Toil Documentation

Toil is an open-source pure-Python workflow engine that lets people write better pipelines.

Check out our website for a comprehensive list of Toil’s features and read our paper to learn what Toil can do in the real world. Please subscribe to our low-volume announce mailing list and feel free to also join us on GitHub and Gitter.

If using Toil for your research, please cite

Vivian, J., Rao, A. A., Nothaft, F. A., Ketchum, C., Armstrong, J., Novak, A., … Paten, B. (2017). Toil enables reproducible, open source, big biomedical data analyses. Nature Biotechnology, 35(4), 314–316. http://doi.org/10.1038/nbt.3772

Installation

This document describes how to prepare for and install Toil. Note that Toil requires that the user run all commands inside of a Python virtualenv. Instructions for installing and creating a Python virtual environment are provided below.

Preparing Your Python Runtime Environment

Toil currently supports Python 3.7, 3.8, 3.9, and 3.10, and requires a virtualenv to be active to install.

If not already present, please install the latest Python virtualenv using pip:

$ sudo pip install virtualenv

And create a virtual environment called venv in your home directory:

$ virtualenv ~/venv

If the user does not have root privileges, there are a few more steps, but one can download a specific virtualenv package directly, untar the file, create, and source the virtualenv (version 15.1.0 as an example) using

$ curl -O https://pypi.python.org/packages/d4/0c/9840c08189e030873387a73b90ada981885010dd9aea134d6de30cd24cb8/virtualenv-15.1.0.tar.gz
$ tar xvfz virtualenv-15.1.0.tar.gz
$ cd virtualenv-15.1.0
$ python virtualenv.py ~/venv

Now that you’ve created your virtualenv, activate your virtual environment:

$ source ~/venv/bin/activate

Basic Installation

If you need only the basic version of Toil, it can be easily installed using pip:

$ pip install toil

Now you’re ready to run your first Toil workflow!

(If you need any of the extra features don’t do this yet and instead skip to the next section.)

Installing Toil with Extra Features

Python headers and static libraries

Needed for the mesos, aws, google, and encryption extras.

On Ubuntu:

$ sudo apt-get install build-essential python-dev

On macOS:

$ xcode-select --install

Encryption specific headers and library

Needed for the encryption extra.

On Ubuntu:

$ sudo apt-get install libssl-dev libffi-dev

On macOS:

$ brew install libssl libffi

Or see Cryptography for other systems.

Some optional features, called extras, are not included in the basic installation of Toil. To install Toil with all its bells and whistles, first install any necessary headers and libraries (python-dev, libffi-dev). Then run

$ pip install toil[aws,google,mesos,encryption,cwl,wdl,kubernetes,server]

or

$ pip install toil[all]

Here’s what each extra provides:

Extra	Description
all	Installs all extras (though htcondor is linux-only and will be skipped if not on a linux computer).
aws	Provides support for managing a cluster on Amazon Web Service (AWS) using Toil’s built in Cluster Utilities. Clusters can scale up and down automatically. It also supports storing workflow state.
google	Experimental. Stores workflow state in Google Cloud Storage.
mesos	Provides support for running Toil on an Apache Mesos cluster. Note that running Toil on other batch systems does not require an extra. The mesos extra requires the following native dependencies: Apache Mesos (Tested with Mesos v1.0.0) Python headers and static libraries Important If launching toil remotely on a mesos instance, to install Toil with the mesos extra in a virtualenv, be sure to create that virtualenv with the --system-site-packages flag (only use remotely!): $ virtualenv ~/venv --system-site-packages Otherwise, you’ll see something like this: ImportError: No module named mesos.native
htcondor	Support for the htcondor batch system. This currently is a linux only extra.
encryption	Provides client-side encryption for files stored in the AWS job store. This extra requires the following native dependencies: Python headers and static libraries libffi headers and library
cwl	Provides support for running workflows written using the Common Workflow Language.
wdl	Provides support for running workflows written using the Workflow Description Language. This extra has no native dependencies.
kubernetes	Provides support for running workflows written using a Kubernetes cluster.
server	Provides support for Toil server mode, including support for the GA4GH Workflow Execution Service API.

Building from Source

If developing with Toil, you will need to build from source. This allows changes you make to Toil to be reflected immediately in your runtime environment.

First, clone the source:

$ git clone https://github.com/DataBiosphere/toil.git
$ cd toil

Then, create and activate a virtualenv:

$ virtualenv venv
$ . venv/bin/activate

From there, you can list all available Make targets by running make. First and foremost, we want to install Toil’s build requirements (these are additional packages that Toil needs to be tested and built but not to be run):

$ make prepare

Now, we can install Toil in development mode (such that changes to the source code will immediately affect the virtualenv):

$ make develop

Or, to install with support for all optional Installing Toil with Extra Features:

$ make develop extras=[aws,mesos,google,encryption,cwl]

Or:

$ make develop extras=[all]

To build the docs, run make develop with all extras followed by

$ make docs

To run a quick batch of tests (this should take less than 30 minutes) run

$ export TOIL_TEST_QUICK=True; make test

For more information on testing see Running Tests.

Quickstart Examples

Running a basic workflow

A Toil workflow can be run with just three steps:

1. Install Toil (see Installation)
2. Copy and paste the following code block into a new file called helloWorld.py:

from toil.common import Toil
from toil.job import Job


def helloWorld(message, memory="1G", cores=1, disk="1G"):
    return f"Hello, world!, here's a message: {message}"


if __name__ == "__main__":
    parser = Job.Runner.getDefaultArgumentParser()
    options = parser.parse_args()
    options.clean = "always"
    with Toil(options) as toil:
        output = toil.start(Job.wrapFn(helloWorld, "You did it!"))
    print(output)

3. Specify the name of the job store and run the workflow:

   (venv) $ python helloWorld.py file:my-job-store
   

Note

Don’t actually type (venv) $ in at the beginning of each command. This is intended only to remind the user that they should have their virtual environment running.

Congratulations! You’ve run your first Toil workflow using the default Batch System, singleMachine, using the file job store.

Toil uses batch systems to manage the jobs it creates.

The singleMachine batch system is primarily used to prepare and debug workflows on a local machine. Once validated, try running them on a full-fledged batch system (see Batch System API). Toil supports many different batch systems such as Apache Mesos and Grid Engine; its versatility makes it easy to run your workflow in all kinds of places.

Toil is totally customizable! Run python helloWorld.py --help to see a complete list of available options.

For something beyond a “Hello, world!” example, refer to A (more) real-world example.

Running a basic CWL workflow

The Common Workflow Language (CWL) is an emerging standard for writing workflows that are portable across multiple workflow engines and platforms. Running CWL workflows using Toil is easy.

1. First ensure that Toil is installed with the cwl extra (see Installing Toil with Extra Features):

   (venv) $ pip install 'toil[cwl]'
   

   This installs the toil-cwl-runner executable.

2. Copy and paste the following code block into example.cwl:

   cwlVersion: v1.0
   class: CommandLineTool
   baseCommand: echo
   stdout: output.txt
   inputs:
     message:
       type: string
       inputBinding:
         position: 1
   outputs:
     output:
       type: stdout
   

   and this code into example-job.yaml:

   message: Hello world!
   

3. To run the workflow simply enter

   (venv) $ toil-cwl-runner example.cwl example-job.yaml
   

   Your output will be in output.txt:

   (venv) $ cat output.txt
   Hello world!
   

To learn more about CWL, see the CWL User Guide (from where this example was shamelessly borrowed).

To run this workflow on an AWS cluster have a look at Running a CWL Workflow on AWS.

For information on using CWL with Toil see the section CWL in Toil

Running a basic WDL workflow

The Workflow Description Language (WDL) is another emerging language for writing workflows that are portable across multiple workflow engines and platforms. Running WDL workflows using Toil is still in alpha, and currently experimental. Toil currently supports basic workflow syntax (see WDL in Toil for more details and examples). Here we go over running a basic WDL helloworld workflow.

1. First ensure that Toil is installed with the wdl extra (see Installing Toil with Extra Features):

   (venv) $ pip install 'toil[wdl]'
   

   This installs the toil-wdl-runner executable.

2. Copy and paste the following code block into wdl-helloworld.wdl:

       workflow write_simple_file {
         call write_file
       }
       task write_file {
         String message
         command { echo ${message} > wdl-helloworld-output.txt }
         output { File test = "wdl-helloworld-output.txt" }
       }
   
   and this code into ``wdl-helloworld.json``::
   
       {
         "write_simple_file.write_file.message": "Hello world!"
       }
   

3. To run the workflow simply enter

   (venv) $ toil-wdl-runner wdl-helloworld.wdl wdl-helloworld.json
   

   Your output will be in wdl-helloworld-output.txt:

   (venv) $ cat wdl-helloworld-output.txt
   Hello world!
   

To learn more about WDL, see the main WDL website .

A (more) real-world example

For a more detailed example and explanation, we’ve developed a sample pipeline that merge-sorts a temporary file. This is not supposed to be an efficient sorting program, rather a more fully worked example of what Toil is capable of.

Running the example

1. Download the example code

2. Run it with the default settings:

   (venv) $ python sort.py file:jobStore
   

   The workflow created a file called sortedFile.txt in your current directory. Have a look at it and notice that it contains a whole lot of sorted lines!

   This workflow does a smart merge sort on a file it generates, fileToSort.txt. The sort is smart because each step of the process—splitting the file into separate chunks, sorting these chunks, and merging them back together—is compartmentalized into a job. Each job can specify its own resource requirements and will only be run after the jobs it depends upon have run. Jobs without dependencies will be run in parallel.

Note

Delete fileToSort.txt before moving on to #3. This example introduces options that specify dimensions for fileToSort.txt, if it does not already exist. If it exists, this workflow will use the existing file and the results will be the same as #2.

3. Run with custom options:

   (venv) $ python sort.py file:jobStore \
                --numLines=5000 \
                --lineLength=10 \
                --overwriteOutput=True \
                --workDir=/tmp/
   

   Here we see that we can add our own options to a Toil script. As noted above, the first two options, --numLines and --lineLength, determine the number of lines and how many characters are in each line. --overwriteOutput causes the current contents of sortedFile.txt to be overwritten, if it already exists. The last option, --workDir, is an option built into Toil to specify where temporary files unique to a job are kept.

Describing the source code

To understand the details of what’s going on inside. Let’s start with the main() function. It looks like a lot of code, but don’t worry—we’ll break it down piece by piece.

def main(options=None):
    if not options:
        # deal with command line arguments
        parser = ArgumentParser()
        Job.Runner.addToilOptions(parser)
        parser.add_argument('--numLines', default=defaultLines, help='Number of lines in file to sort.', type=int)
        parser.add_argument('--lineLength', default=defaultLineLen, help='Length of lines in file to sort.', type=int)
        parser.add_argument("--fileToSort", help="The file you wish to sort")
        parser.add_argument("--outputFile", help="Where the sorted output will go")
        parser.add_argument("--overwriteOutput", help="Write over the output file if it already exists.", default=True)
        parser.add_argument("--N", dest="N",
                            help="The threshold below which a serial sort function is used to sort file. "
                                 "All lines must of length less than or equal to N or program will fail",
                            default=10000)
        parser.add_argument('--downCheckpoints', action='store_true',
                            help='If this option is set, the workflow will make checkpoints on its way through'
                                 'the recursive "down" part of the sort')
        parser.add_argument("--sortMemory", dest="sortMemory",
                        help="Memory for jobs that sort chunks of the file.",
                        default=None)

        parser.add_argument("--mergeMemory", dest="mergeMemory",
                        help="Memory for jobs that collate results.",
                        default=None)

        options = parser.parse_args()
    if not hasattr(options, "sortMemory") or not options.sortMemory:
        options.sortMemory = sortMemory
    if not hasattr(options, "mergeMemory") or not options.mergeMemory:
        options.mergeMemory = sortMemory

    # do some input verification
    sortedFileName = options.outputFile or "sortedFile.txt"
    if not options.overwriteOutput and os.path.exists(sortedFileName):
        print(f'Output file {sortedFileName} already exists.  '
              f'Delete it to run the sort example again or use --overwriteOutput=True')
        exit()

    fileName = options.fileToSort
    if options.fileToSort is None:
        # make the file ourselves
        fileName = 'fileToSort.txt'
        if os.path.exists(fileName):
            print(f'Sorting existing file: {fileName}')
        else:
            print(f'No sort file specified. Generating one automatically called: {fileName}.')
            makeFileToSort(fileName=fileName, lines=options.numLines, lineLen=options.lineLength)
    else:
        if not os.path.exists(options.fileToSort):
            raise RuntimeError("File to sort does not exist: %s" % options.fileToSort)

    if int(options.N) <= 0:
        raise RuntimeError("Invalid value of N: %s" % options.N)

    # Now we are ready to run
    with Toil(options) as workflow:
        sortedFileURL = 'file://' + os.path.abspath(sortedFileName)
        if not workflow.options.restart:
            sortFileURL = 'file://' + os.path.abspath(fileName)
            sortFileID = workflow.importFile(sortFileURL)
            sortedFileID = workflow.start(Job.wrapJobFn(setup,
                                                        sortFileID,
                                                        int(options.N),
                                                        options.downCheckpoints,
                                                        options=options,
                                                        memory=sortMemory))
        else:
            sortedFileID = workflow.restart()
        workflow.exportFile(sortedFileID, sortedFileURL)

First we make a parser to process command line arguments using the argparse module. It’s important that we add the call to Job.Runner.addToilOptions() to initialize our parser with all of Toil’s default options. Then we add the command line arguments unique to this workflow, and parse the input. The help message listed with the arguments should give you a pretty good idea of what they can do.

Next we do a little bit of verification of the input arguments. The option --fileToSort allows you to specify a file that needs to be sorted. If this option isn’t given, it’s here that we make our own file with the call to makeFileToSort().

Finally we come to the context manager that initializes the workflow. We create a path to the input file prepended with 'file://' as per the documentation for toil.common.Toil() when staging a file that is stored locally. Notice that we have to check whether or not the workflow is restarting so that we don’t import the file more than once. Finally we can kick off the workflow by calling toil.common.Toil.start() on the job setup. When the workflow ends we capture its output (the sorted file’s fileID) and use that in toil.common.Toil.exportFile() to move the sorted file from the job store back into “userland”.

Next let’s look at the job that begins the actual workflow, setup.

def setup(job, inputFile, N, downCheckpoints, options):
    """
    Sets up the sort.
    Returns the FileID of the sorted file
    """
    RealtimeLogger.info("Starting the merge sort")
    return job.addChildJobFn(down,
                             inputFile, N, 'root',
                             downCheckpoints,
                             options = options,
                             preemptable=True,
                             memory=sortMemory).rv()

setup really only does two things. First it writes to the logs using Job.log() and then calls addChildJobFn(). Child jobs run directly after the current job. This function turns the ‘job function’ down into an actual job and passes in the inputs including an optional resource requirement, memory. The job doesn’t actually get run until the call to Job.rv(). Once the job down finishes, its output is returned here.

Now we can look at what down does.

def down(job, inputFileStoreID, N, path, downCheckpoints, options, memory=sortMemory):
    """
    Input is a file, a subdivision size N, and a path in the hierarchy of jobs.
    If the range is larger than a threshold N the range is divided recursively and
    a follow on job is then created which merges back the results else
    the file is sorted and placed in the output.
    """

    RealtimeLogger.info("Down job starting: %s" % path)

    # Read the file
    inputFile = job.fileStore.readGlobalFile(inputFileStoreID, cache=False)
    length = os.path.getsize(inputFile)
    if length > N:
        # We will subdivide the file
        RealtimeLogger.critical("Splitting file: %s of size: %s"
                % (inputFileStoreID, length))
        # Split the file into two copies
        midPoint = getMidPoint(inputFile, 0, length)
        t1 = job.fileStore.getLocalTempFile()
        with open(t1, 'w') as fH:
            fH.write(copySubRangeOfFile(inputFile, 0, midPoint+1))
        t2 = job.fileStore.getLocalTempFile()
        with open(t2, 'w') as fH:
            fH.write(copySubRangeOfFile(inputFile, midPoint+1, length))
        # Call down recursively. By giving the rv() of the two jobs as inputs to the follow-on job, up,
        # we communicate the dependency without hindering concurrency.
        result = job.addFollowOnJobFn(up,
                                    job.addChildJobFn(down, job.fileStore.writeGlobalFile(t1), N, path + '/0',
                                                      downCheckpoints, checkpoint=downCheckpoints, options=options,
                                                      preemptable=True, memory=options.sortMemory).rv(),
                                    job.addChildJobFn(down, job.fileStore.writeGlobalFile(t2), N, path + '/1',
                                                      downCheckpoints, checkpoint=downCheckpoints, options=options,
                                                      preemptable=True, memory=options.mergeMemory).rv(),
                                    path + '/up', preemptable=True, options=options, memory=options.sortMemory).rv()
    else:
        # We can sort this bit of the file
        RealtimeLogger.critical("Sorting file: %s of size: %s"
                % (inputFileStoreID, length))
        # Sort the copy and write back to the fileStore
        shutil.copyfile(inputFile, inputFile + '.sort')
        sort(inputFile + '.sort')
        result = job.fileStore.writeGlobalFile(inputFile + '.sort')

    RealtimeLogger.info("Down job finished: %s" % path)
    return result

Down is the recursive part of the workflow. First we read the file into the local filestore by calling job.fileStore.readGlobalFile(). This puts a copy of the file in the temp directory for this particular job. This storage will disappear once this job ends. For a detailed explanation of the filestore, job store, and their interfaces have a look at Managing files within a workflow.

Next down checks the base case of the recursion: is the length of the input file less than N (remember N was an option we added to the workflow in main)? In the base case, we just sort the file, and return the file ID of this new sorted file.

If the base case fails, then the file is split into two new tempFiles using job.fileStore.getLocalTempFile() and the helper function copySubRangeOfFile. Finally we add a follow on Job up with job.addFollowOnJobFn(). We’ve already seen child jobs. A follow-on Job is a job that runs after the current job and all of its children (and their children and follow-ons) have completed. Using a follow-on makes sense because up is responsible for merging the files together and we don’t want to merge the files together until we know they are sorted. Again, the return value of the follow-on job is requested using Job.rv().

Looking at up

def up(job, inputFileID1, inputFileID2, path, options, memory=sortMemory):
    """
    Merges the two files and places them in the output.
    """

    RealtimeLogger.info("Up job starting: %s" % path)

    with job.fileStore.writeGlobalFileStream() as (fileHandle, outputFileStoreID):
        fileHandle = codecs.getwriter('utf-8')(fileHandle)
        with job.fileStore.readGlobalFileStream(inputFileID1) as inputFileHandle1:
            inputFileHandle1 = codecs.getreader('utf-8')(inputFileHandle1)
            with job.fileStore.readGlobalFileStream(inputFileID2) as inputFileHandle2:
                inputFileHandle2 = codecs.getreader('utf-8')(inputFileHandle2)
                RealtimeLogger.info("Merging %s and %s to %s"
                    % (inputFileID1, inputFileID2, outputFileStoreID))
                merge(inputFileHandle1, inputFileHandle2, fileHandle)
        # Cleanup up the input files - these deletes will occur after the completion is successful.
        job.fileStore.deleteGlobalFile(inputFileID1)
        job.fileStore.deleteGlobalFile(inputFileID2)

        RealtimeLogger.info("Up job finished: %s" % path)

        return outputFileStoreID

we see that the two input files are merged together and the output is written to a new file using job.fileStore.writeGlobalFileStream(). After a little cleanup, the output file is returned.

Once the final up finishes and all of the rv() promises are fulfilled, main receives the sorted file’s ID which it uses in exportFile to send it to the user.

There are other things in this example that we didn’t go over such as Checkpoints and the details of much of the Toil Class API.

At the end of the script the lines

if __name__ == '__main__'
    main()

are included to ensure that the main function is only run once in the ‘__main__’ process invoked by you, the user. In Toil terms, by invoking the script you created the leader process in which the main() function is run. A worker process is a separate process whose sole purpose is to host the execution of one or more jobs defined in that script. In any Toil workflow there is always one leader process, and potentially many worker processes.

When using the single-machine batch system (the default), the worker processes will be running on the same machine as the leader process. With full-fledged batch systems like Mesos the worker processes will typically be started on separate machines. The boilerplate ensures that the pipeline is only started once—on the leader—but not when its job functions are imported and executed on the individual workers.

Typing python sort.py --help will show the complete list of arguments for the workflow which includes both Toil’s and ones defined inside sort.py. A complete explanation of Toil’s arguments can be found in Commandline Options.

Logging

By default, Toil logs a lot of information related to the current environment in addition to messages from the batch system and jobs. This can be configured with the --logLevel flag. For example, to only log CRITICAL level messages to the screen:

(venv) $ python sort.py file:jobStore \
             --logLevel=critical \
             --overwriteOutput=True

This hides most of the information we get from the Toil run. For more detail, we can run the pipeline with --logLevel=debug to see a comprehensive output. For more information, see Commandline Options.

Error Handling and Resuming Pipelines

With Toil, you can recover gracefully from a bug in your pipeline without losing any progress from successfully completed jobs. To demonstrate this, let’s add a bug to our example code to see how Toil handles a failure and how we can resume a pipeline after that happens. Add a bad assertion at line 52 of the example (the first line of down()):

def down(job, inputFileStoreID, N, downCheckpoints, memory=sortMemory):
    ...
    assert 1 == 2, "Test error!"

When we run the pipeline, Toil will show a detailed failure log with a traceback:

(venv) $ python sort.py file:jobStore
...
---TOIL WORKER OUTPUT LOG---
...
m/j/jobonrSMP    Traceback (most recent call last):
m/j/jobonrSMP      File "toil/src/toil/worker.py", line 340, in main
m/j/jobonrSMP        job._runner(jobGraph=jobGraph, jobStore=jobStore, fileStore=fileStore)
m/j/jobonrSMP      File "toil/src/toil/job.py", line 1270, in _runner
m/j/jobonrSMP        returnValues = self._run(jobGraph, fileStore)
m/j/jobonrSMP      File "toil/src/toil/job.py", line 1217, in _run
m/j/jobonrSMP        return self.run(fileStore)
m/j/jobonrSMP      File "toil/src/toil/job.py", line 1383, in run
m/j/jobonrSMP        rValue = userFunction(*((self,) + tuple(self._args)), **self._kwargs)
m/j/jobonrSMP      File "toil/example.py", line 30, in down
m/j/jobonrSMP        assert 1 == 2, "Test error!"
m/j/jobonrSMP    AssertionError: Test error!

If we try and run the pipeline again, Toil will give us an error message saying that a job store of the same name already exists. By default, in the event of a failure, the job store is preserved so that the workflow can be restarted, starting from the previously failed jobs. We can restart the pipeline by running

(venv) $ python sort.py file:jobStore \
             --restart \
             --overwriteOutput=True

We can also change the number of times Toil will attempt to retry a failed job:

(venv) $ python sort.py file:jobStore \
             --retryCount 2 \
             --restart \
             --overwriteOutput=True

You’ll now see Toil attempt to rerun the failed job until it runs out of tries. --retryCount is useful for non-systemic errors, like downloading a file that may experience a sporadic interruption, or some other non-deterministic failure.

To successfully restart our pipeline, we can edit our script to comment out line 30, or remove it, and then run

(venv) $ python sort.py file:jobStore \
             --restart \
             --overwriteOutput=True

The pipeline will run successfully, and the job store will be removed on the pipeline’s completion.

Collecting Statistics

Please see the Stats Command section for more on gathering runtime and resource info on jobs.

Launching a Toil Workflow in AWS

After having installed the aws extra for Toil during the Installation and set up AWS (see Preparing your AWS environment), the user can run the basic helloWorld.py script (Running a basic workflow) on a VM in AWS just by modifying the run command.

Note that when running in AWS, users can either run the workflow on a single instance or run it on a cluster (which is running across multiple containers on multiple AWS instances). For more information on running Toil workflows on a cluster, see Running in AWS.

Also! Remember to use the Destroy-Cluster Command command when finished to destroy the cluster! Otherwise things may not be cleaned up properly.

1. Launch a cluster in AWS using the Launch-Cluster Command command:

   (venv) $ toil launch-cluster <cluster-name> \
                --keyPairName <AWS-key-pair-name> \
                --leaderNodeType t2.medium \
                --zone us-west-2a
   

   The arguments keyPairName, leaderNodeType, and zone are required to launch a cluster.

2. Copy helloWorld.py to the /tmp directory on the leader node using the Rsync-Cluster Command command:

   (venv) $ toil rsync-cluster --zone us-west-2a <cluster-name> helloWorld.py :/tmp
   

   Note that the command requires defining the file to copy as well as the target location on the cluster leader node.

3. Login to the cluster leader node using the Ssh-Cluster Command command:

   (venv) $ toil ssh-cluster --zone us-west-2a <cluster-name>
   

   Note that this command will log you in as the root user.

4. Run the Toil script in the cluster:

   $ python /tmp/helloWorld.py aws:us-west-2:my-S3-bucket
   

   In this particular case, we create an S3 bucket called my-S3-bucket in the us-west-2 availability zone to store intermediate job results.

   Along with some other INFO log messages, you should get the following output in your terminal window: Hello, world!, here's a message: You did it!.

5. Exit from the SSH connection.

   $ exit
   

6. Use the Destroy-Cluster Command command to destroy the cluster:

   (venv) $ toil destroy-cluster --zone us-west-2a <cluster-name>
   

   Note that this command will destroy the cluster leader node and any resources created to run the job, including the S3 bucket.

Running a CWL Workflow on AWS

After having installed the aws and cwl extras for Toil during the Installation and set up AWS (see Preparing your AWS environment), the user can run a CWL workflow with Toil on AWS.

Also! Remember to use the Destroy-Cluster Command command when finished to destroy the cluster! Otherwise things may not be cleaned up properly.

1. First launch a node in AWS using the Launch-Cluster Command command:

   (venv) $ toil launch-cluster <cluster-name> \
                --keyPairName <AWS-key-pair-name> \
                --leaderNodeType t2.medium \
                --zone us-west-2a
   

2. Copy example.cwl and example-job.yaml from the CWL example to the node using the Rsync-Cluster Command command:

   (venv) $ toil rsync-cluster --zone us-west-2a <cluster-name> example.cwl :/tmp
   (venv) $ toil rsync-cluster --zone us-west-2a <cluster-name> example-job.yaml :/tmp
   

3. SSH into the cluster’s leader node using the Ssh-Cluster Command utility:

   (venv) $ toil ssh-cluster --zone us-west-2a <cluster-name>
   

4. Once on the leader node, it’s a good idea to update and install the following:

   sudo apt-get update
   sudo apt-get -y upgrade
   sudo apt-get -y dist-upgrade
   sudo apt-get -y install git
   sudo pip install mesos.cli
   

5. Now create a new virtualenv with the --system-site-packages option and activate:

   virtualenv --system-site-packages venv
   source venv/bin/activate
   

6. Now run the CWL workflow:

   (venv) $ toil-cwl-runner \
                --provisioner aws \
                --jobStore aws:us-west-2a:any-name \
                /tmp/example.cwl /tmp/example-job.yaml
   

   Tip

   When running a CWL workflow on AWS, input files can be provided either on the local file system or in S3 buckets using s3:// URI references. Final output files will be copied to the local file system of the leader node.

7. Finally, log out of the leader node and from your local computer, destroy the cluster:

   (venv) $ toil destroy-cluster --zone us-west-2a <cluster-name>
   

Running a Workflow with Autoscaling - Cactus

Cactus is a reference-free, whole-genome multiple alignment program that can be run on any of the cloud platforms Toil supports.

Note

Cloud Independence:

This example provides a “cloud agnostic” view of running Cactus with Toil. Most options will not change between cloud providers. However, each provisioner has unique inputs for --leaderNodeType, --nodeType and --zone. We recommend the following:

Option	Used in	AWS	Google
--leaderNodeType	launch-cluster	t2.medium	n1-standard-1
--zone	launch-cluster	us-west-2a	us-west1-a
--zone	cactus	us-west-2
--nodeType	cactus	c3.4xlarge	n1-standard-8

When executing toil launch-cluster with gce specified for --provisioner, the option --boto must be specified and given a path to your .boto file. See Running in Google Compute Engine (GCE) for more information about the --boto option.

Also! Remember to use the Destroy-Cluster Command command when finished to destroy the cluster! Otherwise things may not be cleaned up properly.

1. Download pestis.tar.gz

2. Launch a leader node using the Launch-Cluster Command command:

   (venv) $ toil launch-cluster <cluster-name> \
                --provisioner <aws, gce> \
                --keyPairName <key-pair-name> \
                --leaderNodeType <type> \
                --zone <zone>
   

   Note

   A Helpful Tip

   When using AWS, setting the environment variable eliminates having to specify the --zone option for each command. This will be supported for GCE in the future.

   (venv) $ export TOIL_AWS_ZONE=us-west-2c
   

3. Create appropriate directory for uploading files:

   (venv) $ toil ssh-cluster --provisioner <aws, gce> <cluster-name>
   $ mkdir /root/cact_ex
   $ exit
   

4. Copy the required files, i.e., seqFile.txt (a text file containing the locations of the input sequences as well as their phylogenetic tree, see here), organisms’ genome sequence files in FASTA format, and configuration files (e.g. blockTrim1.xml, if desired), up to the leader node:

   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> pestis-short-aws-seqFile.txt :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000169655.1_ASM16965v1_genomic.fna :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000006645.1_ASM664v1_genomic.fna :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000182485.1_ASM18248v1_genomic.fna :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000013805.1_ASM1380v1_genomic.fna :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> setup_leaderNode.sh :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> blockTrim1.xml :/root/cact_ex
   (venv) $ toil rsync-cluster --provisioner <aws, gce> <cluster-name> blockTrim3.xml :/root/cact_ex
   

5. Log in to the leader node:

   (venv) $ toil ssh-cluster --provisioner <aws, gce> <cluster-name>
   

6. Set up the environment of the leader node to run Cactus:

   $ bash /root/cact_ex/setup_leaderNode.sh
   $ source cact_venv/bin/activate
   (cact_venv) $ cd cactus
   (cact_venv) $ pip install --upgrade .
   

7. Run Cactus as an autoscaling workflow:

   (cact_venv) $ TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:3.14.0 cactus \
                     --provisioner <aws, gce> \
                     --nodeType <type> \
                     --maxNodes 2 \
                     --minNodes 0 \
                     --retry 10 \
                     --batchSystem mesos \
                     --logDebug \
                     --logFile /logFile_pestis3 \
                     --configFile \
                     /root/cact_ex/blockTrim3.xml <aws, google>:<zone>:cactus-pestis \
                     /root/cact_ex/pestis-short-aws-seqFile.txt \
                     /root/cact_ex/pestis_output3.hal
   

   Note

   Pieces of the Puzzle:

   TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:3.14.0 — specifies the version of Toil being used, 3.14.0; if the latest one is desired, please eliminate.

   --nodeType — determines the instance type used for worker nodes. The instance type specified here must be on the same cloud provider as the one specified with --leaderNodeType

   --maxNodes 2 — creates up to two instances of the type specified with --nodeType and launches Mesos worker containers inside them.

   --logDebug — equivalent to --logLevel DEBUG.

   --logFile /logFile_pestis3 — writes logs in a file named logFile_pestis3 under / folder.

   --configFile — this is not required depending on whether a specific configuration file is intended to run the alignment.

   <aws, google>:<zone>:cactus-pestis — creates a bucket, named cactus-pestis, with the specified cloud provider to store intermediate job files and metadata. NOTE: If you want to use a GCE-based jobstore, specify google here, not gce.

   The result file, named pestis_output3.hal, is stored under /root/cact_ex folder of the leader node.

   Use cactus --help to see all the Cactus and Toil flags available.

8. Log out of the leader node:

   (cact_venv) $ exit
   

9. Download the resulted output to local machine:

   (venv) $ toil rsync-cluster \
                --provisioner <aws, gce> <cluster-name> \
                :/root/cact_ex/pestis_output3.hal \
                <path-of-folder-on-local-machine>
   

10. Destroy the cluster:

    (venv) $ toil destroy-cluster --provisioner <aws, gce> <cluster-name>
    

Introduction

Toil runs in various environments, including locally and in the cloud (Amazon Web Services and Google Compute Engine). Toil also supports two DSLs: CWL and (Amazon Web Services and Google Compute Engine). Toil also supports two DSLs: CWL and WDL (experimental).

Toil is built in a modular way so that it can be used on lots of different systems, and with different configurations. The three configurable pieces are the

• Job Store API: A filepath or url that can host and centralize all files for a workflow (e.g. a local folder, or an AWS s3 bucket url).
• Batch System API: Specifies either a local single-machine or a currently supported HPC environment (lsf, parasol, mesos, slurm, torque, htcondor, kubernetes, or grid_engine). Mesos is a special case, and is launched for cloud environments.
• Provisioner: For running in the cloud only. This specifies which cloud provider provides instances to do the “work” of your workflow.

Job Store

The job store is a storage abstraction which contains all of the information used in a Toil run. This centralizes all of the files used by jobs in the workflow and also the details of the progress of the run. If a workflow crashes or fails, the job store contains all of the information necessary to resume with minimal repetition of work.

Several different job stores are supported, including the file job store and cloud job stores.

File Job Store

The file job store is for use locally, and keeps the workflow information in a directory on the machine where the workflow is launched. This is the simplest and most convenient job store for testing or for small runs.

For an example that uses the file job store, see Running a basic workflow.

Cloud Job Stores

Toil currently supports the following cloud storage systems as job stores:

• AWS Job Store: An AWS S3 bucket formatted as “aws:<zone>:<bucketname>” where only numbers, letters, and dashes are allowed in the bucket name. Example: aws:us-west-2:my-aws-jobstore-name.
• Google Job Store: A Google Cloud Storage bucket formatted as “gce:<zone>:<bucketname>” where only numbers, letters, and dashes are allowed in the bucket name. Example: gce:us-west2-a:my-google-jobstore-name.

These use cloud buckets to house all of the files. This is useful if there are several different worker machines all running jobs that need to access the job store.

Batch System

A Toil batch system is either a local single-machine (one computer) or a currently supported HPC cluster of computers (lsf, parasol, mesos, slurm, torque, htcondor, or grid_engine). Mesos is a special case, and is launched for cloud environments. These environments manage individual worker nodes under a leader node to process the work required in a workflow. The leader and its workers all coordinate their tasks and files through a centralized job store location.

See Batch System API for a more detailed description of different batch systems.

Provisioner

The Toil provisioner provides a tool set for running a Toil workflow on a particular cloud platform.

The Cluster Utilities are command line tools used to provision nodes in your desired cloud platform. They allows you to launch nodes, ssh to the leader, and rsync files back and forth.

For detailed instructions for using the provisioner see Running in AWS or Running in Google Compute Engine (GCE).

Commandline Options

A quick way to see all of Toil’s commandline options is by executing the following on a toil script:

$ toil example.py --help

For a basic toil workflow, Toil has one mandatory argument, the job store. All other arguments are optional.

The Job Store

Running toil scripts requires a filepath or url to a centralizing location for all of the files of the workflow. This is Toil’s one required positional argument: the job store. To use the quickstart example, if you’re on a node that has a large /scratch volume, you can specify that the jobstore be created there by executing: python HelloWorld.py /scratch/my-job-store, or more explicitly, python HelloWorld.py file:/scratch/my-job-store.

