Provided by: toil_3.24.0-1_all 
NAME
toil - 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
QUICKSTART EXAMPLES
Running a basic workflow
A Toil workflow can be run with just two steps:
1. 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 "Hello, world!, here's a message: %s" % 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)
2. Specify the name of the job store and run the workflow:
python helloWorld.py file:my-job-store
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 batchsysteminterface). 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. 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!
2. To run the workflow simply enter
$ toil-cwl-runner example.cwl example-job.yaml
Your output will be in output.txt:
$ 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
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 for more
details and examples). Here we go over running a basic WDL helloworld workflow.
1. 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!"
}
2. To run the workflow simply enter
$ toil-wdl-runner wdl-helloworld.wdl wdl-helloworld.json
Your output will be in wdl-helloworld-output.txt:
$ 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:
$ 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:
$ 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("the output file {} already exists. Delete it to run the sort example again or use --overwriteOutput=True".format(sortedFileName))
exit()
fileName = options.fileToSort
if options.fileToSort is None:
# make the file ourselves
fileName = 'fileToSort.txt'
if os.path.exists(fileName):
print("Sorting existing file: {}".format(fileName))
else:
print('No sort file specified. Generating one automatically called: {}.'.format(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 managingFiles.
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 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
commandRef.
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:
$ 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 workflowOptions.
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:
$ 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
$ python sort.py file:jobStore --restart --overwriteOutput=True
We can also change the number of times Toil will attempt to retry a failed job:
$ 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
$ 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 cli_status 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-ref and set up AWS (see
prepareAWS), 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 runningAWS.
Also! Remember to use the destroyCluster command when finished to destroy the cluster! Otherwise things
may not be cleaned up properly.
1. Launch a cluster in AWS using the launchCluster command:
$ 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 rsyncCluster command:
$ 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 sshCluster command:
$ 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 destroyCluster command to destroy the cluster:
$ 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-ref and set up AWS (see
prepareAWS), the user can run a CWL workflow with Toil on AWS.
Also! Remember to use the destroyCluster command when finished to destroy the cluster! Otherwise things
may not be cleaned up properly.
1. First launch a node in AWS using the launchCluster command:
$ 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 rsyncCluster command:
$ toil rsync-cluster --zone us-west-2a <cluster-name> example.cwl :/tmp
$ toil rsync-cluster --zone us-west-2a <cluster-name> example-job.yaml :/tmp
3. SSH into the cluster's leader node using the sshCluster utility:
$ 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:
$ 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:
$ 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 runningGCE for more information about the --boto
option.
Also! Remember to use the destroyCluster 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 launchCluster command:
$ 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.
$ export TOIL_AWS_ZONE=us-west-2c
3. Create appropriate directory for uploading files:
$ 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:
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> pestis-short-aws-seqFile.txt :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000169655.1_ASM16965v1_genomic.fna :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000006645.1_ASM664v1_genomic.fna :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000182485.1_ASM18248v1_genomic.fna :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> GCF_000013805.1_ASM1380v1_genomic.fna :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> setup_leaderNode.sh :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> blockTrim1.xml :/root/cact_ex
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> blockTrim3.xml :/root/cact_ex
5. Log in to the leader node:
$ 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 --disableCaching --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:
$ toil rsync-cluster --provisioner <aws, gce> <cluster-name> :/root/cact_ex/pestis_output3.hal <path-of-folder-on-local-machine>
10. Destroy the cluster:
$ 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
• jobStoreInterface: 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).
• batchSystemInterface: Specifies either a local single-machine or a currently supported HPC
environment (lsf, parasol, mesos, slurm, torque, htcondor, or gridengine). 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 quickstart.
Cloud Job Stores
Toil currently supports the following cloud storage systems as job stores:
• awsJobStore: 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.
• googleJobStore: 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 gridengine). 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 batchSystemInterface 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 clusterRef 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 runningAWS or runningGCE.
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
--workDir WORKDIR
Absolute path to directory where temporary files generated during the Toil run should be
placed. Temp files and folders, as well as standard output and error from batch system jobs
(unless --noStdOutErr), will be placed in a directory 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.
