miniwdl lsf backend using singularity
Project description
miniwdl-lsf
Extends miniwdl to run workflows on LSF clusters in singularity containers.
This LSF backend plugin for miniwdl runs WDL task containers by creating a job script that is submitted to a LSF cluster. In case the job description has a container, singularity will be used as container runtime.
Installation
For the development version::
pip install git+https://github.com/adthrasher/miniwdl-lsf.git
Configuration
The following miniwdl configuration example can be used to use miniwdl on a LSF cluster:
[scheduler]
container_backend=lsf_singularity
# Sets the maximum concurrent tasks. Since LSF handles scheduling, we only
# limit to avoid excessive overhead in miniwdl.
task_concurrency=200
# This setting allows running tasks to continue, even if one other tasks fails.
# Useful in combination with call caching. Prevents wasting resources by
# cancelling jobs half-way that would probably succeed.
fail_fast = false
[call_cache]
# The following settings create a call cache under the current directory.
# This prevents wasting unnecessary resources on the cluster by rerunning
# jobs that have already succeeded.
put = true
get = true
dir = "$PWD/miniwdl_call_cache"
[task_runtime]
# Setting a 'maxRetries' default allows jobs that fail due to intermittent
# errors on the cluster to be retried.
defaults = {
"maxRetries": 2,
"docker": "ubuntu:20.04"
}
command_shell = /bin/bash
[singularity]
# This plugin wraps the singularity backend. Make sure the settings are
# appropriate for your cluster.
exe = ["singularity"]
# the miniwdl default options contain options to run as a fake root, which
# is not available on most clusters.
run_options = [
"--containall",
"--cleanenv"
]
# Location of the singularity images (optional). The miniwdl-lsf plugin
# will set it to a directory inside $PWD. This location must be reachable
# for the submit nodes.
image_cache = "$PWD/miniwdl_singularity_cache"
[lsf]
# extra arguments passed to the bsub command (optional).
extra_args=""
# Task memory specifications should be interpreted as per-job not per-core (LSF default)
memory_per_job = true
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