Syntax for specifying different job stores:

Local: file:job-store-name

AWS: aws:region-here:job-store-name

Google: google:projectID-here:job-store-name

Different types of job store options can be found below.

Commandline Options

Core Toil Options Options to specify the location of the Toil workflow and turn on stats collation about the performance of jobs.

--workDir WORKDIR
	Absolute path to directory where temporary files generated during the Toil run should be placed. Standard output and error from batch system jobs (unless –noStdOutErr) will be placed in this directory. A cache directory may be placed in this directory. Temp files and folders will be placed in a toil-<workflowID> within workDir. The workflowID is generated by Toil and will be reported in the workflow logs. Default is determined by the variables (TMPDIR, TEMP, TMP) via mkdtemp. This directory needs to exist on all machines running jobs; if capturing standard output and error from batch system jobs is desired, it will generally need to be on a shared file system. When sharing a cache between containers on a host, this directory must be shared between the containers.
--coordinationDir COORDINATION_DIR
	Absolute path to directory where Toil will keep state and lock files. When sharing a cache between containers on a host, this directory must be shared between the containers.
--noStdOutErr	Do not capture standard output and error from batch system jobs.
--stats	Records statistics about the toil workflow to be used by ‘toil stats’.
--clean=STATE	Determines the deletion of the jobStore upon completion of the program. Choices: ‘always’, ‘onError’,’never’, or ‘onSuccess’. The --stats option requires information from the jobStore upon completion so the jobStore will never be deleted with that flag. If you wish to be able to restart the run, choose ‘never’ or ‘onSuccess’. Default is ‘never’ if stats is enabled, and ‘onSuccess’ otherwise
--cleanWorkDir STATE
	Determines deletion of temporary worker directory upon completion of a job. Choices: ‘always’, ‘onError’, ‘never’, or ‘onSuccess’. Default = always. WARNING: This option should be changed for debugging only. Running a full pipeline with this option could fill your disk with intermediate data.
--clusterStats FILEPATH
	If enabled, writes out JSON resource usage statistics to a file. The default location for this file is the current working directory, but an absolute path can also be passed to specify where this file should be written. This option only applies when using scalable batch systems.
--restart	If --restart is specified then will attempt to restart existing workflow at the location pointed to by the --jobStore option. Will raise an exception if the workflow does not exist.

Logging Options Toil hides stdout and stderr by default except in case of job failure. Log levels in toil are based on priority from the logging module:

--logOff	Only CRITICAL log levels are shown. Equivalent to --logLevel=OFF or --logLevel=CRITICAL.
--logCritical	Only CRITICAL log levels are shown. Equivalent to --logLevel=OFF or --logLevel=CRITICAL.
--logError	Only ERROR, and CRITICAL log levels are shown. Equivalent to --logLevel=ERROR.
--logWarning	Only WARN, ERROR, and CRITICAL log levels are shown. Equivalent to --logLevel=WARNING.
--logInfo	All log statements are shown, except DEBUG. Equivalent to --logLevel=INFO.
--logDebug	All log statements are shown. Equivalent to --logLevel=DEBUG.
--logLevel=LOGLEVEL
	May be set to: OFF (or CRITICAL), ERROR, WARN (or WARNING), INFO, or DEBUG.
--logFile FILEPATH
	Specifies a file path to write the logging output to.
--rotatingLogging
	Turn on rotating logging, which prevents log files from getting too big (set using --maxLogFileSize BYTESIZE).
--maxLogFileSize BYTESIZE
	The maximum size of a job log file to keep (in bytes), log files larger than this will be truncated to the last X bytes. Setting this option to zero will prevent any truncation. Setting this option to a negative value will truncate from the beginning. Default=62.5KiB Sets the maximum log file size in bytes (--rotatingLogging must be active).
--log-dir DIRPATH
	For CWL and local file system only. Log stdout and stderr (if tool requests stdout/stderr) to the DIRPATH.

Batch System Options

--batchSystem BATCHSYSTEM
	The type of batch system to run the job(s) with, currently can be one of aws_batch, parasol, single_machine, grid_engine, lsf, mesos, slurm, tes, torque, htcondor, kubernetes. (default: single_machine)
--disableAutoDeployment
	Should auto-deployment of the user script be deactivated? If True, the user script/package should be present at the same location on all workers. Default = False.
--maxLocalJobs MAXLOCALJOBS
	For batch systems that support a local queue for housekeeping jobs (Mesos, GridEngine, htcondor, lsf, slurm, torque). Specifies the maximum number of these housekeeping jobs to run on the local system. The default (equal to the number of cores) is a maximum of concurrent local housekeeping jobs.
--manualMemArgs
	Do not add the default arguments: ‘hv=MEMORY’ & ‘h_vmem=MEMORY’ to the qsub call, and instead rely on TOIL_GRIDGENGINE_ARGS to supply alternative arguments. Requires that TOIL_GRIDGENGINE_ARGS be set.
--runCwlInternalJobsOnWorkers
	Whether to run CWL internal jobs (e.g. CWLScatter) on the worker nodes instead of the primary node. If false (default), then all such jobs are run on the primary node. Setting this to true can speed up the pipeline for very large workflows with many sub-workflows and/or scatters, provided that the worker pool is large enough.
--coalesceStatusCalls
	Coalese status calls to prevent the batch system from being overloaded. Currently only supported for LSF.
--statePollingWait STATEPOLLINGWAIT
	Time, in seconds, to wait before doing a scheduler query for job state. Return cached results if within the waiting period. Only works for grid engine batch systems such as gridengine, htcondor, torque, slurm, and lsf.
--parasolCommand PARASOLCOMMAND
	The name or path of the parasol program. Will be looked up on PATH unless it starts with a slash. (default: parasol)
--parasolMaxBatches PARASOLMAXBATCHES
	Maximum number of job batches the Parasol batch is allowed to create. One batch is created for jobs with a unique set of resource requirements. (default: 1000)
--mesosEndpoint MESOSENDPOINT
	The host and port of the Mesos server separated by a colon. (default: <leader IP>:5050)
--kubernetesHostPath KUBERNETES_HOST_PATH
	Path on Kubernetes hosts to use as shared inter-pod temp directory.
--kubernetesOwner KUBERNETES_OWNER
	Username to mark Kubernetes jobs with.
--kubernetesServiceAccount KUBERNETES_SERVICE_ACCOUNT
	Service account to run jobs as.
--kubernetesPodTimeout KUBERNETES_POD_TIMEOUT
	Seconds to wait for a scheduled Kubernetes pod to start running. (default: 120s)
--tesEndpoint TES_ENDPOINT
	The http(s) URL of the TES server. (default: http://<leader IP>:8000)
--tesUser TES_USER
	User name to use for basic authentication to TES server.
--tesPassword TES_PASSWORD
	Password to use for basic authentication to TES server.
--tesBearerToken TES_BEARER_TOKEN
	Bearer token to use for authentication to TES server.
--awsBatchRegion AWS_BATCH_REGION
	The AWS region containing the AWS Batch queue to submit to.
--awsBatchQueue AWS_BATCH_QUEUE
	The name or ARN of the AWS Batch queue to submit to.
--awsBatchJobRoleArn AWS_BATCH_JOB_ROLE_ARN
	The ARN of an IAM role to run AWS Batch jobs as, so they can e.g. access a job store. Must be assumable by ecs-tasks.amazonaws.com
--scale SCALE	A scaling factor to change the value of all submitted tasks’ submitted cores. Used in single_machine batch system. Useful for running workflows on smaller machines than they were designed for, by setting a value less than 1. (default: 1)

Data Storage Options Allows configuring Toil’s data storage.

--linkImports	When using a filesystem based job store, CWL input files are by default symlinked in. Specifying this option instead copies the files into the job store, which may protect them from being modified externally. When not specified and as long as caching is enabled, Toil will protect the file automatically by changing the permissions to read-only.
--moveExports	When using a filesystem based job store, output files are by default moved to the output directory, and a symlink to the moved exported file is created at the initial location. Specifying this option instead copies the files into the output directory. Applies to filesystem-based job stores only.
--disableCaching
	Disables caching in the file store. This flag must be set to use a batch system that does not support cleanup, such as Parasol.

Autoscaling Options Allows the specification of the minimum and maximum number of nodes in an autoscaled cluster, as well as parameters to control the level of provisioning.

--provisioner CLOUDPROVIDER
	The provisioner for cluster auto-scaling. This is the main Toil --provisioner option, and defaults to None for running on single_machine and non-auto-scaling batch systems. The currently supported choices are ‘aws’ or ‘gce’.
--nodeTypes NODETYPES
	Specifies a list of comma-separated node types, each of which is composed of slash-separated instance types, and an optional spot bid set off by a colon, making the node type preemptable. Instance types may appear in multiple node types, and the same node type may appear as both preemptable and non-preemptable. Valid argument specifying two node types: c5.4xlarge/c5a.4xlarge:0.42,t2.large Node types: c5.4xlarge/c5a.4xlarge:0.42 and t2.large Instance types: c5.4xlarge, c5a.4xlarge, and t2.large Semantics: Bid $0.42/hour for either c5.4xlarge or c5a.4xlarge instances, treated interchangeably, while they are available at that price, and buy t2.large instances at full price
--minNodes MINNODES
	Minimum number of nodes of each type in the cluster, if using auto-scaling. This should be provided as a comma-separated list of the same length as the list of node types. default=0
--maxNodes MAXNODES
	Maximum number of nodes of each type in the cluster, if using autoscaling, provided as a comma-separated list. The first value is used as a default if the list length is less than the number of nodeTypes. default=10
--targetTime TARGETTIME
	Sets how rapidly you aim to complete jobs in seconds. Shorter times mean more aggressive parallelization. The autoscaler attempts to scale up/down so that it expects all queued jobs will complete within targetTime seconds. (Default: 1800)
--betaInertia BETAINERTIA
	A smoothing parameter to prevent unnecessary oscillations in the number of provisioned nodes. This controls an exponentially weighted moving average of the estimated number of nodes. A value of 0.0 disables any smoothing, and a value of 0.9 will smooth so much that few changes will ever be made. Must be between 0.0 and 0.9. (Default: 0.1)
--scaleInterval SCALEINTERVAL
	The interval (seconds) between assessing if the scale of the cluster needs to change. (Default: 60)
--preemptableCompensation PREEMPTABLECOMPENSATION
	The preference of the autoscaler to replace preemptable nodes with non-preemptable nodes, when preemptable nodes cannot be started for some reason. Defaults to 0.0. This value must be between 0.0 and 1.0, inclusive. A value of 0.0 disables such compensation, a value of 0.5 compensates two missing preemptable nodes with a non-preemptable one. A value of 1.0 replaces every missing pre-emptable node with a non-preemptable one.
--nodeStorage NODESTORAGE
	Specify the size of the root volume of worker nodes when they are launched in gigabytes. You may want to set this if your jobs require a lot of disk space. The default value is 50.
--nodeStorageOverrides NODESTORAGEOVERRIDES
	Comma-separated list of nodeType:nodeStorage that are used to override the default value from --nodeStorage for the specified nodeType(s). This is useful for heterogeneous jobs where some tasks require much more disk than others.
--metrics	Enable the prometheus/grafana dashboard for monitoring CPU/RAM usage, queue size, and issued jobs.
--assumeZeroOverhead
	Ignore scheduler and OS overhead and assume jobs can use every last byte of memory and disk on a node when autoscaling.

Service Options Allows the specification of the maximum number of service jobs in a cluster. By keeping this limited we can avoid nodes occupied with services causing deadlocks. (Not for CWL).

--maxServiceJobs MAXSERVICEJOBS
	The maximum number of service jobs that can be run concurrently, excluding service jobs running on preemptable nodes. default=9223372036854775807
--maxPreemptableServiceJobs MAXPREEMPTABLESERVICEJOBS
	The maximum number of service jobs that can run concurrently on preemptable nodes. default=9223372036854775807
--deadlockWait DEADLOCKWAIT
	Time, in seconds, to tolerate the workflow running only the same service jobs, with no jobs to use them, before declaring the workflow to be deadlocked and stopping. default=60
--deadlockCheckInterval DEADLOCKCHECKINTERVAL
	Time, in seconds, to wait between checks to see if the workflow is stuck running only service jobs, with no jobs to use them. Should be shorter than --deadlockWait. May need to be increased if the batch system cannot enumerate running jobs quickly enough, or if polling for running jobs is placing an unacceptable load on a shared cluster. default=30

Resource Options The options to specify default cores/memory requirements (if not specified by the jobs themselves), and to limit the total amount of memory/cores requested from the batch system.

--defaultMemory INT
	The default amount of memory to request for a job. Only applicable to jobs that do not specify an explicit value for this requirement. Standard suffixes like K, Ki, M, Mi, G or Gi are supported. Default is 2.0G
--defaultCores FLOAT
	The default number of CPU cores to dedicate a job. Only applicable to jobs that do not specify an explicit value for this requirement. Fractions of a core (for example 0.1) are supported on some batch systems, namely Mesos and singleMachine. Default is 1.0
--defaultDisk INT
	The default amount of disk space to dedicate a job. Only applicable to jobs that do not specify an explicit value for this requirement. Standard suffixes like K, Ki, M, Mi, G or Gi are supported. Default is 2.0G
--defaultAccelerators ACCELERATOR
	The default amount of accelerators to request for a job. Only applicable to jobs that do not specify an explicit value for this requirement. Each accelerator specification can have a type (gpu [default], nvidia, amd, cuda, rocm, opencl, or a specific model like nvidia-tesla-k80), and a count [default: 1]. If both a type and a count are used, they must be separated by a colon. If multiple types of accelerators are used, the specifications are separated by commas. Default is [].
--defaultPreemptable BOOL
	Make all jobs able to run on preemptable (spot) nodes by default.
--maxCores INT	The maximum number of CPU cores to request from the batch system at any one time. Standard suffixes like K, Ki, M, Mi, G or Gi are supported.
--maxMemory INT
	The maximum amount of memory to request from the batch system at any one time. Standard suffixes like K, Ki, M, Mi, G or Gi are supported.
--maxDisk INT	The maximum amount of disk space to request from the batch system at any one time. Standard suffixes like K, Ki, M, Mi, G or Gi are supported.

Options for rescuing/killing/restarting jobs. The options for jobs that either run too long/fail or get lost (some batch systems have issues!).

--retryCount RETRYCOUNT
	Number of times to retry a failing job before giving up and labeling job failed. default=1
--enableUnlimitedPreemptableRetries
	If set, preemptable failures (or any failure due to an instance getting unexpectedly terminated) will not count towards job failures and --retryCount.
--doubleMem	If set, batch jobs which die due to reaching memory limit on batch schedulers will have their memory doubled and they will be retried. The remaining retry count will be reduced by 1. Currently only supported by LSF. default=False.
--maxJobDuration MAXJOBDURATION
	Maximum runtime of a job (in seconds) before we kill it (this is a lower bound, and the actual time before killing the job may be longer).
--rescueJobsFrequency RESCUEJOBSFREQUENCY
	Period of time to wait (in seconds) between checking for missing/overlong jobs, that is jobs which get lost by the batch system. Expert parameter.

Log Management Options

--maxLogFileSize MAXLOGFILESIZE
	The maximum size of a job log file to keep (in bytes), log files larger than this will be truncated to the last X bytes. Setting this option to zero will prevent any truncation. Setting this option to a negative value will truncate from the beginning. Default=62.5 K
--writeLogs FILEPATH
	Write worker logs received by the leader into their own files at the specified path. Any non-empty standard output and error from failed batch system jobs will also be written into files at this path. The current working directory will be used if a path is not specified explicitly. Note: By default only the logs of failed jobs are returned to leader. Set log level to ‘debug’ or enable --writeLogsFromAllJobs to get logs back from successful jobs, and adjust --maxLogFileSize to control the truncation limit for worker logs.
--writeLogsGzip FILEPATH
	Identical to --writeLogs except the logs files are gzipped on the leader.
--writeMessages FILEPATH
	File to send messages from the leader’s message bus to.
--realTimeLogging
	Enable real-time logging from workers to leader.

Miscellaneous Options

--disableChaining
	Disables chaining of jobs (chaining uses one job’s resource allocation for its successor job if possible).
--disableJobStoreChecksumVerification
	Disables checksum verification for files transferred to/from the job store. Checksum verification is a safety check to ensure the data is not corrupted during transfer. Currently only supported for non-streaming AWS files
--sseKey SSEKEY
	Path to file containing 32 character key to be used for server-side encryption on awsJobStore or googleJobStore. SSE will not be used if this flag is not passed.
--setEnv NAME, -e NAME
	NAME=VALUE or NAME, -e NAME=VALUE or NAME are also valid. Set an environment variable early on in the worker. If VALUE is omitted, it will be looked up in the current environment. Independently of this option, the worker will try to emulate the leader’s environment before running a job, except for some variables known to vary across systems. Using this option, a variable can be injected into the worker process itself before it is started.
--servicePollingInterval SERVICEPOLLINGINTERVAL
	Interval of time service jobs wait between polling for the existence of the keep-alive flag (default=60)
--forceDockerAppliance
	Disables sanity checking the existence of the docker image specified by TOIL_APPLIANCE_SELF, which Toil uses to provision mesos for autoscaling.
--statusWait INT
	Seconds to wait between reports of running jobs. (default=3600)
--disableProgress
	Disables the progress bar shown when standard error is a terminal.

Debug Options Debug options for finding problems or helping with testing.

--debugWorker	Experimental no forking mode for local debugging. Specifically, workers are not forked and stderr/stdout are not redirected to the log. (default=False)
--disableWorkerOutputCapture
	Let worker output go to worker’s standard out/error instead of per-job logs.
--badWorker BADWORKER
	For testing purposes randomly kill --badWorker proportion of jobs using SIGKILL. (Default: 0.0)
--badWorkerFailInterval BADWORKERFAILINTERVAL
	When killing the job pick uniformly within the interval from 0.0 to --badWorkerFailInterval seconds after the worker starts. (Default: 0.01)
--kill_polling_interval KILL_POLLING_INTERVAL
	Interval of time (in seconds) the leader waits between polling for the kill flag inside the job store set by the “toil kill” command. (default=5)

Restart Option

In the event of failure, Toil can resume the pipeline by adding the argument --restart and rerunning the python script. Toil pipelines (but not CWL pipelines) can even be edited and resumed which is useful for development or troubleshooting.

Running Workflows with Services

Toil supports jobs, or clusters of jobs, that run as services to other accessor jobs. Example services include server databases or Apache Spark Clusters. As service jobs exist to provide services to accessor jobs their runtime is dependent on the concurrent running of their accessor jobs. The dependencies between services and their accessor jobs can create potential deadlock scenarios, where the running of the workflow hangs because only service jobs are being run and their accessor jobs can not be scheduled because of too limited resources to run both simultaneously. To cope with this situation Toil attempts to schedule services and accessors intelligently, however to avoid a deadlock with workflows running service jobs it is advisable to use the following parameters:

• --maxServiceJobs: The maximum number of service jobs that can be run concurrently, excluding service jobs running on preemptable nodes.
• --maxPreemptableServiceJobs: The maximum number of service jobs that can run concurrently on preemptable nodes.

Specifying these parameters so that at a maximum cluster size there will be sufficient resources to run accessors in addition to services will ensure that such a deadlock can not occur.

If too low a limit is specified then a deadlock can occur in which toil can not schedule sufficient service jobs concurrently to complete the workflow. Toil will detect this situation if it occurs and throw a toil.DeadlockException exception. Increasing the cluster size and these limits will resolve the issue.

Setting Options directly with the Toil Script

It’s good to remember that commandline options can be overridden in the Toil script itself. For example, toil.job.Job.Runner.getDefaultOptions() can be used to run toil with all default options, and in this example, it will override commandline args to run the default options and always run with the “./toilWorkflow” directory specified as the jobstore:

options = Job.Runner.getDefaultOptions("./toilWorkflow") # Get the options object

with Toil(options) as toil:
    toil.start(Job())  # Run the script

However, each option can be explicitly set within the script by supplying arguments (in this example, we are setting logLevel = "DEBUG" (all log statements are shown) and clean="ALWAYS" (always delete the jobstore) like so:

options = Job.Runner.getDefaultOptions("./toilWorkflow") # Get the options object
options.logLevel = "DEBUG" # Set the log level to the debug level.
options.clean = "ALWAYS" # Always delete the jobStore after a run

with Toil(options) as toil:
    toil.start(Job())  # Run the script

However, the usual incantation is to accept commandline args from the user with the following:

parser = Job.Runner.getDefaultArgumentParser() # Get the parser
options = parser.parse_args() # Parse user args to create the options object

with Toil(options) as toil:
    toil.start(Job())  # Run the script

Which can also, of course, then accept script supplied arguments as before (which will overwrite any user supplied args):

parser = Job.Runner.getDefaultArgumentParser() # Get the parser
options = parser.parse_args() # Parse user args to create the options object
options.logLevel = "DEBUG" # Set the log level to the debug level.
options.clean = "ALWAYS" # Always delete the jobStore after a run

with Toil(options) as toil:
    toil.start(Job())  # Run the script

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.

Introspecting the Jobstore

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 \
      --listFilesInJobStore

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 \
      --localFilePath=/home/user/localpath

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 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, Toil will not fork in order to run jobs, which means you can either use pdb, or an IDE that supports debugging Python as you would normally. Note that the --debugWorker flag will only work with the singleMachine batch system (the default), and not any of the custom job schedulers.

Running in the Cloud

Toil supports Amazon Web Services (AWS) and Google Compute Engine (GCE) in the cloud and has autoscaling capabilities that can adapt to the size of your workflow, whether your workflow requires 10 instances or 20,000.

Toil does this by creating a virtual cluster with Apache Mesos. Apache Mesos requires a leader node to coordinate the workflow, and worker nodes to execute the various tasks within the workflow. As the workflow runs, Toil will “autoscale”, creating and terminating workers as needed to meet the demands of the workflow.

Once a user is familiar with the basics of running toil locally (specifying a jobStore, and how to write a toil script), they can move on to the guides below to learn how to translate these workflows into cloud ready workflows.

Managing a Cluster of Virtual Machines (Provisioning)

Toil can launch and manage a cluster of virtual machines to run using the provisioner to run a workflow distributed over several nodes. The provisioner also has the ability to automatically scale up or down the size of the cluster to handle dynamic changes in computational demand (autoscaling). Currently we have working provisioners with AWS and GCE (Azure support has been deprecated).

Toil uses Apache Mesos as the Batch System.

See here for instructions for Running in AWS.

See here for instructions for Running in Google Compute Engine (GCE).

Storage (Toil jobStore)

Toil can make use of cloud storage such as AWS or Google buckets to take care of storage needs.

This is useful when running Toil in single machine mode on any cloud platform since it allows you to make use of their integrated storage systems.

For an overview of the job store see Job Store.

For instructions configuring a particular job store see:

• AWS Job Store
• Google Job Store

Cloud Platforms

Running on Kubernetes

Kubernetes is a very popular container orchestration tool that has become a de facto cross-cloud-provider API for accessing cloud resources. Major cloud providers like Amazon, Microsoft, Kubernetes owner Google, and DigitalOcean have invested heavily in making Kubernetes work well on their platforms, by writing their own deployment documentation and developing provider-managed Kubernetes-based products. Using minikube, Kubernetes can even be run on a single machine.

Toil supports running Toil workflows against a Kubernetes cluster, either in the cloud or deployed on user-owned hardware.

Preparing your Kubernetes environment

1. Get a Kubernetes cluster

   To run Toil workflows on Kubernetes, you need to have a Kubernetes cluster set up. This will not be covered here, but there are many options available, and which one you choose will depend on which cloud ecosystem if any you use already, and on pricing. If you are just following along with the documentation, use minikube on your local machine.

   Note that currently the only way to run a Toil workflow on Kubernetes is to use the AWS Job Store, so your Kubernetes workflow will currently have to store its data in Amazon’s cloud regardless of where you run it. This can result in significant egress charges from Amazon if you run it outside of Amazon.

   Kubernetes Cluster Providers:

   • Your own institution
   • Amazon EKS
   • Microsoft Azure AKS
   • Google GKE
   • DigitalOcean Kubernetes
   • minikube

2. Get a Kubernetes context on your local machine

   There are two main ways to run Toil workflows on Kubernetes. You can either run the Toil leader on a machine outside the cluster, with jobs submitted to and run on the cluster, or you can submit the Toil leader itself as a job and have it run inside the cluster. Either way, you will need to configure your own machine to be able to submit jobs to the Kubernetes cluster. Generally, this involves creating and populating a file named .kube/config in your user’s home directory, and specifying the cluster to connect to, the certificate and token information needed for mutual authentication, and the Kubernetes namespace within which to work. However, Kubernetes configuration can also be picked up from other files in the .kube directory, environment variables, and the enclosing host when running inside a Kubernetes-managed container.

   You will have to do different things here depending on where you got your Kubernetes cluster:

   • Configuring for Amazon EKS
   • Configuring for Microsoft Azure AKS
   • Configuring for Google GKE
   • Configuring for DigitalOcean Kubernetes Clusters
   • Configuring for minikube

   Toil’s internal Kubernetes configuration logic mirrors that of the kubectl command. Toil workflows will use the current kubectl context to launch their Kubernetes jobs.

3. If running the Toil leader in the cluster, get a service account

   If you are going to run your workflow’s leader within the Kubernetes cluster (see Option 1: Running the Leader Inside Kubernetes), you will need a service account in your chosen Kubernetes namespace. Most namespaces should have a service account named default which should work fine. If your cluster requires you to use a different service account, you will need to obtain its name and use it when launching the Kubernetes job containing the Toil leader.

4. Set up appropriate permissions

   Your local Kubernetes context and/or the service account you are using to run the leader in the cluster will need to have certain permissions in order to run the workflow. Toil needs to be able to interact with jobs and pods in the cluster, and to retrieve pod logs. You as a user may need permission to set up an AWS credentials secret, if one is not already available. Additionally, it is very useful for you as a user to have permission to interact with nodes, and to shell into pods.

   The appropriate permissions may already be available to you and your service account by default, especially in managed or ease-of-use-optimized setups such as EKS or minikube.

   However, if the appropriate permissions are not already available, you or your cluster administrator will have to grant them manually. The following Role (toil-user) and ClusterRole (node-reader), to be applied with kubectl apply -f filename.yaml, should grant sufficient permissions to run Toil workflows when bound to your account and the service account used by Toil workflows. Be sure to replace YOUR_NAMESPACE_HERE with the namespace you are running your workflows in

   apiVersion: rbac.authorization.k8s.io/v1
   kind: Role
   metadata:
     namespace: YOUR_NAMESPACE_HERE
     name: toil-user
   rules:
   - apiGroups: ["*"]
     resources: ["*"]
     verbs: ["explain", "get", "watch", "list", "describe", "logs", "attach", "exec", "port-forward", "proxy", "cp", "auth"]
   - apiGroups: ["batch"]
     resources: ["*"]
     verbs: ["get", "watch", "list", "create", "run", "set", "delete"]
   - apiGroups: [""]
     resources: ["secrets", "pods", "pods/attach", "podtemplates", "configmaps", "events", "services"]
     verbs: ["patch", "get", "update", "watch", "list", "create", "run", "set", "delete", "exec"]
   - apiGroups: [""]
     resources: ["pods", "pods/log"]
     verbs: ["get", "list"]
   - apiGroups: [""]
     resources: ["pods/exec"]
     verbs: ["create"]
   

   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRole
   metadata:
     name: node-reader
   rules:
   - apiGroups: [""]
     resources: ["nodes"]
     verbs: ["get", "list", "describe"]
   - apiGroups: [""]
     resources: ["namespaces"]
     verbs: ["get", "list", "describe"]
   - apiGroups: ["metrics.k8s.io"]
     resources: ["*"]
     verbs: ["*"]
   

   To bind a user or service account to the Role or ClusterRole and actually grant the permissions, you will need a RoleBinding and a ClusterRoleBinding, respectively. Make sure to fill in the namespace, username, and service account name, and add more user stanzas if your cluster is to support multiple Toil users.

   apiVersion: rbac.authorization.k8s.io/v1
   kind: RoleBinding
   metadata:
     name: toil-developer-member
     namespace: toil
   subjects:
   - kind: User
     name: YOUR_KUBERNETES_USERNAME_HERE
     apiGroup: rbac.authorization.k8s.io
   - kind: ServiceAccount
     name: YOUR_SERVICE_ACCOUNT_NAME_HERE
     namespace: YOUR_NAMESPACE_HERE
   roleRef:
     kind: Role
     name: toil-user
     apiGroup: rbac.authorization.k8s.io
   

   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRoleBinding
   metadata:
     name: read-nodes
   subjects:
   - kind: User
     name: YOUR_KUBERNETES_USERNAME_HERE
     apiGroup: rbac.authorization.k8s.io
   - kind: ServiceAccount
     name: YOUR_SERVICE_ACCOUNT_NAME_HERE
     namespace: YOUR_NAMESPACE_HERE
   roleRef:
     kind: ClusterRole
     name: node-reader
     apiGroup: rbac.authorization.k8s.io
   

AWS Job Store for Kubernetes

Currently, the only job store, which is what Toil uses to exchange data between jobs, that works with jobs running on Kubernetes is the AWS Job Store. This requires that the Toil leader and Kubernetes jobs be able to connect to and use Amazon S3 and Amazon SimpleDB. It also requires that you have an Amazon Web Services account.

1. Get access to AWS S3 and SimpleDB

   In your AWS account, you need to create an AWS access key. First go to the IAM dashboard; for “us-west1”, the link would be:

   https://console.aws.amazon.com/iam/home?region=us-west-1#/home
   

   Then create an access key, and save the Access Key ID and the Secret Key. As documented in the AWS documentation:

   1. On the IAM Dashboard page, choose your account name in the navigation bar, and then choose My Security Credentials.
   2. Expand the Access keys (access key ID and secret access key) section.
   3. Choose Create New Access Key. Then choose Download Key File to save the access key ID and secret access key to a file on your computer. After you close the dialog box, you can’t retrieve this secret access key again.

   Make sure that, if your AWS infrastructure requires your user to authenticate with a multi-factor authentication (MFA) token, you obtain a second secret key and access key that don’t have this requirement. The secret key and access key used to populate the Kubernetes secret that allows the jobs to contact the job store need to be usable without human intervention.

2. Configure AWS access from the local machine

   This only really needs to happen if you run the leader on the local machine. But we need the files in place to fill in the secret in the next step. Run:

   $ aws configure
   

   Then when prompted, enter your secret key and access key. This should create a file ~/.aws/credentials that looks like this:

   [default]
   aws_access_key_id =  BLAH
   aws_secret_access_key =  blahblahblah
   

3. Create a Kubernetes secret to give jobs access to AWS

Go into the directory where the credentials file is:

$ cd ~/.aws

Then, create a Kubernetes secret that contains it. We’ll call it aws-credentials:

$ kubectl create secret generic aws-credentials --from-file credentials

Configuring Toil for your Kubernetes environment

To configure your workflow to run on Kubernetes, you will have to configure several environment variables, in addition to passing the --batchSystem kubernetes option. Doing the research to figure out what values to give these variables may require talking to your cluster provider.

1. TOIL_AWS_SECRET_NAME is the most important, and must be set to the secret that contains your AWS credentials file, if your cluster nodes don’t otherwise have access to S3 and SimpleDB (such as through IAM roles). This is required for the AWS job store to work, which is currently the only job store that can be used on Kubernetes. In this example we are using aws-credentials.
2. TOIL_KUBERNETES_HOST_PATH can be set to allow Toil jobs on the same physical host to share a cache. It should be set to a path on the host where the shared cache should be stored. It will be mounted as /var/lib/toil, or at TOIL_WORKDIR if specified, inside the container. This path must already exist on the host, and must have as much free space as your Kubernetes node offers to jobs. In this example, we are using /data/scratch. To actually make use of caching, make sure not to use --disableCaching.
3. TOIL_KUBERNETES_OWNER should be set to the username of the user running the Toil workflow. The jobs that Toil creates will include this username, so they can be more easily recognized, and cleaned up by the user if anything happens to the Toil leader. In this example we are using demo-user.

Note that Docker containers cannot be run inside of unprivileged Kubernetes pods (which are themselves containers). The Docker daemon does not (yet) support this. Other tools, such as Singularity in its user-namespace mode, are able to run containers from within containers. If using Singularity to run containerized tools, and you want downloaded container images to persist between Toil jobs, you will also want to set TOIL_KUBERNETES_HOST_PATH and make sure that Singularity is downloading its containers under the Toil work directory (/var/lib/toil buy default) by setting SINGULARITY_CACHEDIR. However, you will need to make sure that no two jobs try to download the same container at the same time; Singularity has no synchronization or locking around its cache, but the cache is also not safe for simultaneous access by multiple Singularity invocations. Some Toil workflows use their own custom workaround logic for this problem; this work is likely to be made part of Toil in a future release.

Running workflows

To run the workflow, you will need to run the Toil leader process somewhere. It can either be run inside Kubernetes as a Kubernetes job, or outside Kubernetes as a normal command.

Option 1: Running the Leader Inside Kubernetes

Once you have determined a set of environment variable values for your workflow run, write a YAML file that defines a Kubernetes job to run your workflow with that configuration. Some configuration items (such as your username, and the name of your AWS credentials secret) need to be written into the YAML so that they can be used from the leader as well.

Note that the leader pod will need your workflow script, its other dependencies, and Toil all installed. An easy way to get Toil installed is to start with the Toil appliance image for the version of Toil you want to use. In this example, we use quay.io/ucsc_cgl/toil:5.5.0.

Here’s an example YAML file to run a test workflow:

apiVersion: batch/v1
kind: Job
metadata:
  # It is good practice to include your username in your job name.
  # Also specify it in TOIL_KUBERNETES_OWNER
  name: demo-user-toil-test
# Do not try and rerun the leader job if it fails

spec:
 backoffLimit: 0
 template:
   spec:
     # Do not restart the pod when the job fails, but keep it around so the
     # log can be retrieved
     restartPolicy: Never
     volumes:
     - name: aws-credentials-vol
       secret:
         # Make sure the AWS credentials are available as a volume.
         # This should match TOIL_AWS_SECRET_NAME
         secretName: aws-credentials
     # You may need to replace this with a different service account name as
     # appropriate for your cluster.
     serviceAccountName: default
     containers:
     - name: main
       image: quay.io/ucsc_cgl/toil:5.5.0
       env:
       # Specify your username for inclusion in job names
       - name: TOIL_KUBERNETES_OWNER
         value: demo-user
       # Specify where to find the AWS credentials to access the job store with
       - name: TOIL_AWS_SECRET_NAME
         value: aws-credentials
       # Specify where per-host caches should be stored, on the Kubernetes hosts.
       # Needs to be set for Toil's caching to be efficient.
       - name: TOIL_KUBERNETES_HOST_PATH
         value: /data/scratch
       volumeMounts:
       # Mount the AWS credentials volume
       - mountPath: /root/.aws
         name: aws-credentials-vol
       resources:
         # Make sure to set these resource limits to values large enough
         # to accommodate the work your workflow does in the leader
         # process, but small enough to fit on your cluster.
         #
         # Since no request values are specified, the limits are also used
         # for the requests.
         limits:
           cpu: 2
           memory: "4Gi"
           ephemeral-storage: "10Gi"
       command:
       - /bin/bash
       - -c
       - |
         # This Bash script will set up Toil and the workflow to run, and run them.
         set -e
         # We make sure to create a work directory; Toil can't hot-deploy a
         # script from the root of the filesystem, which is where we start.
         mkdir /tmp/work
         cd /tmp/work
         # We make a virtual environment to allow workflow dependencies to be
         # hot-deployed.
         #
         # We don't really make use of it in this example, but for workflows
         # that depend on PyPI packages we will need this.
         #
         # We use --system-site-packages so that the Toil installed in the
         # appliance image is still available.
         virtualenv --python python3 --system-site-packages venv
         . venv/bin/activate
         # Now we install the workflow. Here we're using a demo workflow
         # script from Toil itself.
         wget https://raw.githubusercontent.com/DataBiosphere/toil/releases/4.1.0/src/toil/test/docs/scripts/tutorial_helloworld.py
         # Now we run the workflow. We make sure to use the Kubernetes batch
         # system and an AWS job store, and we set some generally useful
         # logging options. We also make sure to enable caching.
         python3 tutorial_helloworld.py \
             aws:us-west-2:demouser-toil-test-jobstore \
             --batchSystem kubernetes \
             --realTimeLogging \
             --logInfo

You can save this YAML as leader.yaml, and then run it on your Kubernetes installation with:

$ kubectl apply -f leader.yaml

To monitor the progress of the leader job, you will want to read its logs. If you are using a Kubernetes dashboard such as k9s, you can simply find the pod created for the job in the dashboard, and view its logs there. If not, you will need to locate the pod by hand.

