Provided by: toil_5.6.0-2_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 f"Hello, world!, here's a message: {message}"
if __name__ == "__main__":
parser = Job.Runner.getDefaultArgumentParser()
options = parser.parse_args()
options.clean = "always"
with Toil(options) as toil:
output = toil.start(Job.wrapFn(helloWorld, "You did it!"))
print(output)
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(f'Output file {sortedFileName} already exists. '
f'Delete it to run the sort example again or use --overwriteOutput=True')
exit()
fileName = options.fileToSort
if options.fileToSort is None:
# make the file ourselves
fileName = 'fileToSort.txt'
if os.path.exists(fileName):
print(f'Sorting existing file: {fileName}')
else:
print(f'No sort file specified. Generating one automatically called: {fileName}.')
makeFileToSort(fileName=fileName, lines=options.numLines, lineLen=options.lineLength)
else:
if not os.path.exists(options.fileToSort):
raise RuntimeError("File to sort does not exist: %s" % options.fileToSort)
if int(options.N) <= 0:
raise RuntimeError("Invalid value of N: %s" % options.N)
# Now we are ready to run
with Toil(options) as workflow:
sortedFileURL = 'file://' + os.path.abspath(sortedFileName)
if not workflow.options.restart:
sortFileURL = 'file://' + os.path.abspath(fileName)
sortFileID = workflow.importFile(sortFileURL)
sortedFileID = workflow.start(Job.wrapJobFn(setup,
sortFileID,
int(options.N),
options.downCheckpoints,
options=options,
memory=sortMemory))
else:
sortedFileID = workflow.restart()
workflow.exportFile(sortedFileID, sortedFileURL)
First we make a parser to process command line arguments using the argparse module. It's
important that we add the call to Job.Runner.addToilOptions() to initialize our parser
with all of Toil's default options. Then we add the command line arguments unique to this
workflow, and parse the input. The help message listed with the arguments should give you
a pretty good idea of what they can do.
Next we do a little bit of verification of the input arguments. The option --fileToSort
allows you to specify a file that needs to be sorted. If this option isn't given, it's
here that we make our own file with the call to makeFileToSort().
Finally we come to the context manager that initializes the workflow. We create a path to
the input file prepended with 'file://' as per the documentation for toil.common.Toil()
when staging a file that is stored locally. Notice that we have to check whether or not
the workflow is restarting so that we don't import the file more than once. Finally we
can kick off the workflow by calling toil.common.Toil.start() on the job setup. When the
workflow ends we capture its output (the sorted file's fileID) and use that in
toil.common.Toil.exportFile() to move the sorted file from the job store back into
"userland".
Next let's look at the job that begins the actual workflow, setup.
def setup(job, inputFile, N, downCheckpoints, options):
"""
Sets up the sort.
Returns the FileID of the sorted file
"""
RealtimeLogger.info("Starting the merge sort")
return job.addChildJobFn(down,
inputFile, N, 'root',
downCheckpoints,
options = options,
preemptable=True,
memory=sortMemory).rv()
setup really only does two things. First it writes to the logs using Job.log() and then
calls addChildJobFn(). Child jobs run directly after the current job. This function turns
the 'job function' down into an actual job and passes in the inputs including an optional
resource requirement, memory. The job doesn't actually get run until the call to Job.rv().
Once the job down finishes, its output is returned here.
Now we can look at what down does.
def down(job, inputFileStoreID, N, path, downCheckpoints, options, memory=sortMemory):
"""
Input is a file, a subdivision size N, and a path in the hierarchy of jobs.
If the range is larger than a threshold N the range is divided recursively and
a follow on job is then created which merges back the results else
the file is sorted and placed in the output.
"""
RealtimeLogger.info("Down job starting: %s" % path)
# Read the file
inputFile = job.fileStore.readGlobalFile(inputFileStoreID, cache=False)
length = os.path.getsize(inputFile)
if length > N:
# We will subdivide the file
RealtimeLogger.critical("Splitting file: %s of size: %s"
% (inputFileStoreID, length))
# Split the file into two copies
midPoint = getMidPoint(inputFile, 0, length)
t1 = job.fileStore.getLocalTempFile()
with open(t1, 'w') as fH:
fH.write(copySubRangeOfFile(inputFile, 0, midPoint+1))
t2 = job.fileStore.getLocalTempFile()
with open(t2, 'w') as fH:
fH.write(copySubRangeOfFile(inputFile, midPoint+1, length))
# Call down recursively. By giving the rv() of the two jobs as inputs to the follow-on job, up,
# we communicate the dependency without hindering concurrency.
result = job.addFollowOnJobFn(up,
job.addChildJobFn(down, job.fileStore.writeGlobalFile(t1), N, path + '/0',
downCheckpoints, checkpoint=downCheckpoints, options=options,
preemptable=True, memory=options.sortMemory).rv(),
job.addChildJobFn(down, job.fileStore.writeGlobalFile(t2), N, path + '/1',
downCheckpoints, checkpoint=downCheckpoints, options=options,
preemptable=True, memory=options.mergeMemory).rv(),
path + '/up', preemptable=True, options=options, memory=options.sortMemory).rv()
else:
# We can sort this bit of the file
RealtimeLogger.critical("Sorting file: %s of size: %s"
% (inputFileStoreID, length))
# Sort the copy and write back to the fileStore
shutil.copyfile(inputFile, inputFile + '.sort')
sort(inputFile + '.sort')
result = job.fileStore.writeGlobalFile(inputFile + '.sort')
RealtimeLogger.info("Down job finished: %s" % path)
return result
Down is the recursive part of the workflow. First we read the file into the local
filestore by calling job.fileStore.readGlobalFile(). This puts a copy of the file in the
temp directory for this particular job. This storage will disappear once this job ends.
For a detailed explanation of the filestore, job store, and their interfaces have a look
at 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:
(venv) $ toil-cwl-runner \
--provisioner aws \
--jobStore aws:us-west-2a:any-name \
/tmp/example.cwl /tmp/example-job.yaml
TIP:
When running a CWL workflow on AWS, input files can be provided either on the local
file system or in S3 buckets using s3:// URI references. Final output files will be
copied to the local file system of the leader node.
7. Finally, log out of the leader node and from your local computer, destroy the cluster:
$ 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:
(venv) $ toil launch-cluster <cluster-name> \
--provisioner <aws, gce> \
--keyPairName <key-pair-name> \
--leaderNodeType <type> \
--zone <zone>
NOTE:
A Helpful Tip
When using AWS, setting the environment variable eliminates having to specify the
--zone option for each command. This will be supported for GCE in the future.
$ 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 \
--logDebug \
--logFile /logFile_pestis3 \
--configFile \
/root/cact_ex/blockTrim3.xml <aws, google>:<zone>:cactus-pestis \
/root/cact_ex/pestis-short-aws-seqFile.txt \
/root/cact_ex/pestis_output3.hal
NOTE:
Pieces of the Puzzle:
TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:3.14.0 --- specifies the version of Toil
being used, 3.14.0; if the latest one is desired, please eliminate.
--nodeType --- determines the instance type used for worker nodes. The instance
type specified here must be on the same cloud provider as the one specified with
--leaderNodeType
--maxNodes 2 --- creates up to two instances of the type specified with --nodeType
and launches Mesos worker containers inside them.
--logDebug --- equivalent to --logLevel DEBUG.
--logFile /logFile_pestis3 --- writes logs in a file named logFile_pestis3 under /
folder.
--configFile --- this is not required depending on whether a specific configuration
file is intended to run the alignment.
<aws, google>:<zone>:cactus-pestis --- creates a bucket, named cactus-pestis, with
the specified cloud provider to store intermediate job files and metadata. NOTE:
If you want to use a GCE-based jobstore, specify google here, not gce.
