Stian Soiland-Reyes

eScience lab, The University of Manchester

@soilandreyes

https://orcid.org/0000-0001-9842-9718

https://slides.com/soilandreyes/2018-07-03-cwl

BioExcel/MolSSI symposium, PASC18
2018-07-03 Basel, CH

This work has been done as part of the BioExcel CoE (www.bioexcel.eu), a project funded by the European Union contract H2020-EINFRA-2015-1-675728.

Facing Compute Platform Portability Challenges with Scientific Workflows

Experiences from Common Workflow Language

cwlVersion: v1.0
class: Workflow
inputs:
  inp: File
  ex: string

outputs:
  classout:
    type: File
    outputSource: compile/classfile

steps:
  untar:
    run: tar-param.cwl
    in:
      tarfile: inp
      extractfile: ex
    out: [example_out]

  compile:
    run: arguments.cwl
    in:
      src: untar/example_out
    out: [classfile]

cwltool: Local (Linux, OS X, Windows)

Arvados: AWS, GCP, Azure, Slurm

Toil: AWS, Azure, GCP, Grid Engine, LSF, Mesos, OpenStack, Slurm, PBS/Torque, HTcondor

Rabix Bunny: Local(Linux, OS X), GA4GH TES

cwl-tes: Local, GCP, AWS, HTCondor, Grid Engine, PBS/Torque, Slurm

CWL-Airflow: Linux, OS X

REANA: Kubernetes, CERN OpenStack

cromwell: local, HPC, Google, HtCondor

CWLEXEC: IBM Spectrum LSF

XENON: any Xenon backend: local, ssh, SLURM, Torque, Grid Engine

Which CWL engine runs where?

Over 5000 CWL Descriptions on GitHub

 

 

Efficient checking of job completion with maximum parallelism
Support LSF submission (bsub) options
Self-healing of workflows
Docker integration
Cloud bursting
Rerun and interruption

cwlexec

{
    "queue": "high",
    "steps": {
        "step1": {
            "app": "dockerapp"
        },
        "step2": {
            "res_req": "select[type==X86_64] order[ut] 
                        rusage[mem=512MB:swp=1GB:tmp=500GB]"
        }
    }
}

cwlexec Run Profile

#!/usr/bin/env cwl-runner

cwlVersion: v1.0
class: Workflow
inputs:
  inp: File
  ex: string

outputs:
  classout:
    type: File
    outputSource: compile/classfile

steps:
  untar:
    run: tar-param.cwl
    in:
      tarfile: inp
      extractfile: ex
    out: [example_out]

  compile:
    run: arguments.cwl
    in:
      src: untar/example_out
    out: [classfile]

Composing a workflow

cwlVersion: v1.0
class: Workflow
label: EMG QC workflow, (paired end version). Benchmarking with MG-RAST expt.

requirements:
 - class: SubworkflowFeatureRequirement
 - class: SchemaDefRequirement
   types: 
    - $import: ../tools/FragGeneScan-model.yaml
    - $import: ../tools/trimmomatic-sliding_window.yaml
    - $import: ../tools/trimmomatic-end_mode.yaml
    - $import: ../tools/trimmomatic-phred.yaml

inputs:
  reads:
    type: File
    format: edam:format_1930  # FASTQ

outputs:
  processed_sequences:
    type: File
    outputSource: clean_fasta_headers/sequences_with_cleaned_headers

steps:
  trim_quality_control:
    doc: |
      Low quality trimming (low quality ends and sequences with < quality scores
      less than 15 over a 4 nucleotide wide window are removed)
    run: ../tools/trimmomatic.cwl
    in:
      reads1: reads
      phred: { default: '33' }
      leading: { default: 3 }
      trailing: { default: 3 }
      end_mode: { default: SE }
      minlen: { default: 100 }
      slidingwindow:
        default:
          windowSize: 4
          requiredQuality: 15
    out: [reads1_trimmed]

  convert_trimmed-reads_to_fasta:
    run: ../tools/fastq_to_fasta.cwl
    in:
      fastq: trim_quality_control/reads1_trimmed
    out: [ fasta ]

  clean_fasta_headers:
    run: ../tools/clean_fasta_headers.cwl
    in:
      sequences: convert_trimmed-reads_to_fasta/fasta
    out: [ sequences_with_cleaned_headers ]


$namespaces:
 edam: http://edamontology.org/
 s: http://schema.org/
$schemas:
 - http://edamontology.org/EDAM_1.16.owl
 - https://schema.org/docs/schema_org_rdfa.html

s:license: "https://www.apache.org/licenses/LICENSE-2.0"
s:copyrightHolder: "EMBL - European Bioinformatics Institute"
#!/usr/bin/env cwl-runner

cwlVersion: v1.0
class: CommandLineTool
baseCommand: [tar, xf]
inputs:
  tarfile:
    type: File
    inputBinding:
      position: 1
outputs:
  example_out:
    type: File
    outputBinding:
      glob: hello.txt

Command line tool


class: CommandLineTool
hints:
  SoftwareRequirement:
    packages:
      samtools:
        version: [ "0.1.19" ]

baseCommand: ["samtools", "index"]
#..