--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',
'never', '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
Sets the maximum log file size in bytes (--rotatingLogging must be active).
Batch System Options
--batchSystem BATCHSYSTEM
The type of batch system to run the job(s) with, currently can be one of LSF, Mesos, Slurm,
Torque, HTCondor, singleMachine, parasol, gridEngine'. (default: singleMachine)
--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)
--scale SCALE
A scaling factor to change the value of all submitted tasks' submitted cores. Used in
singleMachine batch system. (default: 1)
--linkImports
When using Toil's importFile function for staging, input files are copied to the job store.
Specifying this option saves space by sym-linking imported files. As long as caching is
enabled Toil will protect the file automatically by changing the permissions to read-only.
--mesosMaster MESOSMASTERADDRESS
The host and port of the Mesos master separated by a colon. (default: 169.233.147.202:5050)
Autoscaling Options
--provisioner CLOUDPROVIDER
The provisioner for cluster auto-scaling. The currently supported choices are 'aws' or 'gce'.
The default is None.
--nodeTypes NODETYPES
List of node types separated by commas. The syntax for each node type depends on the
provisioner used. For the cgcloud and AWS provisioners this is the name of an EC2 instance
type, optionally followed by a colon and the price in dollars to bid for a spot instance of
that type, for example 'c3.8xlarge:0.42'. If no spot bid is specified, nodes of this type will
be non-preemptable. It is acceptable to specify an instance as both preemptable and
non-preemptable, including it twice in the list. In that case, preemptable nodes of that type
will be preferred when creating new nodes once the maximum number of preemptable-nodes have
been reached.
--nodeOptions NODEOPTIONS
Options for provisioning the nodes. The syntax depends on the provisioner used. Neither the
CGCloud nor the AWS provisioner support any node options.
--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
--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.
--metrics
Enable the prometheus/grafana dashboard for monitoring CPU/RAM usage, queue size, and issued
jobs.
--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
--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.
--retryCount RETRYCOUNT
Number of times to retry a failing job before giving up and labeling job failed. default=1
--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.
--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
The minimum number of seconds to observe the cluster stuck running only the same service jobs
before throwing a deadlock exception. default=60
--statePollingWait STATEPOLLINGWAIT
Time, in seconds, to wait before doing a scheduler query for job state. Return cached results
if within the waiting period.
Miscellaneous Options
--disableCaching
Disables caching in the file store. This flag must be set to use a batch system that does not
support caching such as Grid Engine, Parasol, LSF, or Slurm.
--disableChaining
Disables chaining of jobs (chaining uses one job's resource allocation for its successor job if
possible).
--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' 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.
--realTimeLogging
Enable real-time logging from workers to masters.
--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
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. 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)
--debugWorker
Experimental no forking mode for local debugging. Specifically, workers are not forked and
stderr/stdout are not redirected to the log. (default=False)
Restart Option
In the event of failure, Toil can resume the pipeline by adding the argument --restart and rerunning the
python script. Toil 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 cli_status 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 batchSystemOverview.
See here for instructions for runningAWS.
See here for instructions for runningGCE.
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 jobStoreOverview.
For instructions configuring a particular job store see:
• awsJobStore
• googleJobStore
CLOUD PLATFORMS
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 clusterRef 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. Only needed once, but AWS requires that users "subscribe" to use the Container Linux by CoreOS AMI.
You will encounter errors if this is not done.
3. Next, generate a key pair for AWS with the command (do NOT generate your key pair with the Amazon
browser):
$ ssh-keygen -t rsa
4. This should prompt you to save your key. Please save it in
~/.ssh/id_rsa
5. Now move this to where your OS can see it as an authorized key:
$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
$ eval `ssh-agent -s`
$ ssh-add
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:
Adding an Amazon Key Pair.UNINDENT
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]: "
14. Toil also relies on boto, and you'll need to create a boto file containing your credentials as
well. To do this, run:
$ nano ~/.boto
15. Paste in the following (with your actual "AWS Access Key ID" and "AWS Secret Access Key"):
[Credentials]
aws_access_key_id = ****************Q65Q
aws_secret_access_key = ****************G0ys
16. If not done already, install toil (example uses version 3.12.0, but we recommend the latest
release):
$ virtualenv venv
$ source venv/bin/activate
$ pip install toil[all]==3.12.0
17. 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
3.12.0 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:3.12.0 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.