The result file, named pestis_output3.hal, is stored under /root/cact_ex folder of
the leader node.
Use cactus --help to see all the Cactus and Toil flags available.
8. Log out of the leader node:
(cact_venv) $ exit
9. Download the resulted output to local machine:
(venv) $ toil rsync-cluster \
--provisioner <aws, gce> <cluster-name> \
:/root/cact_ex/pestis_output3.hal \
<path-of-folder-on-local-machine>
10. Destroy the cluster:
(venv) $ toil destroy-cluster --provisioner <aws, gce> <cluster-name>
INTRODUCTION
Toil runs in various environments, including locally and in the cloud (Amazon Web Services
and Google Compute Engine). Toil also supports two DSLs: CWL and (Amazon Web Services and
Google Compute Engine). Toil also supports two DSLs: CWL and WDL (experimental).
Toil is built in a modular way so that it can be used on lots of different systems, and
with different configurations. The three configurable pieces are the
· 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, kubernetes,
or grid_engine). Mesos is a special case, and is launched for cloud environments.
· Provisioner: For running in the cloud only. This specifies which cloud provider
provides instances to do the "work" of your workflow.
Job Store
The job store is a storage abstraction which contains all of the information used in a
Toil run. This centralizes all of the files used by jobs in the workflow and also the
details of the progress of the run. If a workflow crashes or fails, the job store contains
all of the information necessary to resume with minimal repetition of work.
Several different job stores are supported, including the file job store and cloud job
stores.
File Job Store
The file job store is for use locally, and keeps the workflow information in a directory
on the machine where the workflow is launched. This is the simplest and most convenient
job store for testing or for small runs.
For an example that uses the file job store, see 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
grid_engine). Mesos is a special case, and is launched for cloud environments. These
environments manage individual worker nodes under a leader node to process the work
required in a workflow. The leader and its workers all coordinate their tasks and files
through a centralized job store location.
See 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, single_machine, parasol, grid_engine,
kubernetes, tes. (default: single_machine)
--parasolCommand PARASOLCOMMAND
The name or path of the parasol program. Will be looked up on PATH unless it
starts with a slash. (default: parasol)
--parasolMaxBatches PARASOLMAXBATCHES
Maximum number of job batches the Parasol batch is allowed to create. One batch
is created for jobs with a unique set of resource requirements. (default: 1000)
— mesosEndpoint MESOSENDPOINT The host and port of the Mesos server separated by a
colon. (default: <leader IP>:5050)
--kubernetesHostPath KUBERNETES_HOST_PATH
A path on Kubernetes hosts that will be mounted as the Toil work directory in
the workers, to allow for shared caching.
--kubernetesOwner KUBERNETES_OWNER
A name prefix for easy identification of Kubernetes jobs. If not set, Toil will
use the current user name.
--kubernetesServiceAccount KUBERNETES_SERVICE_ACCOUNT
A service account name to apply when creating Kubernetes pods.
--tesEndpoint TES_ENDPOINT
The http(s) URL of the TES server. (default: http://<leader IP>:8000)
--tesUser TES_USER
User name to use for basic authentication to TES server.
--tesPassword TES_PASSWORD
Password to use for basic authentication to TES server.
--tesBearerToken TES_BEARER_TOKEN
Bearer token to use for authentication to TES server.
--scale SCALE
A scaling factor to change the value of all submitted tasks' submitted cores.
Used in singleMachine batch system. Useful for running workflows on smaller
machines than they were designed for, by setting a value less than 1. (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.
--coalesceStatusCalls Coalese status calls to prevent the batch system from
being overloaded. Currently only supported for LSF.
Autoscaling Options
--provisioner CLOUDPROVIDER
The provisioner for cluster auto-scaling. The currently supported choices are
'aws' or 'gce'. The default is None.
--nodeTypes NODETYPES
Specifies a list of comma-separated node types, each of which is composed of
slash-separated instance types, and an optional spot bid set off by a colon,
making the node type preemptable. Instance types may appear in multiple node
types, and the same node type may appear as both preemptable and
non-preemptable. Valid argument specifying two node types:
c5.4xlarge/c5a.4xlarge:0.42,t2.large
Node types:
c5.4xlarge/c5a.4xlarge:0.42 and t2.large
Instance types:
c5.4xlarge, c5a.4xlarge, and t2.large
Semantics:
Bid $0.42/hour for either c5.4xlarge or c5a.4xlarge instances, treated
interchangeably, while they are available at that price, and buy t2.large
instances at full price
--minNodes MINNODES
Minimum number of nodes of each type in the cluster, if using auto-scaling. This
should be provided as a comma-separated list of the same length as the list of
node types. default=0
--maxNodes MAXNODES
Maximum number of nodes of each type in the cluster, if using autoscaling,
provided as a comma-separated list. The first value is used as a default if the
list length is less than the number of nodeTypes. default=10
--preemptableCompensation PREEMPTABLECOMPENSATION
The preference of the autoscaler to replace preemptable nodes with
non-preemptable nodes, when preemptable nodes cannot be started for some reason.
Defaults to 0.0. This value must be between 0.0 and 1.0, inclusive. A value of
0.0 disables such compensation, a value of 0.5 compensates two missing
preemptable nodes with a non-preemptable one. A value of 1.0 replaces every
missing pre-emptable node with a non-preemptable one.
--nodeStorage NODESTORAGE
Specify the size of the root volume of worker nodes when they are launched in
gigabytes. You may want to set this if your jobs require a lot of disk space.
The default value is 50.
--nodeStorageOverrides NODESTORAGEOVERRIDES
Comma-separated list of nodeType:nodeStorage that are used to override the
default value from --nodeStorage for the specified nodeType(s). This is useful
for heterogeneous jobs where some tasks require much more disk than others.
--metrics
Enable the prometheus/grafana dashboard for monitoring CPU/RAM usage, queue
size, and issued jobs.
--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
--defaultPreemptable BOOL
Set if jobs that do not specifically prohibit it should able to run on
preemptable (spot) nodes.
--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
--doubleMem
If set, batch jobs which die due to reaching memory limit on batch schedulers
will have their memory doubled and they will be retried. The remaining retry
count will be reduced by 1. Currently only supported by LSF. default=False.
--maxJobDuration MAXJOBDURATION
Maximum runtime of a job (in seconds) before we kill it (this is a lower bound,
and the actual time before killing the job may be longer).
--rescueJobsFrequency RESCUEJOBSFREQUENCY
Period of time to wait (in seconds) between checking for missing/overlong jobs,
that is jobs which get lost by the batch system.
--maxServiceJobs MAXSERVICEJOBS
The maximum number of service jobs that can be run concurrently, excluding
service jobs running on preemptable nodes. default=9223372036854775807
--maxPreemptableServiceJobs MAXPREEMPTABLESERVICEJOBS
The maximum number of service jobs that can run concurrently on preemptable
nodes. default=9223372036854775807
--deadlockWait DEADLOCKWAIT
Time, in seconds, to tolerate the workflow running only the same service jobs,
with no jobs to use them, before declaring the workflow to be deadlocked and
stopping. default=60
--deadlockCheckInterval DEADLOCKCHECKINTERVAL
Time, in seconds, to wait between checks to see if the workflow is stuck running
only service jobs, with no jobs to use them. Should be shorter than
--deadlockWait. May need to be increased if the batch system cannot enumerate
running jobs quickly enough, or if polling for running jobs is placing an
unacceptable load on a shared cluster. default=30
--statePollingWait STATEPOLLINGWAIT
Time, in seconds, to wait before doing a scheduler query for job state. Return
cached results if within the waiting period. Only works for grid engine batch
systems such as gridengine, htcondor, torque, slurm, and lsf.
