nf-core/configs: embl_hd
The EMBL Heidelberg HPC cluster profile
nf-core/configs: EMBL HD Cluster Configuration
To use, run the pipeline with -profile embl_hd. This will download and launch the embl_hd.config which has been pre-configured with a setup suitable for the embl cluster.
Config file
params { config_profile_contact = "gitlab-data-science-sci-workflows-nextflow-config-6076-issue-@embl.de" config_profile_description = "The EMBL Heidelberg HPC cluster profile" config_profile_name = 'EMBL_HD' config_profile_url = 'https://git.embl.de/data-science/sci-workflows/nextflow-config'}
conda { channels = ['http://hof.cluster.embl.de/conda-forge', 'http://hof.cluster.embl.de/bioconda']}
docker { enabled = false}
apptainer { autoMounts = true pullTimeout = "3h" // the default is 20 minutes and fails with large images envWhitelist = 'CUDA_VISIBLE_DEVICES' // allow the bounding of GPU visible device variable into the containers libraryDir = '/cvmfs/singularity.galaxyproject.org/all/'}
singularity { autoMounts = true pullTimeout = "3h" envWhitelist = 'CUDA_VISIBLE_DEVICES'}
process { executor = 'slurm' maxRetries = 3 errorStrategy = { task.attempt<3 ? 'retry' : 'ignore' } cache = 'lenient' afterScript = 'sleep 10' queue = { (task.time <= 14.d && task.memory < 256.GB && (task.memory.div(task.cpus)) <= 4.GB) ? "htc-el8" : "bigmem" }
withLabel:gpu { queue = 'gpu-el8' containerOptions = '--nv' //enable Nvidia support }
withLabel:bigmem { queue = 'bigmem' }
withLabel:low_qos { clusterOptions = '--qos=low' }}
executor { name = 'slurm' queueSize = 200 submitRateLimit = "10/1sec" pollInterval = '10sec' exitReadTimeout = "5 min" retry.reason = ".*"}