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 extras.
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 appliance_dev 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
envars 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 appliance_dev.
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.
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 clusterRef. 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 destroyCluster utility.
Uploading Workflows
Now that our cluster is launched, we use the rsyncCluster 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 autoDeploying 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 launchCluster 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 <cluster-name> sort.py :/root
4. Login to the leader node:
(venv) $ toil ssh-cluster <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 workflowOptions 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
sshCluster 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.
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. 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 (extras) 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. [image]
Near the top of the screen click on 'SSH Keys', then edit, add item, and paste the key. Then save:
[image]
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 (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 launchCluster 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 installProvisioner. The
cluster utilities are used for runningAWS 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 runningGCE and runningAWS.
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.
runningGCE 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 subprocess
import os
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(discoverFiles, self).__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.
--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 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.
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 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 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 optionsRef. 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 v1.0.1 specification.
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 cwlquickstart.
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 \
--disableCaching \
--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 fornat containers specified should be run using the
Singularity container engine instead of the Docker container engine.
--jobStore: Path to a folder that already exists, 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.
--disableCaching: Currently required for batch systems (LSF, slurm, gridengine, htcondor, torque)
--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.
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 awscwl.
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:
from toil.job import Job
from toil.common import Toil
import subprocess
import os
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(["cwltoil", 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)
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 '-\-output_directory': Specifies the output folder, and defaults to the current working directory
if not specified by the user.
'-\-gen_parse_files': 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.
'-\-dont_delete_compiled': 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:
from toil.job import Job
from toil.common import Toil
import subprocess
import os
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://')
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 "Hello, world!, here's a message: %s" % 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 "Hello, world!, here's a message: %s" % 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 logs a
message using toil.job.Job.log(), which will be registered in the log output of the leader process of the
workflow.
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. This
uses the toil.job.Job.Runner class, which is used to start and resume Toil workflows. 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__":
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.
Alternatively, the more powerful toil.common.Toil class can be used to run and resume 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.job import Job
from toil.common import Toil
class HelloWorld(Job):
def __init__(self, message):
Job.__init__(self, memory="2G", cores=2, disk="3G")
self.message = message
def run(self, fileStore):
self.log("Hello, world!, I have a message: {}".format(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!")
toil.start(job)
else:
toil.restart()
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 "Hello, world!, here's a message: %s" % 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("Hello world, I have a message: {}".format(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("Hello world, I have a message: {}".format(message))
if __name__=="__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
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("Hello world, I have a message: {}".format(message))
if __name__=="__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
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("Hello world, I have a message: {}".format(message))
if __name__=="__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
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("Binary string: {}".format(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:
from toil.common import Toil
from toil.job import Job, PromisedRequirement
import os
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.importFile(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:
from toil.common import Toil
from toil.job import Job
import os
import sys
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):
if sys.version_info >= (3, 0):
# if python 3
fH.write(b"Out brief candle")
else:
# if python 2
fH.write("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. 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 self.fileStore.readGlobalFileStream(self.inputFileID) as fi:
with self.fileStore.writeGlobalFileStream() 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 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:
from toil.common import Toil
from toil.job import Job
from toil.lib.docker import apiDockerCall
import os
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)
A context manager that represents a Toil workflow, specifically the batch system, job store, and
its configuration.
__init__(options)
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 (argparse.Namespace) -- command line options specified by the user
config = None
Type toil.common.Config
start(rootJob)
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 (toil.job.Job) -- The root job of the workflow
Returns
The root job's return value
restart()
Restarts a workflow that has been interrupted.
Returns
The root job's return value
classmethod getJobStore(locator)
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
Return type
toil.jobStores.abstractJobStore.AbstractJobStore
static createBatchSystem(config)
Creates an instance of the batch system specified in the given config.
Parameters
config (toil.common.Config) -- the current configuration
Return type
batchSystems.abstractBatchSystem.AbstractBatchSystem
Returns
an instance of a concrete subclass of AbstractBatchSystem
importFile(srcUrl, sharedFileName=None)
Imports the file at the given URL into job store.