Finding the tool

module load samtools/0.1.19
apt-get install samtools=0.1.19*
conda install samtools=0.1.19
<dependency_resolvers>
  <modules modulecmd="/opt/bin/modulecmd" />
  <tool_shed_packages />
  <galaxy_packages />
  <conda />

  <modules modulecmd="/opt/bin/modulecmd" versionless="true" />
  <galaxy_packages versionless="true" />
  <conda versionless="true" />
</dependency_resolvers>

Package resolution

tool_dependency_dir/
  samtools/
    0.1.19/
      bin/
      env.sh

Dependency resolution by CWLTool and Toil

Where to find command line tools?

cwlVersion: v1.0
class: CommandLineTool
baseCommand: node
hints:
  DockerRequirement:
    dockerPull: mgibio/samtools:1.3.1

= anaconda

   + 4000 bioinformatics packages

Let's add some identifiers!

hints:
  SoftwareRequirement:
    packages:
    - package: bowtie
      version:
      - '2.2.8'
      specs:
      - https://packages.debian.org/bowtie
      - https://anaconda.org/bioconda/bowtie
      - https://bio.tools/tool/bowtie2/version/2.2.8
      - https://identifiers.org/rrid/RRID:SCR_005476
      - https://hpc.example.edu/modules/bowtie-tbb/2.2

Khan et al,
CWLProv – Interoperable retrospective provenance capture and its challenges,
BOSC 2018

https://doi.org/10.7490/f1000research.1115721.1

document
prefix wfprov <http://purl.org/wf4ever/wfprov#>
prefix prov <http://www.w3.org/ns/prov#>
prefix wfdesc <http://purl.org/wf4ever/wfdesc#>
prefix wf <https://w3id.org/cwl/view/git/933bf2a1a1cce32d88f88f136275535da9df0954/workflows/hello/hello.cwl#>
prefix input <app://579c1b74-b328-4da6-80a8-a2ffef2ac9b5/workflow/input.json#>
prefix run <urn:uuid:>
prefix engine <urn:uuid:>
prefix data <urn:hash:sha256:>

default <app://579c1b74-b328-4da6-80a8-a2ffef2ac9b5/>

// Level 1 provenance of workflow run

activity(run:2e1287e0-6dfb-11e7-8acf-0242ac110002, , , [prov:type='wfprov:WorkflowRun', prov:label="Run of workflow/packed.cwl#main"])    
    wasStartedBy(run:2e1287e0-6dfb-11e7-8acf-0242ac110002, -, -, -, 2017-10-27T14:24:00+01:00)  

    // The engine is the SoftwareAgent that is executing our Workflow plan
    wasAssociatedWith(run:2e1287e0-6dfb-11e7-8acf-0242ac110002, engine:b2210211-8acb-4d58-bd28-2a36b18d3b4f, wf:main)
        agent(engine:b2210211-8acb-4d58-bd28-2a36b18d3b4f, prov:type='prov:SoftwareAgent', prov:type='wfprov:WorkflowEngine', prov:label="cwltool v1.2.5")
        // prov has no term to relate sub-plans - we'll use wfdesc:hasSubProcess
        entity(wf:main,[prov:type='wfdesc:Workflow', prov:type='prov:Plan', wfdesc:hasSubProcess='wf:main/step1', wfdesc:hasSubProcess='wf:main/step2'])
            alternateOf(wf:main, workflow/packed.cwl)
            entity(wf:main/step1,[prov:type='wfdesc:Process', prov:type='prov:Plan'])
            entity(wf:main/step2,[prov:type='wfdesc:Process', prov:type='prov:Plan'])            

    // First the workflow uses some data; here with a urn:sha:sha256 identifier
    used(run:2e1287e0-6dfb-11e7-8acf-0242ac110002, data:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03, 2017-10-27T14:29:00+01:00, [prov:role='wf:main/input1']))
        entity(data:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03, [prov:type='wfprov:Artifact'])
            // which we have stored a copy of within the research object
            specializationOf(data/58/5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03, data:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03)

    // Then there was another activity - wfprov:ProcessRun indicating a command line tool
    activity(run:4305467e-6dfb-11e7-885d-0242ac110002, -, -, [prov:type='wfprov:ProcessRun', prov:label="Run of workflow/packed.cwl#main/step1"])
        // started by the mother activity
        wasStartedBy(run:4305467e-6dfb-11e7-885d-0242ac110002, -, -, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T15:00:00+01:00)
        // same engine using step1 as plan. In a distributed scenario there might be a different engine
        wasAssociatedWith(run:4305467e-6dfb-11e7-885d-0242ac110002, engine:b2210211-8acb-4d58-bd28-2a36b18d3b4f, wf:main/step1)
        // This activity also use the same data, but in a different role (e.g. input parameter)
        used(run:4305467e-6dfb-11e7-885d-0242ac110002, data:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03, 2017-10-27T14:00:00+01:00, [prov:role='wf:main/step1/in1'])