Monitoring and Debugging Kubernetes Jobs and Pods

The following techniques are most useful for looking at the pod which holds the Toil leader, but they can also be applied to individual Toil jobs on Kubernetes, even when the leader is outside the cluster.

Kubernetes names pods for jobs by appending a short random string to the name of the job. You can find the name of the pod for your job by doing:

$ kubectl get pods | grep demo-user-toil-test
demo-user-toil-test-g5496                                         1/1     Running     0          2m

Assuming you have set TOIL_KUBERNETES_OWNER correctly, you should be able to find all of your workflow’s pods by searching for your username:

$ kubectl get pods | grep demo-user

If the status of a pod is anything other than Pending, you will be able to view its logs with:

$ kubectl logs demo-user-toil-test-g5496

This will dump the pod’s logs from the beginning to now and terminate. To follow along with the logs from a running pod, add the -f option:

$ kubectl logs -f demo-user-toil-test-g5496

A status of ImagePullBackoff suggests that you have requested to use an image that is not available. Check the image section of your YAML if you are looking at a leader, or the value of TOIL_APPLIANCE_SELF if you are delaying with a worker job. You also might want to check your Kubernetes node’s Internet connectivity and DNS function; in Kubernetes, DNS depends on system-level pods which can be terminated or evicted in cases of resource oversubscription, just like user workloads.

If your pod seems to be stuck Pending, ContainerCreating, you can get information on what is wrong with it by using kubectl describe pod:

$ kubectl describe pod demo-user-toil-test-g5496

Pay particular attention to the Events: section at the end of the output. An indication that a job is too big for the available nodes on your cluster, or that your cluster is too busy for your jobs, is FailedScheduling events:

Type     Reason            Age                  From               Message
----     ------            ----                 ----               -------
Warning  FailedScheduling  13s (x79 over 100m)  default-scheduler  0/4 nodes are available: 1 Insufficient cpu, 1 Insufficient ephemeral-storage, 4 Insufficient memory.

If a pod is running but seems to be behaving erratically, or seems stuck, you can shell into it and look around:

$ kubectl exec -ti demo-user-toil-test-g5496 /bin/bash

One common cause of stuck pods is attempting to use more memory than allowed by Kubernetes (or by the Toil job’s memory resource requirement), but in a way that does not trigger the Linux OOM killer to terminate the pod’s processes. In these cases, the pod can remain stuck at nearly 100% memory usage more or less indefinitely, and attempting to shell into the pod (which needs to start a process within the pod, using some of its memory) will fail. In these cases, the recommended solution is to kill the offending pod and increase its (or its Toil job’s) memory requirement, or reduce its memory needs by adapting user code.

When Things Go Wrong

The Toil Kubernetes batch system includes cleanup code to terminate worker jobs when the leader shuts down. However, if the leader pod is removed by Kubernetes, is forcibly killed or otherwise suffers a sudden existence failure, it can go away while its worker jobs live on. It is not recommended to restart a workflow in this state, as jobs from the previous invocation will remain running and will be trying to modify the job store concurrently with jobs from the new invocation.

To clean up dangling jobs, you can use the following snippet:

$ kubectl get jobs | grep demo-user | cut -f1 -d' ' | xargs -n10 kubectl delete job

This will delete all jobs with demo-user’s username in their names, in batches of 10. You can also use the UUID that Toil assigns to a particular workflow invocation in the filter, to clean up only the jobs pertaining to that workflow invocation.

Option 2: Running the Leader Outside Kubernetes

If you don’t want to run your Toil leader inside Kubernetes, you can run it locally instead. This can be useful when developing a workflow; files can be hot-deployed from your local machine directly to Kubernetes. However, your local machine will have to have (ideally role-assumption- and MFA-free) access to AWS, and access to Kubernetes. Real time logging will not work unless your local machine is able to listen for incoming UDP packets on arbitrary ports on the address it uses to contact the IPv4 Internet; Toil does no NAT traversal or detection.

Note that if you set TOIL_WORKDIR when running your workflow like this, it will need to be a directory that exists both on the host and in the Toil appliance.

Here is an example of running our test workflow leader locally, outside of Kubernetes:

$ export TOIL_KUBERNETES_OWNER=demo-user  # This defaults to your local username if not set
$ export TOIL_AWS_SECRET_NAME=aws-credentials
$ export TOIL_KUBERNETES_HOST_PATH=/data/scratch
$ virtualenv --python python3 --system-site-packages venv
$ . venv/bin/activate
$ wget https://raw.githubusercontent.com/DataBiosphere/toil/releases/4.1.0/src/toil/test/docs/scripts/tutorial_helloworld.py
$ python3 tutorial_helloworld.py \
      aws:us-west-2:demouser-toil-test-jobstore \
      --batchSystem kubernetes \
      --realTimeLogging \
      --logInfo

Running in AWS

Toil jobs can be run on a variety of cloud platforms. Of these, Amazon Web Services (AWS) is currently the best-supported solution. Toil provides the Cluster Utilities to conveniently create AWS clusters, connect to the leader of the cluster, and then launch a workflow. The leader handles distributing the jobs over the worker nodes and autoscaling to optimize costs.

The Running a Workflow with Autoscaling section details how to create a cluster and run a workflow that will dynamically scale depending on the workflow’s needs.

The Static Provisioning section explains how a static cluster (one that won’t automatically change in size) can be created and provisioned (grown, shrunk, destroyed, etc.).

Preparing your AWS environment

To use Amazon Web Services (AWS) to run Toil or to just use S3 to host the files during the computation of a workflow, first set up and configure an account with AWS:

1. If necessary, create and activate an AWS account

2. Next, generate a key pair for AWS with the command (do NOT generate your key pair with the Amazon browser):

   $ ssh-keygen -t rsa
   

3. This should prompt you to save your key. Please save it in

   ~/.ssh/id_rsa
   

4. Now move this to where your OS can see it as an authorized key:

   $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
   

5. Next, you’ll need to add your key to the ssh-agent:

   $ eval `ssh-agent -s`
   $ ssh-add
   

   If your key has a passphrase, you will be prompted to enter it here once.

6. You’ll also need to chmod your private key (good practice but also enforced by AWS):

   $ chmod 400 id_rsa
   

7. Now you’ll need to add the key to AWS via the browser. For example, on us-west1, this address would accessible at:

   https://us-west-1.console.aws.amazon.com/ec2/v2/home?region=us-west-1#KeyPairs:sort=keyName
   

8. Now click on the “Import Key Pair” button to add your key:

   

9. Next, you need to create an AWS access key. First go to the IAM dashboard, again; for “us-west1”, the example link would be here:

   https://console.aws.amazon.com/iam/home?region=us-west-1#/home
   

10. The directions (transcribed from: https://docs.aws.amazon.com/general/latest/gr/managing-aws-access-keys.html ) are now:

    1. On the IAM Dashboard page, choose your account name in the navigation bar, and then choose My Security Credentials.
    2. Expand the Access keys (access key ID and secret access key) section.
    3. Choose Create New Access Key. Then choose Download Key File to save the access key ID and secret access key to a file on your computer. After you close the dialog box, you can’t retrieve this secret access key again.

11. Now you should have a newly generated “AWS Access Key ID” and “AWS Secret Access Key”. We can now install the AWS CLI and make sure that it has the proper credentials:

    $ pip install awscli --upgrade --user
    

12. Now configure your AWS credentials with:

    $ aws configure
    

13. Add your “AWS Access Key ID” and “AWS Secret Access Key” from earlier and your region and output format:

    " AWS Access Key ID [****************Q65Q]: "
    " AWS Secret Access Key [****************G0ys]: "
    " Default region name [us-west-1]: "
    " Default output format [json]: "
    

    This will create the files ~/.aws/config and ~/.aws/credentials.

14. If not done already, install toil (example uses version 5.3.0, but we recommend the latest release):

    $ virtualenv venv
    $ source venv/bin/activate
    $ pip install toil[all]==5.3.0
    

15. Now that toil is installed and you are running a virtualenv, an example of launching a toil leader node would be the following (again, note that we set TOIL_APPLIANCE_SELF to toil version 5.3.0 in this example, but please set the version to the installed version that you are using if you’re using a different version):

    $ TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:5.3.0 \
          toil launch-cluster clustername \
          --leaderNodeType t2.medium \
          --zone us-west-1a \
          --keyPairName id_rsa
    

To further break down each of these commands:

TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:latest — This is optional. It specifies a mesos docker image that we maintain with the latest version of toil installed on it. If you want to use a different version of toil, please specify the image tag you need from https://quay.io/repository/ucsc_cgl/toil?tag=latest&tab=tags.

toil launch-cluster — Base command in toil to launch a cluster.

clustername — Just choose a name for your cluster.

–leaderNodeType t2.medium — Specify the leader node type. Make a t2.medium (2CPU; 4Gb RAM; $0.0464/Hour). List of available AWS instances: https://aws.amazon.com/ec2/pricing/on-demand/

–zone us-west-1a — Specify the AWS zone you want to launch the instance in. Must have the same prefix as the zone in your awscli credentials (which, in the example of this tutorial is: “us-west-1”).

–keyPairName id_rsa — The name of your key pair, which should be “id_rsa” if you’ve followed this tutorial.

Note

You can set the TOIL_AWS_TAGS environment variable to a JSON object to specify arbitrary tags for AWS resources. For example, if you export TOIL_AWS_TAGS='{"project-name": "variant-calling"}' in your shell before using Toil, AWS resources created by Toil will be tagged with a project-name tag with the value variant-calling.

AWS Job Store

Using the AWS job store is straightforward after you’ve finished Preparing your AWS environment; all you need to do is specify the prefix for the job store name.

To run the sort example sort example with the AWS job store you would type

$ python sort.py aws:us-west-2:my-aws-sort-jobstore

Toil Provisioner

The Toil provisioner is included in Toil alongside the [aws] extra and allows us to spin up a cluster.

Getting started with the provisioner is simple:

1. Make sure you have Toil installed with the AWS extras. For detailed instructions see Installing Toil with Extra Features.
2. You will need an AWS account and you will need to save your AWS credentials on your local machine. For help setting up an AWS account see here. For setting up your AWS credentials follow instructions here.

The Toil provisioner is built around the Toil Appliance, a Docker image that bundles Toil and all its requirements (e.g. Mesos). This makes deployment simple across platforms, and you can even simulate a cluster locally (see Developing with Docker for details).

Choosing Toil Appliance Image

When using the Toil provisioner, the appliance image will be automatically chosen based on the pip-installed version of Toil on your system. That choice can be overridden by setting the environment variables TOIL_DOCKER_REGISTRY and TOIL_DOCKER_NAME or TOIL_APPLIANCE_SELF. See Environment Variables for more information on these variables. If you are developing with autoscaling and want to test and build your own appliance have a look at Developing with Docker.

For information on using the Toil Provisioner have a look at Running a Workflow with Autoscaling.

Details about Launching a Cluster in AWS

Using the provisioner to launch a Toil leader instance is simple using the launch-cluster command. For example, to launch a cluster named “my-cluster” with a t2.medium leader in the us-west-2a zone, run

(venv) $ toil launch-cluster my-cluster \
             --leaderNodeType t2.medium \
             --zone us-west-2a \
             --keyPairName <your-AWS-key-pair-name>

The cluster name is used to uniquely identify your cluster and will be used to populate the instance’s Name tag. Also, the Toil provisioner will automatically tag your cluster with an Owner tag that corresponds to your keypair name to facilitate cost tracking. In addition, the ToilNodeType tag can be used to filter “leader” vs. “worker” nodes in your cluster.

The leaderNodeType is an EC2 instance type. This only affects the leader node.

The --zone parameter specifies which EC2 availability zone to launch the cluster in. Alternatively, you can specify this option via the TOIL_AWS_ZONE environment variable. Note: the zone is different from an EC2 region. A region corresponds to a geographical area like us-west-2 (Oregon), and availability zones are partitions of this area like us-west-2a.

By default, Toil creates an IAM role for each cluster with sufficient permissions to perform cluster operations (e.g. full S3, EC2, and SDB access). If the default permissions are not sufficient for your use case (e.g. if you need access to ECR), you may create a custom IAM role with all necessary permissions and set the --awsEc2ProfileArn parameter when launching the cluster. Note that your custom role must at least have these permissions in order for the Toil cluster to function properly.

In addition, Toil creates a new security group with the same name as the cluster name with default rules (e.g. opens port 22 for SSH access). If you require additional security groups, you may use the --awsEc2ExtraSecurityGroupId parameter when launching the cluster. Note: Do not use the same name as the cluster name for the extra security groups as any security group matching the cluster name will be deleted once the cluster is destroyed.

For more information on options try:

(venv) $ toil launch-cluster --help

Static Provisioning

Toil can be used to manage a cluster in the cloud by using the Cluster Utilities. The cluster utilities also make it easy to run a toil workflow directly on this cluster. We call this static provisioning because the size of the cluster does not change. This is in contrast with Running a Workflow with Autoscaling.

To launch worker nodes alongside the leader we use the -w option:

(venv) $ toil launch-cluster my-cluster \
             --leaderNodeType t2.small -z us-west-2a \
             --keyPairName your-AWS-key-pair-name \
             --nodeTypes m3.large,t2.micro -w 1,4

This will spin up a leader node of type t2.small with five additional workers — one m3.large instance and four t2.micro.

Currently static provisioning is only possible during the cluster’s creation. The ability to add new nodes and remove existing nodes via the native provisioner is in development. Of course the cluster can always be deleted with the Destroy-Cluster Command utility.

Uploading Workflows

Now that our cluster is launched, we use the Rsync-Cluster Command utility to copy the workflow to the leader. For a simple workflow in a single file this might look like

(venv) $ toil rsync-cluster -z us-west-2a my-cluster toil-workflow.py :/

Note

If your toil workflow has dependencies have a look at the Auto-Deployment section for a detailed explanation on how to include them.

Running a Workflow with Autoscaling

Autoscaling is a feature of running Toil in a cloud whereby additional cloud instances are launched to run the workflow. Autoscaling leverages Mesos containers to provide an execution environment for these workflows.

Note

Make sure you’ve done the AWS setup in Preparing your AWS environment.

1. Download sort.py

2. Launch the leader node in AWS using the Launch-Cluster Command command:

   (venv) $ toil launch-cluster <cluster-name> \
                --keyPairName <AWS-key-pair-name> \
                --leaderNodeType t2.medium \
                --zone us-west-2a
   

3. Copy the sort.py script up to the leader node:

   (venv) $ toil rsync-cluster -z us-west-2a <cluster-name> sort.py :/root
   

4. Login to the leader node:

   (venv) $ toil ssh-cluster -z us-west-2a <cluster-name>
   

5. Run the script as an autoscaling workflow:

   $ python /root/sort.py aws:us-west-2:<my-jobstore-name> \
         --provisioner aws \
         --nodeTypes c3.large \
         --maxNodes 2 \
         --batchSystem mesos
   

Note

In this example, the autoscaling Toil code creates up to two instances of type c3.large and launches Mesos slave containers inside them. The containers are then available to run jobs defined by the sort.py script. Toil also creates a bucket in S3 called aws:us-west-2:autoscaling-sort-jobstore to store intermediate job results. The Toil autoscaler can also provision multiple different node types, which is useful for workflows that have jobs with varying resource requirements. For example, one could execute the script with --nodeTypes c3.large,r3.xlarge --maxNodes 5,1, which would allow the provisioner to create up to five c3.large nodes and one r3.xlarge node for memory-intensive jobs. In this situation, the autoscaler would avoid creating the more expensive r3.xlarge node until needed, running most jobs on the c3.large nodes.

1. View the generated file to sort:

   $ head fileToSort.txt
   

2. View the sorted file:

   $ head sortedFile.txt
   

For more information on other autoscaling (and other) options have a look at Commandline Options and/or run

$ python my-toil-script.py --help

Important

Some important caveats about starting a toil run through an ssh session are explained in the Ssh-Cluster Command section.

Preemptability

Toil can run on a heterogeneous cluster of both preemptable and non-preemptable nodes. Being preemptable node simply means that the node may be shut down at any time, while jobs are running. These jobs can then be restarted later somewhere else.

A node type can be specified as preemptable by adding a spot bid to its entry in the list of node types provided with the --nodeTypes flag. If spot instance prices rise above your bid, the preemptable node whill be shut down.

While individual jobs can each explicitly specify whether or not they should be run on preemptable nodes via the boolean preemptable resource requirement, the --defaultPreemptable flag will allow jobs without a preemptable requirement to run on preemptable machines.

Specify Preemptability Carefully

Ensure that your choices for --nodeTypes and --maxNodes <> make sense for your workflow and won’t cause it to hang. You should make sure the provisioner is able to create nodes large enough to run the largest job in the workflow, and that non-preemptable node types are allowed if there are non-preemptable jobs in the workflow.

Finally, the --preemptableCompensation flag can be used to handle cases where preemptable nodes may not be available but are required for your workflow. With this flag enabled, the autoscaler will attempt to compensate for a shortage of preemptable nodes of a certain type by creating non-preemptable nodes of that type, if non-preemptable nodes of that type were specified in --nodeTypes.

Using MinIO and S3-Compatible object stores

Toil can be configured to access files stored in an S3-compatible object store such as MinIO. The following environment variables can be used to configure the S3 connection used:

• TOIL_S3_HOST: the IP address or hostname to use for connecting to S3
• TOIL_S3_PORT: the port number to use for connecting to S3, if needed
• TOIL_S3_USE_SSL: enable or disable the usage of SSL for connecting to S3 (True by default)

Examples:

TOIL_S3_HOST=127.0.0.1
TOIL_S3_PORT=9010
TOIL_S3_USE_SSL=False

Dashboard

Toil provides a dashboard for viewing the RAM and CPU usage of each node, the number of issued jobs of each type, the number of failed jobs, and the size of the jobs queue. To launch this dashboard for a toil workflow, include the --metrics flag in the toil script command. The dashboard can then be viewed in your browser at localhost:3000 while connected to the leader node through toil ssh-cluster:

To change the default port number, you can use the --grafana_port argument:

(venv) $ toil ssh-cluster -z us-west-2a --grafana_port 8000 <cluster-name>

On AWS, the dashboard keeps track of every node in the cluster to monitor CPU and RAM usage, but it can also be used while running a workflow on a single machine. The dashboard uses Grafana as the front end for displaying real-time plots, and Prometheus for tracking metrics exported by toil:

In order to use the dashboard for a non-released toil version, you will have to build the containers locally with make docker, since the prometheus, grafana, and mtail containers used in the dashboard are tied to a specific toil version.

Running in Google Compute Engine (GCE)

Toil supports a provisioner with Google, and a Google Job Store. To get started, follow instructions for Preparing your Google environment.

Preparing your Google environment

Toil supports using the Google Cloud Platform. Setting this up is easy!

1. Make sure that the google extra (Installing Toil with Extra Features) is installed

2. Follow Google’s Instructions to download credentials and set the GOOGLE_APPLICATION_CREDENTIALS environment variable

3. Create a new ssh key with the proper format. To create a new ssh key run the command

   $ ssh-keygen -t rsa -f ~/.ssh/id_rsa -C [USERNAME]
   

   where [USERNAME] is something like jane@example.com. Make sure to leave your password blank.

   Warning

   This command could overwrite an old ssh key you may be using. If you have an existing ssh key you would like to use, it will need to be called id_rsa and it needs to have no password set.

   Make sure only you can read the SSH keys:

   $ chmod 400 ~/.ssh/id_rsa ~/.ssh/id_rsa.pub
   

4. Add your newly formatted public key to Google. To do this, log into your Google Cloud account and go to metadata section under the Compute tab.

   

   Near the top of the screen click on ‘SSH Keys’, then edit, add item, and paste the key. Then save:

   

For more details look at Google’s instructions for adding SSH keys.

Google Job Store

To use the Google Job Store you will need to set the GOOGLE_APPLICATION_CREDENTIALS environment variable by following Google’s instructions.

Then to run the sort example with the Google job store you would type

$ python sort.py google:my-project-id:my-google-sort-jobstore

Running a Workflow with Autoscaling

Warning

Google Autoscaling is in beta!

The steps to run a GCE workflow are similar to those of AWS (Running a Workflow with Autoscaling), except you will need to explicitly specify the --provisioner gce option which otherwise defaults to aws.

1. Download sort.py

2. Launch the leader node in GCE using the Launch-Cluster Command command:

   (venv) $ toil launch-cluster <CLUSTER-NAME> \
                --provisioner gce \
                --leaderNodeType n1-standard-1 \
                --keyPairName <SSH-KEYNAME> \
                --zone us-west1-a
   

   Where <SSH-KEYNAME> is the first part of [USERNAME] used when setting up your ssh key. For example if [USERNAME] was jane@example.com, <SSH-KEYNAME> should be jane.

   The --keyPairName option is for an SSH key that was added to the Google account. If your ssh key [USERNAME] was jane@example.com, then your key pair name will be just jane.

3. Upload the sort example and ssh into the leader:

   (venv) $ toil rsync-cluster --provisioner gce <CLUSTER-NAME> sort.py :/root
   (venv) $ toil ssh-cluster --provisioner gce <CLUSTER-NAME>
   

4. Run the workflow:

   $ python /root/sort.py  google:<PROJECT-ID>:<JOBSTORE-NAME> \
         --provisioner gce \
         --batchSystem mesos \
         --nodeTypes n1-standard-2 \
         --maxNodes 2
   

5. Clean up:

   $ exit  # this exits the ssh from the leader node
   (venv) $ toil destroy-cluster --provisioner gce <CLUSTER-NAME>
   

Cluster Utilities

There are several utilities used for starting and managing a Toil cluster using the AWS provisioner. They are installed via the [aws] or [google] extra. For installation details see Toil Provisioner. The cluster utilities are used for Running in AWS and are comprised of toil launch-cluster, toil rsync-cluster, toil ssh-cluster, and toil destroy-cluster entry points.

Cluster commands specific to toil are:

status — Reports runtime and resource usage for all jobs in a specified jobstore (workflow must have originally been run using the --stats option).

stats — Inspects a job store to see which jobs have failed, run successfully, etc.

destroy-cluster — For autoscaling. Terminates the specified cluster and associated resources.

launch-cluster — For autoscaling. This is used to launch a toil leader instance with the specified provisioner.

rsync-cluster — For autoscaling. Used to transfer files to a cluster launched with toil launch-cluster.

ssh-cluster — SSHs into the toil appliance container running on the leader of the cluster.

clean — Delete the job store used by a previous Toil workflow invocation.

kill — Kills any running jobs in a rogue toil.

For information on a specific utility run:

toil launch-cluster --help

for a full list of its options and functionality.

The cluster utilities can be used for Running in Google Compute Engine (GCE) and Running in AWS.

Tip

By default, all of the cluster utilities expect to be running on AWS. To run with Google you will need to specify the --provisioner gce option for each utility.

Note

Boto must be configured with AWS credentials before using cluster utilities.

Running in Google Compute Engine (GCE) contains instructions for

Stats Command

To use the stats command, a workflow must first be run using the --stats option. Using this command makes certain that toil does not delete the job store, no matter what other options are specified (i.e. normally the option --clean=always would delete the job, but --stats will override this).

An example of this would be running the following:

python discoverfiles.py file:my-jobstore --stats

Where discoverfiles.py is the following:

import os
import subprocess

from toil.common import Toil
from toil.job import Job


class discoverFiles(Job):
    """Views files at a specified path using ls."""

    def __init__(self, path, *args, **kwargs):
        self.path = path
        super().__init__(*args, **kwargs)

    def run(self, fileStore):
        if os.path.exists(self.path):
            subprocess.check_call(["ls", self.path])


def main():
    options = Job.Runner.getDefaultArgumentParser().parse_args()
    options.clean = "always"

    job1 = discoverFiles(path="/sys/", displayName='sysFiles')
    job2 = discoverFiles(path=os.path.expanduser("~"), displayName='userFiles')
    job3 = discoverFiles(path="/tmp/")

    job1.addChild(job2)
    job2.addChild(job3)

    with Toil(options) as toil:
        if not toil.options.restart:
            toil.start(job1)
        else:
            toil.restart()


if __name__ == '__main__':
    main()

Notice the displayName key, which can rename a job, giving it an alias when it is finally displayed in stats. Running this workflow file should record three job names: sysFiles (job1), userFiles (job2), and discoverFiles (job3). To see the runtime and resources used for each job when it was run, type

toil stats file:my-jobstore

This should output the following:

Batch System: singleMachine
Default Cores: 1  Default Memory: 2097152K
Max Cores: 9.22337e+18
Total Clock: 0.56  Total Runtime: 1.01
Worker
    Count |                                    Time* |                                    Clock |                                     Wait |                                   Memory
        n |      min    med*     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total
        1 |     0.14    0.14    0.14    0.14    0.14 |     0.13    0.13    0.13    0.13    0.13 |     0.01    0.01    0.01    0.01    0.01 |      76K     76K     76K     76K     76K
Job
 Worker Jobs  |     min    med    ave    max
              |       3      3      3      3
    Count |                                    Time* |                                    Clock |                                     Wait |                                   Memory
        n |      min    med*     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total
        3 |     0.01    0.06    0.05    0.07    0.14 |     0.00    0.06    0.04    0.07    0.12 |     0.00    0.01    0.00    0.01    0.01 |      76K     76K     76K     76K    229K
 sysFiles
    Count |                                    Time* |                                    Clock |                                     Wait |                                   Memory
        n |      min    med*     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total
        1 |     0.01    0.01    0.01    0.01    0.01 |     0.00    0.00    0.00    0.00    0.00 |     0.01    0.01    0.01    0.01    0.01 |      76K     76K     76K     76K     76K
 userFiles
    Count |                                    Time* |                                    Clock |                                     Wait |                                   Memory
        n |      min    med*     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total
        1 |     0.06    0.06    0.06    0.06    0.06 |     0.06    0.06    0.06    0.06    0.06 |     0.01    0.01    0.01    0.01    0.01 |      76K     76K     76K     76K     76K
 discoverFiles
    Count |                                    Time* |                                    Clock |                                     Wait |                                   Memory
        n |      min    med*     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total |      min     med     ave     max   total
        1 |     0.07    0.07    0.07    0.07    0.07 |     0.07    0.07    0.07    0.07    0.07 |     0.00    0.00    0.00    0.00    0.00 |      76K     76K     76K     76K     76K

Once we’re done, we can clean up the job store by running

toil clean file:my-jobstore

Status Command

Continuing the example from the stats section above, if we ran our workflow with the command

python discoverfiles.py file:my-jobstore --stats

We could interrogate our jobstore with the status command, for example:

toil status file:my-jobstore

If the run was successful, this would not return much valuable information, something like

2018-01-11 19:31:29,739 - toil.lib.bioio - INFO - Root logger is at level 'INFO', 'toil' logger at level 'INFO'.
2018-01-11 19:31:29,740 - toil.utils.toilStatus - INFO - Parsed arguments
2018-01-11 19:31:29,740 - toil.utils.toilStatus - INFO - Checking if we have files for Toil
The root job of the job store is absent, the workflow completed successfully.

Otherwise, the status command should return the following:

There are x unfinished jobs, y parent jobs with children, z jobs with services, a services, and b totally failed jobs currently in c.

Clean Command

If a Toil pipeline didn’t finish successfully, or was run using --clean=always or --stats, the job store will exist until it is deleted. toil clean <jobStore> ensures that all artifacts associated with a job store are removed. This is particularly useful for deleting AWS job stores, which reserves an SDB domain as well as an S3 bucket.

The deletion of the job store can be modified by the --clean argument, and may be set to always, onError, never, or onSuccess (default).

Temporary directories where jobs are running can also be saved from deletion using the --cleanWorkDir, which has the same options as --clean. This option should only be run when debugging, as intermediate jobs will fill up disk space.

Launch-Cluster Command

Running toil launch-cluster starts up a leader for a cluster. Workers can be added to the initial cluster by specifying the -w option. An example would be

$ toil launch-cluster my-cluster \
      --leaderNodeType t2.small -z us-west-2a \
      --keyPairName your-AWS-key-pair-name \
      --nodeTypes m3.large,t2.micro -w 1,4

Options are listed below. These can also be displayed by running

$ toil launch-cluster --help

launch-cluster’s main positional argument is the clusterName. This is simply the name of your cluster. If it does not exist yet, Toil will create it for you.

Launch-Cluster Options

--help	-h also accepted. Displays this help menu.
--tempDirRoot TEMPDIRROOT
	Path to the temporary directory where all temp files are created, by default uses the current working directory as the base.
--version	Display version.
--provisioner CLOUDPROVIDER
	-p CLOUDPROVIDER also accepted. The provisioner for cluster auto-scaling. Both AWS and GCE are currently supported.
--zone ZONE	-z ZONE also accepted. The availability zone of the leader. This parameter can also be set via the TOIL_AWS_ZONE or TOIL_GCE_ZONE environment variables, or by the ec2_region_name parameter in your .boto file if using AWS, or derived from the instance metadata if using this utility on an existing EC2 instance.
--leaderNodeType LEADERNODETYPE
	Non-preemptable node type to use for the cluster leader.
--keyPairName KEYPAIRNAME
	The name of the AWS or ssh key pair to include on the instance.
--owner OWNER	The owner tag for all instances. If not given, the value in TOIL_OWNER_TAG will be used, or else the value of –keyPairName.
--boto BOTOPATH
	The path to the boto credentials directory. This is transferred to all nodes in order to access the AWS jobStore from non-AWS instances.
--tag KEYVALUE	KEYVALUE is specified as KEY=VALUE. -t KEY=VALUE also accepted. Tags are added to the AWS cluster for this node and all of its children. Tags are of the form: -t key1=value1 –tag key2=value2. Multiple tags are allowed and each tag needs its own flag. By default the cluster is tagged with: { “Name”: clusterName, “Owner”: IAM username }.
--vpcSubnet VPCSUBNET
	VPC subnet ID to launch cluster leader in. Uses default subnet if not specified. This subnet needs to have auto assign IPs turned on.
--nodeTypes NODETYPES
	Comma-separated list of node types to create while launching the leader. The syntax for each node type depends on the provisioner used. For the AWS provisioner this is the name of an EC2 instance type followed by a colon and the price in dollars to bid for a spot instance, for example ‘c3.8xlarge:0.42’. Must also provide the –workers argument to specify how many workers of each node type to create.
--workers WORKERS
	-w WORKERS also accepted. Comma-separated list of the number of workers of each node type to launch alongside the leader when the cluster is created. This can be useful if running toil without auto-scaling but with need of more hardware support.
--leaderStorage LEADERSTORAGE
	Specify the size (in gigabytes) of the root volume for the leader instance. This is an EBS volume.
--nodeStorage NODESTORAGE
	Specify the size (in gigabytes) of the root volume for any worker instances created when using the -w flag. This is an EBS volume.
--nodeStorageOverrides NODESTORAGEOVERRIDES
	Comma-separated list of nodeType:nodeStorage that are used to override the default value from –nodeStorage for the specified nodeType(s). This is useful for heterogeneous jobs where some tasks require much more disk than others.

Logging Options

--logOff	Same as --logCritical.
--logCritical	Turn on logging at level CRITICAL and above. (default is INFO)
--logError	Turn on logging at level ERROR and above. (default is INFO)
--logWarning	Turn on logging at level WARNING and above. (default is INFO)
--logInfo	Turn on logging at level INFO and above. (default is INFO)
--logDebug	Turn on logging at level DEBUG and above. (default is INFO)
--logLevel LOGLEVEL
	Log at given level (may be either OFF (or CRITICAL), ERROR, WARN (or WARNING), INFO or DEBUG). (default is INFO)
--logFile LOGFILE
	File to log in.
--rotatingLogging
	Turn on rotating logging, which prevents log files getting too big.

Ssh-Cluster Command

Toil provides the ability to ssh into the leader of the cluster. This can be done as follows:

$ toil ssh-cluster CLUSTER-NAME-HERE

This will open a shell on the Toil leader and is used to start an Running a Workflow with Autoscaling run. Issues with docker prevent using screen and tmux when sshing the cluster (The shell doesn’t know that it is a TTY which prevents it from allocating a new screen session). This can be worked around via

$ script
$ screen

Simply running screen within script will get things working properly again.

Finally, you can execute remote commands with the following syntax:

$ toil ssh-cluster CLUSTER-NAME-HERE remoteCommand

It is not advised that you run your Toil workflow using remote execution like this unless a tool like nohup is used to ensure the process does not die if the SSH connection is interrupted.

For an example usage, see Running a Workflow with Autoscaling.

Rsync-Cluster Command

The most frequent use case for the rsync-cluster utility is deploying your Toil script to the Toil leader. Note that the syntax is the same as traditional rsync with the exception of the hostname before the colon. This is not needed in toil rsync-cluster since the hostname is automatically determined by Toil.

Here is an example of its usage:

$ toil rsync-cluster CLUSTER-NAME-HERE \
   ~/localFile :/remoteDestination

Destroy-Cluster Command

The destroy-cluster command is the advised way to get rid of any Toil cluster launched using the Launch-Cluster Command command. It ensures that all attached nodes, volumes, security groups, etc. are deleted. If a node or cluster is shut down using Amazon’s online portal residual resources may still be in use in the background. To delete a cluster run

$ toil destroy-cluster CLUSTER-NAME-HERE

Kill Command

To kill all currently running jobs for a given jobstore, use the command

toil kill file:my-jobstore

HPC Environments

Toil is a flexible framework that can be leveraged in a variety of environments, including high-performance computing (HPC) environments. Toil provides support for a number of batch systems, including Grid Engine, Slurm, Torque and LSF, which are popular schedulers used in these environments. Toil also supports HTCondor, which is a popular scheduler for high-throughput computing (HTC). To use one of these batch systems specify the “--batchSystem” argument to the toil script.