See toil.jobStores.abstractJobStore.AbstractJobStore.importFile() for a full description
exportFile(jobStoreFileID, dstUrl)
Exports file to destination pointed at by the destination URL.
See toil.jobStores.abstractJobStore.AbstractJobStore.exportFile() for a full description
static getWorkflowDir(workflowID, configWorkDir=None)
Returns a path to the directory where worker directories and the cache will be located for
this workflow.
Parameters
• workflowID (str) -- Unique identifier for the workflow
• configWorkDir (str) -- Value passed to the program using the --workDir flag
Returns
Path to the workflow directory
Return type
str
writePIDFile()
Write a the pid of this process to a file in the jobstore.
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.
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
Represents the physical storage for the jobs and files in a Toil workflow.
__init__()
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.
initialize(config)
Create the physical storage for this job store, allocate a workflow ID and persist the
given Toil configuration to the store.
Parameters
config (toil.common.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
writeConfig()
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()
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 The Toil configuration associated with this job store.
Return type
toil.common.Config
setRootJob(rootJobStoreID)
Set the root job of the workflow backed by this job store
Parameters
rootJobStoreID (str) -- The ID of the job to set as root
loadRootJob()
Loads 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.
Return type
toil.jobGraph.JobGraph
createRootJob(*args, **kwargs)
Create a new job and set it as the root job in this job store
Return type
toil.jobGraph.JobGraph
getRootJobReturnValue()
Parse the return value from the root job.
Raises an exception if the root job hasn't fulfilled its promise yet.
importFile(srcUrl, sharedFileName=None, hardlink=False)
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.
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
• srcUrl (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.
• sharedFileName (str) -- Optional name to assign to the imported file within the
job store
Returns
The jobStoreFileId of the imported file or None if sharedFileName was given
Return type
toil.fileStores.FileID or None
exportFile(jobStoreFileID, dstUrl)
Exports file to destination pointed at by the destination URL.
Refer to AbstractJobStore.importFile() 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
• jobStoreFileID (str) -- The id of the file in the job store that should be
exported.
• dstUrl (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 getSize(url)
returns the size in bytes of the file at the given URL
Parameters
url (urlparse.ParseResult) -- 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()
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.
getEnv()
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=None)
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 (dict[str,toil.jobGraph.JobGraph]) -- if a value it must be a dict from job
ID keys to JobGraph 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.
batch()
All calls to create() with this context manager active will be performed in a batch after
the context manager is released.
Return type
None
create(jobNode)
Creates a job graph from the given job node & writes it to the job store.
Return type
toil.jobGraph.JobGraph
exists(jobStoreID)
Indicates whether the job with the specified jobStoreID exists in the job store
Return type
bool
getPublicUrl(fileName)
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
fileName (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
getSharedPublicUrl(sharedFileName)
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
sharedFileName (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(jobStoreID)
Loads the job referenced by the given ID and returns it.
Parameters
jobStoreID (str) -- the ID of the job to load
Raises NoSuchJobException -- if there is no job with the given ID
Return type
toil.jobGraph.JobGraph
update(job)
Persists the job in this store atomically.
Parameters
job (toil.jobGraph.JobGraph) -- the job to write to this job store
delete(jobStoreID)
Removes from store atomically, can not then subsequently call load(), write(), update(),
etc. with the job.
This operation is idempotent, i.e. deleting a job twice or deleting a non-existent job will
succeed silently.
Parameters
jobStoreID (str) -- the ID of the job to delete from this job store
jobs() Best effort attempt to return iterator on 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.jobGraph.JobGraph]
writeFile(localFilePath, jobStoreID=None, cleanup=False)
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
• localFilePath (str) -- the path to the local file that will be uploaded to the job
store.
• jobStoreID (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
writeFileStream(jobStoreID=None, cleanup=False)
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
• jobStoreID (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 that references the newly created file and can be used to read the file in the
future.
Return type
str
getEmptyFileStoreID(jobStoreID=None, cleanup=False)
Creates an empty file in the job store and returns its ID. Call to
fileExists(getEmptyFileStoreID(jobStoreID)) will return True.
