withLabel:process_high { | ||
cpus = { check_max( 24 * task.attempt, 'cpus' ) } | ||
memory = { check_max( 72.GB * task.attempt, 'memory' ) } | ||
time = { check_max( 20.h * task.attempt, 'time' ) } | ||
} | ||
withLabel:process_long { | ||
time = { check_max( 72.h, 'time' ) } | ||
} | ||
withLabel:process_high_memory { | ||
memory = { check_max( 249.GB, 'memory' ) } | ||
} | ||
} |
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params { | ||
config_profile_name = 'Medium profile' | ||
config_profile_description = 'Profile for 100-1000 sample datasets' | ||
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use_ppanggolin = true | ||
use_fasttree = true | ||
} | ||
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process { | ||
withLabel:process_medium { | ||
cpus = { check_max( 12 * task.attempt, 'cpus' ) } | ||
memory = { check_max( 36.GB * task.attempt, 'memory' ) } | ||
time = { check_max( 8.h * task.attempt, 'time' ) } | ||
} | ||
withLabel:process_high { | ||
cpus = { check_max( 16 * task.attempt, 'cpus' ) } | ||
memory = { check_max( 72.GB * task.attempt, 'memory' ) } | ||
time = { check_max( 20.h * task.attempt, 'time' ) } | ||
} | ||
withLabel:process_high_memory { | ||
memory = { check_max( 200.GB, 'memory' ) } | ||
} | ||
} |
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# ARETE and dataset size | ||
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Currently ARETE has three distinct profiles that change the pipeline execution in some ways: The default profile (which we can call `small`), the `medium` profile and the `large` profile. | ||
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These three profiles were developed based on the size and diversity of the input dataset and change some parameter defaults based on tests we have performed on similar-sized datasets. | ||
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If you want to first gauge the potential diversity of your dataset and have some input assemblies you can try the [PopPUNK entry](https://beiko-lab.github.io/arete/usage/#poppunk-entry). One of the outputs will provide insight into how many clusters, or lineages, your dataset divides into. | ||
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The sizes are: | ||
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- For the default or `small` profile, we expect datasets with 100 samples/assemblies or fewer. | ||
It runs on the default pipeline parameters, with no changes. | ||
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- For the `medium` profile, we expect datasets with >100 and <1000 samples. | ||
It increases the default resource requirements for most processes and also uses [PPanGGoLiN](https://github.com/labgem/PPanGGOLiN) for pangenome construction, instead of [Panaroo](https://github.com/gtonkinhill/panaroo/). | ||
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- For the `large` profile, we expect datasets with >1000 samples. | ||
It also increases default resource requirements for some processes and uses PPanGGoLin. | ||
Additionally, **it enables [PopPUNK subsampling](subsampling.md), with default parameters**. |
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@@ -1,5 +1,5 @@ | ||
process POPPUNK_MAKE_SAMPLESHEET { | ||
label "process_low" | ||
label "process_single" | ||
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input: | ||
path(samplesheets) | ||
|
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