        // And we generate some new data
        wasGeneratedBy(data:00688350913f2f292943a274b57019d58889eda272370af261c84e78e204743c, run:4305467e-6dfb-11e7-885d-0242ac110002, 2017-10-27T16:00:00+01:00, [prov:role='wf:main/step1/out1']))
            entity(data:00688350913f2f292943a274b57019d58889eda272370af261c84e78e204743c, [prov:type='wfprov:Artifact'])
                // again stored in the RO
                specializationOf(data/00/00688350913f2f292943a274b57019d58889eda272370af261c84e78e204743c, data:00688350913f2f292943a274b57019d58889eda272370af261c84e78e204743c)

        // step1 finished
        wasEndedBy(run:4305467e-6dfb-11e7-885d-0242ac110002, -, -, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T15:30:00+01:00)

    // the master workflow then "generate" that same value, but now at a different time and role (the resultA master workflow output)
    wasGeneratedBy(data:00688350913f2f292943a274b57019d58889eda272370af261c84e78e204743c, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T15:00:00+01:00, [prov:role='wf:main/resultA'])

    // next step activity
    activity(run:c42dc36e-6dfd-11e7-bc24-0242ac110002, -, - [prov:type='wfprov:ProcessRun', prov:label="Run of workflow/packed.cwl#main/step2"])
        wasStartedBy(run:c42dc36e-6dfd-11e7-bc24-0242ac110002, -, -, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T16:00:00+01:00)
        // associated with step2
        wasAssociatedWith(run:c42dc36e-6dfd-11e7-bc24-0242ac110002, engine:b2210211-8acb-4d58-bd28-2a36b18d3b4f, wf:main/step2)
        
        // Uses two data artifacts; one which came from previous step, other as workflow input
        used(run:4305467e-6dfb-11e7-885d-0242ac110002, data:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03, 2017-10-27T15:00:00+01:00, [prov:role='wf:main/step2/valueA'])
        used(run:4305467e-6dfb-11e7-885d-0242ac110002, data:00688350913f2f292943a274b57019d58889eda272370af261c84e78e204743c, 2017-10-27T15:00:00+01:00, [prov:role='wf:main/step2/valueB'])
        
        // and generate two new data artifacts
        wasGeneratedBy(data:952f537d1f3116db56703787ace248fe00ae46fa77ea3803aa3d8dc01d221a9d, run:c42dc36e-6dfd-11e7-bc24-0242ac110002,  2017-10-27T16:34:20+01:00, [prov:role='wf:main/step2/out1'])))
            entity(data:952f537d1f3116db56703787ace248fe00ae46fa77ea3803aa3d8dc01d221a9d, [prov:type='wfprov:Artifact'])
                specializationOf(data/95/2f537d1f3116db56703787ace248fe00ae46fa77ea3803aa3d8dc01d221a9d, data:952f537d1f3116db56703787ace248fe00ae46fa77ea3803aa3d8dc01d221a9d)

        wasGeneratedBy(data:3deb00bd0decd1f21d015a178c4f23a5eb537588c08eeee9d55059ec29637be0, run:c42dc36e-6dfd-11e7-bc24-0242ac110002,  2017-10-27T16:34:20+01:00, [prov:role='wf:main/step2/out2'])))
            entity(data:3deb00bd0decd1f21d015a178c4f23a5eb537588c08eeee9d55059ec29637be0, [prov:type='wfprov:Artifact'])
                specializationOf(data/3d/eb00bd0decd1f21d015a178c4f23a5eb537588c08eeee9d55059ec29637be0, data:3deb00bd0decd1f21d015a178c4f23a5eb537588c08eeee9d55059ec29637be0)
        // step2 ends
        wasEndedBy(run:c42dc36e-6dfd-11e7-bc24-0242ac110002, -, -, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T16:30:00+01:00)

    // only step output out1 captured by mother workflow, sent to resultB workflow output
    wasGeneratedBy(data:952f537d1f3116db56703787ace248fe00ae46fa77ea3803aa3d8dc01d221a9d, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T15:00:00+01:00, [prov:role='wf:main/resultB'])

    // mother workflow ends
    wasEndedBy(run:2e1287e0-6dfb-11e7-8acf-0242ac110002, -, -, run:2e1287e0-6dfb-11e7-8acf-0242ac110002, 2017-10-27T16:34:40+01:00)

endDocument

CWLProv

Using provenance to improve performance

Mondelli et al: BioWorkbench
https://arxiv.org/abs/1801.03915

Challenges

Determining hardware allocations

Automatic rescale+retry

Configuration or Prediction?

Machine learning from provenance

 

Scheduling scattered jobs

Can't determine total number of jobs until runtime

..but usually early on you can find the number

Accessing the scheduler

How can tasks influence their own allocations?

 

Lesson learnt from cloud approach:

Avoid "master" node dependency

 

Workarounds: Task Service (TES), worker nodes

 Moving workflows to Exascale

 

Don't throw the baby out with the bathwater!

Automation (but is is interoperable?)

Scalability (faster! bigger!)

Abstraction (can humans still understand it?)

Provenance (what actually ran?)

Findable

Accessable

Interoperable

Reproducible

2018-07-03 PASC Common Workflow Language

By Stian Soiland-Reyes

2018-07-03 PASC Common Workflow Language

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