Miscellaneous Options
--disableCaching
Disables caching in the file store. This flag must be set to use a batch system
that does not support cleanup, such as Parasol.
--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 leader.
--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)
--statusWait INT
Seconds to wait between reports of running jobs. (default=3600)
--disableProgress
Disables the progress bar shown when standard error is a terminal.
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 on Kubernetes
Kubernetes is a very popular container orchestration tool that has become a de facto
cross-cloud-provider API for accessing cloud resources. Major cloud providers like Amazon,
Microsoft, Kubernetes owner Google, and DigitalOcean have invested heavily in making
Kubernetes work well on their platforms, by writing their own deployment documentation and
developing provider-managed Kubernetes-based products. Using minikube, Kubernetes can even
be run on a single machine.
Toil supports running Toil workflows against a Kubernetes cluster, either in the cloud or
deployed on user-owned hardware.
Preparing your Kubernetes environment
1. Get a Kubernetes cluster
To run Toil workflows on Kubernetes, you need to have a Kubernetes cluster set up. This
will not be covered here, but there are many options available, and which one you
choose will depend on which cloud ecosystem if any you use already, and on pricing. If
you are just following along with the documentation, use minikube on your local
machine.
Note that currently the only way to run a Toil workflow on Kubernetes is to use the AWS
Job Store, so your Kubernetes workflow will currently have to store its data in
Amazon's cloud regardless of where you run it. This can result in significant egress
charges from Amazon if you run it outside of Amazon.
Kubernetes Cluster Providers:
· Your own institution
· Amazon EKS
· Microsoft Azure AKS
· Google GKE
· DigitalOcean Kubernetes
· minikube
2. Get a Kubernetes context on your local machine
There are two main ways to run Toil workflows on Kubernetes. You can either run the
Toil leader on a machine outside the cluster, with jobs submitted to and run on the
cluster, or you can submit the Toil leader itself as a job and have it run inside the
cluster. Either way, you will need to configure your own machine to be able to submit
jobs to the Kubernetes cluster. Generally, this involves creating and populating a file
named .kube/config in your user's home directory, and specifying the cluster to connect
to, the certificate and token information needed for mutual authentication, and the
Kubernetes namespace within which to work. However, Kubernetes configuration can also
be picked up from other files in the .kube directory, environment variables, and the
enclosing host when running inside a Kubernetes-managed container.
You will have to do different things here depending on where you got your Kubernetes
cluster:
· Configuring for Amazon EKS
· Configuring for Microsoft Azure AKS
· Configuring for Google GKE
· Configuring for DigitalOcean Kubernetes Clusters
· Configuring for minikube
Toil's internal Kubernetes configuration logic mirrors that of the kubectl command.
Toil workflows will use the current kubectl context to launch their Kubernetes jobs.
3. If running the Toil leader in the cluster, get a service account
If you are going to run your workflow's leader within the Kubernetes cluster (see
Option 1: Running the Leader Inside Kubernetes), you will need a service account in
your chosen Kubernetes namespace. Most namespaces should have a service account named
default which should work fine. If your cluster requires you to use a different service
account, you will need to obtain its name and use it when launching the Kubernetes job
containing the Toil leader.
4. Set up appropriate permissions
Your local Kubernetes context and/or the service account you are using to run the
leader in the cluster will need to have certain permissions in order to run the
workflow. Toil needs to be able to interact with jobs and pods in the cluster, and to
retrieve pod logs. You as a user may need permission to set up an AWS credentials
secret, if one is not already available. Additionally, it is very useful for you as a
user to have permission to interact with nodes, and to shell into pods.
The appropriate permissions may already be available to you and your service account by
default, especially in managed or ease-of-use-optimized setups such as EKS or minikube.
However, if the appropriate permissions are not already available, you or your cluster
administrator will have to grant them manually. The following Role (toil-user) and
ClusterRole (node-reader), to be applied with kubectl apply -f filename.yaml, should
grant sufficient permissions to run Toil workflows when bound to your account and the
service account used by Toil workflows. Be sure to replace YOUR_NAMESPACE_HERE with the
namespace you are running your workflows in
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: YOUR_NAMESPACE_HERE
name: toil-user
rules:
- apiGroups: ["*"]
resources: ["*"]
verbs: ["explain", "get", "watch", "list", "describe", "logs", "attach", "exec", "port-forward", "proxy", "cp", "auth"]
- apiGroups: ["batch"]
resources: ["*"]
verbs: ["get", "watch", "list", "create", "run", "set", "delete"]
- apiGroups: [""]
resources: ["secrets", "pods", "pods/attach", "podtemplates", "configmaps", "events", "services"]
verbs: ["patch", "get", "update", "watch", "list", "create", "run", "set", "delete", "exec"]
- apiGroups: [""]
resources: ["pods", "pods/log"]
verbs: ["get", "list"]
- apiGroups: [""]
resources: ["pods/exec"]
verbs: ["create"]
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: node-reader
rules:
- apiGroups: [""]
resources: ["nodes"]
verbs: ["get", "list", "describe"]
- apiGroups: [""]
resources: ["namespaces"]
verbs: ["get", "list", "describe"]
- apiGroups: ["metrics.k8s.io"]
resources: ["*"]
verbs: ["*"]
To bind a user or service account to the Role or ClusterRole and actually grant the
permissions, you will need a RoleBinding and a ClusterRoleBinding, respectively. Make
sure to fill in the namespace, username, and service account name, and add more user
stanzas if your cluster is to support multiple Toil users.
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: toil-developer-member
namespace: toil
subjects:
- kind: User
name: YOUR_KUBERNETES_USERNAME_HERE
apiGroup: rbac.authorization.k8s.io
- kind: ServiceAccount
name: YOUR_SERVICE_ACCOUNT_NAME_HERE
namespace: YOUR_NAMESPACE_HERE
roleRef:
kind: Role
name: toil-user
apiGroup: rbac.authorization.k8s.io
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: read-nodes
subjects:
- kind: User
name: YOUR_KUBERNETES_USERNAME_HERE
apiGroup: rbac.authorization.k8s.io
- kind: ServiceAccount
name: YOUR_SERVICE_ACCOUNT_NAME_HERE
namespace: YOUR_NAMESPACE_HERE
roleRef:
kind: ClusterRole
name: node-reader
apiGroup: rbac.authorization.k8s.io
AWS Job Store for Kubernetes
Currently, the only job store, which is what Toil uses to exchange data between jobs, that
works with jobs running on Kubernetes is the AWS Job Store. This requires that the Toil
leader and Kubernetes jobs be able to connect to and use Amazon S3 and Amazon SimpleDB. It
also requires that you have an Amazon Web Services account.
1. Get access to AWS S3 and SimpleDB
In your AWS account, you need to create an AWS access key. First go to the IAM
dashboard; for "us-west1", the link would be:
https://console.aws.amazon.com/iam/home?region=us-west-1#/home
Then create an access key, and save the Access Key ID and the Secret Key. As documented
in the AWS documentation:
1. On the IAM Dashboard page, choose your account name in the navigation bar, and then
choose My Security Credentials.
2. Expand the Access keys (access key ID and secret access key) section.
3. Choose Create New Access Key. Then choose Download Key File to save the access key
ID and secret access key to a file on your computer. After you close the dialog box,
you can't retrieve this secret access key again.
Make sure that, if your AWS infrastructure requires your user to authenticate with a
multi-factor authentication (MFA) token, you obtain a second secret key and access key
that don't have this requirement. The secret key and access key used to populate the
Kubernetes secret that allows the jobs to contact the job store need to be usable
without human intervention.