Due to the cost and complexity of maintaining support for these schedulers we currently consider them to be “community supported”, that is the core development team does not regularly test or develop support for these systems. However, there are members of the Toil community currently deploying Toil in HPC environments and we welcome external contributions.

Developing the support of a new or existing batch system involves extending the abstract batch system class toil.batchSystems.abstractBatchSystem.AbstractBatchSystem.

Standard Output/Error from Batch System Jobs

Standard output and error from batch system jobs (except for the Parasol and Mesos batch systems) are redirected to files in the toil-<workflowID> directory created within the temporary directory specified by the --workDir option; see Commandline Options. Each file is named as follows: toil_job_<Toil job ID>_batch_<name of batch system>_<job ID from batch system>_<file description>.log, where <file description> is std_output for standard output, and std_error for standard error. HTCondor will also write job event log files with <file description> = job_events.

If capturing standard output and error is desired, --workDir will generally need to be on a shared file system; otherwise if these are written to local temporary directories on each node (e.g. /tmp) Toil will not be able to retrieve them. Alternatively, the --noStdOutErr option forces Toil to discard all standard output and error from batch system jobs.

CWL in Toil

The Common Workflow Language (CWL) is an emerging standard for writing workflows that are portable across multiple workflow engines and platforms. Toil has full support for the CWL v1.0, v1.1, and v1.2 standards.

Running CWL Locally

The toil-cwl-runner command provides cwl-parsing functionality using cwltool, and leverages the job-scheduling and batch system support of Toil.

To run in local batch mode, provide the CWL file and the input object file:

$ toil-cwl-runner example.cwl example-job.yml

For a simple example of CWL with Toil see Running a basic CWL workflow.

Note for macOS + Docker + Toil

When invoking CWL documents that make use of Docker containers if you see errors that look like

docker: Error response from daemon: Mounts denied:
The paths /var/...tmp are not shared from OS X and are not known to Docker.

you may need to add

export TMPDIR=/tmp/docker_tmp

either in your startup file (.bashrc) or add it manually in your shell before invoking toil.

Detailed Usage Instructions

Help information can be found by using this toil command:

$ toil-cwl-runner -h

A more detailed example shows how we can specify both Toil and cwltool arguments for our workflow:

$ toil-cwl-runner \
    --singularity \
    --jobStore my_jobStore \
    --batchSystem lsf \
    --workDir `pwd` \
    --outdir `pwd` \
    --logFile cwltoil.log \
    --writeLogs `pwd` \
    --logLevel DEBUG \
    --retryCount 2 \
    --maxLogFileSize 20000000000 \
    --stats \
    standard_bam_processing.cwl \
    inputs.yaml

In this example, we set the following options, which are all passed to Toil:

--singularity: Specifies that all jobs with Docker format containers specified should be run using the Singularity container engine instead of the Docker container engine.

--jobStore: Path to a folder which doesn’t exist yet, which will contain the Toil jobstore and all related job-tracking information.

--batchSystem: Use the specified HPC or Cloud-based cluster platform.

--workDir: The directory where all temporary files will be created for the workflow. A subdirectory of this will be set as the $TMPDIR environment variable and this subdirectory can be referenced using the CWL parameter reference $(runtime.tmpdir) in CWL tools and workflows.

--outdir: Directory where final File and Directory outputs will be written. References to these and other output types will be in the JSON object printed to the stdout stream after workflow execution.

--logFile: Path to the main logfile with logs from all jobs.

--writeLogs: Directory where all job logs will be stored.

--retryCount: How many times to retry each Toil job.

--maxLogFileSize: Logs that get larger than this value will be truncated.

--stats: Save resources usages in json files that can be collected with the toil stats command after the workflow is done.

--disable-streaming: Does not allow streaming of input files. This is enabled by default for files marked with streamable flag True and only for remote files when the jobStore is not on local machine.

Running CWL in the Cloud

To run in cloud and HPC configurations, you may need to provide additional command line parameters to select and configure the batch system to use.

To run a CWL workflow in AWS with toil see Running a CWL Workflow on AWS.

Running CWL within Toil Scripts

A CWL workflow can be run indirectly in a native Toil script. However, this is not the standard way to run CWL workflows with Toil and doing so comes at the cost of job efficiency. For some use cases, such as running one process on multiple files, it may be useful. For example, if you want to run a CWL workflow with 3 YML files specifying different samples inputs, it could look something like:

import os
import subprocess

from toil.common import Toil
from toil.job import Job


def initialize_jobs(job):
    job.fileStore.logToMaster('initialize_jobs')


def runQC(job, cwl_file, cwl_filename, yml_file, yml_filename, outputs_dir, output_num):
    job.fileStore.logToMaster("runQC")
    tempDir = job.fileStore.getLocalTempDir()

    cwl = job.fileStore.readGlobalFile(cwl_file, userPath=os.path.join(tempDir, cwl_filename))
    yml = job.fileStore.readGlobalFile(yml_file, userPath=os.path.join(tempDir, yml_filename))

    subprocess.check_call(["toil-cwl-runner", cwl, yml])

    output_filename = "output.txt"
    output_file = job.fileStore.writeGlobalFile(output_filename)
    job.fileStore.readGlobalFile(output_file, userPath=os.path.join(outputs_dir, "sample_" + output_num + "_" + output_filename))
    return output_file


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"
    with Toil(options) as toil:

        # specify the folder where the cwl and yml files live
        inputs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "cwlExampleFiles")
        # specify where you wish the outputs to be written
        outputs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "cwlExampleFiles")

        job0 = Job.wrapJobFn(initialize_jobs)

        cwl_filename = "hello.cwl"
        cwl_file = toil.importFile("file://" + os.path.abspath(os.path.join(inputs_dir, cwl_filename)))

        # add list of yml config inputs here or import and construct from file
        yml_files = ["hello1.yml", "hello2.yml", "hello3.yml"]
        i = 0
        for yml in yml_files:
            i = i + 1
            yml_file = toil.importFile("file://" + os.path.abspath(os.path.join(inputs_dir, yml)))
            yml_filename = yml
            job = Job.wrapJobFn(runQC, cwl_file, cwl_filename, yml_file, yml_filename, outputs_dir, output_num=str(i))
            job0.addChild(job)

        toil.start(job0)

Running CWL workflows with InplaceUpdateRequirement

Some CWL workflows use the InplaceUpdateRequirement feature, which requires that operations on files have visible side effects that Toil’s file store cannot support. If you need to run a workflow like this, you can make sure that all of your worker nodes have a shared filesystem, and use the --bypass-file-store option to toil-cwl-runner. This will make it leave all CWL intermediate files on disk and share them between jobs using file paths, instead of storing them in the file store and downloading them when jobs need them.

Toil & CWL Tips

See logs for just one job by using the full log file

This requires knowing the job’s toil-generated ID, which can be found in the log files.

cat cwltoil.log | grep jobVM1fIs

Grep for full tool commands from toil logs

This gives you a more concise view of the commands being run (note that this information is only available from Toil when running with –logDebug).

pcregrep -M "\[job .*\.cwl.*$\n(.*        .*$\n)*" cwltoil.log
#         ^allows for multiline matching

Find Bams that have been generated for specific step while pipeline is running:

find . | grep -P '^./out_tmpdir.*_MD\.bam$'

See what jobs have been run

cat log/cwltoil.log | grep -oP "\[job .*.cwl\]" | sort | uniq

or:

cat log/cwltoil.log | grep -i "issued job"

Get status of a workflow

$ toil status /home/johnsoni/TEST_RUNS_3/TEST_run/tmp/jobstore-09ae0acc-c800-11e8-9d09-70106fb1697e
<hostname> 2018-10-04 15:01:44,184 MainThread INFO toil.lib.bioio: Root logger is at level 'INFO', 'toil' logger at level 'INFO'.
<hostname> 2018-10-04 15:01:44,185 MainThread INFO toil.utils.toilStatus: Parsed arguments
<hostname> 2018-10-04 15:01:47,081 MainThread INFO toil.utils.toilStatus: Traversing the job graph gathering jobs. This may take a couple of minutes.

Of the 286 jobs considered, there are 179 jobs with children, 107 jobs ready to run, 0 zombie jobs, 0 jobs with services, 0 services, and 0 jobs with log files currently in file:/home/user/jobstore-09ae0acc-c800-11e8-9d09-70106fb1697e.

Toil Stats

You can get run statistics broken down by CWL file. This only works once the workflow is finished:

$ toil stats /path/to/jobstore

The output will contain CPU, memory, and walltime information for all CWL job types:

<hostname> 2018-10-15 12:06:19,003 MainThread INFO toil.lib.bioio: Root logger is at level 'INFO', 'toil' logger at level 'INFO'.
<hostname> 2018-10-15 12:06:19,004 MainThread INFO toil.utils.toilStats: Parsed arguments
<hostname> 2018-10-15 12:06:19,004 MainThread INFO toil.utils.toilStats: Checking if we have files for toil
<hostname> 2018-10-15 12:06:19,004 MainThread INFO toil.utils.toilStats: Checked arguments
Batch System: lsf
Default Cores: 1  Default Memory: 10485760K
Max Cores: 9.22337e+18
Total Clock: 106608.01  Total Runtime: 86634.11
Worker
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
     1659 |     0.00    0.80  264.87 12595.59 439424.40 |     0.00    0.46   449.05 42240.74 744968.80 |  -35336.69     0.16   -184.17  4230.65 -305544.39 |      48K    223K   1020K  40235K 1692300K
Job
 Worker Jobs  |     min    med    ave    max
              |    1077   1077   1077   1077
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
     1077 |     0.04    1.18  407.06 12593.43 438404.73 |     0.01    0.28   691.17 42240.35 744394.14 |  -35336.83     0.27   -284.11  4230.49 -305989.41 |     135K    268K   1633K  40235K 1759734K
 ResolveIndirect
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
      205 |     0.04    0.07    0.16     2.29     31.95 |     0.01    0.02     0.02     0.14      3.60 |       0.02     0.05      0.14     2.28      28.35 |     190K    266K    256K    314K   52487K
 CWLGather
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
       40 |     0.05    0.17    0.29     1.90     11.62 |     0.01    0.02     0.02     0.05      0.80 |       0.03     0.14      0.27     1.88      10.82 |     188K    265K    250K    316K   10039K
 CWLWorkflow
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
      205 |     0.09    0.40    0.98    13.70    200.82 |     0.04    0.15     0.16     1.08     31.78 |       0.04     0.26      0.82    12.62     169.04 |     190K    270K    257K    316K   52826K
 file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/expression_tools/group_waltz_files.cwl
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
       99 |     0.29    0.49    0.59     2.50     58.11 |     0.14    0.26     0.29     1.04     28.95 |       0.14     0.22      0.29     1.48      29.16 |     135K    135K    135K    136K   13459K
 file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/expression_tools/make_sample_output_dirs.cwl
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
       11 |     0.34    0.52    0.74     2.63      8.18 |     0.20    0.30     0.41     1.17      4.54 |       0.14     0.20      0.33     1.45       3.65 |     136K    136K    136K    136K    1496K
 file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/expression_tools/consolidate_files.cwl
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
        8 |     0.31    0.59    0.71     1.80      5.69 |     0.18    0.35     0.37     0.63      2.94 |       0.13     0.27      0.34     1.17       2.75 |     136K    136K    136K    136K    1091K
 file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/bwa-mem/bwa-mem.cwl
    Count |                                       Time* |                                        Clock |                                              Wait |                                    Memory
        n |      min    med*     ave      max     total |      min     med      ave      max     total |        min      med       ave      max      total |      min     med     ave     max    total
       22 |   895.76 3098.13 3587.34 12593.43  78921.51 |  2127.02 7910.31  8123.06 16959.13 178707.34 |  -11049.84 -3827.96  -4535.72    19.49  -99785.83 |    5659K   5950K   5854K   6128K  128807K

Understanding toil log files

There is a worker_log.txt file for each job, this file is written to while the job is running, and deleted after the job finishes. The contents are printed to the main log file and transferred to a log file in the –logDir folder once the job is completed successfully.

The new log file will be named something like:

file:<path to cwl tool>.cwl_<job ID>.log

file:---home-johnsoni-pipeline_1.1.14-ACCESS--Pipeline-cwl_tools-marianas-ProcessLoopUMIFastq.cwl_I-O-jobfGsQQw000.log

This is the toil job command with spaces replaced by dashes.

WDL in Toil

Support is still in the alpha phase and should be able to handle basic wdl files. See the specification below for more details.

How to Run a WDL file in Toil

Recommended best practice when running wdl files is to first use the Broad’s wdltool for syntax validation and generating the needed json input file. Full documentation can be found on the repository, and a precompiled jar binary can be downloaded here: wdltool (this requires java7).

That means two steps. First, make sure your wdl file is valid and devoid of syntax errors by running

java -jar wdltool.jar validate example_wdlfile.wdl

Second, generate a complementary json file if your wdl file needs one. This json will contain keys for every necessary input that your wdl file needs to run:

java -jar wdltool.jar inputs example_wdlfile.wdl

When this json template is generated, open the file, and fill in values as necessary by hand. WDL files all require json files to accompany them. If no variable inputs are needed, a json file containing only ‘{}’ may be required.

Once a wdl file is validated and has an appropriate json file, workflows can be run in toil using:

toil-wdl-runner example_wdlfile.wdl example_jsonfile.json

See options below for more parameters.

ENCODE Example from ENCODE-DCC

To follow this example, you will need docker installed. The original workflow can be found here: https://github.com/ENCODE-DCC/pipeline-container

We’ve included the wdl file and data files in the toil repository needed to run this example. First, download the example code and unzip. The file needed is “testENCODE/encode_mapping_workflow.wdl”.

Next, use wdltool (this requires java7) to validate this file:

java -jar wdltool.jar validate encode_mapping_workflow.wdl

Next, use wdltool to generate a json file for this wdl file:

java -jar wdltool.jar inputs encode_mapping_workflow.wdl

This json file once opened should look like this:

{
"encode_mapping_workflow.fastqs": "Array[File]",
"encode_mapping_workflow.trimming_parameter": "String",
"encode_mapping_workflow.reference": "File"
}

The trimming_parameter should be set to ‘native’. Download the example code and unzip. Inside are two data files required for the run

ENCODE_data/reference/GRCh38_chr21_bwa.tar.gz ENCODE_data/ENCFF000VOL_chr21.fq.gz

Editing the json to include these as inputs, the json should now look something like this:

{
"encode_mapping_workflow.fastqs": ["/path/to/unzipped/ENCODE_data/ENCFF000VOL_chr21.fq.gz"],
"encode_mapping_workflow.trimming_parameter": "native",
"encode_mapping_workflow.reference": "/path/to/unzipped/ENCODE_data/reference/GRCh38_chr21_bwa.tar.gz"
}

The wdl and json files can now be run using the command

toil-wdl-runner encode_mapping_workflow.wdl encode_mapping_workflow.json

This should deposit the output files in the user’s current working directory (to change this, specify a new directory with the ‘-o’ option).

GATK Examples from the Broad

Simple examples of WDL can be found on the Broad’s website as tutorials: https://software.broadinstitute.org/wdl/documentation/topic?name=wdl-tutorials.

One can follow along with these tutorials, write their own wdl files following the directions and run them using either cromwell or toil. For example, in tutorial 1, if you’ve followed along and named your wdl file ‘helloHaplotypeCall.wdl’, then once you’ve validated your wdl file using wdltool (this requires java7) using

java -jar wdltool.jar validate helloHaplotypeCaller.wdl

and generated a json file (and subsequently typed in appropriate filepaths* and variables) using

java -jar wdltool.jar inputs helloHaplotypeCaller.wdl

• Absolute filepath inputs are recommended for local testing.

then the wdl script can be run using

toil-wdl-runner helloHaplotypeCaller.wdl helloHaplotypeCaller_inputs.json

toilwdl.py Options

‘-o’ or ‘--outdir’: Specifies the output folder, and defaults to the current working directory if not specified by the user.

‘--dev_mode’: Creates “AST.out”, which holds a printed AST of the wdl file and “mappings.out”, which holds the printed task, workflow, csv, and tsv dictionaries generated by the parser. Also saves the compiled toil python workflow file for debugging.

Any number of arbitrary options may also be specified. These options will not be parsed immediately, but passed down as toil options once the wdl/json files are processed. For valid toil options, see the documentation: http://toil.readthedocs.io/en/latest/running/cliOptions.html

Running WDL within Toil Scripts

Note

A cromwell.jar file is needed in order to run a WDL workflow.

A WDL workflow can be run indirectly in a native Toil script. However, this is not the standard way to run WDL workflows with Toil and doing so comes at the cost of job efficiency. For some use cases, such as running one process on multiple files, it may be useful. For example, if you want to run a WDL workflow with 3 JSON files specifying different samples inputs, it could look something like:

import os
import subprocess

from toil.common import Toil
from toil.job import Job


def initialize_jobs(job):
    job.fileStore.logToMaster("initialize_jobs")


def runQC(job, wdl_file, wdl_filename, json_file, json_filename, outputs_dir, jar_loc,output_num):
    job.fileStore.logToMaster("runQC")
    tempDir = job.fileStore.getLocalTempDir()

    wdl = job.fileStore.readGlobalFile(wdl_file, userPath=os.path.join(tempDir, wdl_filename))
    json = job.fileStore.readGlobalFile(json_file, userPath=os.path.join(tempDir, json_filename))

    subprocess.check_call(["java","-jar",jar_loc,"run",wdl,"--inputs",json])

    output_filename = "output.txt"
    output_file = job.fileStore.writeGlobalFile(outputs_dir + output_filename)
    job.fileStore.readGlobalFile(output_file, userPath=os.path.join(outputs_dir, "sample_" + output_num + "_" + output_filename))
    return output_file


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:

        # specify the folder where the wdl and json files live
        inputs_dir = "wdlExampleFiles/"
        # specify where you wish the outputs to be written
        outputs_dir = "wdlExampleFiles/"
        # specify the location of your cromwell jar
        jar_loc = os.path.abspath("wdlExampleFiles/cromwell-35.jar")

        job0 = Job.wrapJobFn(initialize_jobs)

        wdl_filename = "hello.wdl"
        wdl_file = toil.importFile("file://" + os.path.abspath(os.path.join(inputs_dir, wdl_filename)))


        # add list of yml config inputs here or import and construct from file
        json_files = ["hello1.json", "hello2.json", "hello3.json"]
        i = 0
        for json in json_files:
            i = i + 1
            json_file = toil.importFile("file://" + os.path.join(inputs_dir, json))
            json_filename = json
            job = Job.wrapJobFn(runQC, wdl_file, wdl_filename, json_file, json_filename, outputs_dir, jar_loc, output_num=str(i))
            job0.addChild(job)

        toil.start(job0)

WDL Specifications

WDL language specifications can be found here: https://github.com/broadinstitute/wdl/blob/develop/SPEC.md

Implementing support for more features is currently underway, but a basic roadmap so far is:

CURRENTLY IMPLEMENTED:

• Scatter
• Many Built-In Functions
• Docker Calls
• Handles Priority, and Output File Wrangling
• Currently Handles Primitives and Arrays

TO BE IMPLEMENTED:

• Integrate Cloud Autoscaling Capacity More Robustly
• WDL Files That “Import” Other WDL Files (Including URI Handling for ‘http://’ and ‘https://’)

Workflow Execution Service (WES)

The GA4GH Workflow Execution Service (WES) is a standardized API for submitting and monitoring workflows. Toil has experimental support for setting up a WES server and executing CWL, WDL, and Toil workflows using the WES API. More information about the WES API specification can be found here.

To get started with the Toil WES server, make sure that the server extra (Installing Toil with Extra Features) is installed.

Preparing your WES environment

The WES server requires Celery to distribute and execute workflows. To set up Celery:

1. Start RabbitMQ, which is the broker between the WES server and Celery workers:

   docker run -d --name wes-rabbitmq -p 5672:5672 rabbitmq:3.9.5
   

2. Start Celery workers:

   celery -A toil.server.celery_app worker --loglevel=INFO
   

Starting a WES server

To start a WES server on the default port 8080, run the Toil command:

$ toil server

The WES API will be hosted on the following URL:

http://localhost:8080/ga4gh/wes/v1

To use another port, e.g.: 3000, you can specify the --port argument:

$ toil server --port 3000

There are many other command line options. Help information can be found by using this command:

$ toil server --help

Below is a detailed summary of all server-specific options:

--debug	Enable debug mode.
--bypass_celery
	Skip sending workflows to Celery and just run them under the server. For testing.
--host HOST	The host interface that the Toil server binds on. (default: “127.0.0.1”).
--port PORT	The port that the Toil server listens on. (default: 8080).
--swagger_ui	If True, the swagger UI will be enabled and hosted on the {api_base_path}/ui endpoint. (default: False)
--cors	Enable Cross Origin Resource Sharing (CORS). This should only be turned on if the server is intended to be used by a website or domain. (default: False).
--cors_origins CORS_ORIGIN
	Ignored if -//-cors is False. This sets the allowed origins for CORS. For details about CORS and its security risks, see the GA4GH docs on CORS. (default: “*”).
--workers WORKERS, -w WORKERS
	Ignored if --debug is True. The number of worker processes launched by the WSGI server. (default: 2).
--work_dir WORK_DIR
	The directory where workflows should be stored. This directory should be empty or only contain previous workflows. (default: ‘./workflows’).
--state_store STATE_STORE
	The local path or S3 URL where workflow state metadata should be stored. (default: in --work_dir)
--opt OPT, -o OPT
	Specify the default parameters to be sent to the workflow engine for each run. Options taking arguments must use = syntax. Accepts multiple values. Example: --opt=--logLevel=CRITICAL --opt=--workDir=/tmp.
--dest_bucket_base DEST_BUCKET_BASE
	Direct CWL workflows to save output files to dynamically generated unique paths under the given URL. Supports AWS S3.
--wes_dialect DIALECT
	Restrict WES responses to a dialect compatible with clients that do not fully implement the WES standard. (default: ‘standard’)

Running the Server with docker-compose

Instead of manually setting up the server components (toil server, RabbitMQ, and Celery), you can use the following docker-compose.yml file to orchestrate and link them together.

Make sure to change the credentials for basic authentication by updating the traefik.http.middlewares.auth.basicauth.users label. The passwords can be generated with tools like htpasswd like this. (Note that single $ signs need to be replaced with $$ in the yaml file).

When running on a different host other than localhost, make sure to change the Host to your tartget host in the traefik.http.routers.wes.rule and traefik.http.routers.wespublic.rule labels.

You can also change /tmp/toil-workflows if you want Toil workflows to live somewhere else, and create the directory before starting the server.

In order to run workflows that require Docker, the docker.sock socket must be mounted as volume for Celery. Additionally, the TOIL_WORKDIR directory (defaults to: /var/lib/toil) and /var/lib/cwl (if running CWL workflows with DockerRequirement) should exist on the host and also be mounted as volumes.

Also make sure to run it behind a firewall; it opens up the Toil server on port 8080 to anyone who connects.

# docker-compose.yml
version: "3.8"

services:
  rabbitmq:
    image: rabbitmq:3.9.5
    hostname: rabbitmq
  celery:
    image: ${TOIL_APPLIANCE_SELF}
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - /var/lib/docker:/var/lib/docker
      - /var/lib/toil:/var/lib/toil
      - /var/lib/cwl:/var/lib/cwl
      - /tmp/toil-workflows:/tmp/toil-workflows
    command: celery --broker=amqp://guest:guest@rabbitmq:5672// -A toil.server.celery_app worker --loglevel=INFO
    depends_on:
      - rabbitmq
  wes-server:
    image: ${TOIL_APPLIANCE_SELF}
    volumes:
      - /tmp/toil-workflows:/tmp/toil-workflows
    environment:
      - TOIL_WES_BROKER_URL=amqp://guest:guest@rabbitmq:5672//
    command: toil server --host 0.0.0.0 --port 8000 --work_dir /tmp/toil-workflows
    expose:
      - 8000
    labels:
      - "traefik.enable=true"
      - "traefik.http.routers.wes.rule=Host(`localhost`)"
      - "traefik.http.routers.wes.entrypoints=web"
      - "traefik.http.routers.wes.middlewares=auth"
      - "traefik.http.middlewares.auth.basicauth.users=test:$$2y$$12$$ci.4U63YX83CwkyUrjqxAucnmi2xXOIlEF6T/KdP9824f1Rf1iyNG"
      - "traefik.http.routers.wespublic.rule=Host(`localhost`) && Path(`/ga4gh/wes/v1/service-info`)"
    depends_on:
      - rabbitmq
      - celery
  traefik:
    image: traefik:v2.2
    command:
      - "--providers.docker"
      - "--providers.docker.exposedbydefault=false"
      - "--entrypoints.web.address=:8080"
    ports:
      - "8080:8080"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock

Further customization can also be made as needed. For example, if you have a domain, you can set up HTTPS with Let’s Encrypt.

Once everything is configured, simply run docker-compose up to start the containers. Run docker-compose down to stop and remove all containers.

Note

docker-compose is not installed on the Toil appliance by default. See the following section to set up the WES server on a Toil cluster.

Running on a Toil cluster

To run the server on a Toil leader instance on EC2:

1. Launch a Toil cluster with the toil launch-cluster command with the AWS provisioner

2. SSH into your cluster with the --sshOption=-L8080:localhost:8080 option to forward port 8080

3. Install Docker Compose by running the following commands from the Docker docs:

   curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
   chmod +x /usr/local/bin/docker-compose
   
   # check installation
   docker-compose --version
   

   or, install a different version of Docker Compose by changing "1.29.2" to another version.

4. Copy the docker-compose.yml file from (Running the Server with docker-compose) to an empty directory, and modify the configuration as needed.

5. Now, run docker-compose up -d to start the WES server in detach mode on the Toil appliance.

6. To stop the server, run docker-compose down.

WES API Endpoints

As defined by the GA4GH WES API specification, the following endpoints with base path ga4gh/wes/v1/ are supported by Toil:

GET /service-info	Get information about the Workflow Execution Service.
GET /runs	List the workflow runs.
POST /runs	Run a workflow. This endpoint creates a new workflow run and returns a run_id to monitor its progress.
GET /runs/{run_id}	Get detailed info about a workflow run.
POST /runs/{run_id}/cancel	Cancel a running workflow.
GET /runs/{run_id}/status	Get the status (overall state) of a workflow run.

When running the WES server with the docker-compose setup above, most endpoints (except GET /service-info) will be protected with basic authentication. Make sure to set the Authorization header with the correct credentials when submitting or retrieving a workflow.

Submitting a Workflow

Now that the WES API is up and running, we can submit and monitor workflows remotely using the WES API endpoints. A workflow can be submitted for execution using the POST /runs endpoint.

As a quick example, we can submit the example CWL workflow from Running a basic CWL workflow to our WES API:

# example.cwl
cwlVersion: v1.0
class: CommandLineTool
baseCommand: echo
stdout: output.txt
inputs:
 message:
   type: string
   inputBinding:
     position: 1
outputs:
 output:
   type: stdout

using cURL:

$ curl --location --request POST 'http://localhost:8080/ga4gh/wes/v1/runs' \
    --user test:test \
    --form 'workflow_url="example.cwl"' \
    --form 'workflow_type="cwl"' \
    --form 'workflow_type_version="v1.0"' \
    --form 'workflow_params="{\"message\": \"Hello world!\"}"' \
    --form 'workflow_attachment=@"./toil_test_files/example.cwl"'
{
  "run_id": "4deb8beb24894e9eb7c74b0f010305d1"
}

Note that the --user argument is used to attach the basic authentication credentials along with the request. Make sure to change test:test to the username and password you configured for your WES server. Alternatively, you can also set the Authorization header manually as "Authorization: Basic base64_encoded_auth".

If the workflow is submitted successfully, a JSON object containing a run_id will be returned. The run_id is a unique identifier of your requested workflow, which can be used to monitor or cancel the run.

There are a few required parameters that have to be set for all workflow submissions, which are the following:

workflow_url	The URL of the workflow to run. This can refer to a file from workflow_attachment.
workflow_type	The type of workflow language. Toil currently supports one of the following: "CWL", "WDL", or "py". To run a Toil native python script, set this to "py".
workflow_type_version	The version of the workflow language. Supported versions can be found by accessing the GET /service-info endpoint of your WES server.
workflow_params	A JSON object that specifies the inputs of the workflow.

Additionally, the following optional parameters are also available:

workflow_attachment	A list of files associated with the workflow run.
workflow_engine_parameters	A JSON key-value map of workflow engine parameters to send to the runner. Example: {"--logLevel": "INFO", "--workDir": "/tmp/"}
tags	A JSON key-value map of metadata associated with the workflow.

For more details about these parameters, refer to the Run Workflow section in the WES API spec.

Upload multiple files

Looking at the body of the request of the previous example, note that the workflow_url is a relative URL that refers to the example.cwl file uploaded from the local path ./toil_test_files/example.cwl.

To specify the file name (or subdirectory) of the remote destination file, set the filename field in the Content-Disposition header. You could also upload more than one file by providing the workflow_attachment parameter multiple times with different files.

This can be shown by the following example:

$ curl --location --request POST 'http://localhost:8080/ga4gh/wes/v1/runs' \
    --user test:test \
    --form 'workflow_url="example.cwl"' \
    --form 'workflow_type="cwl"' \
    --form 'workflow_type_version="v1.0"' \
    --form 'workflow_params="{\"message\": \"Hello world!\"}"' \
    --form 'workflow_attachment=@"./toil_test_files/example.cwl"' \
    --form 'workflow_attachment=@"./toil_test_files/2.fasta";filename=inputs/test.fasta' \
    --form 'workflow_attachment=@"./toil_test_files/2.fastq";filename=inputs/test.fastq'

On the server, the execution directory would have the following structure from the above request:

execution/
├── example.cwl
├── inputs
│     ├── test.fasta
|     └── test.fastq
└── wes_inputs.json

Specify Toil options

To pass Toil-specific parameters to the workflow, you can include the workflow_engine_parameters parameter along with your request.

For example, to set the logging level to INFO, and change the working directory of the workflow, simply include the following as workflow_engine_parameters:

{"--logLevel": "INFO", "--workDir": "/tmp/"}

These options would be appended at the end of existing parameters during command construction, which would override the default parameters if provided. (Default parameters that can be passed multiple times would not be overridden).

Monitoring a Workflow

With the run_id returned when submitting the workflow, we can check the status or get the full logs of the workflow run.

Checking the state

The GET /runs/{run_id}/status endpoint can be used to get a simple result with the overall state of your run:

$ curl --user test:test http://localhost:8080/ga4gh/wes/v1/runs/4deb8beb24894e9eb7c74b0f010305d1/status
{
  "run_id": "4deb8beb24894e9eb7c74b0f010305d1",
  "state": "RUNNING"
}

The possible states here are: QUEUED, INITIALIZING, RUNNING, COMPLETE, EXECUTOR_ERROR, SYSTEM_ERROR, CANCELING, and CANCELED.

Getting the full logs

To get the detailed information about a workflow run, use the GET /runs/{run_id} endpoint:

$ curl --user test:test http://localhost:8080/ga4gh/wes/v1/runs/4deb8beb24894e9eb7c74b0f010305d1
{
  "run_id": "4deb8beb24894e9eb7c74b0f010305d1",
  "request": {
    "workflow_attachment": [
      "example.cwl"
    ],
    "workflow_url": "example.cwl",
    "workflow_type": "cwl",
    "workflow_type_version": "v1.0",
    "workflow_params": {
      "message": "Hello world!"
    }
  },
  "state": "RUNNING",
  "run_log": {
    "cmd": [
      "toil-cwl-runner --outdir=/home/toil/workflows/4deb8beb24894e9eb7c74b0f010305d1/outputs --jobStore=file:/home/toil/workflows/4deb8beb24894e9eb7c74b0f010305d1/toil_job_store /home/toil/workflows/4deb8beb24894e9eb7c74b0f010305d1/execution/example.cwl /home/workflows/4deb8beb24894e9eb7c74b0f010305d1/execution/wes_inputs.json"
    ],
    "start_time": "2021-08-30T17:35:50Z",
    "end_time": null,
    "stdout": null,
    "stderr": null,
    "exit_code": null
  },
  "task_logs": [],
  "outputs": {}
}

Canceling a run

To cancel a workflow run, use the POST /runs/{run_id}/cancel endpoint:

$ curl --location --request POST 'http://localhost:8080/ga4gh/wes/v1/runs/4deb8beb24894e9eb7c74b0f010305d1/cancel' \
      --user test:test
{
  "run_id": "4deb8beb24894e9eb7c74b0f010305d1"
}

Developing a Workflow

This tutorial walks through the features of Toil necessary for developing a workflow using the Toil Python API.

Note

“script” and “workflow” will be used interchangeably

Scripting Quick Start

To begin, consider this short toil script which illustrates defining a workflow:

from toil.common import Toil
from toil.job import Job


def helloWorld(message, memory="2G", cores=2, disk="3G"):
    return f"Hello, world!, here's a message: {message}"


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "OFF"
    options.clean = "always"

    hello_job = Job.wrapFn(helloWorld, "Woot")

    with Toil(options) as toil:
        print(toil.start(hello_job))  # prints "Hello, world!, ..."

The workflow consists of a single job. The resource requirements for that job are (optionally) specified by keyword arguments (memory, cores, disk). The script is run using toil.job.Job.Runner.getDefaultOptions(). Below we explain the components of this code in detail.

Job Basics

The atomic unit of work in a Toil workflow is a Job. User scripts inherit from this base class to define units of work. For example, here is a more long-winded class-based version of the job in the quick start example:

from toil.job import Job

class HelloWorld(Job):
    def __init__(self, message):
        Job.__init__(self,  memory="2G", cores=2, disk="3G")
        self.message = message

    def run(self, fileStore):
        return f"Hello, world! Here's a message: {self.message}"

In the example a class, HelloWorld, is defined. The constructor requests 2 gigabytes of memory, 2 cores and 3 gigabytes of local disk to complete the work.

The toil.job.Job.run() method is the function the user overrides to get work done. Here it just returns a message.

It is also possible to log a message using toil.job.Job.log(), which will be registered in the log output of the leader process of the workflow:

...
    def run(self, fileStore):
        self.log(f"Hello, world! Here's a message: {self.message}")

Invoking a Workflow

We can add to the previous example to turn it into a complete workflow by adding the necessary function calls to create an instance of HelloWorld and to run this as a workflow containing a single job. For example:

from toil.common import Toil
from toil.job import Job


class HelloWorld(Job):
    def __init__(self, message):
        Job.__init__(self,  memory="2G", cores=2, disk="3G")
        self.message = message

    def run(self, fileStore):
        return f"Hello, world!, here's a message: {self.message}"


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "OFF"
    options.clean = "always"

    hello_job = HelloWorld("Woot")

    with Toil(options) as toil:
        print(toil.start(hello_job))

Note

Do not include a . in the name of your python script (besides .py at the end). This is to allow toil to import the types and functions defined in your file while starting a new process.

This uses the toil.common.Toil class, which is used to run and resume Toil workflows. It is used as a context manager and allows for preliminary setup, such as staging of files into the job store on the leader node. An instance of the class is initialized by specifying an options object. The actual workflow is then invoked by calling the toil.common.Toil.start() method, passing the root job of the workflow, or, if a workflow is being restarted, toil.common.Toil.restart() should be used. Note that the context manager should have explicit if else branches addressing restart and non restart cases. The boolean value for these if else blocks is toil.options.restart.