Parameters
• jobStoreID (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.
Returns
a jobStoreFileID that references the newly created file and can be used to reference
the file in the future.
Return type
str
readFile(jobStoreFileID, localFilePath, symlink=False)
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!
Parameters
• jobStoreFileID (str) -- ID of the file to be copied
• localFilePath (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.
readFileStream(jobStoreFileID)
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
jobStoreFileID (str) -- ID of the file to get a readable file handle for
deleteFile(jobStoreFileID)
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
jobStoreFileID (str) -- ID of the file to delete
fileExists(jobStoreFileID)
Determine whether a file exists in this job store.
Parameters
jobStoreFileID (str) -- an ID referencing the file to be checked
Return type
bool
getFileSize(jobStoreFileID)
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
jobStoreFileID (str) -- an ID referencing the file to be checked
Return type
int
updateFile(jobStoreFileID, localFilePath)
Replaces the existing version of a file in the job store. Throws an exception if the file
does not exist.
Parameters
• jobStoreFileID (str) -- the ID of the file in the job store to be updated
• localFilePath (str) -- 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
updateFileStream(jobStoreFileID)
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
jobStoreFileID (str) -- the ID of the file in the job store to be updated
Raises
• ConcurrentFileModificationException -- if the file was modified concurrently
during an invocation of this method
• NoSuchFileException -- if the specified file does not exist
writeSharedFileStream(sharedFileName, isProtected=None)
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
• sharedFileName (str) -- A file name matching AbstractJobStore.fileNameRegex,
unique within this job store
• isProtected (bool) -- True if the file must be encrypted, None if it may be
encrypted or False if it must be stored in the clear.
Raises ConcurrentFileModificationException -- if the file was modified concurrently during
an invocation of this method
readSharedFileStream(sharedFileName)
Returns a context manager yielding a readable file handle to the global file referenced by
the given name.
Parameters
sharedFileName (str) -- A file name matching AbstractJobStore.fileNameRegex, unique
within this job store
writeStatsAndLogging(statsAndLoggingString)
Adds the given statistics/logging string to the store of statistics info.
Parameters
statsAndLoggingString (str) -- the string to be written to the stats file
Raises ConcurrentFileModificationException -- if the file was modified concurrently during
an invocation of this method
readStatsAndLogging(callback, readAll=False)
Reads stats/logging strings accumulated by the writeStatsAndLogging() method. For each
stats/logging string this method calls the given callback function with an open, readable
file handle from which the stats string can be read. Returns the number of stats/logging
strings processed. Each stats/logging string is only processed once unless the readAll
parameter is set, in which case the given callback will be invoked for all existing
stats/logging strings, including the ones from a previous invocation of this method.
Parameters
• callback (Callable) -- a function to be applied to each of the stats file handles
found
• readAll (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
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, 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 (toil.fileStores.abstractFileStore.AbstractFileStore) -- 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
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 (toil.fileStores.abstractFileStore.AbstractFileStore) -- 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)
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()
A' = A.encapsulate()
A'.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 encapsulatd 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)
Parameters
job (toil.job.Job) -- the job to encapsulate.
addChild(childJob)
Adds 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.
Parameters
childJob (toil.job.Job) --
Returns
childJob
Return type
toil.job.Job
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 (toil.job.Job.Service) -- Service to add.
• parentService (toil.job.Job.Service) -- 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.
Return type
toil.job.Promise
addFollowOn(followOnJob)
Adds a follow-on job, follow-on jobs will be run after the child jobs and their
successors have been run.
Parameters
followOnJob (toil.job.Job) --
Returns
followOnJob
Return type
toil.job.Job
rv(*path)
Creates a promise (toil.job.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)
Ensure that a promise by this job (the promissor) can register with the promissor when
another job referring to the promise (the promissee) is being serialized. The promissee
holds the reference to the promise (usually as part of the 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.
Returns
Promise
The class used to reference return values of jobs/services not yet run/started.
class toil.job.Promise(job, path)
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, path)
Parameters
• job (Job) -- the job whose return value this promise references
• path -- see Job.rv()
class toil.job.PromisedRequirement(valueOrCallable, *args)
__init__(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()))
Parameters
• valueOrCallable -- A single Promise instance or a function that takes *args as
input parameters.