2. Configure AWS access from the local machine
This only really needs to happen if you run the leader on the local machine. But we
need the files in place to fill in the secret in the next step. Run:
$ aws configure
Then when prompted, enter your secret key and access key. This should create a file
~/.aws/credentials that looks like this:
[default]
aws_access_key_id = BLAH
aws_secret_access_key = blahblahblah
3. Create a Kubernetes secret to give jobs access to AWS
Go into the directory where the credentials file is:
$ cd ~/.aws
Then, create a Kubernetes secret that contains it. We'll call it aws-credentials:
$ kubectl create secret generic aws-credentials --from-file credentials
Configuring Toil for your Kubernetes environment
To configure your workflow to run on Kubernetes, you will have to configure several
environment variables, in addition to passing the --batchSystem kubernetes option. Doing
the research to figure out what values to give these variables may require talking to your
cluster provider.
1. TOIL_AWS_SECRET_NAME is the most important, and must be set to the secret that contains
your AWS credentials file, if your cluster nodes don't otherwise have access to S3 and
SimpleDB (such as through IAM roles). This is required for the AWS job store to work,
which is currently the only job store that can be used on Kubernetes. In this example
we are using aws-credentials.
2. TOIL_KUBERNETES_HOST_PATH can be set to allow Toil jobs on the same physical host to
share a cache. It should be set to a path on the host where the shared cache should be
stored. It will be mounted as /var/lib/toil, or at TOIL_WORKDIR if specified, inside
the container. This path must already exist on the host, and must have as much free
space as your Kubernetes node offers to jobs. In this example, we are using
/data/scratch. To actually make use of caching, make sure not to use --disableCaching.
3. TOIL_KUBERNETES_OWNER should be set to the username of the user running the Toil
workflow. The jobs that Toil creates will include this username, so they can be more
easily recognized, and cleaned up by the user if anything happens to the Toil leader.
In this example we are using demo-user.
Note that Docker containers cannot be run inside of unprivileged Kubernetes pods (which
are themselves containers). The Docker daemon does not (yet) support this. Other tools,
such as Singularity in its user-namespace mode, are able to run containers from within
containers. If using Singularity to run containerized tools, and you want downloaded
container images to persist between Toil jobs, you will also want to set
TOIL_KUBERNETES_HOST_PATH and make sure that Singularity is downloading its containers
under the Toil work directory (/var/lib/toil buy default) by setting SINGULARITY_CACHEDIR.
However, you will need to make sure that no two jobs try to download the same container at
the same time; Singularity has no synchronization or locking around its cache, but the
cache is also not safe for simultaneous access by multiple Singularity invocations. Some
Toil workflows use their own custom workaround logic for this problem; this work is likely
to be made part of Toil in a future release.
Running workflows
To run the workflow, you will need to run the Toil leader process somewhere. It can either
be run inside Kubernetes as a Kubernetes job, or outside Kubernetes as a normal command.
Option 1: Running the Leader Inside Kubernetes
Once you have determined a set of environment variable values for your workflow run, write
a YAML file that defines a Kubernetes job to run your workflow with that configuration.
Some configuration items (such as your username, and the name of your AWS credentials
secret) need to be written into the YAML so that they can be used from the leader as well.
Note that the leader pod will need your workflow script, its other dependencies, and Toil
all installed. An easy way to get Toil installed is to start with the Toil appliance image
for the version of Toil you want to use. In this example, we use
quay.io/ucsc_cgl/toil:5.5.0.
Here's an example YAML file to run a test workflow:
apiVersion: batch/v1
kind: Job
metadata:
# It is good practice to include your username in your job name.
# Also specify it in TOIL_KUBERNETES_OWNER
name: demo-user-toil-test
# Do not try and rerun the leader job if it fails
spec:
backoffLimit: 0
template:
spec:
# Do not restart the pod when the job fails, but keep it around so the
# log can be retrieved
restartPolicy: Never
volumes:
- name: aws-credentials-vol
secret:
# Make sure the AWS credentials are available as a volume.
# This should match TOIL_AWS_SECRET_NAME
secretName: aws-credentials
# You may need to replace this with a different service account name as
# appropriate for your cluster.
serviceAccountName: default
containers:
- name: main
image: quay.io/ucsc_cgl/toil:5.5.0
env:
# Specify your username for inclusion in job names
- name: TOIL_KUBERNETES_OWNER
value: demo-user
# Specify where to find the AWS credentials to access the job store with
- name: TOIL_AWS_SECRET_NAME
value: aws-credentials
# Specify where per-host caches should be stored, on the Kubernetes hosts.
# Needs to be set for Toil's caching to be efficient.
- name: TOIL_KUBERNETES_HOST_PATH
value: /data/scratch
volumeMounts:
# Mount the AWS credentials volume
- mountPath: /root/.aws
name: aws-credentials-vol
resources:
# Make sure to set these resource limits to values large enough
# to accommodate the work your workflow does in the leader
# process, but small enough to fit on your cluster.
#
# Since no request values are specified, the limits are also used
# for the requests.
limits:
cpu: 2
memory: "4Gi"
ephemeral-storage: "10Gi"
command:
- /bin/bash
- -c
- |
# This Bash script will set up Toil and the workflow to run, and run them.
set -e
# We make sure to create a work directory; Toil can't hot-deploy a
# script from the root of the filesystem, which is where we start.
mkdir /tmp/work
cd /tmp/work
# We make a virtual environment to allow workflow dependencies to be
# hot-deployed.
#
# We don't really make use of it in this example, but for workflows
# that depend on PyPI packages we will need this.
#
# We use --system-site-packages so that the Toil installed in the
# appliance image is still available.
virtualenv --python python3 --system-site-packages venv
. venv/bin/activate
# Now we install the workflow. Here we're using a demo workflow
# script from Toil itself.
wget https://raw.githubusercontent.com/DataBiosphere/toil/releases/4.1.0/src/toil/test/docs/scripts/tutorial_helloworld.py
# Now we run the workflow. We make sure to use the Kubernetes batch
# system and an AWS job store, and we set some generally useful
# logging options. We also make sure to enable caching.
python3 tutorial_helloworld.py \
aws:us-west-2:demouser-toil-test-jobstore \
--batchSystem kubernetes \
--realTimeLogging \
--logInfo
You can save this YAML as leader.yaml, and then run it on your Kubernetes installation
with:
$ kubectl apply -f leader.yaml
To monitor the progress of the leader job, you will want to read its logs. If you are
using a Kubernetes dashboard such as k9s, you can simply find the pod created for the job
in the dashboard, and view its logs there. If not, you will need to locate the pod by
hand.
Monitoring and Debugging Kubernetes Jobs and Pods
The following techniques are most useful for looking at the pod which holds the Toil
leader, but they can also be applied to individual Toil jobs on Kubernetes, even when the
leader is outside the cluster.
Kubernetes names pods for jobs by appending a short random string to the name of the job.
You can find the name of the pod for your job by doing:
$ kubectl get pods | grep demo-user-toil-test
demo-user-toil-test-g5496 1/1 Running 0 2m
Assuming you have set TOIL_KUBERNETES_OWNER correctly, you should be able to find all of
your workflow's pods by searching for your username:
$ kubectl get pods | grep demo-user
If the status of a pod is anything other than Pending, you will be able to view its logs
with:
$ kubectl logs demo-user-toil-test-g5496
This will dump the pod's logs from the beginning to now and terminate. To follow along
with the logs from a running pod, add the -f option:
$ kubectl logs -f demo-user-toil-test-g5496
A status of ImagePullBackoff suggests that you have requested to use an image that is not
available. Check the image section of your YAML if you are looking at a leader, or the
value of TOIL_APPLIANCE_SELF if you are delaying with a worker job. You also might want to
check your Kubernetes node's Internet connectivity and DNS function; in Kubernetes, DNS
depends on system-level pods which can be terminated or evicted in cases of resource
oversubscription, just like user workloads.
