Run on Docker¶
A C-PAC Docker image is available so that you can easily get an analysis running without needing to install C-PAC.
The Docker image is designed following the specification established by the BIDS-Apps project, an initiative to create a collection of reproducible neuroimaging workflows that can be executed as self-contained environments using Docker containers. These workflows take as input any dataset that is organized according to the Brain Imaging Data Structure (BIDS) standard and generating first-level outputs for this dataset. However, you can provide the C-PAC Docker image with a custom non-BIDS dataset by entering your own data configuration file. More details below.
In addition, we have created a Docker default pipeline configuration as part of this initiative that allows you to run the C-PAC pipeline on your data in an environment that is fully provisioned with all of C-PAC’s dependencies - more details about the default pipeline are available further below. If you wish to run your own pipeline configuration, you can also provide this to the Docker image at run-time.
To start, first pull the image from Docker Hub:
docker pull fcpindi/c-pac:latest
Once this is complete, you can use the fcpindi/c-pac:latest image tag to invoke runs. The full C-PAC Docker image usage options are shown here, with some specific use cases.
As a quick example, in order to run the C-PAC Docker container in participant mode, for one participant, using a BIDS dataset stored on your machine or server, and using the Docker image’s default pipeline configuration (broken into multiple lines for visual clarity):
docker run -i --rm \
-v /Users/You/local_bids_data:/bids_dataset \
-v /Users/You/some_folder:/outputs \
-v /tmp:/tmp \
fcpindi/c-pac:latest /bids_dataset /outputs participant
Note, the -v flags map your local filesystem locations to a “location” within the Docker image. (For example, the /bids_dataset and /outputs directories in the command above are arbitrary names). If you provided /Users/You/local_bids_data to the bids_dir input parameter, Docker would not be able to access or see that directory, so it needs to be mapped first. In this example, the local machine’s /tmp directory has been mapped to the /tmp name because the C-PAC Docker image’s default pipeline sets the working directory to /tmp. If you wish to keep your working directory somewhere more permanent, you can simply map this like so: -v /Users/You/working_dir:/tmp.
You can also provide a link to an AWS S3 bucket containing a BIDS directory as the data source:
docker run -i --rm \
-v /Users/You/some_folder:/outputs \
-v /tmp:/tmp \
fcpindi/c-pac:latest s3://fcp-indi/data/Projects/ADHD200/RawDataBIDS /outputs participant
In addition to the default pipeline, C-PAC comes packaged with a growing library of pre-configured pipelines that are ready to use. To run the C-PAC Docker container with one of the pre-packaged pre-configured pipelines, simply invoke the --preconfig flag, shown below. See the full selection of pre-configured pipelines here.
docker run -i --rm \
-v /Users/You/local_bids_data:/bids_dataset \
-v /Users/You/some_folder:/outputs \
-v /tmp:/tmp \
fcpindi/c-pac:latest /bids_dataset /outputs --preconfig anat-only
To run the C-PAC Docker container with a pipeline configuration file other than one of the pre-configured pipelines, assuming the configuration file is in the /Users/You/Documents directory:
docker run -i --rm \
-v /Users/You/local_bids_data:/bids_dataset \
-v /Users/You/some_folder:/outputs \
-v /tmp:/tmp \
-v /Users/You/Documents:/configs \
-v /Users/You/resources:/resources \
fcpindi/c-pac:latest /bids_dataset /outputs participant --pipeline_file /configs/pipeline_config.yml
In this case, we need to map the directory containing the pipeline configuration file /Users/You/Documents to a Docker image virtual directory /configs. Note we are using this /configs directory in the --pipeline_file input flag. In addition, if there are any ROIs, masks, or input files listed in your pipeline configuration file, the directory these are in must be mapped as well- assuming /Users/You/resources is your directory of ROI and/or mask files, we map it with -v /Users/You/resources:/resources. In the pipeline configuration file you are providing, these ROI and mask files must be listed as /resources/ROI.nii.gz (etc.) because we have mapped /Users/You/resources to /resources.
Finally, to run the Docker container with a specific data configuration file (instead of providing a BIDS data directory):
docker run -i --rm \
-v /Users/You/any_directory:/bids_dataset \
-v /Users/You/some_folder:/outputs \
-v /tmp:/tmp \
-v /Users/You/Documents:/configs \
fcpindi/c-pac:latest /bids_dataset /outputs participant --data_config_file /configs/data_config.yml
Note: we are still providing /bids_dataset to the bids_dir input parameter. However, we have mapped this to any directory on your machine, as C-PAC will not look for data in this directory when you provide a data configuration YAML with the --data_config_file flag. In addition, if the dataset in your data configuration file is not in BIDS format, just make sure to add the --skip_bids_validator flag at the end of your command to bypass the BIDS validation process.
The full list of parameters and options that can be passed to the Docker container are shown below:
Usage: cpac run¶
$ cpac run --help
Loading 🐳 Docker
Loading 🐳 fcpindi/c-pac:latest with these directory bindings:
local Docker mode
---------------------------- -------------------- ------
/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.
INFO: //matlab/startup.m does not exist ... creating
mkdir: cannot create directory ‘//matlab’: Permission denied
touch: cannot touch '//matlab/startup.m': No such file or directory
/usr/lib/freesurfer/FreeSurferEnv.sh: line 231: //matlab/startup.m: No such file or directory
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grep: //matlab/startup.m: No such file or directory
grep: //matlab/startup.m: No such file or directory
grep: //matlab/startup.m: No such file or directory
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".
Note that any of the optional arguments above will over-ride any pipeline settings in the default pipeline or in the pipeline configuration file you provide via the --pipeline_file parameter.
Further usage notes:
You can run only anatomical preprocessing easily, without modifying your data or pipeline configuration files, by providing the
--anat_onlyflag.As stated, the default behavior is to read data that is organized in the BIDS format. This includes data that is in Amazon AWS S3 by using the format
s3://<bucket_name>/<bids_dir>for thebids_dircommand line argument. Outputs can be written to S3 using the same format for theoutput_dir. Credentials for accessing these buckets can be specified on the command line (using--aws_input_credsor--aws_output_creds).When the app is run, a data configuration file is written to the working directory. This file can be passed into subsequent runs, which avoids the overhead of re-parsing the BIDS input directory on each run (i.e. for cluster or cloud runs). These files can be generated without executing the C-PAC pipeline using the test_run command line argument.
The
participant_labelandparticipant_ndxarguments allow the user to specify which of the many datasets should be processed, which is useful when parallelizing the run of multiple participants.