• *args (int or Promise) -- variable length argument list
getValue()
Returns PromisedRequirement value
static convertPromises(kwargs)
Returns True if reserved resource keyword is a Promise or PromisedRequirement instance.
Converts Promise instance to PromisedRequirement.
Parameters
kwargs -- function keyword arguments
Returns
bool
JOB METHODS API
Jobs are the units of work in Toil which are composed into workflows.
class toil.job.Job(memory=None, cores=None, disk=None, preemptable=None, unitName=None, checkpoint=False,
displayName=None)
Class represents a unit of work in toil.
__init__(memory=None, cores=None, disk=None, preemptable=None, unitName=None, checkpoint=False,
displayName=None)
This method must be called by any overriding constructor.
Parameters
• memory (int or string convertible by toil.lib.humanize.human2bytes to an int) --
the maximum number of bytes of memory the job will require to run.
• cores (int or string convertible by toil.lib.humanize.human2bytes to an int) --
the number of CPU cores required.
• disk (int or string convertible by toil.lib.humanize.human2bytes to an int) -- the
amount of local disk space required by the job, expressed in bytes.
• preemptable (bool) -- if the job can be run on a preemptable node.
• checkpoint -- 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().
run(fileStore)
Override this function to perform work and dynamically create successor jobs.
Parameters
fileStore (toil.fileStores.abstractFileStore.AbstractFileStore) -- 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)
Adds 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.
Parameters
childJob (toil.job.Job) --
Returns
childJob
Return type
toil.job.Job
hasChild(childJob)
Check if childJob is already a child of this job.
Parameters
childJob (toil.job.Job) --
Returns
True if childJob is a child of the job, else False.
Return type
bool
addFollowOn(followOnJob)
Adds a follow-on job, follow-on jobs will be run after the child jobs and their
successors have been run.
Parameters
followOnJob (toil.job.Job) --
Returns
followOnJob
Return type
toil.job.Job
hasFollowOn(followOnJob)
Check if given job is already a follow-on of this job.
Parameters
followOnJob (toil.job.Job) --
Returns
True if the followOnJob is a follow-on of this job, else False.
Return type
bool
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 (toil.job.Job.Service) -- Service to add.
• parentService (toil.job.Job.Service) -- 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.
Return type
toil.job.Promise
addChildFn(fn, *args, **kwargs)
Adds 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.
Return type
toil.job.FunctionWrappingJob
addFollowOnFn(fn, *args, **kwargs)
Adds 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.
Return type
toil.job.FunctionWrappingJob
addChildJobFn(fn, *args, **kwargs)
Adds 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.
Return type
toil.job.JobFunctionWrappingJob
addFollowOnJobFn(fn, *args, **kwargs)
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.
Return type
toil.job.JobFunctionWrappingJob
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 :rtype: str
log(text, level=20)
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()
Encapsulates the job, see toil.job.EncapsulatedJob. Convenience function for constructor
of toil.job.EncapsulatedJob.
Returns
an encapsulated version of this job.
Return type
toil.job.EncapsulatedJob
rv(*path)
Creates a promise (toil.job.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)
Ensure that a promise by this job (the promissor) can register with the promissor when
another job referring to the promise (the promissee) is being serialized. The promissee
holds the reference to the promise (usually as part of the 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.
Returns
checkJobGraphForDeadlocks()
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()
Returns
The roots of the connected component of jobs that contains this job. A root
is a job with no predecessors.
:rtype : set of toil.job.Job instances
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.
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!
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.
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.
Return type
list
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()
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)
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)
Deprecated by toil.common.Toil.start. Runs the toil workflow using the given options (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, jobGraph, localTempDir,
waitForPreviousCommit)
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, jobGraph, localTempDir, waitForPreviousCommit)
Create a new file store object.
Parameters
• jobStore (toil.jobStores.abstractJobStore.AbstractJobStore) -- the job store in
use for the current Toil run.
• jobGraph (toil.jobGraph.JobGraph) -- the job graph object for the currently
running job.