For example:

from toil.common import Toil
from toil.job import Job


class HelloWorld(Job):
    def __init__(self, message):
        Job.__init__(self,  memory="2G", cores=2, disk="3G")
        self.message = message

    def run(self, fileStore):
        return f"Hello, world!, I have a message: {self.message}"


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        if not toil.options.restart:
            job = HelloWorld("Woot!")
            output = toil.start(job)
        else:
            output = toil.restart()
    print(output)

The call to toil.job.Job.Runner.getDefaultOptions() creates a set of default options for the workflow. The only argument is a description of how to store the workflow’s state in what we call a job-store. Here the job-store is contained in a directory within the current working directory called “toilWorkflowRun”. Alternatively this string can encode other ways to store the necessary state, e.g. an S3 bucket object store location. By default the job-store is deleted if the workflow completes successfully.

The workflow is executed in the final line, which creates an instance of HelloWorld and runs it as a workflow. Note all Toil workflows start from a single starting job, referred to as the root job. The return value of the root job is returned as the result of the completed workflow (see promises below to see how this is a useful feature!).

Specifying Commandline Arguments

To allow command line control of the options we can use the toil.job.Job.Runner.getDefaultArgumentParser() method to create a argparse.ArgumentParser object which can be used to parse command line options for a Toil script. For example:

from toil.common import Toil
from toil.job import Job


class HelloWorld(Job):
    def __init__(self, message):
        Job.__init__(self,  memory="2G", cores=2, disk="3G")
        self.message = message

    def run(self, fileStore):
        return "Hello, world!, here's a message: %s" % self.message


if __name__ == "__main__":
    parser = Job.Runner.getDefaultArgumentParser()
    options = parser.parse_args()
    options.logLevel = "OFF"
    options.clean = "always"

    hello_job = HelloWorld("Woot")

    with Toil(options) as toil:
        print(toil.start(hello_job))

Creates a fully fledged script with all the options Toil exposed as command line arguments. Running this script with “–help” will print the full list of options.

Alternatively an existing argparse.ArgumentParser or optparse.OptionParser object can have Toil script command line options added to it with the toil.job.Job.Runner.addToilOptions() method.

Resuming a Workflow

In the event that a workflow fails, either because of programmatic error within the jobs being run, or because of node failure, the workflow can be resumed. Workflows can only not be reliably resumed if the job-store itself becomes corrupt.

Critical to resumption is that jobs can be rerun, even if they have apparently completed successfully. Put succinctly, a user defined job should not corrupt its input arguments. That way, regardless of node, network or leader failure the job can be restarted and the workflow resumed.

To resume a workflow specify the “restart” option in the options object passed to toil.common.Toil.start(). If node failures are expected it can also be useful to use the integer “retryCount” option, which will attempt to rerun a job retryCount number of times before marking it fully failed.

In the common scenario that a small subset of jobs fail (including retry attempts) within a workflow Toil will continue to run other jobs until it can do no more, at which point toil.common.Toil.start() will raise a toil.leader.FailedJobsException exception. Typically at this point the user can decide to fix the script and resume the workflow or delete the job-store manually and rerun the complete workflow.

Functions and Job Functions

Defining jobs by creating class definitions generally involves the boilerplate of creating a constructor. To avoid this the classes toil.job.FunctionWrappingJob and toil.job.JobFunctionWrappingTarget allow functions to be directly converted to jobs. For example, the quick start example (repeated here):

from toil.common import Toil
from toil.job import Job


def helloWorld(message, memory="2G", cores=2, disk="3G"):
    return f"Hello, world!, here's a message: {message}"


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "OFF"
    options.clean = "always"

    hello_job = Job.wrapFn(helloWorld, "Woot")

    with Toil(options) as toil:
        print(toil.start(hello_job))  # prints "Hello, world!, ..."

Is equivalent to the previous example, but using a function to define the job.

The function call:

Job.wrapFn(helloWorld, "Woot")

Creates the instance of the toil.job.FunctionWrappingTarget that wraps the function.

The keyword arguments memory, cores and disk allow resource requirements to be specified as before. Even if they are not included as keyword arguments within a function header they can be passed as arguments when wrapping a function as a job and will be used to specify resource requirements.

We can also use the function wrapping syntax to a job function, a function whose first argument is a reference to the wrapping job. Just like a self argument in a class, this allows access to the methods of the wrapping job, see toil.job.JobFunctionWrappingTarget. For example:

from toil.common import Toil
from toil.job import Job


def helloWorld(job, message):
    job.log(f"Hello world, I have a message: {message}")


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    hello_job = Job.wrapJobFn(helloWorld, "Woot!")

    with Toil(options) as toil:
        toil.start(hello_job)

Here helloWorld() is a job function. It uses the toil.job.Job.log() to log a message that will be printed to the output console. Here the only subtle difference to note is the line:

hello_job = Job.wrapJobFn(helloWorld, "Woot")

Which uses the function toil.job.Job.wrapJobFn() to wrap the job function instead of toil.job.Job.wrapFn() which wraps a vanilla function.

Workflows with Multiple Jobs

A parent job can have child jobs and follow-on jobs. These relationships are specified by methods of the job class, e.g. toil.job.Job.addChild() and toil.job.Job.addFollowOn().

Considering a set of jobs the nodes in a job graph and the child and follow-on relationships the directed edges of the graph, we say that a job B that is on a directed path of child/follow-on edges from a job A in the job graph is a successor of A, similarly A is a predecessor of B.

A parent job’s child jobs are run directly after the parent job has completed, and in parallel. The follow-on jobs of a job are run after its child jobs and their successors have completed. They are also run in parallel. Follow-ons allow the easy specification of cleanup tasks that happen after a set of parallel child tasks. The following shows a simple example that uses the earlier helloWorld() job function:

from toil.common import Toil
from toil.job import Job


def helloWorld(job, message, memory="2G", cores=2, disk="3G"):
    job.log(f"Hello world, I have a message: {message}")


if __name__ == "__main__":
    parser = Job.Runner.getDefaultArgumentParser()
    options = parser.parse_args()
    options.logLevel = "INFO"
    options.clean = "always"

    j1 = Job.wrapJobFn(helloWorld, "first")
    j2 = Job.wrapJobFn(helloWorld, "second or third")
    j3 = Job.wrapJobFn(helloWorld, "second or third")
    j4 = Job.wrapJobFn(helloWorld, "last")

    j1.addChild(j2)
    j1.addChild(j3)
    j1.addFollowOn(j4)

    with Toil(options) as toil:
        toil.start(j1)

In the example four jobs are created, first j1 is run, then j2 and j3 are run in parallel as children of j1, finally j4 is run as a follow-on of j1.

There are multiple short hand functions to achieve the same workflow, for example:

from toil.common import Toil
from toil.job import Job


def helloWorld(job, message, memory="2G", cores=2, disk="3G"):
    job.log(f"Hello world, I have a message: {message}")


if __name__ == "__main__":
    parser = Job.Runner.getDefaultArgumentParser()
    options = parser.parse_args()
    options.logLevel = "INFO"
    options.clean = "always"

    j1 = Job.wrapJobFn(helloWorld, "first")
    j2 = j1.addChildJobFn(helloWorld, "second or third")
    j3 = j1.addChildJobFn(helloWorld, "second or third")
    j4 = j1.addFollowOnJobFn(helloWorld, "last")

    with Toil(options) as toil:
        toil.start(j1)

Equivalently defines the workflow, where the functions toil.job.Job.addChildJobFn() and toil.job.Job.addFollowOnJobFn() are used to create job functions as children or follow-ons of an earlier job.

Jobs graphs are not limited to trees, and can express arbitrary directed acyclic graphs. For a precise definition of legal graphs see toil.job.Job.checkJobGraphForDeadlocks(). The previous example could be specified as a DAG as follows:

from toil.common import Toil
from toil.job import Job


def helloWorld(job, message, memory="2G", cores=2, disk="3G"):
    job.log(f"Hello world, I have a message: {message}")


if __name__ == "__main__":
    parser = Job.Runner.getDefaultArgumentParser()
    options = parser.parse_args()
    options.logLevel = "INFO"
    options.clean = "always"

    j1 = Job.wrapJobFn(helloWorld, "first")
    j2 = j1.addChildJobFn(helloWorld, "second or third")
    j3 = j1.addChildJobFn(helloWorld, "second or third")
    j4 = j2.addChildJobFn(helloWorld, "last")
    j3.addChild(j4)

    with Toil(options) as toil:
        toil.start(j1)

Note the use of an extra child edge to make j4 a child of both j2 and j3.

Dynamic Job Creation

The previous examples show a workflow being defined outside of a job. However, Toil also allows jobs to be created dynamically within jobs. For example:

from toil.common import Toil
from toil.job import Job


def binaryStringFn(job, depth, message=""):
    if depth > 0:
        job.addChildJobFn(binaryStringFn, depth-1, message + "0")
        job.addChildJobFn(binaryStringFn, depth-1, message + "1")
    else:
        job.log(f"Binary string: {message}")


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        toil.start(Job.wrapJobFn(binaryStringFn, depth=5))

The job function binaryStringFn logs all possible binary strings of length n (here n=5), creating a total of 2^(n+2) - 1 jobs dynamically and recursively. Static and dynamic creation of jobs can be mixed in a Toil workflow, with jobs defined within a job or job function being created at run time.

Promises

The previous example of dynamic job creation shows variables from a parent job being passed to a child job. Such forward variable passing is naturally specified by recursive invocation of successor jobs within parent jobs. This can also be achieved statically by passing around references to the return variables of jobs. In Toil this is achieved with promises, as illustrated in the following example:

from toil.common import Toil
from toil.job import Job


def fn(job, i):
    job.log("i is: %s" % i, level=100)
    return i + 1


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    j1 = Job.wrapJobFn(fn, 1)
    j2 = j1.addChildJobFn(fn, j1.rv())
    j3 = j1.addFollowOnJobFn(fn, j2.rv())

    with Toil(options) as toil:
        toil.start(j1)

Running this workflow results in three log messages from the jobs: i is 1 from j1, i is 2 from j2 and i is 3 from j3.

The return value from the first job is promised to the second job by the call to toil.job.Job.rv() in the following line:

j2 = j1.addChildFn(fn, j1.rv())

The value of j1.rv() is a promise, rather than the actual return value of the function, because j1 for the given input has at that point not been evaluated. A promise (toil.job.Promise) is essentially a pointer to for the return value that is replaced by the actual return value once it has been evaluated. Therefore, when j2 is run the promise becomes 2.

Promises also support indexing of return values:

def parent(job):
    indexable = Job.wrapJobFn(fn)
    job.addChild(indexable)
    job.addFollowOnFn(raiseWrap, indexable.rv(2))

def raiseWrap(arg):
    raise RuntimeError(arg) # raises "2"

def fn(job):
    return (0, 1, 2, 3)

Promises can be quite useful. For example, we can combine dynamic job creation with promises to achieve a job creation process that mimics the functional patterns possible in many programming languages:

from toil.common import Toil
from toil.job import Job


def binaryStrings(job, depth, message=""):
    if depth > 0:
        s = [job.addChildJobFn(binaryStrings, depth - 1, message + "0").rv(),
             job.addChildJobFn(binaryStrings, depth - 1, message + "1").rv()]
        return job.addFollowOnFn(merge, s).rv()
    return [message]


def merge(strings):
    return strings[0] + strings[1]


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.loglevel = "OFF"
    options.clean = "always"

    with Toil(options) as toil:
        print(toil.start(Job.wrapJobFn(binaryStrings, depth=5)))

The return value l of the workflow is a list of all binary strings of length 10, computed recursively. Although a toy example, it demonstrates how closely Toil workflows can mimic typical programming patterns.

Promised Requirements

Promised requirements are a special case of Promises that allow a job’s return value to be used as another job’s resource requirements.

This is useful when, for example, a job’s storage requirement is determined by a file staged to the job store by an earlier job:

import os

from toil.common import Toil
from toil.job import Job, PromisedRequirement


def parentJob(job):
    downloadJob = Job.wrapJobFn(stageFn, "file://" + os.path.realpath(__file__), cores=0.1, memory='32M', disk='1M')
    job.addChild(downloadJob)

    analysis = Job.wrapJobFn(analysisJob,
                             fileStoreID=downloadJob.rv(0),
                             disk=PromisedRequirement(downloadJob.rv(1)))
    job.addFollowOn(analysis)


def stageFn(job, url, cores=1):
    importedFile = job.fileStore.import_file(url)
    return importedFile, importedFile.size


def analysisJob(job, fileStoreID, cores=2):
    # now do some analysis on the file
    pass


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        toil.start(Job.wrapJobFn(parentJob))

Note that this also makes use of the size attribute of the FileID object. This promised requirements mechanism can also be used in combination with an aggregator for multiple jobs’ output values:

def parentJob(job):
    aggregator = []
    for fileNum in range(0, 10):
        downloadJob = Job.wrapJobFn(stageFn, "file://" + os.path.realpath(__file__), cores=0.1, memory='32M', disk='1M')
        job.addChild(downloadJob)
        aggregator.append(downloadJob)

    analysis = Job.wrapJobFn(analysisJob,
                             fileStoreID=downloadJob.rv(0),
                             disk=PromisedRequirement(lambda xs: sum(xs), [j.rv(1) for j in aggregator]))
    job.addFollowOn(analysis)

Limitations

Just like regular promises, the return value must be determined prior to scheduling any job that depends on the return value. In our example above, notice how the dependent jobs were follow ons to the parent while promising jobs are children of the parent. This ordering ensures that all promises are properly fulfilled.

FileID

The toil.fileStore.FileID class is a small wrapper around Python’s builtin string class. It is used to represent a file’s ID in the file store, and has a size attribute that is the file’s size in bytes. This object is returned by importFile and writeGlobalFile.

Managing files within a workflow

It is frequently the case that a workflow will want to create files, both persistent and temporary, during its run. The toil.fileStores.abstractFileStore.AbstractFileStore class is used by jobs to manage these files in a manner that guarantees cleanup and resumption on failure.

The toil.job.Job.run() method has a file store instance as an argument. The following example shows how this can be used to create temporary files that persist for the length of the job, be placed in a specified local disk of the node and that will be cleaned up, regardless of failure, when the job finishes:

from toil.common import Toil
from toil.job import Job


class LocalFileStoreJob(Job):
    def run(self, fileStore):
        # self.tempDir will always contain the name of a directory within the allocated disk space reserved for the job
        scratchDir = self.tempDir

        # Similarly create a temporary file.
        scratchFile = fileStore.getLocalTempFile()


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    # Create an instance of FooJob which will have at least 2 gigabytes of storage space.
    j = LocalFileStoreJob(disk="2G")

    # Run the workflow
    with Toil(options) as toil:
        toil.start(j)

Job functions can also access the file store for the job. The equivalent of the LocalFileStoreJob class is

def localFileStoreJobFn(job):
    scratchDir = job.tempDir
    scratchFile = job.fileStore.getLocalTempFile()

Note that the fileStore attribute is accessed as an attribute of the job argument.

In addition to temporary files that exist for the duration of a job, the file store allows the creation of files in a global store, which persists during the workflow and are globally accessible (hence the name) between jobs. For example:

import os

from toil.common import Toil
from toil.job import Job


def globalFileStoreJobFn(job):
    job.log("The following example exercises all the methods provided "
            "by the toil.fileStores.abstractFileStore.AbstractFileStore class")

    # Create a local temporary file.
    scratchFile = job.fileStore.getLocalTempFile()

    # Write something in the scratch file.
    with open(scratchFile, 'w') as fH:
        fH.write("What a tangled web we weave")

    # Write a copy of the file into the file-store; fileID is the key that can be used to retrieve the file.
    # This write is asynchronous by default
    fileID = job.fileStore.writeGlobalFile(scratchFile)

    # Write another file using a stream; fileID2 is the
    # key for this second file.
    with job.fileStore.writeGlobalFileStream(cleanup=True) as (fH, fileID2):
        fH.write(b"Out brief candle")

    # Now read the first file; scratchFile2 is a local copy of the file that is read-only by default.
    scratchFile2 = job.fileStore.readGlobalFile(fileID)

    # Read the second file to a desired location: scratchFile3.
    scratchFile3 = os.path.join(job.tempDir, "foo.txt")
    job.fileStore.readGlobalFile(fileID2, userPath=scratchFile3)

    # Read the second file again using a stream.
    with job.fileStore.readGlobalFileStream(fileID2) as fH:
        print(fH.read())  # This prints "Out brief candle"

    # Delete the first file from the global file-store.
    job.fileStore.deleteGlobalFile(fileID)

    # It is unnecessary to delete the file keyed by fileID2 because we used the cleanup flag,
    # which removes the file after this job and all its successors have run (if the file still exists)


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        toil.start(Job.wrapJobFn(globalFileStoreJobFn))

The example demonstrates the global read, write and delete functionality of the file-store, using both local copies of the files and streams to read and write the files. It covers all the methods provided by the file store interface.

What is obvious is that the file-store provides no functionality to update an existing “global” file, meaning that files are, barring deletion, immutable. Also worth noting is that there is no file system hierarchy for files in the global file store. These limitations allow us to fairly easily support different object stores and to use caching to limit the amount of network file transfer between jobs.

Staging of Files into the Job Store

External files can be imported into or exported out of the job store prior to running a workflow when the toil.common.Toil context manager is used on the leader. The context manager provides methods toil.common.Toil.importFile(), and toil.common.Toil.exportFile() for this purpose. The destination and source locations of such files are described with URLs passed to the two methods. Local files can be imported and exported as relative paths, and should be relative to the directory where the toil workflow is initially run from.

Using absolute paths and appropriate schema where possible (prefixing with “file://” or “s3:/” for example), make imports and exports less ambiguous and is recommended.

A list of the currently supported URLs can be found at toil.jobStores.abstractJobStore.AbstractJobStore.importFile(). To import an external file into the job store as a shared file, pass the optional sharedFileName parameter to that method.

If a workflow fails for any reason an imported file acts as any other file in the job store. If the workflow was configured such that it not be cleaned up on a failed run, the file will persist in the job store and needs not be staged again when the workflow is resumed.

Example:

import os

from toil.common import Toil
from toil.job import Job


class HelloWorld(Job):
    def __init__(self, id):
        Job.__init__(self, memory="2G", cores=2, disk="3G")
        self.inputFileID = id

    def run(self, fileStore):
        with fileStore.readGlobalFileStream(self.inputFileID, encoding='utf-8') as fi:
            with fileStore.writeGlobalFileStream(encoding='utf-8') as (fo, outputFileID):
                fo.write(fi.read() + 'World!')
        return outputFileID


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        if not toil.options.restart:
            ioFileDirectory = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stagingExampleFiles")
            inputFileID = toil.importFile("file://" + os.path.abspath(os.path.join(ioFileDirectory, "in.txt")))
            outputFileID = toil.start(HelloWorld(inputFileID))
        else:
            outputFileID = toil.restart()

        toil.exportFile(outputFileID, "file://" + os.path.abspath(os.path.join(ioFileDirectory, "out.txt")))

Using Docker Containers in Toil

Docker containers are commonly used with Toil. The combination of Toil and Docker allows for pipelines to be fully portable between any platform that has both Toil and Docker installed. Docker eliminates the need for the user to do any other tool installation or environment setup.

In order to use Docker containers with Toil, Docker must be installed on all workers of the cluster. Instructions for installing Docker can be found on the Docker website.

When using Toil-based autoscaling, Docker will be automatically set up on the cluster’s worker nodes, so no additional installation steps are necessary. Further information on using Toil-based autoscaling can be found in the Running a Workflow with Autoscaling documentation.

In order to use docker containers in a Toil workflow, the container can be built locally or downloaded in real time from an online docker repository like Quay. If the container is not in a repository, the container’s layers must be accessible on each node of the cluster.

When invoking docker containers from within a Toil workflow, it is strongly recommended that you use dockerCall(), a toil job function provided in toil.lib.docker. dockerCall leverages docker’s own python API, and provides container cleanup on job failure. When docker containers are run without this feature, failed jobs can result in resource leaks. Docker’s API can be found at docker-py.

In order to use dockerCall, your installation of Docker must be set up to run without sudo. Instructions for setting this up can be found here.

An example of a basic dockerCall is below:

dockerCall(job=job,
            tool='quay.io/ucsc_cgl/bwa',
            workDir=job.tempDir,
            parameters=['index', '/data/reference.fa'])

Note the assumption that reference.fa file is located in /data. This is Toil’s standard convention as a mount location to reduce boilerplate when calling dockerCall. Users can choose their own mount locations by supplying a volumes kwarg to dockerCall, such as: volumes={working_dir: {‘bind’: ‘/data’, ‘mode’: ‘rw’}}, where working_dir is an absolute path on the user’s filesystem.

dockerCall can also be added to workflows like any other job function:

import os

from toil.common import Toil
from toil.job import Job
from toil.lib.docker import apiDockerCall

align = Job.wrapJobFn(apiDockerCall,
                      image='ubuntu',
                      working_dir=os.getcwd(),
                      parameters=['ls', '-lha'])

if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        toil.start(align)

cgl-docker-lib contains dockerCall-compatible Dockerized tools that are commonly used in bioinformatics analysis.

The documentation provides guidelines for developing your own Docker containers that can be used with Toil and dockerCall. In order for a container to be compatible with dockerCall, it must have an ENTRYPOINT set to a wrapper script, as described in cgl-docker-lib containerization standards. This can be set by passing in the optional keyword argument, ‘entrypoint’. Example:

entrypoint=[“/bin/bash”,”-c”]

dockerCall supports currently the 75 keyword arguments found in the python Docker API, under the ‘run’ command.

Services

It is sometimes desirable to run services, such as a database or server, concurrently with a workflow. The toil.job.Job.Service class provides a simple mechanism for spawning such a service within a Toil workflow, allowing precise specification of the start and end time of the service, and providing start and end methods to use for initialization and cleanup. The following simple, conceptual example illustrates how services work:

from toil.common import Toil
from toil.job import Job


class DemoService(Job.Service):
    def start(self, fileStore):
        # Start up a database/service here
        # Return a value that enables another process to connect to the database
        return "loginCredentials"

    def check(self):
        # A function that if it returns False causes the service to quit
        # If it raises an exception the service is killed and an error is reported
        return True

    def stop(self, fileStore):
        # Cleanup the database here
        pass


j = Job()
s = DemoService()
loginCredentialsPromise = j.addService(s)


def dbFn(loginCredentials):
    # Use the login credentials returned from the service's start method to connect to the service
    pass


j.addChildFn(dbFn, loginCredentialsPromise)


if __name__ == "__main__":
    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        toil.start(j)

In this example the DemoService starts a database in the start method, returning an object from the start method indicating how a client job would access the database. The service’s stop method cleans up the database, while the service’s check method is polled periodically to check the service is alive.

A DemoService instance is added as a service of the root job j, with resource requirements specified. The return value from toil.job.Job.addService() is a promise to the return value of the service’s start method. When the promised is fulfilled it will represent how to connect to the database. The promise is passed to a child job of j, which uses it to make a database connection. The services of a job are started before any of its successors have been run and stopped after all the successors of the job have completed successfully.

Multiple services can be created per job, all run in parallel. Additionally, services can define sub-services using toil.job.Job.Service.addChild(). This allows complex networks of services to be created, e.g. Apache Spark clusters, within a workflow.

Checkpoints

Services complicate resuming a workflow after failure, because they can create complex dependencies between jobs. For example, consider a service that provides a database that multiple jobs update. If the database service fails and loses state, it is not clear that just restarting the service will allow the workflow to be resumed, because jobs that created that state may have already finished. To get around this problem Toil supports checkpoint jobs, specified as the boolean keyword argument checkpoint to a job or wrapped function, e.g.:

j = Job(checkpoint=True)

A checkpoint job is rerun if one or more of its successors fails its retry attempts, until it itself has exhausted its retry attempts. Upon restarting a checkpoint job all its existing successors are first deleted, and then the job is rerun to define new successors. By checkpointing a job that defines a service, upon failure of the service the database and the jobs that access the service can be redefined and rerun.

To make the implementation of checkpoint jobs simple, a job can only be a checkpoint if when first defined it has no successors, i.e. it can only define successors within its run method.

Encapsulation

Let A be a root job potentially with children and follow-ons. Without an encapsulated job the simplest way to specify a job B which runs after A and all its successors is to create a parent of A, call it Ap, and then make B a follow-on of Ap. e.g.:

from toil.common import Toil
from toil.job import Job

if __name__ == "__main__":
    # A is a job with children and follow-ons, for example:
    A = Job()
    A.addChild(Job())
    A.addFollowOn(Job())

    # B is a job which needs to run after A and its successors
    B = Job()

    # The way to do this without encapsulation is to make a parent of A, Ap, and make B a follow-on of Ap.
    Ap = Job()
    Ap.addChild(A)
    Ap.addFollowOn(B)

    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        print(toil.start(Ap))

An encapsulated job E(A) of A saves making Ap, instead we can write:

from toil.common import Toil
from toil.job import Job

if __name__ == "__main__":
    # A
    A = Job()
    A.addChild(Job())
    A.addFollowOn(Job())

    # Encapsulate A
    A = A.encapsulate()

    # B is a job which needs to run after A and its successors
    B = Job()

    # With encapsulation A and its successor subgraph appear to be a single job, hence:
    A.addChild(B)

    options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
    options.logLevel = "INFO"
    options.clean = "always"

    with Toil(options) as toil:
        print(toil.start(A))

Note the call to toil.job.Job.encapsulate() creates the toil.job.Job.EncapsulatedJob.

Depending on Toil

If you are packing your workflow(s) as a pip-installable distribution on PyPI, you might be tempted to declare Toil as a dependency in your setup.py, via the install_requires keyword argument to setup(). Unfortunately, this does not work, for two reasons: For one, Toil uses Setuptools’ extra mechanism to manage its own optional dependencies. If you explicitly declared a dependency on Toil, you would have to hard-code a particular combination of extras (or no extras at all), robbing the user of the choice what Toil extras to install. Secondly, and more importantly, declaring a dependency on Toil would only lead to Toil being installed on the leader node of a cluster, but not the worker nodes. Auto-deployment does not work here because Toil cannot auto-deploy itself, the classic “Which came first, chicken or egg?” problem.

In other words, you shouldn’t explicitly depend on Toil. Document the dependency instead (as in “This workflow needs Toil version X.Y.Z to be installed”) and optionally add a version check to your setup.py. Refer to the check_version() function in the toil-lib project’s setup.py for an example. Alternatively, you can also just depend on toil-lib and you’ll get that check for free.

If your workflow depends on a dependency of Toil, consider not making that dependency explicit either. If you do, you risk a version conflict between your project and Toil. The pip utility may silently ignore that conflict, breaking either Toil or your workflow. It is safest to simply assume that Toil installs that dependency for you. The only downside is that you are locked into the exact version of that dependency that Toil declares. But such is life with Python, which, unlike Java, has no means of dependencies belonging to different software components within the same process, and whose favored software distribution utility is incapable of properly resolving overlapping dependencies and detecting conflicts.

Best Practices for Dockerizing Toil Workflows

Computational Genomics Lab’s Dockstore based production system provides workflow authors a way to run Dockerized versions of their pipeline in an automated, scalable fashion. To be compatible with this system of a workflow should meet the following requirements. In addition to the Docker container, a common workflow language descriptor file is needed. For inputs:

• Only command line arguments should be used for configuring the workflow. If the workflow relies on a configuration file, like Toil-RNAseq or ProTECT, a wrapper script inside the Docker container can be used to parse the CLI and generate the necessary configuration file.
• All inputs to the pipeline should be explicitly enumerated rather than implicit. For example, don’t rely on one FASTQ read’s path to discover the location of its pair. This is necessary since all inputs are mapped to their own isolated directories when the Docker is called via Dockstore.
• All inputs must be documented in the CWL descriptor file. Examples of this file can be seen in both Toil-RNAseq and ProTECT.

For outputs:

• All outputs should be written to a local path rather than S3.
• Take care to package outputs in a local and user-friendly way. For example, don’t tar up all output if there are specific files that will care to see individually.
• All output file names should be deterministic and predictable. For example, don’t prepend the name of an output file with PASS/FAIL depending on the outcome of the pipeline.
• All outputs must be documented in the CWL descriptor file. Examples of this file can be seen in both Toil-RNAseq and ProTECT.

Toil Class API

The Toil class configures and starts a Toil run.

class toil.common.Toil(options: argparse.Namespace)

A context manager that represents a Toil workflow.

Specifically the batch system, job store, and its configuration.

__init__(options: argparse.Namespace) → None

Initialize a Toil object from the given options.

Note that this is very light-weight and that the bulk of the work is done when the context is entered.

Parameters:options – command line options specified by the user

start(rootJob: Job) → Any

Invoke a Toil workflow with the given job as the root for an initial run.

This method must be called in the body of a with Toil(...) as toil: statement. This method should not be called more than once for a workflow that has not finished.

Parameters:rootJob – The root job of the workflow Returns:The root job’s return value

restart() → Any

Restarts a workflow that has been interrupted.

Returns:The root job’s return value

classmethod getJobStore(locator: str) → AbstractJobStore

Create an instance of the concrete job store implementation that matches the given locator.

Parameters:locator (str) – The location of the job store to be represent by the instance Returns:an instance of a concrete subclass of AbstractJobStore

static createBatchSystem(config: toil.common.Config) → AbstractBatchSystem

Create an instance of the batch system specified in the given config.

Parameters:config – the current configuration Returns:an instance of a concrete subclass of AbstractBatchSystem

import_file(src_uri: str, shared_file_name: Optional[str] = None, symlink: bool = False) → Optional[toil.fileStores.FileID]

Import the file at the given URL into the job store.

See toil.jobStores.abstractJobStore.AbstractJobStore.importFile() for a full description

export_file(file_id: toil.fileStores.FileID, dst_uri: str) → None

Export file to destination pointed at by the destination URL.

See toil.jobStores.abstractJobStore.AbstractJobStore.exportFile() for a full description

static normalize_uri(uri: str, check_existence: bool = False) → str

Given a URI, if it has no scheme, prepend “file:”.

Parameters:check_existence – If set, raise an error if a URI points to a local file that does not exist.

static getToilWorkDir(configWorkDir: Optional[str] = None) → str

Return a path to a writable directory under which per-workflow directories exist.

This directory is always required to exist on a machine, even if the Toil worker has not run yet. If your workers and leader have different temp directories, you may need to set TOIL_WORKDIR.

Parameters:configWorkDir – Value passed to the program using the –workDir flag Returns:Path to the Toil work directory, constant across all machines

classmethod get_toil_coordination_dir(config_work_dir: Optional[str], config_coordination_dir: Optional[str]) → str

Return a path to a writable directory, which will be in memory if convenient. Ought to be used for file locking and coordination.

Parameters:

• config_work_dir – Value passed to the program using the –workDir flag
• config_coordination_dir – Value passed to the program using the –coordinationDir flag

Returns:

Path to the Toil coordination directory. Ought to be on a POSIX filesystem that allows directories containing open files to be deleted.

classmethod getLocalWorkflowDir(workflowID: str, configWorkDir: Optional[str] = None) → str

Return the directory where worker directories and the cache will be located for this workflow on this machine.

Parameters:configWorkDir – Value passed to the program using the –workDir flag Returns:Path to the local workflow directory on this machine

classmethod get_local_workflow_coordination_dir(workflow_id: str, config_work_dir: Optional[str], config_coordination_dir: Optional[str]) → str

Return the directory where coordination files should be located for this workflow on this machine. These include internal Toil databases and lock files for the machine.

If an in-memory filesystem is available, it is used. Otherwise, the local workflow directory, which may be on a shared network filesystem, is used.

Parameters:

• workflow_id – Unique ID of the current workflow.
• config_work_dir – Value used for the work directory in the current Toil Config.
• config_coordination_dir – Value used for the coordination directory in the current Toil Config.

Returns:

Path to the local workflow coordination directory on this machine.

Job Store API

The job store interface is an abstraction layer that that hides the specific details of file storage, for example standard file systems, S3, etc. The AbstractJobStore API is implemented to support a give file store, e.g. S3. Implement this API to support a new file store.

class toil.jobStores.abstractJobStore.AbstractJobStore(locator: str)

Represents the physical storage for the jobs and files in a Toil workflow.

JobStores are responsible for storing toil.job.JobDescription (which relate jobs to each other) and files.

Actual toil.job.Job objects are stored in files, referenced by JobDescriptions. All the non-file CRUD methods the JobStore provides deal in JobDescriptions and not full, executable Jobs.

To actually get ahold of a toil.job.Job, use toil.job.Job.loadJob() with a JobStore and the relevant JobDescription.

__init__(locator: str) → None

Create an instance of the job store.

The instance will not be fully functional until either initialize() or resume() is invoked. Note that the destroy() method may be invoked on the object with or without prior invocation of either of these two methods.

Takes and stores the locator string for the job store, which will be accessible via self.locator.

initialize(config: toil.common.Config) → None

Initialize this job store.

Create the physical storage for this job store, allocate a workflow ID and persist the given Toil configuration to the store.

Parameters:config – the Toil configuration to initialize this job store with. The given configuration will be updated with the newly allocated workflow ID. Raises:JobStoreExistsException – if the physical storage for this job store already exists

write_config() → None

Persists the value of the AbstractJobStore.config attribute to the job store, so that it can be retrieved later by other instances of this class.

resume() → None

Connect this instance to the physical storage it represents and load the Toil configuration into the AbstractJobStore.config attribute.

Raises:NoSuchJobStoreException – if the physical storage for this job store doesn’t exist

config

Return the Toil configuration associated with this job store.

locator

Get the locator that defines the job store, which can be used to connect to it.

setRootJob(rootJobStoreID: toil.fileStores.FileID) → None

Set the root job of the workflow backed by this job store.

set_root_job(job_id: toil.fileStores.FileID) → None

Set the root job of the workflow backed by this job store.

Parameters:job_id – The ID of the job to set as root

load_root_job() → toil.job.JobDescription

Loads the JobDescription for the root job in the current job store.

Raises:toil.job.JobException – If no root job is set or if the root job doesn’t exist in this job store Returns:The root job.

create_root_job(job_description: toil.job.JobDescription) → toil.job.JobDescription

Create the given JobDescription and set it as the root job in this job store.

Parameters:job_description – JobDescription to save and make the root job.

get_root_job_return_value() → Any

Parse the return value from the root job.

Raises an exception if the root job hasn’t fulfilled its promise yet.

import_file(src_uri: str, shared_file_name: Optional[str] = None, hardlink: bool = False, symlink: bool = False) → Optional[toil.fileStores.FileID]

Imports the file at the given URL into job store. The ID of the newly imported file is returned. If the name of a shared file name is provided, the file will be imported as such and None is returned. If an executable file on the local filesystem is uploaded, its executability will be preserved when it is downloaded.

Currently supported schemes are:

• ‘s3’ for objects in Amazon S3

  e.g. s3://bucket/key

• ‘file’ for local files

  e.g. file:///local/file/path

• ‘http’

  e.g. http://someurl.com/path

• ‘gs’

  e.g. gs://bucket/file

Parameters:

• src_uri (str) – URL that points to a file or object in the storage mechanism of a supported URL scheme e.g. a blob in an AWS s3 bucket.
• shared_file_name (str) – Optional name to assign to the imported file within the job store

Returns:

The jobStoreFileID of the imported file or None if shared_file_name was given

Return type:

toil.fileStores.FileID or None

export_file(file_id: toil.fileStores.FileID, dst_uri: str) → None

Exports file to destination pointed at by the destination URL. The exported file will be executable if and only if it was originally uploaded from an executable file on the local filesystem.