If your pod seems to be stuck Pending, ContainerCreating, you can get information on what
is wrong with it by using kubectl describe pod:
$ kubectl describe pod demo-user-toil-test-g5496
Pay particular attention to the Events: section at the end of the output. An indication
that a job is too big for the available nodes on your cluster, or that your cluster is too
busy for your jobs, is FailedScheduling events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedScheduling 13s (x79 over 100m) default-scheduler 0/4 nodes are available: 1 Insufficient cpu, 1 Insufficient ephemeral-storage, 4 Insufficient memory.
If a pod is running but seems to be behaving erratically, or seems stuck, you can shell
into it and look around:
$ kubectl exec -ti demo-user-toil-test-g5496 /bin/bash
One common cause of stuck pods is attempting to use more memory than allowed by Kubernetes
(or by the Toil job's memory resource requirement), but in a way that does not trigger the
Linux OOM killer to terminate the pod's processes. In these cases, the pod can remain
stuck at nearly 100% memory usage more or less indefinitely, and attempting to shell into
the pod (which needs to start a process within the pod, using some of its memory) will
fail. In these cases, the recommended solution is to kill the offending pod and increase
its (or its Toil job's) memory requirement, or reduce its memory needs by adapting user
code.
When Things Go Wrong
The Toil Kubernetes batch system includes cleanup code to terminate worker jobs when the
leader shuts down. However, if the leader pod is removed by Kubernetes, is forcibly killed
or otherwise suffers a sudden existence failure, it can go away while its worker jobs live
on. It is not recommended to restart a workflow in this state, as jobs from the previous
invocation will remain running and will be trying to modify the job store concurrently
with jobs from the new invocation.
To clean up dangling jobs, you can use the following snippet:
$ kubectl get jobs | grep demo-user | cut -f1 -d' ' | xargs -n10 kubectl delete job
This will delete all jobs with demo-user's username in their names, in batches of 10. You
can also use the UUID that Toil assigns to a particular workflow invocation in the filter,
to clean up only the jobs pertaining to that workflow invocation.
Option 2: Running the Leader Outside Kubernetes
If you don't want to run your Toil leader inside Kubernetes, you can run it locally
instead. This can be useful when developing a workflow; files can be hot-deployed from
your local machine directly to Kubernetes. However, your local machine will have to have
(ideally role-assumption- and MFA-free) access to AWS, and access to Kubernetes. Real time
logging will not work unless your local machine is able to listen for incoming UDP packets
on arbitrary ports on the address it uses to contact the IPv4 Internet; Toil does no NAT
traversal or detection.
Note that if you set TOIL_WORKDIR when running your workflow like this, it will need to be
a directory that exists both on the host and in the Toil appliance.
Here is an example of running our test workflow leader locally, outside of Kubernetes:
$ export TOIL_KUBERNETES_OWNER=demo-user # This defaults to your local username if not set
$ export TOIL_AWS_SECRET_NAME=aws-credentials
$ export TOIL_KUBERNETES_HOST_PATH=/data/scratch
$ virtualenv --python python3 --system-site-packages venv
$ . venv/bin/activate
$ wget https://raw.githubusercontent.com/DataBiosphere/toil/releases/4.1.0/src/toil/test/docs/scripts/tutorial_helloworld.py
$ python3 tutorial_helloworld.py \
aws:us-west-2:demouser-toil-test-jobstore \
--batchSystem kubernetes \
--realTimeLogging \
--logInfo
Running in AWS
Toil jobs can be run on a variety of cloud platforms. Of these, Amazon Web Services (AWS)
is currently the best-supported solution. Toil provides the 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. Next, generate a key pair for AWS with the command (do NOT generate your key pair with
the Amazon browser):
$ ssh-keygen -t rsa
3. This should prompt you to save your key. Please save it in
~/.ssh/id_rsa
4. Now move this to where your OS can see it as an authorized key:
$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
5. Next, you'll need to add your key to the ssh-agent:
$ eval `ssh-agent -s`
$ ssh-add
If your key has a passphrase, you will be prompted to enter it here once.
6. You'll also need to chmod your private key (good practice but also enforced by AWS):
$ chmod 400 id_rsa
7. Now you'll need to add the key to AWS via the browser. For example, on us-west1, this
address would accessible at:
https://us-west-1.console.aws.amazon.com/ec2/v2/home?region=us-west-1#KeyPairs:sort=keyName
8. Now click on the "Import Key Pair" button to add your key:
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]: "
This will create the files ~/.aws/config and ~/.aws/credentials.
14. If not done already, install toil (example uses version 5.3.0, but we recommend
the latest release):
$ virtualenv venv
$ source venv/bin/activate
$ pip install toil[all]==5.3.0
15. Now that toil is installed and you are running a virtualenv, an example of
launching a toil leader node would be the following (again, note that we set
TOIL_APPLIANCE_SELF to toil version 5.3.0 in this example, but please set the
version to the installed version that you are using if you're using a different
version):
$ TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:5.3.0 \
toil launch-cluster clustername \
--leaderNodeType t2.medium \
--zone us-west-1a \
--keyPairName id_rsa
To further break down each of these commands:
TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:latest --- This is optional. It specifies a
mesos docker image that we maintain with the latest version of toil installed on it.
If you want to use a different version of toil, please specify the image tag you need
from https://quay.io/repository/ucsc_cgl/toil?tag=latest&tab=tags.
toil launch-cluster --- Base command in toil to launch a cluster.
clustername --- Just choose a name for your cluster.
--leaderNodeType t2.medium --- Specify the leader node type. Make a t2.medium (2CPU;
4Gb RAM; $0.0464/Hour). List of available AWS instances:
https://aws.amazon.com/ec2/pricing/on-demand/
--zone us-west-1a --- Specify the AWS zone you want to launch the instance in. Must
have the same prefix as the zone in your awscli credentials (which, in the example of
this tutorial is: "us-west-1").
--keyPairName id_rsa --- The name of your key pair, which should be "id_rsa" if you've
followed this tutorial.
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.
In addition, Toil creates a new security group with the same name as the cluster name with
default rules (e.g. opens port 22 for SSH access). If you require additional security
groups, you may use the --awsEc2ExtraSecurityGroupId parameter when launching the cluster.
Note: Do not use the same name as the cluster name for the extra security groups as any
security group matching the cluster name will be deleted once the cluster is destroyed.
For more information on options try:
(venv) $ toil launch-cluster --help
Static Provisioning
Toil can be used to manage a cluster in the cloud by using the 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 -z us-west-2a <cluster-name> sort.py :/root
4. Login to the leader node:
(venv) $ toil ssh-cluster -z us-west-2a <cluster-name>
5. Run the script as an autoscaling workflow:
$ python /root/sort.py aws:us-west-2:<my-jobstore-name> \
--provisioner aws \
--nodeTypes c3.large \
--maxNodes 2 \
--batchSystem mesos
NOTE:
In this example, the autoscaling Toil code creates up to two instances of type c3.large
and launches Mesos slave containers inside them. The containers are then available to
run jobs defined by the sort.py script. Toil also creates a bucket in S3 called
aws:us-west-2:autoscaling-sort-jobstore to store intermediate job results. The Toil
autoscaler can also provision multiple different node types, which is useful for
workflows that have jobs with varying resource requirements. For example, one could
execute the script with --nodeTypes c3.large,r3.xlarge --maxNodes 5,1, which would
allow the provisioner to create up to five c3.large nodes and one r3.xlarge node for
memory-intensive jobs. In this situation, the autoscaler would avoid creating the more
expensive r3.xlarge node until needed, running most jobs on the c3.large nodes.
1. View the generated file to sort:
$ head fileToSort.txt
2. View the sorted file:
$ head sortedFile.txt
For more information on other autoscaling (and other) options have a look at
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.
