nf-core/configs: xanadu
The UConn HPC profile
nf-core/configs: UConn HPC profile Configuration
nf-core pipelines have been successfully configured for use on the UConn HPC cluster at Xanadu.
To use the xanadu profile, run the pipeline with -profile xanadu. This will download and apply xanadu.config which has been pre-configured for the UConn HPC cluster “Xanadu”. Using this profile, all Nextflow processes will be run within singularity containers, which can download and convert from docker containers when necesary.
A Nextflow module is available on the Xanadu HPC cluster, to use run module load nextflow or module load nextflow/<version> prior to running your pipeline. If you are expecting the NextFlow pipeline to consume more space than is available, you can set the work directory to /scratch/<userid> which can handle 84.TB with export NXF_WORK=/scratch/<userid>. CAUTION make sure to remove items from this directoy, it is not intended for long-term storage.
Config file
params { config_profile_description = 'The UConn HPC profile' config_profile_contact = 'noah.reid@uconn.edu' config_profile_url = 'https://bioinformatics.uconn.edu/'
// max resources max_memory = 2.TB max_cpus = 64 max_time = 21.d
// Path to shared singularity images singularity_cache_dir = '/isg/shared/databases/nfx_singularity_cache'}
process { resourceLimits = [ memory: 2.TB, cpus: 64, time: 21.d ] executor = 'slurm' queue = { task.memory <= 245.GB ? 'general' : (task.memory <= 512.GB ? 'himem' : 'himem2') }
clusterOptions = { [ task.memory <= 245.GB ? '--qos=general' : '--qos=himem' ].join(' ').trim() }}
executor { name = 'slurm' submitRateLimit = '2 sec' queueSize = 100}
singularity { enabled = true cacheDir = params.singularity_cache_dir autoMounts = true conda.enabled = false}