What kind of pipelines can
Looper can run samples through any pipeline that runs on the command line. The flexible pipeline interface file allows
looper to execute arbitrary shell commands. A pipeline may consist of scripts in languages like Perl, Python, or bash, or it may be built with a particular framework. Typically, we use Python pipelines built using the
pypiper package, which provides some additional power to
looper, but that's optional.
Why isn't the
looper executable available on
By default, Python packages are installed to
You can add that location to your path by appending it (
How can I run my jobs on a cluster?
Looper uses the external package divvy for cluster computing, making it flexible enough to use with any cluster resource environment. Please see the tutorial on cluster computing with looper and divvy.
What's the difference between
pypiper is a more traditional workflow-building framework; it helps you build pipelines to process individual samples.
looper is completely pipeline-agnostic, and has nothing to do with individual processing steps; it operates groups of samples (as in a project), submitting the appropriate pipeline(s) to a cluster or server (or running them locally). The two projects are independent and can be used separately, but they are most powerful when combined. They complement one another, together constituting a comprehensive pipeline management system.
Why isn't a sample being processed by a pipeline (
Not submitting, flag found: ['*_<status>.flag'])?
When using the
run subcommand, for each sample being processed
looper first checks for "flag" files in the sample's designated output folder for flag files (which can be
_failed.flag). Typically, we don't want to resubmit a job that's already running or already finished, so by default,
looper will not submit a job when it finds a flag file. This is what the message above is indicating.
If you do in fact want to re-rerun a sample (maybe you've updated the pipeline, or you want to run restart a failed attempt), you can do so by just passing to
looper at startup the
--ignore-flags option; this will skip the flag check for all samples. If you only want to re-run or restart a few samples, it's best to just delete the flag files for the samples you want to restart, then use
looper run as normal.
You may be interested in the usage docs for the
looper rerun command, which runs any failed samples.
How can I resubmit a subset of jobs that failed?
As of version
0.11, you can use
looper rerun to submit only jobs with a
failed flag. By default,
looper will not submit a job that has already run. If you want to restart a sample (maybe you've updated the pipeline, or you want to restart a failed attempt), you can either use
looper rerun to restart only failed jobs, or you pass
--ignore-flags, which will resubmit all samples. If you want more specificity, you can just manually delete the "flag" files for the samples you want to restart, then use
looper run as normal.
Why are computing resources defined in the pipeline interface file instead of in the
divvy computing configuration file?
You may notice that the compute config file does not specify resources to request (like memory, CPUs, or time). Yet, these are required in order to submit a job to a cluster. Resources are not handled by the divcfg file because they not relative to a particular computing environment; instead they vary by pipeline and sample. As such, these items should be defined at other stages.
Resources defined in the
pipeline_interface.yaml file that connects looper to a pipeline. The reason for this is that pipeline developers are the most likely to know what sort of resources their pipeline requires, so they are in the best position to define the resources requested. For more information on how to adjust resources, see the
compute section of the pipeline interface page. If all the different configuration files seem confusing, now is a good time to review who's who in configuration files.
Which configuration file has which settings?
There's a list on the config files page.