Using MinIO and S3-Compatible object stores
Toil can be configured to access files stored in an S3-compatible object store such as
MinIO. The following environment variables can be used to configure the S3 connection
used:
· TOIL_S3_HOST: the IP address or hostname to use for connecting to S3
· TOIL_S3_PORT: the port number to use for connecting to S3, if needed
· TOIL_S3_USE_SSL: enable or disable the usage of SSL for connecting to S3 (True by
default)
Examples:
TOIL_S3_HOST=127.0.0.1
TOIL_S3_PORT=9010
TOIL_S3_USE_SSL=False
Dashboard
Toil provides a dashboard for viewing the RAM and CPU usage of each node, the number of
issued jobs of each type, the number of failed jobs, and the size of the jobs queue. To
launch this dashboard for a toil workflow, include the --metrics flag in the toil script
command. The dashboard can then be viewed in your browser at localhost:3000 while
connected to the leader node through toil ssh-cluster:
To change the default port number, you can use the --grafana_port argument:
(venv) $ toil ssh-cluster -z us-west-2a --grafana_port 8000 <cluster-name>
On AWS, the dashboard keeps track of every node in the cluster to monitor CPU and RAM
usage, but it can also be used while running a workflow on a single machine. The dashboard
uses Grafana as the front end for displaying real-time plots, and Prometheus for tracking
metrics exported by toil: [image]
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 os
import subprocess
from toil.common import Toil
from toil.job import Job
class discoverFiles(Job):
"""Views files at a specified path using ls."""
def __init__(self, path, *args, **kwargs):
self.path = path
super().__init__(*args, **kwargs)
def run(self, fileStore):
if os.path.exists(self.path):
subprocess.check_call(["ls", self.path])
def main():
options = Job.Runner.getDefaultArgumentParser().parse_args()
options.clean = "always"
job1 = discoverFiles(path="/sys/", displayName='sysFiles')
job2 = discoverFiles(path=os.path.expanduser("~"), displayName='userFiles')
job3 = discoverFiles(path="/tmp/")
job1.addChild(job2)
job2.addChild(job3)
with Toil(options) as toil:
if not toil.options.restart:
toil.start(job1)
else:
toil.restart()
if __name__ == '__main__':
main()
Notice the displayName key, which can rename a job, giving it an alias when it is finally
displayed in stats. Running this workflow file should record three job names: sysFiles
(job1), userFiles (job2), and discoverFiles (job3). To see the runtime and resources used
for each job when it was run, type
toil stats file:my-jobstore
This should output the following:
Batch System: singleMachine
Default Cores: 1 Default Memory: 2097152K
Max Cores: 9.22337e+18
Total Clock: 0.56 Total Runtime: 1.01
Worker
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
1 | 0.14 0.14 0.14 0.14 0.14 | 0.13 0.13 0.13 0.13 0.13 | 0.01 0.01 0.01 0.01 0.01 | 76K 76K 76K 76K 76K
Job
Worker Jobs | min med ave max
| 3 3 3 3
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
3 | 0.01 0.06 0.05 0.07 0.14 | 0.00 0.06 0.04 0.07 0.12 | 0.00 0.01 0.00 0.01 0.01 | 76K 76K 76K 76K 229K
sysFiles
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
1 | 0.01 0.01 0.01 0.01 0.01 | 0.00 0.00 0.00 0.00 0.00 | 0.01 0.01 0.01 0.01 0.01 | 76K 76K 76K 76K 76K
userFiles
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
1 | 0.06 0.06 0.06 0.06 0.06 | 0.06 0.06 0.06 0.06 0.06 | 0.01 0.01 0.01 0.01 0.01 | 76K 76K 76K 76K 76K
discoverFiles
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
1 | 0.07 0.07 0.07 0.07 0.07 | 0.07 0.07 0.07 0.07 0.07 | 0.00 0.00 0.00 0.00 0.00 | 76K 76K 76K 76K 76K
Once we're done, we can clean up the job store by running
toil clean file:my-jobstore
Status Command
Continuing the example from the stats section above, if we ran our workflow with the
command
python discoverfiles.py file:my-jobstore --stats
We could interrogate our jobstore with the status command, for example:
toil status file:my-jobstore
If the run was successful, this would not return much valuable information, something like
2018-01-11 19:31:29,739 - toil.lib.bioio - INFO - Root logger is at level 'INFO', 'toil' logger at level 'INFO'.
2018-01-11 19:31:29,740 - toil.utils.toilStatus - INFO - Parsed arguments
2018-01-11 19:31:29,740 - toil.utils.toilStatus - INFO - Checking if we have files for Toil
The root job of the job store is absent, the workflow completed successfully.
Otherwise, the status command should return the following:
There are x unfinished jobs, y parent jobs with children, z jobs with services, a
services, and b totally failed jobs currently in c.
Clean Command
If a Toil pipeline didn't finish successfully, or was run using --clean=always or --stats,
the job store will exist until it is deleted. toil clean <jobStore> ensures that all
artifacts associated with a job store are removed. This is particularly useful for
deleting AWS job stores, which reserves an SDB domain as well as an S3 bucket.
The deletion of the job store can be modified by the --clean argument, and may be set to
always, onError, never, or onSuccess (default).
Temporary directories where jobs are running can also be saved from deletion using the
--cleanWorkDir, which has the same options as --clean. This option should only be run
when debugging, as intermediate jobs will fill up disk space.
Launch-Cluster Command
Running toil launch-cluster starts up a leader for a cluster. Workers can be added to the
initial cluster by specifying the -w option. An example would be
$ toil launch-cluster my-cluster \
--leaderNodeType t2.small -z us-west-2a \
--keyPairName your-AWS-key-pair-name \
--nodeTypes m3.large,t2.micro -w 1,4
Options are listed below. These can also be displayed by running
$ toil launch-cluster --help
launch-cluster's main positional argument is the clusterName. This is simply the name of
your cluster. If it does not exist yet, Toil will create it for you.
Launch-Cluster Options
--help -h also accepted. Displays this help menu.
--tempDirRoot TEMPDIRROOT
Path to the temporary directory where all temp files are created, by default
uses the current working directory as the base.
--version
Display version.
--provisioner CLOUDPROVIDER
-p CLOUDPROVIDER also accepted. The provisioner for cluster auto-scaling. Both
AWS and GCE are currently supported.
--zone ZONE
-z ZONE also accepted. The availability zone of the leader. This parameter can
also be set via the TOIL_AWS_ZONE or TOIL_GCE_ZONE environment variables, or by
the ec2_region_name parameter in your .boto file if using AWS, or derived from
the instance metadata if using this utility on an existing EC2 instance.
--leaderNodeType LEADERNODETYPE
Non-preemptable node type to use for the cluster leader.
--keyPairName KEYPAIRNAME
The name of the AWS or ssh key pair to include on the instance.
--boto BOTOPATH
The path to the boto credentials directory. This is transferred to all nodes in
order to access the AWS jobStore from non-AWS instances.
--tag KEYVALUE
KEYVALUE is specified as KEY=VALUE. -t KEY=VALUE also accepted. Tags are added
to the AWS cluster for this node and all of its children. Tags are of the form:
-t key1=value1 --tag key2=value2. Multiple tags are allowed and each tag needs
its own flag. By default the cluster is tagged with: { "Name": clusterName,
"Owner": IAM username }.
--vpcSubnet VPCSUBNET
VPC subnet ID to launch cluster leader in. Uses default subnet if not specified.
This subnet needs to have auto assign IPs turned on.
--nodeTypes NODETYPES
Comma-separated list of node types to create while launching the leader. The
syntax for each node type depends on the provisioner used. For the AWS
provisioner this is the name of an EC2 instance type followed by a colon and the
price in dollars to bid for a spot instance, for example 'c3.8xlarge:0.42'. Must
also provide the --workers argument to specify how many workers of each node
type to create.