Refer to AbstractJobStore.import_file() documentation for currently supported URL schemes.

Note that the helper method _exportFile is used to read from the source and write to destination. To implement any optimizations that circumvent this, the _exportFile method should be overridden by subclasses of AbstractJobStore.

Parameters:

• file_id (str) – The id of the file in the job store that should be exported.
• dst_uri (str) – URL that points to a file or object in the storage mechanism of a supported URL scheme e.g. a blob in an AWS s3 bucket.

classmethod list_url(src_uri: str) → List[str]

List the directory at the given URL. Returned path components can be joined with ‘/’ onto the passed URL to form new URLs. Those that end in ‘/’ correspond to directories. The provided URL may or may not end with ‘/’.

Currently supported schemes are:

• ‘s3’ for objects in Amazon S3

  e.g. s3://bucket/prefix/

• ‘file’ for local files

  e.g. file:///local/dir/path/

Parameters:src_uri (str) – URL that points to a directory or prefix in the storage mechanism of a supported URL scheme e.g. a prefix in an AWS s3 bucket. Returns:A list of URL components in the given directory, already URL-encoded.

classmethod get_is_directory(src_uri: str) → bool

Return True if the thing at the given URL is a directory, and False if it is a file. The URL may or may not end in ‘/’.

classmethod read_from_url(src_uri: str, writable: IO[bytes]) → Tuple[int, bool]

Read the given URL and write its content into the given writable stream.

Returns:The size of the file in bytes and whether the executable permission bit is set Return type:Tuple[int, bool]

classmethod get_size(src_uri: urllib.parse.ParseResult) → None

Get the size in bytes of the file at the given URL, or None if it cannot be obtained.

Parameters:src_uri – URL that points to a file or object in the storage mechanism of a supported URL scheme e.g. a blob in an AWS s3 bucket.

destroy() → None

The inverse of initialize(), this method deletes the physical storage represented by this instance. While not being atomic, this method is at least idempotent, as a means to counteract potential issues with eventual consistency exhibited by the underlying storage mechanisms. This means that if the method fails (raises an exception), it may (and should be) invoked again. If the underlying storage mechanism is eventually consistent, even a successful invocation is not an ironclad guarantee that the physical storage vanished completely and immediately. A successful invocation only guarantees that the deletion will eventually happen. It is therefore recommended to not immediately reuse the same job store location for a new Toil workflow.

get_env() → Dict[str, str]

Returns a dictionary of environment variables that this job store requires to be set in order to function properly on a worker.

Return type:dict[str,str]

clean(jobCache: Optional[Dict[Union[str, TemporaryID], toil.job.JobDescription]] = None) → toil.job.JobDescription

Function to cleanup the state of a job store after a restart.

Fixes jobs that might have been partially updated. Resets the try counts and removes jobs that are not successors of the current root job.

Parameters:jobCache – if a value it must be a dict from job ID keys to JobDescription object values. Jobs will be loaded from the cache (which can be downloaded from the job store in a batch) instead of piecemeal when recursed into.

assign_job_id(job_description: toil.job.JobDescription) → None

Get a new jobStoreID to be used by the described job, and assigns it to the JobDescription.

Files associated with the assigned ID will be accepted even if the JobDescription has never been created or updated.

Parameters:job_description (toil.job.JobDescription) – The JobDescription to give an ID to

batch() → Iterator[None]

If supported by the batch system, calls to create() with this context manager active will be performed in a batch after the context manager is released.

create_job(job_description: toil.job.JobDescription) → toil.job.JobDescription

Writes the given JobDescription to the job store. The job must have an ID assigned already.

Must call jobDescription.pre_update_hook()

Returns:The JobDescription passed. Return type:toil.job.JobDescription

job_exists(job_id: str) → bool

Indicates whether a description of the job with the specified jobStoreID exists in the job store

Return type:bool

get_public_url(file_name: str) → str

Returns a publicly accessible URL to the given file in the job store. The returned URL may expire as early as 1h after its been returned. Throw an exception if the file does not exist.

Parameters:file_name (str) – the jobStoreFileID of the file to generate a URL for Raises:NoSuchFileException – if the specified file does not exist in this job store Return type:str

get_shared_public_url(shared_file_name: str) → str

Differs from getPublicUrl() in that this method is for generating URLs for shared files written by writeSharedFileStream().

Returns a publicly accessible URL to the given file in the job store. The returned URL starts with ‘http:’, ‘https:’ or ‘file:’. The returned URL may expire as early as 1h after its been returned. Throw an exception if the file does not exist.

Parameters:shared_file_name (str) – The name of the shared file to generate a publically accessible url for. Raises:NoSuchFileException – raised if the specified file does not exist in the store Return type:str

load_job(job_id: str) → toil.job.JobDescription

Loads the description of the job referenced by the given ID, assigns it the job store’s config, and returns it.

May declare the job to have failed (see toil.job.JobDescription.setupJobAfterFailure()) if there is evidence of a failed update attempt.

Parameters:job_id – the ID of the job to load Raises:NoSuchJobException – if there is no job with the given ID

update_job(job_description: toil.job.JobDescription) → None

Persists changes to the state of the given JobDescription in this store atomically.

Must call jobDescription.pre_update_hook()

Parameters:job (toil.job.JobDescription) – the job to write to this job store

delete_job(job_id: str) → None

Removes the JobDescription from the store atomically. You may not then subsequently call load(), write(), update(), etc. with the same jobStoreID or any JobDescription bearing it.

This operation is idempotent, i.e. deleting a job twice or deleting a non-existent job will succeed silently.

Parameters:job_id (str) – the ID of the job to delete from this job store

jobs() → Iterator[toil.job.JobDescription]

Best effort attempt to return iterator on JobDescriptions for all jobs in the store. The iterator may not return all jobs and may also contain orphaned jobs that have already finished successfully and should not be rerun. To guarantee you get any and all jobs that can be run instead construct a more expensive ToilState object

Returns:Returns iterator on jobs in the store. The iterator may or may not contain all jobs and may contain invalid jobs Return type:Iterator[toil.job.jobDescription]

write_file(local_path: str, job_id: Optional[str] = None, cleanup: bool = False) → str

Takes a file (as a path) and places it in this job store. Returns an ID that can be used to retrieve the file at a later time. The file is written in a atomic manner. It will not appear in the jobStore until the write has successfully completed.

Parameters:

• local_path (str) – the path to the local file that will be uploaded to the job store. The last path component (basename of the file) will remain associated with the file in the file store, if supported, so that the file can be searched for by name or name glob.
• job_id (str) – the id of a job, or None. If specified, the may be associated with that job in a job-store-specific way. This may influence the returned ID.
• cleanup (bool) – Whether to attempt to delete the file when the job whose jobStoreID was given as jobStoreID is deleted with jobStore.delete(job). If jobStoreID was not given, does nothing.

Raises:

• ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method
• NoSuchJobException – if the job specified via jobStoreID does not exist

FIXME: some implementations may not raise this

Returns:an ID referencing the newly created file and can be used to read the file in the future. Return type:str

write_file_stream(job_id: Optional[str] = None, cleanup: bool = False, basename: Optional[str] = None, encoding: Optional[str] = None, errors: Optional[str] = None) → Iterator[Tuple[IO[bytes], str]]

Similar to writeFile, but returns a context manager yielding a tuple of 1) a file handle which can be written to and 2) the ID of the resulting file in the job store. The yielded file handle does not need to and should not be closed explicitly. The file is written in a atomic manner. It will not appear in the jobStore until the write has successfully completed.

Parameters:

• job_id (str) – the id of a job, or None. If specified, the may be associated with that job in a job-store-specific way. This may influence the returned ID.
• cleanup (bool) – Whether to attempt to delete the file when the job whose jobStoreID was given as jobStoreID is deleted with jobStore.delete(job). If jobStoreID was not given, does nothing.
• basename (str) – If supported by the implementation, use the given file basename so that when searching the job store with a query matching that basename, the file will be detected.
• encoding (str) – the name of the encoding used to encode the file. Encodings are the same as for encode(). Defaults to None which represents binary mode.
• errors (str) – an optional string that specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.

Raises:

• ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method
• NoSuchJobException – if the job specified via jobStoreID does not exist

FIXME: some implementations may not raise this

Returns:a context manager yielding a file handle which can be written to and an ID that references the newly created file and can be used to read the file in the future. Return type:Iterator[Tuple[IO[bytes], str]]

get_empty_file_store_id(job_id: Optional[str] = None, cleanup: bool = False, basename: Optional[str] = None) → str

Creates an empty file in the job store and returns its ID. Call to fileExists(getEmptyFileStoreID(jobStoreID)) will return True.

Parameters:

• job_id (str) – the id of a job, or None. If specified, the may be associated with that job in a job-store-specific way. This may influence the returned ID.
• cleanup (bool) – Whether to attempt to delete the file when the job whose jobStoreID was given as jobStoreID is deleted with jobStore.delete(job). If jobStoreID was not given, does nothing.
• basename (str) – If supported by the implementation, use the given file basename so that when searching the job store with a query matching that basename, the file will be detected.

Returns:

a jobStoreFileID that references the newly created file and can be used to reference the file in the future.

Return type:

str

read_file(file_id: str, local_path: str, symlink: bool = False) → None

Copies or hard links the file referenced by jobStoreFileID to the given local file path. The version will be consistent with the last copy of the file written/updated. If the file in the job store is later modified via updateFile or updateFileStream, it is implementation-defined whether those writes will be visible at localFilePath. The file is copied in an atomic manner. It will not appear in the local file system until the copy has completed.

The file at the given local path may not be modified after this method returns!

Note! Implementations of readFile need to respect/provide the executable attribute on FileIDs.

Parameters:

• file_id (str) – ID of the file to be copied
• local_path (str) – the local path indicating where to place the contents of the given file in the job store
• symlink (bool) – whether the reader can tolerate a symlink. If set to true, the job store may create a symlink instead of a full copy of the file or a hard link.

read_file_stream(file_id: Union[toil.fileStores.FileID, str], encoding: Optional[str] = None, errors: Optional[str] = None) → Union[AbstractContextManager[IO[bytes]], AbstractContextManager[IO[str]]]

Similar to readFile, but returns a context manager yielding a file handle which can be read from. The yielded file handle does not need to and should not be closed explicitly.

Parameters:

• file_id (str) – ID of the file to get a readable file handle for
• encoding (str) – the name of the encoding used to decode the file. Encodings are the same as for decode(). Defaults to None which represents binary mode.
• errors (str) – an optional string that specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.

Returns:

a context manager yielding a file handle which can be read from

Return type:

Iterator[Union[IO[bytes], IO[str]]]

delete_file(file_id: str) → None

Deletes the file with the given ID from this job store. This operation is idempotent, i.e. deleting a file twice or deleting a non-existent file will succeed silently.

Parameters:file_id (str) – ID of the file to delete

fileExists(jobStoreFileID: str) → bool

Determine whether a file exists in this job store.

file_exists(file_id: str) → bool

Determine whether a file exists in this job store.

Parameters:file_id – an ID referencing the file to be checked

getFileSize(jobStoreFileID: str) → int

Get the size of the given file in bytes.

get_file_size(file_id: str) → int

Get the size of the given file in bytes, or 0 if it does not exist when queried.

Note that job stores which encrypt files might return overestimates of file sizes, since the encrypted file may have been padded to the nearest block, augmented with an initialization vector, etc.

Parameters:file_id (str) – an ID referencing the file to be checked Return type:int

updateFile(jobStoreFileID: str, localFilePath: str) → None

Replaces the existing version of a file in the job store.

update_file(file_id: str, local_path: str) → None

Replaces the existing version of a file in the job store.

Throws an exception if the file does not exist.

Parameters:

• file_id – the ID of the file in the job store to be updated
• local_path – the local path to a file that will overwrite the current version in the job store

Raises:

• ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method
• NoSuchFileException – if the specified file does not exist

update_file_stream(file_id: str, encoding: Optional[str] = None, errors: Optional[str] = None) → Iterator[IO[Any]]

Replaces the existing version of a file in the job store. Similar to writeFile, but returns a context manager yielding a file handle which can be written to. The yielded file handle does not need to and should not be closed explicitly.

Parameters:

• file_id (str) – the ID of the file in the job store to be updated
• encoding (str) – the name of the encoding used to encode the file. Encodings are the same as for encode(). Defaults to None which represents binary mode.
• errors (str) – an optional string that specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.

Raises:

• ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method
• NoSuchFileException – if the specified file does not exist

write_shared_file_stream(shared_file_name: str, encrypted: Optional[bool] = None, encoding: Optional[str] = None, errors: Optional[str] = None) → Iterator[IO[bytes]]

Returns a context manager yielding a writable file handle to the global file referenced by the given name. File will be created in an atomic manner.

Parameters:

• shared_file_name (str) – A file name matching AbstractJobStore.fileNameRegex, unique within this job store
• encrypted (bool) – True if the file must be encrypted, None if it may be encrypted or False if it must be stored in the clear.
• encoding (str) – the name of the encoding used to encode the file. Encodings are the same as for encode(). Defaults to None which represents binary mode.
• errors (str) – an optional string that specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.

Raises:

ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method

Returns:

a context manager yielding a writable file handle

Return type:

Iterator[IO[bytes]]

read_shared_file_stream(shared_file_name: str, encoding: Optional[str] = None, errors: Optional[str] = None) → Iterator[IO[bytes]]

Returns a context manager yielding a readable file handle to the global file referenced by the given name.

Parameters:

• shared_file_name (str) – A file name matching AbstractJobStore.fileNameRegex, unique within this job store
• encoding (str) – the name of the encoding used to decode the file. Encodings are the same as for decode(). Defaults to None which represents binary mode.
• errors (str) – an optional string that specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.

Returns:

a context manager yielding a readable file handle

Return type:

Iterator[IO[bytes]]

write_logs(msg: str) → None

Stores a message as a log in the jobstore.

Parameters:msg (str) – the string to be written Raises:ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method

read_logs(callback: Callable[[...], Any], read_all: bool = False) → int

Reads logs accumulated by the write_logs() method. For each log this method calls the given callback function with the message as an argument (rather than returning logs directly, this method must be supplied with a callback which will process log messages).

Only unread logs will be read unless the read_all parameter is set.

Parameters:

• callback (Callable) – a function to be applied to each of the stats file handles found
• read_all (bool) – a boolean indicating whether to read the already processed stats files in addition to the unread stats files

Raises:

ConcurrentFileModificationException – if the file was modified concurrently during an invocation of this method

Returns:

the number of stats files processed

Return type:

int

write_leader_pid() → None

Write the pid of this process to a file in the job store.

Overwriting the current contents of pid.log is a feature, not a bug of this method. Other methods will rely on always having the most current pid available. So far there is no reason to store any old pids.

read_leader_pid() → int

Read the pid of the leader process to a file in the job store.

Raises:NoSuchFileException – If the PID file doesn’t exist.

write_leader_node_id() → None

Write the leader node id to the job store. This should only be called by the leader.

read_leader_node_id() → str

Read the leader node id stored in the job store.

Raises:NoSuchFileException – If the node ID file doesn’t exist.

write_kill_flag(kill: bool = False) → None

Write a file inside the job store that serves as a kill flag.

The initialized file contains the characters “NO”. This should only be changed when the user runs the “toil kill” command.

Changing this file to a “YES” triggers a kill of the leader process. The workers are expected to be cleaned up by the leader.

read_kill_flag() → bool

Read the kill flag from the job store, and return True if the leader has been killed. False otherwise.

Toil Job API

Functions to wrap jobs and return values (promises).

FunctionWrappingJob

The subclass of Job for wrapping user functions.

class toil.job.FunctionWrappingJob(userFunction, *args, **kwargs)

Job used to wrap a function. In its run method the wrapped function is called.

__init__(userFunction, *args, **kwargs)

Parameters:userFunction (callable) – The function to wrap. It will be called with *args and **kwargs as arguments.

The keywords memory, cores, disk, accelerators`, ``preemptable and checkpoint are reserved keyword arguments that if specified will be used to determine the resources required for the job, as toil.job.Job.__init__(). If they are keyword arguments to the function they will be extracted from the function definition, but may be overridden by the user (as you would expect).

run(fileStore)

Override this function to perform work and dynamically create successor jobs.

Parameters:fileStore – Used to create local and globally sharable temporary files and to send log messages to the leader process. Returns:The return value of the function can be passed to other jobs by means of toil.job.Job.rv().

JobFunctionWrappingJob

The subclass of FunctionWrappingJob for wrapping user job functions.

class toil.job.JobFunctionWrappingJob(userFunction, *args, **kwargs)

A job function is a function whose first argument is a Job instance that is the wrapping job for the function. This can be used to add successor jobs for the function and perform all the functions the Job class provides.

To enable the job function to get access to the toil.fileStores.abstractFileStore.AbstractFileStore instance (see toil.job.Job.run()), it is made a variable of the wrapping job called fileStore.

To specify a job’s resource requirements the following default keyword arguments can be specified:

• memory
• disk
• cores
• accelerators
• preemptible

Note that the argument is named “preemptible” but internally the requirement is “preemptable”.

For example to wrap a function into a job we would call:

Job.wrapJobFn(myJob, memory='100k', disk='1M', cores=0.1)

run(fileStore)

Override this function to perform work and dynamically create successor jobs.

Parameters:fileStore – Used to create local and globally sharable temporary files and to send log messages to the leader process. Returns:The return value of the function can be passed to other jobs by means of toil.job.Job.rv().

EncapsulatedJob

The subclass of Job for encapsulating a job, allowing a subgraph of jobs to be treated as a single job.

class toil.job.EncapsulatedJob(job, unitName=None)

A convenience Job class used to make a job subgraph appear to be a single job.

Let A be the root job of a job subgraph and B be another job we’d like to run after A and all its successors have completed, for this use encapsulate:

#  Job A and subgraph, Job B
A, B = A(), B()
Aprime = A.encapsulate()
Aprime.addChild(B)
#  B will run after A and all its successors have completed, A and its subgraph of
# successors in effect appear to be just one job.

If the job being encapsulated has predecessors (e.g. is not the root job), then the encapsulated job will inherit these predecessors. If predecessors are added to the job being encapsulated after the encapsulated job is created then the encapsulating job will NOT inherit these predecessors automatically. Care should be exercised to ensure the encapsulated job has the proper set of predecessors.

The return value of an encapsulated job (as accessed by the toil.job.Job.rv() function) is the return value of the root job, e.g. A().encapsulate().rv() and A().rv() will resolve to the same value after A or A.encapsulate() has been run.

__init__(job, unitName=None)

Parameters:

• job (toil.job.Job) – the job to encapsulate.
• unitName (str) – human-readable name to identify this job instance.

addChild(childJob)

Add a childJob to be run as child of this job.

Child jobs will be run directly after this job’s toil.job.Job.run() method has completed.

Returns:childJob: for call chaining

addService(service, parentService=None)

Add a service.

The toil.job.Job.Service.start() method of the service will be called after the run method has completed but before any successors are run. The service’s toil.job.Job.Service.stop() method will be called once the successors of the job have been run.

Services allow things like databases and servers to be started and accessed by jobs in a workflow.

Raises:

toil.job.JobException – If service has already been made the child of a job or another service.

Parameters:

• service – Service to add.
• parentService – Service that will be started before ‘service’ is started. Allows trees of services to be established. parentService must be a service of this job.

Returns:

a promise that will be replaced with the return value from toil.job.Job.Service.start() of service in any successor of the job.

addFollowOn(followOnJob)

Add a follow-on job.

Follow-on jobs will be run after the child jobs and their successors have been run.

Returns:followOnJob for call chaining

rv(*path) → toil.job.Promise

Create a promise (toil.job.Promise).

The “promise” representing a return value of the job’s run method, or, in case of a function-wrapping job, the wrapped function’s return value.

Parameters:path ((Any)) – Optional path for selecting a component of the promised return value. If absent or empty, the entire return value will be used. Otherwise, the first element of the path is used to select an individual item of the return value. For that to work, the return value must be a list, dictionary or of any other type implementing the __getitem__() magic method. If the selected item is yet another composite value, the second element of the path can be used to select an item from it, and so on. For example, if the return value is [6,{‘a’:42}], .rv(0) would select 6 , rv(1) would select {‘a’:3} while rv(1,’a’) would select 3. To select a slice from a return value that is slicable, e.g. tuple or list, the path element should be a slice object. For example, assuming that the return value is [6, 7, 8, 9] then .rv(slice(1, 3)) would select [7, 8]. Note that slicing really only makes sense at the end of path. Returns:A promise representing the return value of this jobs toil.job.Job.run() method. Return type:toil.job.Promise

prepareForPromiseRegistration(jobStore)

Set up to allow this job’s promises to register themselves.

Prepare this job (the promisor) so that its promises can register themselves with it, when the jobs they are promised to (promisees) are serialized.

The promissee holds the reference to the promise (usually as part of the job arguments) and when it is being pickled, so will the promises it refers to. Pickling a promise triggers it to be registered with the promissor.

Promise

The class used to reference return values of jobs/services not yet run/started.

class toil.job.Promise(job: toil.job.Job, path: Any)

References a return value from a method as a promise before the method itself is run.

References a return value from a toil.job.Job.run() or toil.job.Job.Service.start() method as a promise before the method itself is run.

Let T be a job. Instances of Promise (termed a promise) are returned by T.rv(), which is used to reference the return value of T’s run function. When the promise is passed to the constructor (or as an argument to a wrapped function) of a different, successor job the promise will be replaced by the actual referenced return value. This mechanism allows a return values from one job’s run method to be input argument to job before the former job’s run function has been executed.

filesToDelete = {}

A set of IDs of files containing promised values when we know we won’t need them anymore

__init__(job: toil.job.Job, path: Any)

Initialize this promise.

Parameters:

• job (Job) – the job whose return value this promise references
• path – see Job.rv()

class toil.job.PromisedRequirement(valueOrCallable, *args)

Class for dynamically allocating job function resource requirements.

(involving toil.job.Promise instances.)

Use when resource requirements depend on the return value of a parent function. PromisedRequirements can be modified by passing a function that takes the Promise as input.

For example, let f, g, and h be functions. Then a Toil workflow can be defined as follows:: A = Job.wrapFn(f) B = A.addChildFn(g, cores=PromisedRequirement(A.rv()) C = B.addChildFn(h, cores=PromisedRequirement(lambda x: 2*x, B.rv()))

__init__(valueOrCallable, *args)

Initialize this Promised Requirement.

Parameters:

• valueOrCallable – A single Promise instance or a function that takes args as input parameters.
• args (int or Promise) – variable length argument list

getValue()

Return PromisedRequirement value.

static convertPromises(kwargs: Dict[str, Any]) → bool

Return True if reserved resource keyword is a Promise or PromisedRequirement instance.

Converts Promise instance to PromisedRequirement.

Parameters:kwargs – function keyword arguments

Job Methods API

Jobs are the units of work in Toil which are composed into workflows.

class toil.job.Job(memory: Union[str, int, None] = None, cores: Union[str, int, float, None] = None, disk: Union[str, int, None] = None, accelerators: Union[str, int, Mapping[str, Any], toil.job.AcceleratorRequirement, Sequence[Union[str, int, Mapping[str, Any], toil.job.AcceleratorRequirement]], None] = None, preemptable: Union[str, int, bool, None] = None, unitName: Optional[str] = '', checkpoint: Optional[bool] = False, displayName: Optional[str] = '', descriptionClass: Optional[str] = None)

Class represents a unit of work in toil.

__init__(memory: Union[str, int, None] = None, cores: Union[str, int, float, None] = None, disk: Union[str, int, None] = None, accelerators: Union[str, int, Mapping[str, Any], toil.job.AcceleratorRequirement, Sequence[Union[str, int, Mapping[str, Any], toil.job.AcceleratorRequirement]], None] = None, preemptable: Union[str, int, bool, None] = None, unitName: Optional[str] = '', checkpoint: Optional[bool] = False, displayName: Optional[str] = '', descriptionClass: Optional[str] = None) → None

Job initializer.

This method must be called by any overriding constructor.

Parameters:

• memory (int or string convertible by toil.lib.conversions.human2bytes to an int) – the maximum number of bytes of memory the job will require to run.
• cores (float, int, or string convertible by toil.lib.conversions.human2bytes to an int) – the number of CPU cores required.
• disk (int or string convertible by toil.lib.conversions.human2bytes to an int) – the amount of local disk space required by the job, expressed in bytes.
• accelerators (int, string, dict, or list of those. Strings and dicts must be parseable by AcceleratorRequirement.parse.) – the computational accelerators required by the job. If a string, can be a string of a number, or a string specifying a model, brand, or API (with optional colon-delimited count).
• preemptable (bool, int in {0, 1}, or string in {'false', 'true'} in any case) – if the job can be run on a preemptable node.
• unitName (str) – Human-readable name for this instance of the job.
• checkpoint (bool) – if any of this job’s successor jobs completely fails, exhausting all their retries, remove any successor jobs and rerun this job to restart the subtree. Job must be a leaf vertex in the job graph when initially defined, see toil.job.Job.checkNewCheckpointsAreCutVertices().
• displayName (str) – Human-readable job type display name.
• descriptionClass (class) – Override for the JobDescription class used to describe the job.

jobStoreID

Get the ID of this Job.

Return type:str|toil.job.TemporaryID

description

Expose the JobDescription that describes this job.

Return type:toil.job.JobDescription

disk

The maximum number of bytes of disk the job will require to run.

Return type:int

memory

The maximum number of bytes of memory the job will require to run.

Return type:int

cores

The number of CPU cores required.

Return type:int|float

accelerators

Any accelerators, such as GPUs, that are needed.

Return type:list

preemptable

Whether the job can be run on a preemptable node.

Return type:bool

checkpoint

Determine if the job is a checkpoint job or not.

Return type:bool

assignConfig(config: toil.common.Config)

Assign the given config object.

It will be used by various actions implemented inside the Job class.

Parameters:config – Config object to query

run(fileStore: AbstractFileStore) → Any

Override this function to perform work and dynamically create successor jobs.

Parameters:fileStore – Used to create local and globally sharable temporary files and to send log messages to the leader process. Returns:The return value of the function can be passed to other jobs by means of toil.job.Job.rv().

addChild(childJob: toil.job.Job) → toil.job.Job

Add a childJob to be run as child of this job.

Child jobs will be run directly after this job’s toil.job.Job.run() method has completed.

Returns:childJob: for call chaining

hasChild(childJob: toil.job.Job) → bool

Check if childJob is already a child of this job.

Returns:True if childJob is a child of the job, else False.

addFollowOn(followOnJob: toil.job.Job) → toil.job.Job

Add a follow-on job.

Follow-on jobs will be run after the child jobs and their successors have been run.

Returns:followOnJob for call chaining

hasPredecessor(job: toil.job.Job) → bool

Check if a given job is already a predecessor of this job.

hasFollowOn(followOnJob: toil.job.Job) → bool

Check if given job is already a follow-on of this job.

Returns:True if the followOnJob is a follow-on of this job, else False.

addService(service: Service, parentService: Optional[Service] = None) → Promise

Add a service.

The toil.job.Job.Service.start() method of the service will be called after the run method has completed but before any successors are run. The service’s toil.job.Job.Service.stop() method will be called once the successors of the job have been run.

Services allow things like databases and servers to be started and accessed by jobs in a workflow.

Raises:

toil.job.JobException – If service has already been made the child of a job or another service.

Parameters:

• service – Service to add.
• parentService – Service that will be started before ‘service’ is started. Allows trees of services to be established. parentService must be a service of this job.

Returns:

a promise that will be replaced with the return value from toil.job.Job.Service.start() of service in any successor of the job.

hasService(service: Service) → bool

Return True if the given Service is a service of this job, and False otherwise.

addChildFn(fn: Callable, *args, **kwargs) → toil.job.FunctionWrappingJob

Add a function as a child job.

Parameters:fn – Function to be run as a child job with *args and **kwargs as arguments to this function. See toil.job.FunctionWrappingJob for reserved keyword arguments used to specify resource requirements. Returns:The new child job that wraps fn.

addFollowOnFn(fn: Callable, *args, **kwargs) → toil.job.FunctionWrappingJob

Add a function as a follow-on job.

Parameters:fn – Function to be run as a follow-on job with *args and **kwargs as arguments to this function. See toil.job.FunctionWrappingJob for reserved keyword arguments used to specify resource requirements. Returns:The new follow-on job that wraps fn.

addChildJobFn(fn: Callable, *args, **kwargs) → toil.job.FunctionWrappingJob

Add a job function as a child job.

See toil.job.JobFunctionWrappingJob for a definition of a job function.

Parameters:fn – Job function to be run as a child job with *args and **kwargs as arguments to this function. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements. Returns:The new child job that wraps fn.

addFollowOnJobFn(fn: Callable, *args, **kwargs) → toil.job.FunctionWrappingJob

Add a follow-on job function.

See toil.job.JobFunctionWrappingJob for a definition of a job function.

Parameters:fn – Job function to be run as a follow-on job with *args and **kwargs as arguments to this function. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements. Returns:The new follow-on job that wraps fn.

tempDir

Shortcut to calling job.fileStore.getLocalTempDir().

Temp dir is created on first call and will be returned for first and future calls :return: Path to tempDir. See job.fileStore.getLocalTempDir

log(text: str, level=20) → None

Convenience wrapper for fileStore.logToMaster().

static wrapFn(fn, *args, **kwargs)

Makes a Job out of a function. Convenience function for constructor of toil.job.FunctionWrappingJob.

Parameters:fn – Function to be run with *args and **kwargs as arguments. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements. Returns:The new function that wraps fn. Return type:toil.job.FunctionWrappingJob

static wrapJobFn(fn, *args, **kwargs)

Makes a Job out of a job function. Convenience function for constructor of toil.job.JobFunctionWrappingJob.

Parameters:fn – Job function to be run with *args and **kwargs as arguments. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements. Returns:The new job function that wraps fn. Return type:toil.job.JobFunctionWrappingJob

encapsulate(name=None)

Encapsulates the job, see toil.job.EncapsulatedJob. Convenience function for constructor of toil.job.EncapsulatedJob.

Parameters:name (str) – Human-readable name for the encapsulated job. Returns:an encapsulated version of this job. Return type:toil.job.EncapsulatedJob

rv(*path) → Any

Create a promise (toil.job.Promise).

The “promise” representing a return value of the job’s run method, or, in case of a function-wrapping job, the wrapped function’s return value.

Parameters:path ((Any)) – Optional path for selecting a component of the promised return value. If absent or empty, the entire return value will be used. Otherwise, the first element of the path is used to select an individual item of the return value. For that to work, the return value must be a list, dictionary or of any other type implementing the __getitem__() magic method. If the selected item is yet another composite value, the second element of the path can be used to select an item from it, and so on. For example, if the return value is [6,{‘a’:42}], .rv(0) would select 6 , rv(1) would select {‘a’:3} while rv(1,’a’) would select 3. To select a slice from a return value that is slicable, e.g. tuple or list, the path element should be a slice object. For example, assuming that the return value is [6, 7, 8, 9] then .rv(slice(1, 3)) would select [7, 8]. Note that slicing really only makes sense at the end of path. Returns:A promise representing the return value of this jobs toil.job.Job.run() method. Return type:toil.job.Promise

prepareForPromiseRegistration(jobStore: AbstractJobStore) → None

Set up to allow this job’s promises to register themselves.

Prepare this job (the promisor) so that its promises can register themselves with it, when the jobs they are promised to (promisees) are serialized.

The promissee holds the reference to the promise (usually as part of the job arguments) and when it is being pickled, so will the promises it refers to. Pickling a promise triggers it to be registered with the promissor.

checkJobGraphForDeadlocks()

Ensures that a graph of Jobs (that hasn’t yet been saved to the JobStore) doesn’t contain any pathological relationships between jobs that would result in deadlocks if we tried to run the jobs.

See toil.job.Job.checkJobGraphConnected(), toil.job.Job.checkJobGraphAcyclic() and toil.job.Job.checkNewCheckpointsAreLeafVertices() for more info.

Raises:toil.job.JobGraphDeadlockException – if the job graph is cyclic, contains multiple roots or contains checkpoint jobs that are not leaf vertices when defined (see toil.job.Job.checkNewCheckpointsAreLeaves()).

getRootJobs() → Set[toil.job.Job]

Returns the set of root job objects that contain this job. A root job is a job with no predecessors (i.e. which are not children, follow-ons, or services).

Only deals with jobs created here, rather than loaded from the job store.

checkJobGraphConnected()

Raises:toil.job.JobGraphDeadlockException – if toil.job.Job.getRootJobs() does not contain exactly one root job.

As execution always starts from one root job, having multiple root jobs will cause a deadlock to occur.

Only deals with jobs created here, rather than loaded from the job store.

checkJobGraphAcylic()

Raises:toil.job.JobGraphDeadlockException – if the connected component of jobs containing this job contains any cycles of child/followOn dependencies in the augmented job graph (see below). Such cycles are not allowed in valid job graphs.

A follow-on edge (A, B) between two jobs A and B is equivalent to adding a child edge to B from (1) A, (2) from each child of A, and (3) from the successors of each child of A. We call each such edge an edge an “implied” edge. The augmented job graph is a job graph including all the implied edges.

For a job graph G = (V, E) the algorithm is O(|V|^2). It is O(|V| + |E|) for a graph with no follow-ons. The former follow-on case could be improved!

Only deals with jobs created here, rather than loaded from the job store.

checkNewCheckpointsAreLeafVertices()

A checkpoint job is a job that is restarted if either it fails, or if any of its successors completely fails, exhausting their retries.

A job is a leaf it is has no successors.

A checkpoint job must be a leaf when initially added to the job graph. When its run method is invoked it can then create direct successors. This restriction is made to simplify implementation.

Only works on connected components of jobs not yet added to the JobStore.

Raises:toil.job.JobGraphDeadlockException – if there exists a job being added to the graph for which checkpoint=True and which is not a leaf.

defer(function, *args, **kwargs)

Register a deferred function, i.e. a callable that will be invoked after the current attempt at running this job concludes. A job attempt is said to conclude when the job function (or the toil.job.Job.run() method for class-based jobs) returns, raises an exception or after the process running it terminates abnormally. A deferred function will be called on the node that attempted to run the job, even if a subsequent attempt is made on another node. A deferred function should be idempotent because it may be called multiple times on the same node or even in the same process. More than one deferred function may be registered per job attempt by calling this method repeatedly with different arguments. If the same function is registered twice with the same or different arguments, it will be called twice per job attempt.

Examples for deferred functions are ones that handle cleanup of resources external to Toil, like Docker containers, files outside the work directory, etc.

Parameters:

• function (callable) – The function to be called after this job concludes.
• args (list) – The arguments to the function
• kwargs (dict) – The keyword arguments to the function

getTopologicalOrderingOfJobs()

Returns:a list of jobs such that for all pairs of indices i, j for which i < j, the job at index i can be run before the job at index j.

Only considers jobs in this job’s subgraph that are newly added, not loaded from the job store.

