$ cpac run --help
Loading 🐳 Docker
Loading 🐳 fcpindi/c-pac:latest with these directory bindings:
local Docker mode
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/home/circleci/build /home/circleci/build rw
/home/circleci/build /tmp rw
/home/circleci/build/log /crash rw
/home/circleci/build/outputs /output rw
Logging messages will refer to the Docker paths.
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usage: run.py [-h] [--pipeline_file PIPELINE_FILE] [--group_file GROUP_FILE]
[--data_config_file DATA_CONFIG_FILE] [--preconfig PRECONFIG]
[--aws_input_creds AWS_INPUT_CREDS]
[--aws_output_creds AWS_OUTPUT_CREDS] [--n_cpus N_CPUS]
[--mem_mb MEM_MB] [--mem_gb MEM_GB]
[--save_working_dir [SAVE_WORKING_DIR]] [--disable_file_logging]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--participant_ndx PARTICIPANT_NDX] [-v]
[--bids_validator_config BIDS_VALIDATOR_CONFIG]
[--skip_bids_validator] [--anat_only] [--tracking_opt-out]
[--monitoring]
bids_dir output_dir {participant,group,test_config,gui,cli}
C-PAC Pipeline Runner
positional arguments:
bids_dir The directory with the input dataset formatted
according to the BIDS standard. Use the format
s3://bucket/path/to/bidsdir to read data directly from
an S3 bucket. This may require AWS S3 credentials
specified via the --aws_input_creds option.
output_dir The directory where the output files should be stored.
If you are running group level analysis this folder
should be prepopulated with the results of the
participant level analysis. Use the format
s3://bucket/path/to/bidsdir to write data directly to
an S3 bucket. This may require AWS S3 credentials
specified via the --aws_output_creds option.
{participant,group,test_config,gui,cli}
Level of the analysis that will be performed. Multiple
participant level analyses can be run independently
(in parallel) using the same output_dir. GUI will open
the CPAC gui (currently only works with singularity)
and test_config will run through the entire
configuration process but will not execute the
pipeline.
optional arguments:
-h, --help show this help message and exit
--pipeline_file PIPELINE_FILE
Path for the pipeline configuration file to use. Use
the format s3://bucket/path/to/pipeline_file to read
data directly from an S3 bucket. This may require AWS
S3 credentials specified via the --aws_input_creds
option.
--group_file GROUP_FILE
Path for the group analysis configuration file to use.
Use the format s3://bucket/path/to/pipeline_file to
read data directly from an S3 bucket. This may require
AWS S3 credentials specified via the --aws_input_creds
option. The output directory needs to refer to the
output of a preprocessing individual pipeline.
--data_config_file DATA_CONFIG_FILE
Yaml file containing the location of the data that is
to be processed. Can be generated from the CPAC gui.
This file is not necessary if the data in bids_dir is
organized according to the BIDS format. This enables
support for legacy data organization and cloud based
storage. A bids_dir must still be specified when using
this option, but its value will be ignored. Use the
format s3://bucket/path/to/data_config_file to read
data directly from an S3 bucket. This may require AWS
S3 credentials specified via the --aws_input_creds
option.
--preconfig PRECONFIG
Name of the pre-configured pipeline to run.
--aws_input_creds AWS_INPUT_CREDS
Credentials for reading from S3. If not provided and
s3 paths are specified in the data config we will try
to access the bucket anonymously use the string "env"
to indicate that input credentials should read from
the environment. (E.g. when using AWS iam roles).
--aws_output_creds AWS_OUTPUT_CREDS
Credentials for writing to S3. If not provided and s3
paths are specified in the output directory we will
try to access the bucket anonymously use the string
"env" to indicate that output credentials should read
from the environment. (E.g. when using AWS iam roles).
--n_cpus N_CPUS Number of execution resources per participant
available for the pipeline.
--mem_mb MEM_MB Amount of RAM available to the pipeline in megabytes.
Included for compatibility with BIDS-Apps standard,
but mem_gb is preferred
--mem_gb MEM_GB Amount of RAM available to the pipeline in gigabytes.
if this is specified along with mem_mb, this flag will
take precedence.
--save_working_dir [SAVE_WORKING_DIR]
Save the contents of the working directory.
--disable_file_logging
Disable file logging, this is useful for clusters that
have disabled file locking.
--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
The label of the participant that should be analyzed.
The label corresponds to sub-<participant_label> from
the BIDS spec (so it does not include "sub-"). If this
parameter is not provided all participants should be
analyzed. Multiple participants can be specified with
a space separated list. To work correctly this should
come at the end of the command line.
--participant_ndx PARTICIPANT_NDX
The index of the participant that should be analyzed.
This corresponds to the index of the participant in
the data config file. This was added to make it easier
to accommodate SGE array jobs. Only a single
participant will be analyzed. Can be used with
participant label, in which case it is the index into
the list that follows the participant_label flag. Use
the value "-1" to indicate that the participant index
should be read from the AWS_BATCH_JOB_ARRAY_INDEX
environment variable.
-v, --version show program's version number and exit
--bids_validator_config BIDS_VALIDATOR_CONFIG
JSON file specifying configuration of bids-validator:
See https://github.com/bids-standard/bids-validator
for more info.
--skip_bids_validator
Skips bids validation.
--anat_only run only the anatomical preprocessing
--tracking_opt-out Disable usage tracking. Only the number of
participants on the analysis is tracked.
--monitoring Enable monitoring server on port 8080. You need to
bind the port using the Docker flag "-p".
