Toil Debugging

Toil has a number of tools to assist in debugging. Here we provide help in working through potential problems that a user might encounter in attempting to run a workflow.

Reading the Log

Usually, at the end of a failed Toil worklfow, Toil will reproduce the job logs for the jobs that failed. You can look at the end of your workflow log and use the job logs to identify which jobs are failing and why.

Finding Failed Jobs in the Jobstore

The toil status command (Status Command) can be used with the --failed option to list all failed jobs in a Toil job store.

You can also use it with the --logs option to retrieve per-job logs from the job store, for failed jobs that left them. These logs might be useful for diagnosing and fixing the problem.

Running a Job Locally

If you have a failing job’s ID or name, you can reproduce its failure on your local machine with toil debug-job. See Debug Job Command.

For example, say you have this WDL workflow in test.wdl. This workflow cannot succeed, due to the typo in the echo command:

version 1.0
workflow test {
  call hello
task hello {
  input {
  command <<<
    set -e
    echoo "Hello"
  output {

You could try to run it with:

toil-wdl-runner --jobStore ./store test.wdl --retryCount 0

But it will fail.

If you want to reproduce the failure later, or on another machine, you can first find out what jobs failed with toil status:

toil status --failed --noAggStats ./store

This will produce something like:

[2024-03-14T17:45:15-0400] [MainThread] [I] [toil.utils.toilStatus] Traversing the job graph gathering jobs. This may take a couple of minutes. Failed jobs: ‘WDLTaskJob’ test.hello.command kind-WDLTaskJob/instance-r9u6_dcs v6

And we can see a failed job with the display name test.hello.command, which describes the job’s location in the WDL workflow as the command section of the hello task called from the test workflow. (If you are writing a Toil Python script, this is the job’s displayName.) We can then run that job again locally by name with:

toil debug-job ./store test.hello.command

If there were multiple failed jobs with that name (perhaps because of a WDL scatter), we would need to select one by Toil job ID instead:

toil debug-job ./store kind-WDLTaskJob/instance-r9u6_dcs

And if we know there’s only one failed WDL task, we can just tell Toil to rerun the failed WDLTaskJob by Python class name:

toil debug-job ./store WDLTaskJob

Any of these will run the job (including any containers) on the local machine, where its execution can be observed live or monitored with a debugger.

Fetching Job Inputs

The --retrieveTaskDirectory option to toil debug-job allows you to send the input files for a job to a directory, and then stop running the job. It works for CWL and WDL jobs, and for Python workflows that call toil.job.Job.files_downloaded_hook() after downloading their files. It will make the worker work in the specified directory, so the job’s temporary directory will be at worker/job inside it. For WDL and CWL jobs that mount files into containers, there will also be an inside directory populated with symlinks to the files as they would be visible from the root of the container’s filesystem.

For example, say you have a broken WDL workflow named example_alwaysfail_with_files.wdl, like this:

version 1.0
workflow test {
    call make_file as f1
    call make_file as f2
    call hello {
task make_file {
    input {
    command <<<
        echo "These are the contents" >test.txt
    output {
        File out = "test.txt"
task hello {
    input {
        File name_file
        File? unused_file
    command <<<
        set -e
        echoo "Hello" "$(cat ~{name_file})"
    output {
        File out = stdout()

You can try and fail to run it like this:

toil-wdl-runner --jobStore ./store example_alwaysfail_with_files.wdl --retryCount 0

If you then dump the files from the failing job:

toil debug-job ./store WDLTaskJob --retrieveTaskDirectory dumpdir

You will end up with a directory tree that looks, accorfing to tree, something like this:

├── inside
│   └── mnt
│       └── miniwdl_task_container
│           └── work
│               └── _miniwdl_inputs
│                   ├── 0
│                   │   └── test.txt -> ../../../../../../worker/job/2c6b3dc4-1d21-4abf-9937-db475e6a6bc2/test.txt
│                   └── 1
│                       └── test.txt -> ../../../../../../worker/job/e3d724e1-e6cc-4165-97f1-6f62ab0fb1ef/test.txt
└── worker
    └── job
        ├── 2c6b3dc4-1d21-4abf-9937-db475e6a6bc2
        │   └── test.txt
        ├── e3d724e1-e6cc-4165-97f1-6f62ab0fb1ef
        │   └── test.txt
        ├── tmpr2j5yaic
        ├── tmpxqr9__y4
        └── work

15 directories, 4 files

You can see where Toil downloaded the input files for the job to the worker’s temporary directory, and how they would be mounted into the container.

Introspecting the Job Store

Note: Currently these features are only implemented for use locally (single machine) with the fileJobStore.

To view what files currently reside in the jobstore, run the following command:

$ toil debug-file file:path-to-jobstore-directory \

When run from the commandline, this should generate a file containing the contents of the job store (in addition to displaying a series of log messages to the terminal). This file is named “jobstore_files.txt” by default and will be generated in the current working directory.

If one wishes to copy any of these files to a local directory, one can run for example:

$ toil debug-file file:path-to-jobstore \
      --fetch overview.txt *.bam *.fastq \

To fetch overview.txt, and all .bam and .fastq files. This can be used to recover previously used input and output files for debugging or reuse in other workflows, or use in general debugging to ensure that certain outputs were imported into the jobStore.

Stats and Status

See Stats Command and Status Command for more about gathering statistics about job success, runtime, and resource usage from workflows.

Using a Python debugger

If you execute a workflow using the --debugWorker flag, or if you use toil debug-job, Toil will run the job in the process you started from the command line. This means you can either use pdb, or an IDE that supports debugging Python to interact with the Python process as it runs your job. Note that the --debugWorker flag will only work with the single_machine batch system (the default), and not any of the custom job schedulers.