ReproMan aims to simplify creation and management of computing environments in Neuroimaging. While concentrating on Neuroimaging use-cases, it is by no means is limited to this field of science and tools will find utility in other fields as well.
ReproMan is under rapid development. While the code base is still growing the focus is increasingly shifting towards robust and safe operation with a sensible API. There has been no major public release yet, as organization and configuration are still subject of considerable reorganization and standardization.
See CONTRIBUTING.md if you are interested in internals and/or contributing to the project.
ReproMan requires Python 3 (>= 3.8).
By default, installation via pip (pip install reproman) installs core functionality of reproman
allowing for managing datasets etc. Additional installation schemes
are available, so you could provide enhanced installation via
pip install 'reproman[SCHEME]' where SCHEME could be
- tests to also install dependencies used by unit-tests battery of the reproman
- full to install all of possible dependencies, e.g. DataLad
For installation through pip you would need some external dependencies
not shipped from it (e.g. docker, singularity, etc.) for which please refer to
the next section.
On Debian-based systems we recommend to enable NeuroDebian from which we will soon provide recent releases of ReproMan. We will also provide backports of all necessary packages from that repository.
Python 3.8+ with header files possibly needed to build some extensions without wheels. They are provided by python3-dev on
debian-based systems or python-devel on Red Hat systems.
Our setup.py and corresponding packaging describes all necessary python dependencies.
On Debian-based systems we recommend to enable NeuroDebian
since we use it to provide backports of recent fixed external modules we
depend upon. Additionally, if you would
like to develop and run our tests battery see CONTRIBUTING.md
regarding additional dependencies.
This example is heavily based on the "Typical workflow"
example created for ///repronim/containers
which we refer you to discover more about YODA principles etc. In this reproman example we will
follow exactly the same goal -- running MRIQC on a sample dataset -- but this time utilizing
ReproMan's ability to run computation remotely. DataLad and ///repronim/containers will
still be used for data and containers logistics, while reproman will establish a little HTCondor
cluster in the AWS cloud, run the analysis, and fetch the results.
If it is the first time you are using ReproMan to interact with AWS cloud services, you should first provide
ReproMan with secret credentials to interact with AWS. For that edit its configuration file
(~/.config/reproman/reproman.cfg on Linux, ~/Library/Application Support/reproman/reproman.cfg on OSX)
[aws]
access_key_id = ...
secret_access_key = ...
Disclaimer/Warning: Never share or post those secrets publicly.
filling out the ...s. If reproman fails to find this information, error message Unable to locate credentials will appear.
Run (need to be done once, makes resource available for reproman login or reproman run):
reproman create aws-hpc2 -t aws-condor -b size=2 -b instance_type=t2.mediumto create a new ReproMan resource: 2 AWS EC2 instances, with HTCondor installed (we use NITRC-CE instances).
Disclaimer/Warning: It is important to monitor your cloud resources in the cloud provider dashboard(s) to ensure absent run away instances etc. to help avoid incuring heavy cost for used cloud services.
Following script is an exact replica from ///repronim/containers
where only the datalad containers-run command, which fetches data locally and runs computation locally and serially, is replaced with
reproman run which publishes dataset (without data) to the remote resource, fetches the data, runs computation
via HTCondor in parallel across 2 nodes, and then fetches results back:
#!/bin/sh
( # so it could be just copy pasted or used as a script
PS4='> '; set -xeu # to see what we are doing and exit upon error
# Work in some temporary directory
cd $(mktemp -d ${TMPDIR:-/tmp}/repro-XXXXXXX)
# Create a dataset to contain mriqc output
datalad create -d ds000003-qc -c text2git
cd ds000003-qc
# Install our containers collection:
datalad install -d . ///repronim/containers
# (optionally) Freeze container of interest to the specific version desired
# to facilitate reproducibility of some older results
datalad run -m "Downgrade/Freeze mriqc container version" \
containers/scripts/freeze_versions bids-mriqc=0.16.0
# Install input data:
datalad install -d . -s https://github.com/ReproNim/ds000003-demo sourcedata
# Setup git to ignore workdir to be used by pipelines
echo "workdir/" > .gitignore && datalad save -m "Ignore workdir" .gitignore
# Execute desired preprocessing in parallel across two subjects
# on remote AWS EC2 cluster, creating a provenance record
# in git history containing all condor submission scripts and logs, and
# fetching them locally
reproman run -r aws-hpc2 \
--sub condor --orc datalad-pair \
--jp "container=containers/bids-mriqc" --bp subj=02,13 --follow \
--input 'sourcedata/sub-{p[subj]}' \
--output . \
'{inputs}' . participant group -w workdir --participant_label '{p[subj]}'
)ReproMan: Execute documentation section
provides more information on the underlying principles behind reproman run
command.
Whenever everything is computed and fetched, and you are satisfied with the results, use reproman delete aws-hpc2 to terminate
remote cluster in AWS, to not cause unnecessary charges.
MIT/Expat
It is in a beta stage -- majority of the functionality is usable but Documentation and API enhancements is WiP to make it better. Please do not be shy of filing an issue or a pull request. See CONTRIBUTING.md for the guidance.
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