--workers WORKERS
-w WORKERS also accepted. Comma-separated list of the number of workers of each
node type to launch alongside the leader when the cluster is created. This can
be useful if running toil without auto-scaling but with need of more hardware
support.
--leaderStorage LEADERSTORAGE
Specify the size (in gigabytes) of the root volume for the leader instance. This
is an EBS volume.
--nodeStorage NODESTORAGE
Specify the size (in gigabytes) of the root volume for any worker instances
created when using the -w flag. This is an EBS volume.
--nodeStorageOverrides NODESTORAGEOVERRIDES
Comma-separated list of nodeType:nodeStorage that are used to override the
default value from --nodeStorage for the specified nodeType(s). This is useful
for heterogeneous jobs where some tasks require much more disk than others.
Logging Options
--logOff
Same as -\-logCritical.
--logCritical
Turn on logging at level CRITICAL and above. (default is INFO)
--logError
Turn on logging at level ERROR and above. (default is INFO)
--logWarning
Turn on logging at level WARNING and above. (default is INFO)
--logInfo
Turn on logging at level INFO and above. (default is INFO)
--logDebug
Turn on logging at level DEBUG and above. (default is INFO)
--logLevel LOGLEVEL
Log at given level (may be either OFF (or CRITICAL), ERROR, WARN (or WARNING),
INFO or DEBUG). (default is INFO)
--logFile LOGFILE
File to log in.
--rotatingLogging
Turn on rotating logging, which prevents log files getting too big.
Ssh-Cluster Command
Toil provides the ability to ssh into the leader of the cluster. This can be done as
follows:
$ toil ssh-cluster CLUSTER-NAME-HERE
This will open a shell on the Toil leader and is used to start an 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, v1.1, and v1.2 standards.
Running CWL Locally
The toil-cwl-runner command provides cwl-parsing functionality using cwltool, and
leverages the job-scheduling and batch system support of Toil.
To run in local batch mode, provide the CWL file and the input object file:
$ toil-cwl-runner example.cwl example-job.yml
For a simple example of CWL with Toil see 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 \
--maxLogFileSize 20000000000 \
--stats \
standard_bam_processing.cwl \
inputs.yaml
In this example, we set the following options, which are all passed to Toil:
--singularity: Specifies that all jobs with Docker format containers specified should be
run using the Singularity container engine instead of the Docker container engine.
--jobStore: Path to a folder which doesn't exist yet, which will contain the Toil jobstore
and all related job-tracking information.
--batchSystem: Use the specified HPC or Cloud-based cluster platform.
--workDir: The directory where all temporary files will be created for the workflow. A
subdirectory of this will be set as the $TMPDIR environment variable and this subdirectory
can be referenced using the CWL parameter reference $(runtime.tmpdir) in CWL tools and
workflows.
--outdir: Directory where final File and Directory outputs will be written. References to
these and other output types will be in the JSON object printed to the stdout stream after
workflow execution.
--logFile: Path to the main logfile with logs from all jobs.
--writeLogs: Directory where all job logs will be stored.
--retryCount: How many times to retry each Toil job.
--maxLogFileSize: Logs that get larger than this value will be truncated.
--stats: Save resources usages in json files that can be collected with the toil stats
command after the workflow is done.
--disable-streaming: Does not allow streaming of input files. This is enabled by default
for files marked with streamable flag True and only for remote files when the jobStore is
not on local machine.
Running CWL in the Cloud
To run in cloud and HPC configurations, you may need to provide additional command line
parameters to select and configure the batch system to use.
To run a CWL workflow in AWS with toil see 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:
import os
import subprocess
from toil.common import Toil
from toil.job import Job
def initialize_jobs(job):
job.fileStore.logToMaster('initialize_jobs')
def runQC(job, cwl_file, cwl_filename, yml_file, yml_filename, outputs_dir, output_num):
job.fileStore.logToMaster("runQC")
tempDir = job.fileStore.getLocalTempDir()
cwl = job.fileStore.readGlobalFile(cwl_file, userPath=os.path.join(tempDir, cwl_filename))
yml = job.fileStore.readGlobalFile(yml_file, userPath=os.path.join(tempDir, yml_filename))
subprocess.check_call(["toil-cwl-runner", cwl, yml])
output_filename = "output.txt"
output_file = job.fileStore.writeGlobalFile(output_filename)
job.fileStore.readGlobalFile(output_file, userPath=os.path.join(outputs_dir, "sample_" + output_num + "_" + output_filename))
return output_file
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
# specify the folder where the cwl and yml files live
inputs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "cwlExampleFiles")
# specify where you wish the outputs to be written
outputs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "cwlExampleFiles")
job0 = Job.wrapJobFn(initialize_jobs)
cwl_filename = "hello.cwl"
cwl_file = toil.importFile("file://" + os.path.abspath(os.path.join(inputs_dir, cwl_filename)))
# add list of yml config inputs here or import and construct from file
yml_files = ["hello1.yml", "hello2.yml", "hello3.yml"]
i = 0
for yml in yml_files:
i = i + 1
yml_file = toil.importFile("file://" + os.path.abspath(os.path.join(inputs_dir, yml)))
yml_filename = yml
job = Job.wrapJobFn(runQC, cwl_file, cwl_filename, yml_file, yml_filename, outputs_dir, output_num=str(i))
job0.addChild(job)
toil.start(job0)
Running CWL workflows with InplaceUpdateRequirement
Some CWL workflows use the InplaceUpdateRequirement feature, which requires that
operations on files have visible side effects that Toil's file store cannot support. If
you need to run a workflow like this, you can make sure that all of your worker nodes have
a shared filesystem, and use the --bypass-file-store option to toil-cwl-runner. This will
make it leave all CWL intermediate files on disk and share them between jobs using file
paths, instead of storing them in the file store and downloading them when jobs need them.
Toil & CWL Tips
See logs for just one job by using the full log file
This requires knowing the job's toil-generated ID, which can be found in the log files.
cat cwltoil.log | grep jobVM1fIs
Grep for full tool commands from toil logs
This gives you a more concise view of the commands being run (note that this information
is only available from Toil when running with --logDebug).
pcregrep -M "\[job .*\.cwl.*$\n(.* .*$\n)*" cwltoil.log
# ^allows for multiline matching
Find Bams that have been generated for specific step while pipeline is running:
find . | grep -P '^./out_tmpdir.*_MD\.bam$'
See what jobs have been run
cat log/cwltoil.log | grep -oP "\[job .*.cwl\]" | sort | uniq
or:
cat log/cwltoil.log | grep -i "issued job"
Get status of a workflow
$ toil status /home/johnsoni/TEST_RUNS_3/TEST_run/tmp/jobstore-09ae0acc-c800-11e8-9d09-70106fb1697e
<hostname> 2018-10-04 15:01:44,184 MainThread INFO toil.lib.bioio: Root logger is at level 'INFO', 'toil' logger at level 'INFO'.
<hostname> 2018-10-04 15:01:44,185 MainThread INFO toil.utils.toilStatus: Parsed arguments
<hostname> 2018-10-04 15:01:47,081 MainThread INFO toil.utils.toilStatus: Traversing the job graph gathering jobs. This may take a couple of minutes.
Of the 286 jobs considered, there are 179 jobs with children, 107 jobs ready to run, 0 zombie jobs, 0 jobs with services, 0 services, and 0 jobs with log files currently in file:/home/user/jobstore-09ae0acc-c800-11e8-9d09-70106fb1697e.
Toil Stats
You can get run statistics broken down by CWL file. This only works once the workflow is
finished:
$ toil stats /path/to/jobstore
The output will contain CPU, memory, and walltime information for all CWL job types:
<hostname> 2018-10-15 12:06:19,003 MainThread INFO toil.lib.bioio: Root logger is at level 'INFO', 'toil' logger at level 'INFO'.