• localTempDir (str) -- the per-worker local temporary directory, under which
per-job directories will be created.
• 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 shutdownFileStore(workflowDir, workflowID)
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
• workflowDir (str) -- The path to the cache directory
• workflowID (str) -- The workflow ID for this invocation of the workflow
open(job)
The context manager used to conduct tasks prior-to, and after a job has been run. File
operations are only permitted inside the context manager.
Parameters
job (toil.job.Job) -- The job instance of the toil job to run.
getLocalTempDir()
Get a new local temporary directory in which to write files that 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.
Return type
str
getLocalTempFile()
Get a new local temporary file that will persist for the duration of the job.
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.
Return type
str
getLocalTempFileName()
Get a valid name for a new local file. Don't actually create a file at the path.
Returns
Path to valid file
Return type
str
writeGlobalFile(localFileName, cleanup=False)
Takes a file (as a path) and uploads it to the job store.
Parameters
• localFileName (string) -- The path to the local file to upload.
• cleanup (bool) -- 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.
Return type
toil.fileStores.FileID
writeGlobalFileStream(cleanup=False)
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
cleanup (bool) -- is as in
toil.fileStores.abstractFileStore.AbstractFileStore.writeGlobalFile().
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.
readGlobalFile(fileStoreID, userPath=None, cache=True, mutable=False, symlink=False)
Makes 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.
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.
Parameters
• or str fileStoreID (toil.fileStores.FileID) -- job store id for the file
• userPath (string) -- a path to the name of file to which the global file will be
copied or hard-linked (see below).
• cache (bool) -- Described in toil.fileStores.CachingFileStore.readGlobalFile()
• mutable (bool) -- Described in toil.fileStores.CachingFileStore.readGlobalFile()
Returns
An absolute path to a local, temporary copy of the file keyed by fileStoreID.
Return type
str
readGlobalFileStream(fileStoreID)
Similar to readGlobalFile, but allows a stream to be read from the job store. The yielded
file handle does not need to and should not be closed explicitly.
Returns
a context manager yielding a file handle which can be read from.
getGlobalFileSize(fileStoreID)
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
or str fileStoreID (toil.fileStores.FileID) -- File ID for the file
Returns
File's size in bytes, as stored in the job store
Return type
int
deleteLocalFile(fileStoreID)
Deletes local copies of files associated with the provided job store ID.
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
or str fileStoreID (toil.fileStores.FileID) -- File Store ID of the file to be
deleted.
deleteGlobalFile(fileStoreID)
Deletes local files with the provided job store ID 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
or str fileStoreID (toil.fileStores.FileID) -- the File Store ID of the file to be
deleted.
logToMaster(text, level=20)
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 (int) -- The logging level.
startCommit(jobState=False)
Update the status of the job on the disk.
May start an asynchronous process. Call waitForCommit() to wait on that process.
Parameters
jobState (bool) -- If True, commit the state of the FileStore's job, and file
deletes. Otherwise, commit only file creates/updates.
waitForCommit()
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
Return type
bool
classmethod shutdown(dir_)
Shutdown the filestore on this node.
This is intended to be called on batch system shutdown.
Parameters
dir -- The implementation-specific directory containing the required 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, size)
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, size)
Initialize self. See help(type(self)) for accurate signature.
pack() Pack the FileID into a string so it can be passed through external code.
classmethod unpack(packedFileStoreID)
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:
export TOIL_SLURM_ARGS="-t 1:00:00 -q fatq"
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 (as far as Python currently allows) base class to represent the interface the batch
system must provide to Toil.
classmethod supportsAutoDeployment()
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
Return type
bool
classmethod supportsWorkerCleanup()
Indicates whether this batch system invokes 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.
Return type
bool
setUserScript(userScript)
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 (toil.resource.Resource) -- the resource object representing the user
script or module and the modules it depends on.
issueBatchJob(jobNode)
Issues a job with the specified command to the batch system and returns a unique jobID.
:param jobNode a toil.job.JobNode
Returns
a unique jobID that can be used to reference the newly issued job
Return type
int
killBatchJobs(jobIDs)
Kills the given job IDs. After returning, the killed jobs will not appear in the results of
getRunningBatchJobIDs.
