Best Practices for writing CWL

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

  • A tool may fail to find an input file that could be found when run under the jobs API. This is because tools are limited to accessing collections explicitly listed in the input, and further limited to those individual files or subdirectories that are listed. For example, given an explicit file input /dir/subdir/file1.txt, a tool will not be allowed to implicitly access a file in the parent directory /dir/file2.txt. Use secondaryFiles or a Directory for files that need to be grouped together.
  • A tool may fail when attempting to rename or delete a file in the output directory. This may happen because files listed in InitialWorkDirRequirement appear in the output directory as normal files (not symlinks) but cannot be moved, renamed or deleted unless marked as “writable” in CWL. These files will be added to the output collection but without any additional copies of the underlying data.
  • A tool may fail when attempting to access the network. This may happen because, unlike the jobs API, under the containers API network access is disabled by default. Tools which require network access should add arv:APIRequirement: {} to the requirements section.

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The content of this documentation is licensed under the Creative Commons Attribution-Share Alike 3.0 United States licence.
Code samples in this documentation are licensed under the Apache License, Version 2.0.