Best Practices for writing CWL

• To run on Arvados, a workflow should provide a DockerRequirement in the hints section.

• Build a reusable library of components. Share tool wrappers and subworkflows between projects. Make use of and contribute to community maintained workflows and tools and tool registries such as Dockstore .

• When combining a parameter value with a string, such as adding a filename extension, write $(inputs.file.basename).ext instead of $(inputs.file.basename + 'ext'). The first form is evaluated as a simple text substitution, the second form (using the + operator) is evaluated as an arbitrary Javascript expression and requires that you declare InlineJavascriptRequirement.

• Avoid declaring InlineJavascriptRequirement or ShellCommandRequirement unless you specifically need them. Don’t include them “just in case” because they change the default behavior and may imply extra overhead.

• Don’t write CWL scripts that access the Arvados SDK. This is non-portable; a script that access Arvados directly won’t work with cwltool or crunch v2.

• CommandLineTools wrapping custom scripts should represent the script as an input parameter with the script file as a default value. Use secondaryFiles for scripts that consist of multiple files. For example:

cwlVersion: v1.0
class: CommandLineTool
baseCommand: python
inputs:
  script:
    type: File
    inputBinding: {position: 1}
    default:
      class: File
      location: bclfastq.py
      secondaryFiles:
        - class: File
          location: helper1.py
        - class: File
          location: helper2.py
  inputfile:
    type: File
    inputBinding: {position: 2}
outputs:
  out:
    type: File
    outputBinding:
      glob: "*.fastq"

• You can get the designated temporary directory using $(runtime.tmpdir) in your CWL file, or from the $TMPDIR environment variable in your script.

• Similarly, you can get the designated output directory using $(runtime.outdir), or from the HOME environment variable in your script.

• Use ExpressionTool to efficiently rearrange input files between steps of a Workflow. For example, the following expression accepts a directory containing files paired by _R1_ and _R2_ and produces an array of Directories containing each pair.

class: ExpressionTool
cwlVersion: v1.0
inputs:
  inputdir: Directory
outputs:
  out: Directory[]
requirements:
  InlineJavascriptRequirement: {}
expression: |
  ${
    var samples = {};
    for (var i = 0; i < inputs.inputdir.listing.length; i++) {
      var file = inputs.inputdir.listing[i];
      var groups = file.basename.match(/^(.+)(_R[12]_)(.+)$/);
      if (groups) {
        if (!samples[groups[1]]) {
          samples[groups[1]] = [];
        }
        samples[groups[1]].push(file);
      }
    }
    var dirs = [];
    for (var key in samples) {
      dirs.push({"class": "Directory",
                 "basename": key,
                 "listing": [samples[key]]});
    }
    return {"out": dirs};
  }

• Avoid specifying resource requirements in CommandLineTool. Prefer to specify them in the workflow. You can provide a default resource requirement in the top level hints section, and individual steps can override it with their own resource requirement.

cwlVersion: v1.0
class: Workflow
inputs:
  inp: File
hints:
  ResourceRequirement:
    ramMin: 1000
    coresMin: 1
    tmpdirMin: 45000
steps:
  step1:
    in: {inp: inp}
    out: [out]
    run: tool1.cwl
  step2:
    in: {inp: step1/inp}
    out: [out]
    run: tool2.cwl
    hints:
      ResourceRequirement:
        ramMin: 2000
        coresMin: 2
        tmpdirMin: 90000

• Available compute nodes types vary over time and across different cloud providers, so try to limit the RAM requirement to what the program actually needs. However, if you need to target a specific compute node type, see this discussion on calculating RAM request and choosing instance type for containers.

• Instead of scattering separate steps, prefer to scatter over a subworkflow.

With the following pattern, step1 has to wait for all samples to complete before step2 can start computing on any samples. This means a single long-running sample can prevent the rest of the workflow from moving on:

cwlVersion: v1.0
class: Workflow
inputs:
  inp: File
steps:
  step1:
    in: {inp: inp}
    scatter: inp
    out: [out]
    run: tool1.cwl
  step2:
    in: {inp: step1/inp}
    scatter: inp
    out: [out]
    run: tool2.cwl
  step3:
    in: {inp: step2/inp}
    scatter: inp
    out: [out]
    run: tool3.cwl

Instead, scatter over a subworkflow. In this pattern, a sample can proceed to step2 as soon as step1 is done, independently of any other samples.
Example: (note, the subworkflow can also be put in a separate file)

cwlVersion: v1.0
class: Workflow
steps:
  step1:
    in: {inp: inp}
    scatter: inp
    out: [out]
    run:
      class: Workflow
      inputs:
        inp: File
      outputs:
        out:
          type: File
          outputSource: step3/out
      steps:
        step1:
          in: {inp: inp}
          out: [out]
          run: tool1.cwl
        step2:
          in: {inp: step1/inp}
          out: [out]
          run: tool2.cwl
        step3:
          in: {inp: step2/inp}
          out: [out]
          run: tool3.cwl

Migrating running CWL on jobs API to containers API

• When migrating from jobs API (—api=jobs) (sometimes referred to as “crunch v1”) to the containers API (—api=containers) (“crunch v2”) there are a few differences in behavior:
  • The tool is limited to accessing only collections which are explicitly listed in the input, and further limited to only the subdirectories of collections listed in input. For example, given an explicit file input /dir/subdir/file1.txt, a tool will not be able to implicitly access the file /dir/file2.txt. Use secondaryFiles or a Directory input to describe trees of files.
  • Files listed in InitialWorkDirRequirement appear in the output directory as normal files (not symlinks) but cannot be moved, renamed or deleted. These files will be added to the output collection but without any additional copies of the underlying data.
  • Tools are disallowed network access by default. Tools which require network access must include arv:APIRequirement: {} in their requirements section.

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