Parameters
jobIDs (list[int]) -- list of IDs of jobs to kill
getIssuedBatchJobIDs()
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.
Return type
list[str]
getRunningBatchJobIDs()
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
Return type
dict[int,float]
getUpdatedBatchJob(maxWait)
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.
Parameters
maxWait (float) -- the number of seconds to block, waiting for a result
Return type
tuple(str, int, float) or None
Returns
If a result is available, returns a tuple (jobID, exitValue, wallTime). 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. Returns None for jobs that were killed.
shutdown()
Called at the completion of a toil invocation. Should cleanly terminate all worker
threads.
setEnv(name, value=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 setOptions(setOption)
Process command line or configuration options relevant to this batch system. The
Parameters
setOption -- A function with signature setOption(varName, parsingFn=None,
checkFn=None, default=None) used to update run configuration
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, preemptable=None, unitName=None)
Abstract class used to define the interface to a service.
__init__(memory=None, cores=None, disk=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 job that is being run. 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 job that is being run. 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)
General job exception.
__init__(message)
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)
Indicates that the file was attempted to be modified by multiple processes at once.
__init__(jobStoreFileID)
Parameters
jobStoreFileID (str) -- the ID of the file that was modified by multiple workers or
processes concurrently
exception toil.jobStores.abstractJobStore.JobStoreExistsException(locator)
Indicates that the specified job store already exists.
__init__(locator)
Initialize self. See help(type(self)) for accurate signature.
exception toil.jobStores.abstractJobStore.NoSuchFileException(jobStoreFileID, customName=None, *extra)
Indicates that the specified file does not exist.
__init__(jobStoreFileID, customName=None, *extra)
Parameters
• jobStoreFileID (str) -- the ID of the file that was mistakenly assumed to exist
• customName (str) -- 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)
Indicates that the specified job does not exist.
__init__(jobStoreID)
Parameters
jobStoreID (str) -- the jobStoreID that was mistakenly assumed to exist
exception toil.jobStores.abstractJobStore.NoSuchJobStoreException(locator)
Indicates that the specified job store does not exist.
__init__(locator)
Initialize self. See help(type(self)) for accurate signature.
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
buildFromSource.
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 prepareAWS), │
│ │ which is required to run the AWS │
│ │ tests. │
├──────────────────────┼───────────────────────────────────────┤
│TOIL_GOOGLE_PROJECTID │ A Google Cloud account projectID (see │
│ │ runningGCE), 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 buildFromSource.
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 envars.
7. Now you can launch your cluster! For more information see 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.
• Modified pull requests must be re-reviewed before merging. Note that Github does not enforce this!
• Pull requests will not be merged unless Travis and Gitlab 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.
TOIL ARCHITECTURE
The following diagram layouts out the software architecture of Toil.
[image: Toil's architecture is composed of the leader, the job store, the worker processes, the batch
system, the node provisioner, and the stats and logging monitor.] [image] Figure 1: The basic
components of Toil's architecture..UNINDENT
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.
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.
[image: Graph outlining the efficiency gain from caching.] [image] 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..UNINDENT
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 prepareAWS 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 sshCluster 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 envars section. For more information on the Toil Appliance, see the runningAWS 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. On Mesos nodes, │
│ │ TOIL_WORKDIR generally defaults to │
│ │ the Mesos sandbox, except on │
│ │ CGCloud-provisioned nodes where it │
│ │ defaults to /var/lib/mesos. In all │
│ │ other cases, the system's standard │
│ │ temporary directory is used. │
├────────────────────────────────┼───────────────────────────────────────┤
│TOIL_KUBERNETES_OWNER │ A name prefix for easy identification │
│ │ of Kubernetes jobs. If not set, Toil │
│ │ will use the current user name. │
└────────────────────────────────┴───────────────────────────────────────┘
│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 │ The EC2 zone to provision nodes in if │
│ │ using Toil's provisioner. │
├────────────────────────────────┼───────────────────────────────────────┤
│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_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_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). │
└────────────────────────────────┴───────────────────────────────────────┘
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AUTHOR
UCSC Computational Genomics Lab
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