Overview

Teaching: 15 min
Exercises: 20 min

Questions

• How do I provide input to run a workflow?

• What should I do if the workflow fails?

Objectives

• Write an input parameter file.

• Execute the workflow.

• Diagnose workflow errors.

This lesson has migrated to https://doc.arvados.org/rnaseq-cwl-training/03-running/index.html

The input parameter file

CWL input values are provided in the form of a YAML or JSON file. create a called file

This file gives the values for parameters declared in the inputs section of our workflow. Our workflow takes fq, genome and gtf as input parameters.

When setting inputs, Files and Directories are given as an object with class: File or class: Directory. This distinguishes them from plain strings that may or may not be file paths.

Note: if you don’t have example sequence data or the STAR index files, see setup.

fq:
  class: File
  location: rnaseq/raw_fastq/Mov10_oe_1.subset.fq
  format: http://edamontology.org/format_1930
genome:
  class: Directory
  location: hg19-chr1-STAR-index
gtf:
  class: File
  location: rnaseq/reference_data/chr1-hg19_genes.gtf

fq:
  class: File
  location: keep:9178fe1b80a08a422dbe02adfd439764+925/raw_fastq/Mov10_oe_1.subset.fq
  format: http://edamontology.org/format_1930
genome:
  class: Directory
  location: keep:02a12ce9e2707610991bd29d38796b57+2912
gtf:
  class: File
  location: keep:9178fe1b80a08a422dbe02adfd439764+925/reference_data/chr1-hg19_genes.gtf

Debugging the workflow

Depending on whether and how your workflow platform enforces memory limits, your workflow may fail. Let’s talk about what to do when a workflow fails.

A workflow can fail for many reasons: some possible reasons include bad input, bugs in the code, or running out memory. In our example, the STAR workflow may fail with an out of memory error.

To help diagnose these errors, the workflow runner produces logs that record what happened, either in the terminal or the web interface.

Some errors you might see in the logs that would indicate an out of memory condition:

EXITING: fatal error trying to allocate genome arrays, exception thrown: std::bad_alloc
Possible cause 1: not enough RAM. Check if you have enough RAM 5711762337 bytes
Possible cause 2: not enough virtual memory allowed with ulimit. SOLUTION: run ulimit -v 5711762337

or

Container exited with code: 137

(Exit code 137 most commonly occurs when a container goes “out of memory” and is terminated by the operating system).

If this happens, you will need to request more RAM.

Setting runtime RAM requirements

By default, a step is allocated 256 MB of RAM. From the STAR error message:

Check if you have enough RAM 5711762337 bytes

We can see that STAR requires quite a bit more RAM than 256 MB. To request more RAM, add a “requirements” section with “ResourceRequirement” to the “STAR” step:

  STAR:
    requirements:
      ResourceRequirement:
        ramMin: 9000
    run: bio-cwl-tools/STAR/STAR-Align.cwl
	...

Resource requirements you can set include:

• coresMin: CPU cores
• ramMin: RAM (in megabytes)
• tmpdirMin: temporary directory available space
• outdirMin: output directory available space

Running the workflow

Now that you’ve fixed the workflow, run it again.

Episode solution

• main.cwl

Workflow results

The CWL runner will print a results JSON object to standard output. It will look something like this (it may include additional fields).

{
    "bam_sorted_indexed": {
        "location": "file:///home/username/rnaseq-cwl-training-exercises/Aligned.sortedByCoord.out.bam",
        "basename": "Aligned.sortedByCoord.out.bam",
        "class": "File",
        "size": 25370707,
        "secondaryFiles": [
            {
                "basename": "Aligned.sortedByCoord.out.bam.bai",
                "location": "file:///home/username/rnaseq-cwl-training-exercises/Aligned.sortedByCoord.out.bam.bai",
                "class": "File",
                "size": 176552,
            }
        ]
    },
    "qc_html": {
        "location": "file:///home/username/rnaseq-cwl-training-exercises/Mov10_oe_1.subset_fastqc.html",
        "basename": "Mov10_oe_1.subset_fastqc.html",
        "class": "File",
        "size": 383589
    }
}

{
    "bam_sorted_indexed": {
        "basename": "Aligned.sortedByCoord.out.bam",
        "class": "File",
        "location": "keep:2dbaaef5aefd558e37f14280e47091a9+327/Aligned.sortedByCoord.out.bam",
        "secondaryFiles": [
            {
                "basename": "Aligned.sortedByCoord.out.bam.bai",
                "class": "File",
                "location": "keep:2dbaaef5aefd558e37f14280e47091a9+327/Aligned.sortedByCoord.out.bam.bai"
            }
        ],
        "size": 25370695
    },
    "qc_html": {
        "basename": "Mov10_oe_1.subset_fastqc.html",
        "class": "File",
        "location": "keep:2dbaaef5aefd558e37f14280e47091a9+327/Mov10_oe_1.subset_fastqc.html",
        "size": 383589
    }
}

This has a similar structure as main-input.yaml. The each output parameter is listed, with the location field of each File object indicating where the output file can be found.

Key Points

• The input parameter file is a YAML file with values for each input parameter.

• A common reason for a workflow step fails is insufficient RAM.

• Use ResourceRequirement to set the amount of RAM to be allocated to the job.

• Output parameter values are printed as JSON to standard output at the end of the run.