Running BIDS Apps on Bacalhau

Introduction

In this example tutorial, we will look at how to run BIDS App on Bacalhau. BIDS (Brain Imaging Data Structure) is an emerging standard for organizing and describing neuroimaging datasets. BIDS App is a container image capturing a neuroimaging pipeline that takes a BIDS formatted dataset as input. Each BIDS App has the same core set of command line arguments, making them easy to run and integrate into automated platforms. BIDS Apps are constructed in a way that does not depend on any software outside of the image other than the container engine.

Prerequisite​

To get started, you need to install the Bacalhau client, see more information here

Downloading datasets​

For this tutorial, download file ds005.tar from this Bids dataset folder and untar it in a directory:

mkdir data
tar -xf ds005.tar -C data 

Let's take a look at the structure of the data directory:

data
└── ds005
    ├── CHANGES
    ├── dataset_description.json
    ├── participants.tsv
    ├── README
    ├── sub-01
    │   ├── anat
    │   │   ├── sub-01_inplaneT2.nii.gz
    │   │   └── sub-01_T1w.nii.gz
    │   └── func
    │       ├── sub-01_task-mixedgamblestask_run-01_bold.nii.gz
    │       ├── sub-01_task-mixedgamblestask_run-01_events.tsv
    │       ├── sub-01_task-mixedgamblestask_run-02_bold.nii.gz
    │       ├── sub-01_task-mixedgamblestask_run-02_events.tsv
    │       ├── sub-01_task-mixedgamblestask_run-03_bold.nii.gz
    │       └── sub-01_task-mixedgamblestask_run-03_events.tsv
    ├── sub-02
    │   ├── anat
    │   │   ├── sub-02_inplaneT2.nii.gz
    │   │   └── sub-02_T1w.nii.gz
    ...

Uploading the datasets to IPFS​

The simplest way to upload the data to IPFS is to use a third-party service to "pin" data to the IPFS network, to ensure that the data exists and is available. To do this, you need an account with a pinning service like Pinata or nft.storage. Once registered, you can use their UI or API or SDKs to upload files.

When you pin your data, you'll get a CID which is in a format like this QmaNyzSpJCt1gMCQLd3QugihY6HzdYmA8QMEa45LDBbVPz. Copy the CID as it will be used to access your data

Running a Bacalhau Job​

export JOB_ID=$(bacalhau docker run \
    --id-only \
    --wait \
    --timeout 3600 \
    --wait-timeout-secs 3600 \
    -i ipfs://QmaNyzSpJCt1gMCQLd3QugihY6HzdYmA8QMEa45LDBbVPz:/data \
    nipreps/mriqc:latest \
    -- mriqc ../data/ds005 ../outputs participant --participant_label 01 02 03)

Structure of the command​

Let's look closely at the command above:

1. bacalhau docker run: call to bacalhau

2. -i ipfs://QmaNyzSpJCt1gMCQLd3QugihY6HzdYmA8QMEa45LDBbVPz:/data: mount the CID of the dataset that is uploaded to IPFS and mount it to a folder called data on the container

3. nipreps/mriqc:latest: the name and the tag of the docker image we are using

4. ../data/ds005: path to input dataset

5. ../outputs: path to the output

6. participant --participant_label 01 02 03: run the mriqc on subjects with participant labels 01, 02, and 03

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Declarative job description​

The same job can be presented in the declarative format. In this case, the description will look like this:

Copy

name: Running BIDS
type: batch
count: 1
tasks:
  - name: My main task
    Engine:
      type: docker
      params:
        Image: nipreps/mriqc:latest
        Entrypoint:
          - /bin/bash
        Parameters:
          - -c
          - mriqc ../data/ds005 ../outputs participant --participant_label 01 02 03
    Publisher:
      Type: ipfs
    ResultPaths:
      - Name: outputs
        Path: /outputs
    InputSources:
      - Target: "/data"
        Source:
          Type: "ipfs"
          Params:
            CID: "QmaNyzSpJCt1gMCQLd3QugihY6HzdYmA8QMEa45LDBbVPz"

The job description should be saved in .yaml format, e.g. bids.yaml, and then run with the command:

bacalhau job run bids.yaml

Checking the State of your Jobs​

Job status: You can check the status of the job using bacalhau job list.

bacalhau job list --id-filter ${JOB_ID} --wide

When it says Published or Completed, that means the job is done, and we can get the results.

Job information: You can find out more information about your job by using bacalhau job describe.

bacalhau job describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

rm -rf results && mkdir -p results
bacalhau job get $JOB_ID --output-dir results

Viewing your Job Output​

To view the file, run the following command:

ls results/ # list the contents of the current directory 
cat results/stdout # displays the contents of the current directory 

Support​

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).