Ignores service jobs.

Return type:list[Job]

saveBody(jobStore)

Save the execution data for just this job to the JobStore, and fill in the JobDescription with the information needed to retrieve it.

The Job’s JobDescription must have already had a real jobStoreID assigned to it.

Does not save the JobDescription.

Parameters:jobStore (toil.jobStores.abstractJobStore.AbstractJobStore) – The job store to save the job body into.

saveAsRootJob(jobStore: AbstractJobStore) → toil.job.JobDescription

Save this job to the given jobStore as the root job of the workflow.

Returns:the JobDescription describing this job.

classmethod loadJob(jobStore: AbstractJobStore, jobDescription: toil.job.JobDescription) → Job

Retrieves a toil.job.Job instance from a JobStore

Parameters:

• jobStore – The job store.
• jobDescription – the JobDescription of the job to retrieve.

Returns:

The job referenced by the JobDescription.

JobDescription

The class used to store all the information that the Toil Leader ever needs to know about a Job.

class toil.job.JobDescription(requirements: Mapping[str, Union[int, str, bool]], jobName: str, unitName: str = '', displayName: str = '', command: Optional[str] = None)

Stores all the information that the Toil Leader ever needs to know about a Job.

(requirements information, dependency information, commands to issue, etc.)

Can be obtained from an actual (i.e. executable) Job object, and can be used to obtain the Job object from the JobStore.

Never contains other Jobs or JobDescriptions: all reference is by ID.

Subclassed into variants for checkpoint jobs and service jobs that have their specific parameters.

__init__(requirements: Mapping[str, Union[int, str, bool]], jobName: str, unitName: str = '', displayName: str = '', command: Optional[str] = None) → None

Create a new JobDescription.

Parameters:

• requirements – Dict from string to number, string, or bool describing the resource requirements of the job. ‘cores’, ‘memory’, ‘disk’, and ‘preemptable’ fields, if set, are parsed and broken out into properties. If unset, the relevant property will be unspecified, and will be pulled from the assigned Config object if queried (see toil.job.Requirer.assignConfig()).
• jobName – Name of the kind of job this is. May be used in job store IDs and logging. Also used to let the cluster scaler learn a model for how long the job will take. Ought to be the job class’s name if no real user-defined name is available.
• unitName – Name of this instance of this kind of job. May appear with jobName in logging.
• displayName – A human-readable name to identify this particular job instance. Ought to be the job class’s name if no real user-defined name is available.

serviceHostIDsInBatches() → Iterator[List[str]]

Find all batches of service host job IDs that can be started at the same time.

(in the order they need to start in)

successorsAndServiceHosts() → Iterator[str]

Get an iterator over all child, follow-on, and service job IDs.

allSuccessors()

Get an iterator over all child and follow-on job IDs.

services

Get a collection of the IDs of service host jobs for this job, in arbitrary order.

Will be empty if the job has no unfinished services.

nextSuccessors() → List[str]

Return the collection of job IDs for the successors of this job that are ready to run.

If those jobs have multiple predecessor relationships, they may still be blocked on other jobs.

Returns None when at the final phase (all successors done), and an empty collection if there are more phases but they can’t be entered yet (e.g. because we are waiting for the job itself to run).

stack

Get IDs of successors that need to run still.

Batches of successors are in reverse order of the order they need to run in.

Some successors in each batch may have already been finished. Batches may be empty.

Exists so that code that used the old stack list immutably can work still. New development should use nextSuccessors(), and all mutations should use filterSuccessors() (which automatically removes completed phases).

Returns:Batches of successors that still need to run, in reverse order. An empty batch may exist under a non-empty batch, or at the top when the job itself is not done. Return type:tuple(tuple(str))

filterSuccessors(predicate: Callable[[str], bool]) → None

Keep only successor jobs for which the given predicate function approves.

The predicate function is called with the job’s ID.

Treats all other successors as complete and forgets them.

filterServiceHosts(predicate: Callable[[str], bool]) → None

Keep only services for which the given predicate approves.

The predicate function is called with the service host job’s ID.

Treats all other services as complete and forgets them.

clear_nonexistent_dependents(job_store: AbstractJobStore) → None

Remove all references to child, follow-on, and associated service jobs that do not exist (i.e. have been completed and removed) in the given job store.

clear_dependents() → None

Remove all references to child, follow-on, and associated service jobs.

is_subtree_done() → bool

Return True if the job appears to be done, and all related child, follow-on, and service jobs appear to be finished and removed.

replace(other: toil.job.JobDescription) → None

Take on the ID of another JobDescription, retaining our own state and type.

When updated in the JobStore, we will save over the other JobDescription.

Useful for chaining jobs: the chained-to job can replace the parent job.

Merges cleanup state from the job being replaced into this one.

Parameters:other – Job description to replace.

addChild(childID: str) → None

Make the job with the given ID a child of the described job.

addFollowOn(followOnID: str) → None

Make the job with the given ID a follow-on of the described job.

addServiceHostJob(serviceID, parentServiceID=None)

Make the ServiceHostJob with the given ID a service of the described job.

If a parent ServiceHostJob ID is given, that parent service will be started first, and must have already been added.

hasChild(childID: str) → bool

Return True if the job with the given ID is a child of the described job.

hasFollowOn(followOnID: str) → bool

Test if the job with the given ID is a follow-on of the described job.

hasServiceHostJob(serviceID) → bool

Test if the ServiceHostJob is a service of the described job.

renameReferences(renames: Dict[toil.job.TemporaryID, str]) → None

Apply the given dict of ID renames to all references to jobs.

Does not modify our own ID or those of finished predecessors. IDs not present in the renames dict are left as-is.

Parameters:renames – Rename operations to apply.

addPredecessor() → None

Notify the JobDescription that a predecessor has been added to its Job.

onRegistration(jobStore: AbstractJobStore) → None

Called by the Job saving logic when this JobDescription meets the JobStore and has its ID assigned.

Overridden to perform setup work (like hooking up flag files for service jobs) that requires the JobStore.

Parameters:jobStore – The job store we are being placed into

setupJobAfterFailure(exit_status: Optional[int] = None, exit_reason: Optional[BatchJobExitReason] = None)

Reduce the remainingTryCount if greater than zero and set the memory to be at least as big as the default memory (in case of exhaustion of memory, which is common).

Requires a configuration to have been assigned (see toil.job.Requirer.assignConfig()).

Parameters:

• exit_status – The exit code from the job.
• exit_reason – The reason the job stopped, if available from the batch system.

getLogFileHandle(jobStore)

Returns a context manager that yields a file handle to the log file.

Assumes logJobStoreFileID is set.

remainingTryCount

The try count set on the JobDescription, or the default based on the retry count from the config if none is set.

clearRemainingTryCount() → bool

Clear remainingTryCount and set it back to its default value.

Returns:True if a modification to the JobDescription was made, and False otherwise.

pre_update_hook() → None

Called by the job store before pickling and saving a created or updated version of a job.

get_job_kind() → str

Returns an identifier of the job for use with the message bus. Either the unit name, job name, or display name, which identifies the kind of job it is to toil.

Otherwise returns Unknown Job in case no identifier is available

Job.Runner API

The Runner contains the methods needed to configure and start a Toil run.

class Job.Runner

Used to setup and run Toil workflow.

static getDefaultArgumentParser() → argparse.ArgumentParser

Get argument parser with added toil workflow options.

Returns:The argument parser used by a toil workflow with added Toil options. Return type:argparse.ArgumentParser

static getDefaultOptions(jobStore: str) → argparse.Namespace

Get default options for a toil workflow.

Parameters:jobStore (string) – A string describing the jobStore for the workflow. Returns:The options used by a toil workflow. Return type:argparse.ArgumentParser values object

static addToilOptions(parser)

Adds the default toil options to an optparse or argparse parser object.

Parameters:parser (optparse.OptionParser or argparse.ArgumentParser) – Options object to add toil options to.

static startToil(job, options)

Run the toil workflow using the given options.

Deprecated by toil.common.Toil.start.

(see Job.Runner.getDefaultOptions and Job.Runner.addToilOptions) starting with this job. :param toil.job.Job job: root job of the workflow :raises: toil.leader.FailedJobsException if at the end of function their remain failed jobs. :return: The return value of the root job’s run function. :rtype: Any

job.fileStore API

The AbstractFileStore is an abstraction of a Toil run’s shared storage.

class toil.fileStores.abstractFileStore.AbstractFileStore(jobStore: toil.jobStores.abstractJobStore.AbstractJobStore, jobDesc: toil.job.JobDescription, file_store_dir: str, waitForPreviousCommit: Callable[[], Any])

Interface used to allow user code run by Toil to read and write files.

Also provides the interface to other Toil facilities used by user code, including:

• normal (non-real-time) logging
• finding the correct temporary directory for scratch work
• importing and exporting files into and out of the workflow

Stores user files in the jobStore, but keeps them separate from actual jobs.

May implement caching.

Passed as argument to the toil.job.Job.run() method.

Access to files is only permitted inside the context manager provided by toil.fileStores.abstractFileStore.AbstractFileStore.open().

Also responsible for committing completed jobs back to the job store with an update operation, and allowing that commit operation to be waited for.

__init__(jobStore: toil.jobStores.abstractJobStore.AbstractJobStore, jobDesc: toil.job.JobDescription, file_store_dir: str, waitForPreviousCommit: Callable[[], Any]) → None

Create a new file store object.

Parameters:

• jobStore – the job store in use for the current Toil run.
• jobDesc – the JobDescription object for the currently running job.
• file_store_dir – the per-worker local temporary directory where the file store should store local files. Per-job directories will be created under here by the file store.
• waitForPreviousCommit – the waitForCommit method of the previous job’s file store, when jobs are running in sequence on the same worker. Used to prevent this file store’s startCommit and the previous job’s startCommit methods from running at the same time and racing. If they did race, it might be possible for the later job to be fully marked as completed in the job store before the eralier job was.

static createFileStore(jobStore: toil.jobStores.abstractJobStore.AbstractJobStore, jobDesc: toil.job.JobDescription, file_store_dir: str, waitForPreviousCommit: Callable[[], Any], caching: bool) → Union[NonCachingFileStore, CachingFileStore]

Create a concreate FileStore.

static shutdownFileStore(workflowID: str, config_work_dir: Optional[str], config_coordination_dir: Optional[str]) → None

Carry out any necessary filestore-specific cleanup.

This is a destructive operation and it is important to ensure that there are no other running processes on the system that are modifying or using the file store for this workflow.

This is the intended to be the last call to the file store in a Toil run, called by the batch system cleanup function upon batch system shutdown.

Parameters:

• workflowID – The workflow ID for this invocation of the workflow
• config_work_dir – The path to the work directory in the Toil Config.
• config_coordination_dir – The path to the coordination directory in the Toil Config.

open(job: toil.job.Job) → Generator[None, None, None]

Create the context manager around tasks prior and after a job has been run.

File operations are only permitted inside the context manager.

Implementations must only yield from within with super().open(job):.

Parameters:job – The job instance of the toil job to run.

getLocalTempDir() → str

Get a new local temporary directory in which to write files.

The directory will only persist for the duration of the job.

Returns:The absolute path to a new local temporary directory. This directory will exist for the duration of the job only, and is guaranteed to be deleted once the job terminates, removing all files it contains recursively.

getLocalTempFile(suffix: Optional[str] = None, prefix: Optional[str] = None) → str

Get a new local temporary file that will persist for the duration of the job.

Parameters:

• suffix – If not None, the file name will end with this string. Otherwise, default value “.tmp” will be used
• prefix – If not None, the file name will start with this string. Otherwise, default value “tmp” will be used

Returns:

The absolute path to a local temporary file. This file will exist for the duration of the job only, and is guaranteed to be deleted once the job terminates.

getLocalTempFileName(suffix: Optional[str] = None, prefix: Optional[str] = None) → str

Get a valid name for a new local file. Don’t actually create a file at the path.

Parameters:

• suffix – If not None, the file name will end with this string. Otherwise, default value “.tmp” will be used
• prefix – If not None, the file name will start with this string. Otherwise, default value “tmp” will be used

Returns:

Path to valid file

writeGlobalFile(localFileName: str, cleanup: bool = False) → toil.fileStores.FileID

Upload a file (as a path) to the job store.

If the file is in a FileStore-managed temporary directory (i.e. from toil.fileStores.abstractFileStore.AbstractFileStore.getLocalTempDir()), it will become a local copy of the file, eligible for deletion by toil.fileStores.abstractFileStore.AbstractFileStore.deleteLocalFile().

If an executable file on the local filesystem is uploaded, its executability will be preserved when it is downloaded again.

Parameters:

• localFileName – The path to the local file to upload. The last path component (basename of the file) will remain associated with the file in the file store, if supported by the backing JobStore, so that the file can be searched for by name or name glob.
• cleanup – if True then the copy of the global file will be deleted once the job and all its successors have completed running. If not the global file must be deleted manually.

Returns:

an ID that can be used to retrieve the file.

writeGlobalFileStream(cleanup: bool = False, basename: Optional[str] = None, encoding: Optional[str] = None, errors: Optional[str] = None) → Iterator[Tuple[toil.lib.io.WriteWatchingStream, toil.fileStores.FileID]]

Similar to writeGlobalFile, but allows the writing of a stream to the job store. The yielded file handle does not need to and should not be closed explicitly.

Parameters:

• encoding – The name of the encoding used to decode the file. Encodings are the same as for decode(). Defaults to None which represents binary mode.
• errors – Specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.
• cleanup – is as in toil.fileStores.abstractFileStore.AbstractFileStore.writeGlobalFile().
• basename – If supported by the backing JobStore, use the given file basename so that when searching the job store with a query matching that basename, the file will be detected.

Returns:

A context manager yielding a tuple of 1) a file handle which can be written to and 2) the toil.fileStores.FileID of the resulting file in the job store.

logAccess(fileStoreID: Union[toil.fileStores.FileID, str], destination: Optional[str] = None) → None

Record that the given file was read by the job.

(to be announced if the job fails)

If destination is not None, it gives the path that the file was downloaded to. Otherwise, assumes that the file was streamed.

Must be called by readGlobalFile() and readGlobalFileStream() implementations.

readGlobalFile(fileStoreID: str, userPath: Optional[str] = None, cache: bool = True, mutable: bool = False, symlink: bool = False) → str

Make the file associated with fileStoreID available locally.

If mutable is True, then a copy of the file will be created locally so that the original is not modified and does not change the file for other jobs. If mutable is False, then a link can be created to the file, saving disk resources. The file that is downloaded will be executable if and only if it was originally uploaded from an executable file on the local filesystem.

If a user path is specified, it is used as the destination. If a user path isn’t specified, the file is stored in the local temp directory with an encoded name.

The destination file must not be deleted by the user; it can only be deleted through deleteLocalFile.

Implementations must call logAccess() to report the download.

Parameters:

• fileStoreID – job store id for the file
• userPath – a path to the name of file to which the global file will be copied or hard-linked (see below).
• cache – Described in toil.fileStores.CachingFileStore.readGlobalFile()
• mutable – Described in toil.fileStores.CachingFileStore.readGlobalFile()

Returns:

An absolute path to a local, temporary copy of the file keyed by fileStoreID.

readGlobalFileStream(fileStoreID: str, encoding: Optional[str] = None, errors: Optional[str] = None) → AbstractContextManager[Union[IO[bytes], IO[str]]]

Read a stream from the job store; similar to readGlobalFile.

The yielded file handle does not need to and should not be closed explicitly.

Parameters:

• encoding – the name of the encoding used to decode the file. Encodings are the same as for decode(). Defaults to None which represents binary mode.
• errors – an optional string that specifies how encoding errors are to be handled. Errors are the same as for open(). Defaults to ‘strict’ when an encoding is specified.

Implementations must call logAccess() to report the download.

Returns:a context manager yielding a file handle which can be read from.

getGlobalFileSize(fileStoreID: Union[toil.fileStores.FileID, str]) → int

Get the size of the file pointed to by the given ID, in bytes.

If a FileID or something else with a non-None ‘size’ field, gets that.

Otherwise, asks the job store to poll the file’s size.

Note that the job store may overestimate the file’s size, for example if it is encrypted and had to be augmented with an IV or other encryption framing.

Parameters:fileStoreID – File ID for the file Returns:File’s size in bytes, as stored in the job store

deleteLocalFile(fileStoreID: Union[toil.fileStores.FileID, str]) → None

Delete local copies of files associated with the provided job store ID.

Raises an OSError with an errno of errno.ENOENT if no such local copies exist. Thus, cannot be called multiple times in succession.

The files deleted are all those previously read from this file ID via readGlobalFile by the current job into the job’s file-store-provided temp directory, plus the file that was written to create the given file ID, if it was written by the current job from the job’s file-store-provided temp directory.

Parameters:fileStoreID – File Store ID of the file to be deleted.

deleteGlobalFile(fileStoreID: Union[toil.fileStores.FileID, str]) → None

Delete local files and then permanently deletes them from the job store.

To ensure that the job can be restarted if necessary, the delete will not happen until after the job’s run method has completed.

Parameters:fileStoreID – the File Store ID of the file to be deleted.

logToMaster(text: str, level: int = 20) → None

Send a logging message to the leader. The message will also be logged by the worker at the same level.

Parameters:

• text – The string to log.
• level – The logging level.

startCommit(jobState: bool = False) → None

Update the status of the job on the disk.

May start an asynchronous process. Call waitForCommit() to wait on that process.

Parameters:jobState – If True, commit the state of the FileStore’s job, and file deletes. Otherwise, commit only file creates/updates.

waitForCommit() → bool

Blocks while startCommit is running.

This function is called by this job’s successor to ensure that it does not begin modifying the job store until after this job has finished doing so.

Might be called when startCommit is never called on a particular instance, in which case it does not block.

Returns:Always returns True

classmethod shutdown(shutdown_info: Any) → None

Shutdown the filestore on this node.

This is intended to be called on batch system shutdown.

Parameters:shutdown_info – The implementation-specific shutdown information, for shutting down the file store and removing all its state and all job local temp directories from the node.

class toil.fileStores.FileID(fileStoreID: str, size: int, executable: bool = False)

A small wrapper around Python’s builtin string class.

It is used to represent a file’s ID in the file store, and has a size attribute that is the file’s size in bytes. This object is returned by importFile and writeGlobalFile.

Calls into the file store can use bare strings; size will be queried from the job store if unavailable in the ID.

__init__(fileStoreID: str, size: int, executable: bool = False) → None

Initialize self. See help(type(self)) for accurate signature.

pack() → str

Pack the FileID into a string so it can be passed through external code.

classmethod unpack(packedFileStoreID: str) → toil.fileStores.FileID

Unpack the result of pack() into a FileID object.

Batch System API

The batch system interface is used by Toil to abstract over different ways of running batches of jobs, for example Slurm, GridEngine, Mesos, Parasol and a single node. The toil.batchSystems.abstractBatchSystem.AbstractBatchSystem API is implemented to run jobs using a given job management system, e.g. Mesos.

Batch System Enivronmental Variables

Environmental variables allow passing of scheduler specific parameters.

For SLURM there are two environment variables - the first applies to all jobs, while the second defined the partition to use for parallel jobs:

export TOIL_SLURM_ARGS="-t 1:00:00 -q fatq"
export TOIL_SLURM_PE='multicore'

Depending on your SLURM configuration and Python environment, you may need to add –export=ALL to TOIL_SLURM_ARGS in order for the started jobs to properly inherit the environment.

For TORQUE there are two environment variables - one for everything but the resource requirements, and another - for resources requirements (without the -l prefix):

export TOIL_TORQUE_ARGS="-q fatq"
export TOIL_TORQUE_REQS="walltime=1:00:00"

For GridEngine (SGE, UGE), there is an additional environmental variable to define the parallel environment for running multicore jobs:

export TOIL_GRIDENGINE_PE='smp'
export TOIL_GRIDENGINE_ARGS='-q batch.q'

For HTCondor, additional parameters can be included in the submit file passed to condor_submit:

export TOIL_HTCONDOR_PARAMS='requirements = TARGET.has_sse4_2 == true; accounting_group = test'

The environment variable is parsed as a semicolon-separated string of parameter = value pairs.

Batch System API

class toil.batchSystems.abstractBatchSystem.AbstractBatchSystem

An abstract base class to represent the interface the batch system must provide to Toil.

classmethod supportsAutoDeployment() → bool

Whether this batch system supports auto-deployment of the user script itself.

If it does, the setUserScript() can be invoked to set the resource object representing the user script.

Note to implementors: If your implementation returns True here, it should also override

classmethod supportsWorkerCleanup() → bool

Indicates whether this batch system invokes BatchSystemSupport.workerCleanup() after the last job for a particular workflow invocation finishes. Note that the term worker refers to an entire node, not just a worker process. A worker process may run more than one job sequentially, and more than one concurrent worker process may exist on a worker node, for the same workflow. The batch system is said to shut down after the last worker process terminates.

setUserScript(userScript: toil.resource.Resource) → None

Set the user script for this workflow. This method must be called before the first job is issued to this batch system, and only if supportsAutoDeployment() returns True, otherwise it will raise an exception.

Parameters:userScript – the resource object representing the user script or module and the modules it depends on.

set_message_bus(message_bus: toil.bus.MessageBus) → None

Give the batch system an opportunity to connect directly to the message bus, so that it can send informational messages about the jobs it is running to other Toil components.

issueBatchJob(jobDesc: toil.job.JobDescription, job_environment: Optional[Dict[str, str]] = None) → int

Issues a job with the specified command to the batch system and returns a unique jobID.

Parameters:

• jobDesc – a toil.job.JobDescription
• job_environment – a collection of job-specific environment variables to be set on the worker.

Returns:

a unique jobID that can be used to reference the newly issued job

killBatchJobs(jobIDs: List[int]) → None

Kills the given job IDs. After returning, the killed jobs will not appear in the results of getRunningBatchJobIDs. The killed job will not be returned from getUpdatedBatchJob.

Parameters:jobIDs – list of IDs of jobs to kill

getIssuedBatchJobIDs() → List[int]

Gets all currently issued jobs

Returns:A list of jobs (as jobIDs) currently issued (may be running, or may be waiting to be run). Despite the result being a list, the ordering should not be depended upon.

getRunningBatchJobIDs() → Dict[int, float]

Gets a map of jobs as jobIDs that are currently running (not just waiting) and how long they have been running, in seconds.

Returns:dictionary with currently running jobID keys and how many seconds they have been running as the value

getUpdatedBatchJob(maxWait: int) → Optional[toil.batchSystems.abstractBatchSystem.UpdatedBatchJobInfo]

Returns information about job that has updated its status (i.e. ceased running, either successfully or with an error). Each such job will be returned exactly once.

Does not return info for jobs killed by killBatchJobs, although they may cause None to be returned earlier than maxWait.

Parameters:maxWait – the number of seconds to block, waiting for a result Returns:If a result is available, returns UpdatedBatchJobInfo. Otherwise it returns None. wallTime is the number of seconds (a strictly positive float) in wall-clock time the job ran for, or None if this batch system does not support tracking wall time.

getSchedulingStatusMessage() → Optional[str]

Get a log message fragment for the user about anything that might be going wrong in the batch system, if available.

If no useful message is available, return None.

This can be used to report what resource is the limiting factor when scheduling jobs, for example. If the leader thinks the workflow is stuck, the message can be displayed to the user to help them diagnose why it might be stuck.

Returns:User-directed message about scheduling state.

shutdown() → None

Called at the completion of a toil invocation. Should cleanly terminate all worker threads.

setEnv(name: str, value: Optional[str] = None) → None

Set an environment variable for the worker process before it is launched. The worker process will typically inherit the environment of the machine it is running on but this method makes it possible to override specific variables in that inherited environment before the worker is launched. Note that this mechanism is different to the one used by the worker internally to set up the environment of a job. A call to this method affects all jobs issued after this method returns. Note to implementors: This means that you would typically need to copy the variables before enqueuing a job.

If no value is provided it will be looked up from the current environment.

classmethod add_options(parser: Union[argparse.ArgumentParser, argparse._ArgumentGroup]) → None

If this batch system provides any command line options, add them to the given parser.

classmethod setOptions(setOption: toil.batchSystems.options.OptionSetter) → None

Process command line or configuration options relevant to this batch system.

Parameters:setOption – A function with signature setOption(option_name, parsing_function=None, check_function=None, default=None, env=None) returning nothing, used to update run configuration as a side effect.

getWorkerContexts() → List[AbstractContextManager[Any]]

Get a list of picklable context manager objects to wrap worker work in, in order.

Can be used to ask the Toil worker to do things in-process (such as configuring environment variables, hot-deploying user scripts, or cleaning up a node) that would otherwise require a wrapping “executor” process.

Job.Service API

The Service class allows databases and servers to be spawned within a Toil workflow.

class Job.Service(memory=None, cores=None, disk=None, accelerators=None, preemptable=None, unitName=None)

Abstract class used to define the interface to a service.

Should be subclassed by the user to define services.

Is not executed as a job; runs within a ServiceHostJob.

__init__(memory=None, cores=None, disk=None, accelerators=None, preemptable=None, unitName=None)

Memory, core and disk requirements are specified identically to as in toil.job.Job.__init__().

start(job)

Start the service.

Parameters:job (toil.job.Job) – The underlying host job that the service is being run in. Can be used to register deferred functions, or to access the fileStore for creating temporary files. Returns:An object describing how to access the service. The object must be pickleable and will be used by jobs to access the service (see toil.job.Job.addService()).

stop(job)

Stops the service. Function can block until complete.

Parameters:job (toil.job.Job) – The underlying host job that the service is being run in. Can be used to register deferred functions, or to access the fileStore for creating temporary files.

check()

Checks the service is still running.

Raises:exceptions.RuntimeError – If the service failed, this will cause the service job to be labeled failed. Returns:True if the service is still running, else False. If False then the service job will be terminated, and considered a success. Important point: if the service job exits due to a failure, it should raise a RuntimeError, not return False!

Exceptions API

Toil specific exceptions.

exception toil.job.JobException(message: str)

General job exception.

__init__(message: str) → None

Initialize self. See help(type(self)) for accurate signature.

exception toil.job.JobGraphDeadlockException(string)

An exception raised in the event that a workflow contains an unresolvable dependency, such as a cycle. See toil.job.Job.checkJobGraphForDeadlocks().

__init__(string)

Initialize self. See help(type(self)) for accurate signature.

exception toil.jobStores.abstractJobStore.ConcurrentFileModificationException(jobStoreFileID: toil.fileStores.FileID)

Indicates that the file was attempted to be modified by multiple processes at once.

__init__(jobStoreFileID: toil.fileStores.FileID)

Parameters:jobStoreFileID – the ID of the file that was modified by multiple workers or processes concurrently

exception toil.jobStores.abstractJobStore.JobStoreExistsException(locator: str)

Indicates that the specified job store already exists.

__init__(locator: str)

Parameters:locator (str) – The location of the job store

exception toil.jobStores.abstractJobStore.NoSuchFileException(jobStoreFileID: toil.fileStores.FileID, customName: Optional[str] = None, *extra)

Indicates that the specified file does not exist.

__init__(jobStoreFileID: toil.fileStores.FileID, customName: Optional[str] = None, *extra)

Parameters:

• jobStoreFileID – the ID of the file that was mistakenly assumed to exist
• customName – optionally, an alternate name for the nonexistent file
• extra (list) – optional extra information to add to the error message

exception toil.jobStores.abstractJobStore.NoSuchJobException(jobStoreID: toil.fileStores.FileID)

Indicates that the specified job does not exist.

__init__(jobStoreID: toil.fileStores.FileID)

Parameters:jobStoreID (str) – the jobStoreID that was mistakenly assumed to exist

exception toil.jobStores.abstractJobStore.NoSuchJobStoreException(locator: str)

Indicates that the specified job store does not exist.

__init__(locator: str)

Parameters:locator (str) – The location of the job store

Running Tests

Test make targets, invoked as $ make <target>, subject to which environment variables are set (see Running Integration Tests).

TARGET	DESCRIPTION
test	Invokes all tests.
integration_test	Invokes only the integration tests.
test_offline	Skips building the Docker appliance and only invokes tests that have no docker dependencies.
integration_test_local	Makes integration tests easier to debug locally by running the integration tests serially and doesn’t redirect output. This makes it appears on the terminal as expected.

Before running tests for the first time, initialize your virtual environment following the steps in Building from Source.

Run all tests (including slow tests):

$ make test

Run only quick tests (as of Jul 25, 2018, this was ~ 20 minutes):

$ export TOIL_TEST_QUICK=True; make test

Run an individual test with:

$ make test tests=src/toil/test/sort/sortTest.py::SortTest::testSort

The default value for tests is "src" which includes all tests in the src/ subdirectory of the project root. Tests that require a particular feature will be skipped implicitly. If you want to explicitly skip tests that depend on a currently installed feature, use

$ make test tests="-m 'not aws' src"

This will run only the tests that don’t depend on the aws extra, even if that extra is currently installed. Note the distinction between the terms feature and extra. Every extra is a feature but there are features that are not extras, such as the gridengine and parasol features. To skip tests involving both the parasol feature and the aws extra, use the following:

$ make test tests="-m 'not aws and not parasol' src"

Running Tests with pytest

Often it is simpler to use pytest directly, instead of calling the make wrapper. This usually works as expected, but some tests need some manual preparation. To run a specific test with pytest, use the following:

python -m pytest src/toil/test/sort/sortTest.py::SortTest::testSort

For more information, see the pytest documentation.

Running Integration Tests

These tests are generally only run using in our CI workflow due to their resource requirements and cost. However, they can be made available for local testing:

• Running tests that make use of Docker (e.g. autoscaling tests and Docker tests) require an appliance image to be hosted. First, make sure you have gone through the set up found in Using Docker with Quay. Then to build and host the appliance image run the make target push_docker.

  $ make push_docker
  

• Running integration tests require activation via an environment variable as well as exporting information relevant to the desired tests. Enable the integration tests:

  $ export TOIL_TEST_INTEGRATIVE=True
  

• Finally, set the environment variables for keyname and desired zone:

  $ export TOIL_X_KEYNAME=[Your Keyname]
  $ export TOIL_X_ZONE=[Desired Zone]
  

  Where X is one of our currently supported cloud providers (GCE, AWS).

• See the above sections for guidance on running tests.

Test Environment Variables

TOIL_TEST_TEMP	An absolute path to a directory where Toil tests will write their temporary files. Defaults to the system’s standard temporary directory.
TOIL_TEST_INTEGRATIVE	If True, this allows the integration tests to run. Only valid when running the tests from the source directory via make test or make test_parallel.
TOIL_AWS_KEYNAME	An AWS keyname (see Preparing your AWS environment), which is required to run the AWS tests.
TOIL_GOOGLE_PROJECTID	A Google Cloud account projectID (see Running in Google Compute Engine (GCE)), which is required to to run the Google Cloud tests.
TOIL_TEST_QUICK	If True, long running tests are skipped.

Partial install and failing tests

Some tests may fail with an ImportError if the required extras are not installed. Install Toil with all of the extras do prevent such errors.

Using Docker with Quay

Docker is needed for some of the tests. Follow the appropriate installation instructions for your system on their website to get started.

When running make test you might still get the following error:

$ make test
Please set TOIL_DOCKER_REGISTRY, e.g. to quay.io/USER.

To solve, make an account with Quay and specify it like so:

$ TOIL_DOCKER_REGISTRY=quay.io/USER make test

where USER is your Quay username.

For convenience you may want to add this variable to your bashrc by running

$ echo 'export TOIL_DOCKER_REGISTRY=quay.io/USER' >> $HOME/.bashrc

Running Mesos Tests

If you’re running Toil’s Mesos tests, be sure to create the virtualenv with --system-site-packages to include the Mesos Python bindings. Verify this by activating the virtualenv and running pip list | grep mesos. On macOS, this may come up empty. To fix it, run the following:

for i in /usr/local/lib/python2.7/site-packages/*mesos*; do ln -snf $i venv/lib/python2.7/site-packages/; done

Developing with Docker

To develop on features reliant on the Toil Appliance (the docker image toil uses for AWS autoscaling), you should consider setting up a personal registry on Quay or Docker Hub. Because the Toil Appliance images are tagged with the Git commit they are based on and because only commits on our master branch trigger an appliance build on Quay, as soon as a developer makes a commit or dirties the working copy they will no longer be able to rely on Toil to automatically detect the proper Toil Appliance image. Instead, developers wishing to test any appliance changes in autoscaling should build and push their own appliance image to a personal Docker registry. This is described in the next section.

Making Your Own Toil Docker Image

Note! Toil checks if the docker image specified by TOIL_APPLIANCE_SELF exists prior to launching by using the docker v2 schema. This should be valid for any major docker repository, but there is an option to override this if desired using the option: --forceDockerAppliance.

Here is a general workflow (similar instructions apply when using Docker Hub):

1. Make some changes to the provisioner of your local version of Toil

2. Go to the location where you installed the Toil source code and run

   $ make docker
   

   to automatically build a docker image that can now be uploaded to your personal Quay account. If you have not installed Toil source code yet see Building from Source.

3. If it’s not already you will need Docker installed and need to log into Quay. Also you will want to make sure that your Quay account is public.

4. Set the environment variable TOIL_DOCKER_REGISTRY to your Quay account. If you find yourself doing this often you may want to add

   export TOIL_DOCKER_REGISTRY=quay.io/<MY_QUAY_USERNAME>
   

   to your .bashrc or equivalent.

5. Now you can run

   $ make push_docker
   

   which will upload the docker image to your Quay account. Take note of the image’s tag for the next step.

6. Finally you will need to tell Toil from where to pull the Appliance image you’ve created (it uses the Toil release you have installed by default). To do this set the environment variable TOIL_APPLIANCE_SELF to the url of your image. For more info see Environment Variables.

7. Now you can launch your cluster! For more information see Running a Workflow with Autoscaling.

Running a Cluster Locally

The Toil Appliance container can also be useful as a test environment since it can simulate a Toil cluster locally. An important caveat for this is autoscaling, since autoscaling will only work on an EC2 instance and cannot (at this time) be run on a local machine.

To spin up a local cluster, start by using the following Docker run command to launch a Toil leader container:

docker run \
    --entrypoint=mesos-master \
    --net=host \
    -d \
    --name=leader \
    --volume=/home/jobStoreParentDir:/jobStoreParentDir \
    quay.io/ucsc_cgl/toil:3.6.0 \
    --registry=in_memory \
    --ip=127.0.0.1 \
    --port=5050 \
    --allocation_interval=500ms

A couple notes on this command: the -d flag tells Docker to run in daemon mode so the container will run in the background. To verify that the container is running you can run docker ps to see all containers. If you want to run your own container rather than the official UCSC container you can simply replace the quay.io/ucsc_cgl/toil:3.6.0 parameter with your own container name.

