Build the docker image

docker build . -t fmri-conf-runner

Generate a configuration CSV file

See https://github.com/Inria-Empenn/fmri_feature_model/tree/master

Describe your data

Modify data_desc_sample.json to your usage

Run configurations

• Run configurations list from /config.csv file.
• Read data from /data
• Write NiPype cache in /workdir
• Write results to /results

Locally

Change /local/path/to/... to your local paths

docker run -u root -v "/local/path/to/data:/data" -v "/local/path/to/results:/results" -v "/local/path/to/workdir:/work" -v "/local/path/to/configs:/configs" fmri-conf-runner python -u run.py --configs "/configs/config.csv" --data /data/data_desc.json --ref /configs/config_ref.csv

On Abaca (Inria cluster)

Use run_configs.sh

• Start 40 jobs in parallel.
• Timeout for each job is 4h
• Needs 40 configurations named CSV config_[1..40].csv

oarsub -S -n fmri-conf-runner ./run_configs.sh

Postprocess data

• Read data from /results
• Write results to /results
  • 'mean_result.nii' : mean statistic map
  • 'correlations.csv' : pairwise correlations
  • 'dataset.csv' :

Locally

Change /local/path/to/... to your local paths

docker run -u root -v "/local/path/to/results:/results" fmri-conf-runner python -u postprocess.py --results "/results"

On Abaca (Inria cluster)

On Abaca (Inria cluster), use postprocess.sh

oarsub -S -n postprocess ./postprocess.sh