Also note that we are not mounting the job store directory itself, but rather the location where the job store will be written. Due to complications with running Docker on MacOS, I recommend only mounting directories within your home directory. The next command will launch the Toil worker container with similar parameters:

docker run \
    --entrypoint=mesos-slave \
    --net=host \
    -d \
    --name=worker \
    --volume=/home/jobStoreParentDir:/jobStoreParentDir \
    quay.io/ucsc_cgl/toil:3.6.0 \
    --work_dir=/var/lib/mesos \
    --master=127.0.0.1:5050 \
    --ip=127.0.0.1 \
    —-attributes=preemptable:False \
    --resources=cpus:2

Note here that we are specifying 2 CPUs and a non-preemptable worker. We can easily change either or both of these in a logical way. To change the number of cores we can change the 2 to whatever number you like, and to change the worker to be preemptable we change preemptable:False to preemptable:True. Also note that the same volume is mounted into the worker. This is needed since both the leader and worker write and read from the job store. Now that your cluster is running, you can run

docker exec -it leader bash

to get a shell in your leader ‘node’. You can also replace the leader parameter with worker to get shell access in your worker.

Docker-in-Docker issues

If you want to run Docker inside this Docker cluster (Dockerized tools, perhaps), you should also mount in the Docker socket via -v /var/run/docker.sock:/var/run/docker.sock. This will give the Docker client inside the Toil Appliance access to the Docker engine on the host. Client/engine version mismatches have been known to cause issues, so we recommend using Docker version 1.12.3 on the host to be compatible with the Docker client installed in the Appliance. Finally, be careful where you write files inside the Toil Appliance - ‘child’ Docker containers launched in the Appliance will actually be siblings to the Appliance since the Docker engine is located on the host. This means that the ‘child’ container can only mount in files from the Appliance if the files are located in a directory that was originally mounted into the Appliance from the host - that way the files are accessible to the sibling container. Note: if Docker can’t find the file/directory on the host it will silently fail and mount in an empty directory.

Maintainer’s Guidelines

In general, as developers and maintainers of the code, we adhere to the following guidelines:

• We strive to never break the build on master. All development should be done on branches, in either the main Toil repository or in developers’ forks.
• Pull requests should be used for any and all changes (except truly trivial ones).
• Pull requests should be in response to issues. If you find yourself making a pull request without an issue, you should create the issue first.

Naming Conventions

• Commit messages should be great. Most importantly, they must:

  • Have a short subject line. If in need of more space, drop down two lines and write a body to explain what is changing and why it has to change.
  • Write the subject line as a command: Destroy all humans, not All humans destroyed.
  • Reference the issue being fixed in a Github-parseable format, such as (resolves #1234) at the end of the subject line, or This will fix #1234. somewhere in the body. If no single commit on its own fixes the issue, the cross-reference must appear in the pull request title or body instead.

• Branches in the main Toil repository must start with issues/, followed by the issue number (or numbers, separated by a dash), followed by a short, lowercase, hyphenated description of the change. (There can be many open pull requests with their associated branches at any given point in time and this convention ensures that we can easily identify branches.)

  Say there is an issue numbered #123 titled Foo does not work. The branch name would be issues/123-fix-foo and the title of the commit would be Fix foo in case of bar (resolves #123).

Pull Requests

• All pull requests must be reviewed by a person other than the request’s author. Review the PR by following the Reviewing Pull Requests checklist.

• Modified pull requests must be re-reviewed before merging. Note that Github does not enforce this!

• Merge pull requests by following the Merging Pull Requests checklist.

• When merging a pull request, make sure to update the Draft Changelog on the Github wiki, which we will use to produce the changelog for the next release. The PR template tells you to do this, so don’t forget. New entries should go at the bottom.

• Pull requests will not be merged unless CI tests pass. Gitlab tests are only run on code in the main Toil repository on some branch, so it is the responsibility of the approving reviewer to make sure that pull requests from outside repositories are copied to branches in the main repository. This can be accomplished with (from a Toil clone):

  ./contrib/admin/test-pr theirusername their-branch issues/123-fix-description-here
  

  This must be repeated every time the PR submitter updates their PR, after checking to see that the update is not malicious.

  If there is no issue corresponding to the PR, after which the branch can be named, the reviewer of the PR should first create the issue.

  Developers who have push access to the main Toil repository are encouraged to make their pull requests from within the repository, to avoid this step.

• Prefer using “Squash and marge” when merging pull requests to master especially when the PR contains a “single unit” of work (i.e. if one were to rewrite the PR from scratch with all the fixes included, they would have one commit for the entire PR). This makes the commit history on master more readable and easier to debug in case of a breakage.

  When squashing a PR from multiple authors, please add Co-authored-by to give credit to all contributing authors.

  See Issue #2816 for more details.

Publishing a Release

These are the steps to take to publish a Toil release:

• Determine the release version X.Y.Z. This should follow semantic versioning; if user-workflow-breaking changes are made, X should be incremented, and Y and Z should be zero. If non-breaking changes are made but new functionality is added, X should remain the same as the last release, Y should be incremented, and Z should be zero. If only patches are released, X and Y should be the same as the last release and Z should be incremented.

• If it does not exist already, create a release branch in the Toil repo named X.Y.x, where x is a literal lower-case “x”. For patch releases, find the existing branch and make sure it is up to date with the patch commits that are to be released. They may be cherry-picked over from master.

• On the release branch, edit version_template.py in the root of the repository. Find the line that looks like this (slightly different for patch releases):

  baseVersion = 'X.Y.0a1'
  

  Make it look like this instead:

  baseVersion = 'X.Y.Z'
  

  Commit your change to the branch.

• Tag the current state of the release branch as releases/X.Y.Z.

• Make the Github release here, referencing that tag. For a non-patch release, fill in the description with the changelog from the wiki page, which you should clear. For a patch release, just describe the patch.

• For a non-patch release, set up the main branch so that development builds will declare themselves to be alpha versions of what the next release will probably be. Edit version_template.py in the root of the repository on the main branch to set baseVersion like this:

  baseVersion = 'X.Y+1.0a1'
  

  Make sure to replace X and Y+1 with actual numbers.

Using Git Hooks

In the contrib/hooks directory, there are two scripts, mypy-after-commit.py and mypy-before-push.py, that can be set up as Git hooks to make sure you don’t accidentally push commits that would immediately fail type-checking. These are supposed to eliminate the need to run make mypy constantly. You can install them into your Git working copy like this

ln -rs ./contrib/hooks/mypy-after-commit.py .git/hooks/post-commit
ln -rs ./contrib/hooks/mypy-before-push.py .git/hooks/pre-push

After you make a commit, the post-commit script will start type-checking it, and if it takes too long re-launch the process in the background. When you push, the pre-push script will see if the commit you are pushing type-checked successfully, and if it hasn’t been type-checked but is currently checked out, it will be type-checked. If type-checking fails, the push will be aborted.

Type-checking will only be performed if you are in a Toil development virtual environment. If you aren’t, the scripts won’t do anything.

To bypass or override pre-push hook, if it is wrong or if you need to push something that doesn’t typecheck, you can git push --no-verify. If the scripts get confused about whether a commit actually typechecks, you can clear out the type-checking result cache, which is in /var/run/user/<your UID>/.mypy_toil_result_cache on Linux and in .mypy_toil_result_cache in the Toil repo on Mac.

To uninstall the scripts, delete .git/hooks/post-commit and .git/hooks/pre-push.

Adding Retries to a Function

See toil.lib.retry .

retry() can be used to decorate any function based on the list of errors one wishes to retry on.

This list of errors can contain normal Exception objects, and/or RetryCondition objects wrapping Exceptions to include additional conditions.

For example, retrying on a one Exception (HTTPError):

from requests import get
from requests.exceptions import HTTPError

@retry(errors=[HTTPError])
def update_my_wallpaper():
    return get('https://www.deviantart.com/')

Or:

from requests import get
from requests.exceptions import HTTPError

@retry(errors=[HTTPError, ValueError])
def update_my_wallpaper():
    return get('https://www.deviantart.com/')

The examples above will retry for the default interval on any errors specified the “errors=” arg list.

To retry on specifically 500/502/503/504 errors, you could specify an ErrorCondition object instead, for example:

from requests import get
from requests.exceptions import HTTPError

@retry(errors=[
    ErrorCondition(
               error=HTTPError,
               error_codes=[500, 502, 503, 504]
           )])
def update_my_wallpaper():
    return requests.get('https://www.deviantart.com/')

To retry on specifically errors containing the phrase “NotFound”:

from requests import get
from requests.exceptions import HTTPError

@retry(errors=[
    ErrorCondition(
        error=HTTPError,
        error_message_must_include="NotFound"
    )])
def update_my_wallpaper():
    return requests.get('https://www.deviantart.com/')

To retry on all HTTPError errors EXCEPT an HTTPError containing the phrase “NotFound”:

from requests import get
from requests.exceptions import HTTPError

@retry(errors=[
    HTTPError,
    ErrorCondition(
               error=HTTPError,
               error_message_must_include="NotFound",
               retry_on_this_condition=False
           )])
def update_my_wallpaper():
    return requests.get('https://www.deviantart.com/')

To retry on boto3’s specific status errors, an example of the implementation is:

import boto3
from botocore.exceptions import ClientError

@retry(errors=[
    ErrorCondition(
               error=ClientError,
               boto_error_codes=["BucketNotFound"]
           )])
def boto_bucket(bucket_name):
    boto_session = boto3.session.Session()
    s3_resource = boto_session.resource('s3')
    return s3_resource.Bucket(bucket_name)

Any combination of these will also work, provided the codes are matched to the correct exceptions. A ValueError will not return a 404, for example.

The retry function as a decorator should make retrying functions easier and clearer. It also encourages smaller independent functions, as opposed to lumping many different things that may need to be retried on different conditions in the same function.

The ErrorCondition object tries to take some of the heavy lifting of writing specific retry conditions and boil it down to an API that covers all common use-cases without the user having to write any new bespoke functions.

Use-cases covered currently:

1. Retrying on a normal error, like a KeyError.
2. Retrying on HTTP error codes (use ErrorCondition).
3. Retrying on boto’s specific status errors, like “BucketNotFound” (use ErrorCondition).
4. Retrying when an error message contains a certain phrase (use ErrorCondition).
5. Explicitly NOT retrying on a condition (use ErrorCondition).

If new functionality is needed, it’s currently best practice in Toil to add functionality to the ErrorCondition itself rather than making a new custom retry method.

Pull Request Checklists

This document contains checklists for dealing with PRs. More general PR information is available at Pull Requests.

Reviewing Pull Requests

This checklist is to be kept in sync with the checklist in the pull request template.

When reviewing a PR, do the following:

• Make sure it is coming from issues/XXXX-fix-the-thing in the Toil repo, or from an external repo.

  • If it is coming from an external repo, make sure to pull it in for CI with:

    contrib/admin/test-pr otheruser theirbranchname issues/XXXX-fix-the-thing
    

  • If there is no associated issue, create one.

• Read through the code changes. Make sure that it doesn’t have:

  • Addition of trailing whitespace.
  • New variable or member names in camelCase that want to be in snake_case.
  • New functions without type hints.
  • New functions or classes without informative docstrings.
  • Changes to semantics not reflected in the relevant docstrings.
  • New or changed command line options for Toil workflows that are not reflected in docs/running/cliOptions.rst
  • New features without tests.

• Comment on the lines of code where problems exist with a review comment. You can shift-click the line numbers in the diff to select multiple lines.

• Finish the review with an overall description of your opinion.

Merging Pull Requests

This checklist is to be kept in sync with the checklist in the pull request template.

When merging a PR, do the following:

• Make sure the PR passes tests.
• Make sure the PR has been reviewed since its last modification. If not, review it.

• Merge with the Github “Squash and merge” feature.

  • If there are multiple authors’ commits, add Co-authored-by to give credit to all contributing authors.

• Copy its recommended changelog entry to the Draft Changelog.
• Append the issue number in parentheses to the changelog entry.

Toil Architecture

The following diagram layouts out the software architecture of Toil.

Figure 1: The basic components of Toil’s architecture.

These components are described below:

• the leader:

  The leader is responsible for deciding which jobs should be run. To do this it traverses the job graph. Currently this is a single threaded process, but we make aggressive steps to prevent it becoming a bottleneck (see Read-only Leader described below).

• the job-store:

  Handles all files shared between the components. Files in the job-store are the means by which the state of the workflow is maintained. Each job is backed by a file in the job store, and atomic updates to this state are used to ensure the workflow can always be resumed upon failure. The job-store can also store all user files, allowing them to be shared between jobs. The job-store is defined by the AbstractJobStore class. Multiple implementations of this class allow Toil to support different back-end file stores, e.g.: S3, network file systems, Google file store, etc.

• workers:

  The workers are temporary processes responsible for running jobs, one at a time per worker. Each worker process is invoked with a job argument that it is responsible for running. The worker monitors this job and reports back success or failure to the leader by editing the job’s state in the file-store. If the job defines successor jobs the worker may choose to immediately run them (see Job Chaining below).

• the batch-system:

  Responsible for scheduling the jobs given to it by the leader, creating a worker command for each job. The batch-system is defined by the AbstractBatchSystem class. Toil uses multiple existing batch systems to schedule jobs, including Apache Mesos, GridEngine and a multi-process single node implementation that allows workflows to be run without any of these frameworks. Toil can therefore fairly easily be made to run a workflow using an existing cluster.

• the node provisioner:

  Creates worker nodes in which the batch system schedules workers. It is defined by the AbstractProvisioner class.

• the statistics and logging monitor:

  Monitors logging and statistics produced by the workers and reports them. Uses the job-store to gather this information.

Jobs and JobDescriptions

As noted in Job Basics, a job is the atomic unit of work in a Toil workflow. User scripts inherit from the Job class to define units of work. These jobs are pickled and stored in the job-store by the leader, and are retrieved and un-pickled by the worker when they are scheduled to run.

During scheduling, Toil does not work with the actual Job objects. Instead, JobDescription objects are used to store all the information that the Toil Leader ever needs to know about the Job. This includes requirements information, dependency information, commands to issue, etc.

Internally, the JobDescription object is referenced by its jobStoreID, which is often not human readable. However, the Job and JobDescription objects contain several human-readable names that are useful for logging and identification:

jobName	Name of the kind of job this is. This may be used in job store IDs and logging. Also used to let the cluster scaler learn a model for how long the job will take. Defaults to the job class’s name if no real user-defined name is available. For a FunctionWrappingJob, the jobName is replaced by the wrapped function’s name. For a CWL workflow, the jobName is the class name of the internal job that is running the CWL workflow, such as "CWLJob".
unitName	Name of this instance of this kind of job. If set by the user, it will appear with the jobName in logging. For a CWL workflow, the unitName is set to a descriptive name that includes the CWL file name and the ID in the file if set.
displayName	A human-readable name to identify this particular job instance. Used as an identifier of the job class in the stats report. Defaults to the job class’s name if no real user-defined name is available. For a CWL workflow, the displayName is the absolute workflow URI.

Optimizations

Toil implements lots of optimizations designed for scalability. Here we detail some of the key optimizations.

Read-only leader

The leader process is currently implemented as a single thread. Most of the leader’s tasks revolve around processing the state of jobs, each stored as a file within the job-store. To minimise the load on this thread, each worker does as much work as possible to manage the state of the job it is running. As a result, with a couple of minor exceptions, the leader process never needs to write or update the state of a job within the job-store. For example, when a job is complete and has no further successors the responsible worker deletes the job from the job-store, marking it complete. The leader then only has to check for the existence of the file when it receives a signal from the batch-system to know that the job is complete. This off-loading of state management is orthogonal to future parallelization of the leader.

Job chaining

The scheduling of successor jobs is partially managed by the worker, reducing the number of individual jobs the leader needs to process. Currently this is very simple: if the there is a single next successor job to run and its resources fit within the resources of the current job and closely match the resources of the current job then the job is run immediately on the worker without returning to the leader. Further extensions of this strategy are possible, but for many workflows which define a series of serial successors (e.g. map sequencing reads, post-process mapped reads, etc.) this pattern is very effective at reducing leader workload.

Preemptable node support

Critical to running at large-scale is dealing with intermittent node failures. Toil is therefore designed to always be resumable providing the job-store does not become corrupt. This robustness allows Toil to run on preemptible nodes, which are only available when others are not willing to pay more to use them. Designing workflows that divide into many short individual jobs that can use preemptable nodes allows for workflows to be efficiently scheduled and executed.

Caching

Running bioinformatic pipelines often require the passing of large datasets between jobs. Toil caches the results from jobs such that child jobs running on the same node can directly use the same file objects, thereby eliminating the need for an intermediary transfer to the job store. Caching also reduces the burden on the local disks, because multiple jobs can share a single file. The resulting drop in I/O allows pipelines to run faster, and, by the sharing of files, allows users to run more jobs in parallel by reducing overall disk requirements.

To demonstrate the efficiency of caching, we ran an experimental internal pipeline on 3 samples from the TCGA Lung Squamous Carcinoma (LUSC) dataset. The pipeline takes the tumor and normal exome fastqs, and the tumor rna fastq and input, and predicts MHC presented neoepitopes in the patient that are potential targets for T-cell based immunotherapies. The pipeline was run individually on the samples on c3.8xlarge machines on AWS (60GB RAM,600GB SSD storage, 32 cores). The pipeline aligns the data to hg19-based references, predicts MHC haplotypes using PHLAT, calls mutations using 2 callers (MuTect and RADIA) and annotates them using SnpEff, then predicts MHC:peptide binding using the IEDB suite of tools before running an in-house rank boosting algorithm on the final calls.

To optimize time taken, The pipeline is written such that mutations are called on a per-chromosome basis from the whole-exome bams and are merged into a complete vcf. Running mutect in parallel on whole exome bams requires each mutect job to download the complete Tumor and Normal Bams to their working directories – An operation that quickly fills the disk and limits the parallelizability of jobs. The script was run in Toil, with and without caching, and Figure 2 shows that the workflow finishes faster in the cached case while using less disk on average than the uncached run. We believe that benefits of caching arising from file transfers will be much higher on magnetic disk-based storage systems as compared to the SSD systems we tested this on.

Figure 2: Efficiency gain from caching. The lower half of each plot describes the disk used by the pipeline recorded every 10 minutes over the duration of the pipeline, and the upper half shows the corresponding stage of the pipeline that is being processed. Since jobs requesting the same file shared the same inode, the effective load on the disk is considerably lower than in the uncached case where every job downloads a personal copy of every file it needs. We see that in all cases, the uncached run uses almost 300-400GB more that the cached run in the resource heavy mutation calling step. We also see a benefit in terms of wall time for each stage since we eliminate the time taken for file transfers.

Toil support for Common Workflow Language

The CWL document and input document are loaded using the ‘cwltool.load_tool’ module. This performs normalization and URI expansion (for example, relative file references are turned into absolute file URIs), validates the document against the CWL schema, initializes Python objects corresponding to major document elements (command line tools, workflows, workflow steps), and performs static type checking that sources and sinks have compatible types.

Input files referenced by the CWL document and input document are imported into the Toil file store. CWL documents may use any URI scheme supported by Toil file store, including local files and object storage.

The ‘location’ field of File references are updated to reflect the import token returned by the Toil file store.

For directory inputs, the directory listing is stored in Directory object. Each individual files is imported into Toil file store.

An initial workflow Job is created from the toplevel CWL document. Then, control passes to the Toil engine which schedules the initial workflow job to run.

When the toplevel workflow job runs, it traverses the CWL workflow and creates a toil job for each step. The dependency graph is expressed by making downstream jobs children of upstream jobs, and initializing the child jobs with an input object containing the promises of output from upstream jobs.

Because Toil jobs have a single output, but CWL permits steps to have multiple output parameters that may feed into multiple other steps, the input to a CWLJob is expressed with an “indirect dictionary”. This is a dictionary of input parameters, where each entry value is a tuple of a promise and a promise key. When the job runs, the indirect dictionary is turned into a concrete input object by resolving each promise into its actual value (which is always a dict), and then looking up the promise key to get the actual value for the the input parameter.

If a workflow step specifies a scatter, then a scatter job is created and connected into the workflow graph as described above. When the scatter step runs, it creates child jobs for each parameterizations of the scatter. A gather job is added as a follow-on to gather the outputs into arrays.

When running a command line tool, it first creates output and temporary directories under the Toil local temp dir. It runs the command line tool using the single_job_executor from CWLTool, providing a Toil-specific constructor for filesystem access, and overriding the default PathMapper to use ToilPathMapper.

The ToilPathMapper keeps track of a file’s symbolic identifier (the Toil FileID), its local path on the host (the value returned by readGlobalFile) and the the location of the file inside the Docker container.

After executing single_job_executor from CWLTool, it gets back the output object and status. If the underlying job failed, raise an exception. Files from the output object are added to the file store using writeGlobalFile and the ‘location’ field of File references are updated to reflect the token returned by the Toil file store.

When the workflow completes, it returns an indirect dictionary linking to the outputs of the job steps that contribute to the final output. This is the value returned by toil.start() or toil.restart(). This is resolved to get the final output object. The files in this object are exported from the file store to ‘outdir’ on the host file system, and the ‘location’ field of File references are updated to reflect the final exported location of the output files.

Minimum AWS IAM permissions

Toil requires at least the following permissions in an IAM role to operate on a cluster. These are added by default when launching a cluster. However, ensure that they are present if creating a custom IAM role when launching a cluster with the --awsEc2ProfileArn parameter.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:*",
                "s3:*",
                "sdb:*",
                "iam:PassRole"
            ],
            "Resource": "*"
        }
    ]
}

Auto-Deployment

If you want to run your workflow in a distributed environment, on multiple worker machines, either in the cloud or on a bare-metal cluster, your script needs to be made available to those other machines. If your script imports other modules, those modules also need to be made available on the workers. Toil can automatically do that for you, with a little help on your part. We call this feature auto-deployment of a workflow.

Let’s first examine various scenarios of auto-deploying a workflow, which, as we’ll see shortly cannot be auto-deployed. Lastly, we’ll deal with the issue of declaring Toil as a dependency of a workflow that is packaged as a setuptools distribution.

Toil can be easily deployed to a remote host. First, assuming you’ve followed our Preparing your AWS environment section to install Toil and use it to create a remote leader node on (in this example) AWS, you can now log into this into using Ssh-Cluster Command and once on the remote host, create and activate a virtualenv (noting to make sure to use the --system-site-packages option!):

$ virtualenv --system-site-packages venv
$ . venv/bin/activate

Note the --system-site-packages option, which ensures that globally-installed packages are accessible inside the virtualenv. Do not (re)install Toil after this! The --system-site-packages option has already transferred Toil and the dependencies from your local installation of Toil for you.

From here, you can install a project and its dependencies:

$ tree
.
├── util
│   ├── __init__.py
│   └── sort
│       ├── __init__.py
│       └── quick.py
└── workflow
    ├── __init__.py
    └── main.py

3 directories, 5 files
$ pip install matplotlib
$ cp -R workflow util venv/lib/python2.7/site-packages

Ideally, your project would have a setup.py file (see setuptools) which streamlines the installation process:

$ tree
.
├── util
│   ├── __init__.py
│   └── sort
│       ├── __init__.py
│       └── quick.py
├── workflow
│   ├── __init__.py
│   └── main.py
└── setup.py

3 directories, 6 files
$ pip install .

Or, if your project has been published to PyPI:

$ pip install my-project

In each case, we have created a virtualenv with the --system-site-packages flag in the venv subdirectory then installed the matplotlib distribution from PyPI along with the two packages that our project consists of. (Again, both Python and Toil are assumed to be present on the leader and all worker nodes.)

We can now run our workflow:

$ python main.py --batchSystem=mesos …

Important

If workflow’s external dependencies contain native code (i.e. are not pure Python) then they must be manually installed on each worker.

Warning

Neither python setup.py develop nor pip install -e . can be used in this process as, instead of copying the source files, they create .egg-link files that Toil can’t auto-deploy. Similarly, python setup.py install doesn’t work either as it installs the project as a Python .egg which is also not currently supported by Toil (though it could be in the future).

Also note that using the --single-version-externally-managed flag with setup.py will prevent the installation of your package as an .egg. It will also disable the automatic installation of your project’s dependencies.

Auto Deployment with Sibling Modules

This scenario applies if the user script imports modules that are its siblings:

$ cd my_project
$ ls
userScript.py utilities.py
$ ./userScript.py --batchSystem=mesos …

Here userScript.py imports additional functionality from utilities.py. Toil detects that userScript.py has sibling modules and copies them to the workers, alongside the user script. Note that sibling modules will be auto-deployed regardless of whether they are actually imported by the user script–all .py files residing in the same directory as the user script will automatically be auto-deployed.

Sibling modules are a suitable method of organizing the source code of reasonably complicated workflows.

Auto-Deploying a Package Hierarchy

Recall that in Python, a package is a directory containing one or more .py files—one of which must be called __init__.py—and optionally other packages. For more involved workflows that contain a significant amount of code, this is the recommended way of organizing the source code. Because we use a package hierarchy, we can’t really refer to the user script as such, we call it the user module instead. It is merely one of the modules in the package hierarchy. We need to inform Toil that we want to use a package hierarchy by invoking Python’s -m option. That enables Toil to identify the entire set of modules belonging to the workflow and copy all of them to each worker. Note that while using the -m option is optional in the scenarios above, it is mandatory in this one.

The following shell session illustrates this:

$ cd my_project
$ tree
.
├── utils
│   ├── __init__.py
│   └── sort
│       ├── __init__.py
│       └── quick.py
└── workflow
    ├── __init__.py
    └── main.py

3 directories, 5 files
$ python -m workflow.main --batchSystem=mesos …

Here the user module main.py does not reside in the current directory, but is part of a package called util, in a subdirectory of the current directory. Additional functionality is in a separate module called util.sort.quick which corresponds to util/sort/quick.py. Because we invoke the user module via python -m workflow.main, Toil can determine the root directory of the hierarchy–my_project in this case–and copy all Python modules underneath it to each worker. The -m option is documented here

When -m is passed, Python adds the current working directory to sys.path, the list of root directories to be considered when resolving a module name like workflow.main. Without that added convenience we’d have to run the workflow as PYTHONPATH="$PWD" python -m workflow.main. This also means that Toil can detect the root directory of the user module’s package hierarchy even if it isn’t the current working directory. In other words we could do this:

$ cd my_project
$ export PYTHONPATH="$PWD"
$ cd /some/other/dir
$ python -m workflow.main --batchSystem=mesos …

Also note that the root directory itself must not be package, i.e. must not contain an __init__.py.

Relying on Shared Filesystems

Bare-metal clusters typically mount a shared file system like NFS on each node. If every node has that file system mounted at the same path, you can place your project on that shared filesystem and run your user script from there. Additionally, you can clone the Toil source tree into a directory on that shared file system and you won’t even need to install Toil on every worker. Be sure to add both your project directory and the Toil clone to PYTHONPATH. Toil replicates PYTHONPATH from the leader to every worker.

Using a shared filesystem

Toil currently only supports a tempdir set to a local, non-shared directory.

Toil Appliance

The term Toil Appliance refers to the Mesos Docker image that Toil uses to simulate the machines in the virtual mesos cluster. It’s easily deployed, only needs Docker, and allows for workflows to be run in single-machine mode and for clusters of VMs to be provisioned. To specify a different image, see the Toil Environment Variables section. For more information on the Toil Appliance, see the Running in AWS section.

Environment Variables

There are several environment variables that affect the way Toil runs.

TOIL_CHECK_ENV	A flag that determines whether Toil will try to refer back to a Python virtual environment in which it is installed when composing commands that may be run on other hosts. If set to True, if Toil is installed in the current virtual environment, it will use absolute paths to its own executables (and the virtual environment must thus be available on at the same path on all nodes). Otherwise, Toil internal commands such as _toil_worker will be resolved according to the PATH on the node where they are executed. This setting can be useful in a shared HPC environment, where users may have their own Toil installations in virtual environments.
TOIL_WORKDIR	An absolute path to a directory where Toil will write its temporary files. This directory must exist on each worker node and may be set to a different value on each worker. The --workDir command line option overrides this. When using the Toil docker container, such as on Kubernetes, this defaults to /var/lib/toil. When using Toil autoscaling with Mesos, this is somewhere inside the Mesos sandbox. In all other cases, the system’s standard temporary directory is used.
TOIL_WORKDIR_OVERRIDE	An absolute path to a directory where Toil will write its temporary files. This overrides TOIL_WORKDIR and the --workDir command line option.
TOIL_COORDINATION_DIR	An absolute path to a directory where Toil will write its lock files. This directory must exist on each worker node and may be set to a different value on each worker. The --coordinationDir command line option overrides this.
TOIL_COORDINATION_DIR_OVERRIDE	An absolute path to a directory where Toil will write its lock files. This overrides TOIL_COORDINATION_DIR and the --coordinationDir command line option.
TOIL_KUBERNETES_HOST_PATH	A path on Kubernetes hosts that will be mounted as the Toil work directory in the workers, to allow for shared caching. Will be created if it doesn’t already exist.
TOIL_KUBERNETES_OWNER	A name prefix for easy identification of Kubernetes jobs. If not set, Toil will use the current user name.
TOIL_KUBERNETES_SERVICE_ACCOUNT	A service account name to apply when creating Kubernetes pods.
TOIL_KUBERNETES_POD_TIMEOUT	Seconds to wait for a scheduled Kubernetes pod to start running.
KUBE_WATCH_ENABLED	A boolean variable that allows for users to utilize kubernetes watch stream feature instead of polling for running jobs. Default value is set to False.
TOIL_TES_ENDPOINT	URL to the TES server to run against when using the tes batch system.
TOIL_TES_USER	Username to use with HTTP Basic Authentication to log into the TES server.
TOIL_TES_PASSWORD	Password to use with HTTP Basic Authentication to log into the TES server.
TOIL_TES_BEARER_TOKEN	Token to use to authenticate to the TES server.
TOIL_APPLIANCE_SELF	The fully qualified reference for the Toil Appliance you wish to use, in the form REPO/IMAGE:TAG. quay.io/ucsc_cgl/toil:3.6.0 and cket/toil:3.5.0 are both examples of valid options. Note that since Docker defaults to Dockerhub repos, only quay.io repos need to specify their registry.
TOIL_DOCKER_REGISTRY	The URL of the registry of the Toil Appliance image you wish to use. Docker will use Dockerhub by default, but the quay.io registry is also very popular and easily specifiable by setting this option to quay.io.
TOIL_DOCKER_NAME	The name of the Toil Appliance image you wish to use. Generally this is simply toil but this option is provided to override this, since the image can be built with arbitrary names.
TOIL_AWS_SECRET_NAME	For the Kubernetes batch system, the name of a Kubernetes secret which contains a credentials file granting access to AWS resources. Will be mounted as ~/.aws inside Kubernetes-managed Toil containers. Enables the AWSJobStore to be used with the Kubernetes batch system, if the credentials allow access to S3 and SimpleDB.
TOIL_AWS_ZONE	Zone to use when using AWS. Also determines region. Overrides TOIL_AWS_REGION.
TOIL_AWS_REGION	Region to use when using AWS.
TOIL_AWS_AMI	ID of the AMI to use in node provisioning. If in doubt, don’t set this variable.
TOIL_AWS_NODE_DEBUG	Determines whether to preserve nodes that have failed health checks. If set to True, nodes that fail EC2 health checks won’t immediately be terminated so they can be examined and the cause of failure determined. If any EC2 nodes are left behind in this manner, the security group will also be left behind by necessity as it cannot be deleted until all associated nodes have been terminated.
TOIL_AWS_BATCH_QUEUE	Name or ARN of an AWS Batch Queue to use with the AWS Batch batch system.
TOIL_AWS_BATCH_JOB_ROLE_ARN	ARN of an IAM role to run AWS Batch jobs as with the AWS Batch batch system. If the jobs are not run with an IAM role or on machines that have access to S3 and SimpleDB, the AWS job store will not be usable.
TOIL_GOOGLE_PROJECTID	The Google project ID to use when generating Google job store names for tests or CWL workflows.
TOIL_SLURM_ARGS	Arguments for sbatch for the slurm batch system. Do not pass CPU or memory specifications here. Instead, define resource requirements for the job. There is no default value for this variable.
TOIL_SLURM_PE	Name of the slurm partition to use for parallel jobs. There is no default value for this variable.
TOIL_GRIDENGINE_ARGS	Arguments for qsub for the gridengine batch system. Do not pass CPU or memory specifications here. Instead, define resource requirements for the job. There is no default value for this variable.
TOIL_GRIDENGINE_PE	Parallel environment arguments for qsub and for the gridengine batch system. There is no default value for this variable.
TOIL_TORQUE_ARGS	Arguments for qsub for the Torque batch system. Do not pass CPU or memory specifications here. Instead, define extra parameters for the job such as queue. Example: -q medium Use TOIL_TORQUE_REQS to pass extra values for the -l resource requirements parameter. There is no default value for this variable.
TOIL_TORQUE_REQS	Arguments for the resource requirements for Torque batch system. Do not pass CPU or memory specifications here. Instead, define extra resource requirements as a string that goes after the -l argument to qsub. Example: walltime=2:00:00,file=50gb There is no default value for this variable.
TOIL_LSF_ARGS	Additional arguments for the LSF’s bsub command. Instead, define extra parameters for the job such as queue. Example: -q medium. There is no default value for this variable.
TOIL_HTCONDOR_PARAMS	Additional parameters to include in the HTCondor submit file passed to condor_submit. Do not pass CPU or memory specifications here. Instead define extra parameters which may be required by HTCondor. This variable is parsed as a semicolon-separated string of parameter = value pairs. Example: requirements = TARGET.has_sse4_2 == true; accounting_group = test. There is no default value for this variable.
TOIL_CUSTOM_DOCKER_INIT_COMMAND	Any custom bash command to run in the Toil docker container prior to running the Toil services. Can be used for any custom initialization in the worker and/or primary nodes such as private docker docker authentication. Example for AWS ECR: pip install awscli && eval $(aws ecr get-login --no-include-email --region us-east-1).
TOIL_CUSTOM_INIT_COMMAND	Any custom bash command to run prior to starting the Toil appliance. Can be used for any custom initialization in the worker and/or primary nodes such as private docker authentication for the Toil appliance itself (i.e. from TOIL_APPLIANCE_SELF).
TOIL_S3_HOST	the IP address or hostname to use for connecting to S3. Example: TOIL_S3_HOST=127.0.0.1
TOIL_S3_PORT	a port number to use for connecting to S3. Example: TOIL_S3_PORT=9001
TOIL_S3_USE_SSL	enable or disable the usage of SSL for connecting to S3 (True by default). Example: TOIL_S3_USE_SSL=False
TOIL_WES_BROKER_URL	An optional broker URL to use to communicate between the WES server and Celery task queue. If unset, amqp://guest:guest@localhost:5672// is used.
TOIL_WES_JOB_STORE_TYPE	Type of job store to use by default for workflows run via the WES server. Can be file, aws, or google.
TOIL_OWNER_TAG	This will tag cloud resources with a tag reading: “Owner: $TOIL_OWNER_TAG”. This is used internally at UCSC to stop a bot we have that terminates untagged resources.
TOIL_AWS_PROFILE	The name of an AWS profile to run TOIL with.
TOIL_AWS_TAGS	This will tag cloud resources with any arbitrary tags given in a JSON format. These are overwritten in favor of CLI options when using launch cluster. For information on valid AWS tags, see AWS Tags.
SINGULARITY_DOCKER_HUB_MIRROR	An http or https URL for the Singularity wrapper in the Toil Docker container to use as a mirror for Docker Hub.
OMP_NUM_THREADS	The number of cores set for OpenMP applications in the workers. If not set, Toil will use the number of job threads.
GUNICORN_CMD_ARGS	Specify additional Gunicorn configurations for the Toil WES server. See Gunicorn settings.

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