<hostname> 2018-10-15 12:06:19,004 MainThread INFO toil.utils.toilStats: Parsed arguments
<hostname> 2018-10-15 12:06:19,004 MainThread INFO toil.utils.toilStats: Checking if we have files for toil
<hostname> 2018-10-15 12:06:19,004 MainThread INFO toil.utils.toilStats: Checked arguments
Batch System: lsf
Default Cores: 1 Default Memory: 10485760K
Max Cores: 9.22337e+18
Total Clock: 106608.01 Total Runtime: 86634.11
Worker
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
1659 | 0.00 0.80 264.87 12595.59 439424.40 | 0.00 0.46 449.05 42240.74 744968.80 | -35336.69 0.16 -184.17 4230.65 -305544.39 | 48K 223K 1020K 40235K 1692300K
Job
Worker Jobs | min med ave max
| 1077 1077 1077 1077
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
1077 | 0.04 1.18 407.06 12593.43 438404.73 | 0.01 0.28 691.17 42240.35 744394.14 | -35336.83 0.27 -284.11 4230.49 -305989.41 | 135K 268K 1633K 40235K 1759734K
ResolveIndirect
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
205 | 0.04 0.07 0.16 2.29 31.95 | 0.01 0.02 0.02 0.14 3.60 | 0.02 0.05 0.14 2.28 28.35 | 190K 266K 256K 314K 52487K
CWLGather
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
40 | 0.05 0.17 0.29 1.90 11.62 | 0.01 0.02 0.02 0.05 0.80 | 0.03 0.14 0.27 1.88 10.82 | 188K 265K 250K 316K 10039K
CWLWorkflow
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
205 | 0.09 0.40 0.98 13.70 200.82 | 0.04 0.15 0.16 1.08 31.78 | 0.04 0.26 0.82 12.62 169.04 | 190K 270K 257K 316K 52826K
file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/expression_tools/group_waltz_files.cwl
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
99 | 0.29 0.49 0.59 2.50 58.11 | 0.14 0.26 0.29 1.04 28.95 | 0.14 0.22 0.29 1.48 29.16 | 135K 135K 135K 136K 13459K
file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/expression_tools/make_sample_output_dirs.cwl
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
11 | 0.34 0.52 0.74 2.63 8.18 | 0.20 0.30 0.41 1.17 4.54 | 0.14 0.20 0.33 1.45 3.65 | 136K 136K 136K 136K 1496K
file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/expression_tools/consolidate_files.cwl
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
8 | 0.31 0.59 0.71 1.80 5.69 | 0.18 0.35 0.37 0.63 2.94 | 0.13 0.27 0.34 1.17 2.75 | 136K 136K 136K 136K 1091K
file:///home/johnsoni/pipeline_0.0.39/ACCESS-Pipeline/cwl_tools/bwa-mem/bwa-mem.cwl
Count | Time* | Clock | Wait | Memory
n | min med* ave max total | min med ave max total | min med ave max total | min med ave max total
22 | 895.76 3098.13 3587.34 12593.43 78921.51 | 2127.02 7910.31 8123.06 16959.13 178707.34 | -11049.84 -3827.96 -4535.72 19.49 -99785.83 | 5659K 5950K 5854K 6128K 128807K
Understanding toil log files
There is a worker_log.txt file for each job, this file is written to while the job is
running, and deleted after the job finishes. The contents are printed to the main log file
and transferred to a log file in the --logDir folder once the job is completed
successfully.
The new log file will be named something like:
file:<path to cwl tool>.cwl_<job ID>.log
file:---home-johnsoni-pipeline_1.1.14-ACCESS--Pipeline-cwl_tools-marianas-ProcessLoopUMIFastq.cwl_I-O-jobfGsQQw000.log
This is the toil job command with spaces replaced by dashes.
WDL IN TOIL
Support is still in the alpha phase and should be able to handle basic wdl files. See the
specification below for more details.
How to Run a WDL file in Toil
Recommended best practice when running wdl files is to first use the Broad's wdltool for
syntax validation and generating the needed json input file. Full documentation can be
found on the repository, and a precompiled jar binary can be downloaded here: wdltool
(this requires java7).
That means two steps. First, make sure your wdl file is valid and devoid of syntax errors
by running
java -jar wdltool.jar validate example_wdlfile.wdl
Second, generate a complementary json file if your wdl file needs one. This json will
contain keys for every necessary input that your wdl file needs to run:
java -jar wdltool.jar inputs example_wdlfile.wdl
When this json template is generated, open the file, and fill in values as necessary by
hand. WDL files all require json files to accompany them. If no variable inputs are
needed, a json file containing only '{}' may be required.
Once a wdl file is validated and has an appropriate json file, workflows can be run in
toil using:
toil-wdl-runner example_wdlfile.wdl example_jsonfile.json
See options below for more parameters.
ENCODE Example from ENCODE-DCC
To follow this example, you will need docker installed. The original workflow can be
found here: https://github.com/ENCODE-DCC/pipeline-container
We've included the wdl file and data files in the toil repository needed to run this
example. First, download the example code and unzip. The file needed is
"testENCODE/encode_mapping_workflow.wdl".
Next, use wdltool (this requires java7) to validate this file:
java -jar wdltool.jar validate encode_mapping_workflow.wdl
Next, use wdltool to generate a json file for this wdl file:
java -jar wdltool.jar inputs encode_mapping_workflow.wdl
This json file once opened should look like this:
{
"encode_mapping_workflow.fastqs": "Array[File]",
"encode_mapping_workflow.trimming_parameter": "String",
"encode_mapping_workflow.reference": "File"
}
The trimming_parameter should be set to 'native'. Download the example code and unzip.
Inside are two data files required for the run
ENCODE_data/reference/GRCh38_chr21_bwa.tar.gz ENCODE_data/ENCFF000VOL_chr21.fq.gz
Editing the json to include these as inputs, the json should now look something like this:
{
"encode_mapping_workflow.fastqs": ["/path/to/unzipped/ENCODE_data/ENCFF000VOL_chr21.fq.gz"],
"encode_mapping_workflow.trimming_parameter": "native",
"encode_mapping_workflow.reference": "/path/to/unzipped/ENCODE_data/reference/GRCh38_chr21_bwa.tar.gz"
}
The wdl and json files can now be run using the command
toil-wdl-runner encode_mapping_workflow.wdl encode_mapping_workflow.json
This should deposit the output files in the user's current working directory (to change
this, specify a new directory with the '-o' option).
GATK Examples from the Broad
Simple examples of WDL can be found on the Broad's website as tutorials:
https://software.broadinstitute.org/wdl/documentation/topic?name=wdl-tutorials.
One can follow along with these tutorials, write their own wdl files following the
directions and run them using either cromwell or toil. For example, in tutorial 1, if
you've followed along and named your wdl file 'helloHaplotypeCall.wdl', then once you've
validated your wdl file using wdltool (this requires java7) using
java -jar wdltool.jar validate helloHaplotypeCaller.wdl
and generated a json file (and subsequently typed in appropriate filepaths* and variables)
using
java -jar wdltool.jar inputs helloHaplotypeCaller.wdl
· Absolute filepath inputs are recommended for local testing.
then the wdl script can be run using
toil-wdl-runner helloHaplotypeCaller.wdl helloHaplotypeCaller_inputs.json
toilwdl.py Options
'-o' or '-\-outdir': Specifies the output folder, and defaults to the current working
directory if not specified by the user.
'-\-dev_mode': Creates "AST.out", which holds a printed AST of the wdl file and
"mappings.out", which holds the printed task, workflow, csv, and tsv dictionaries
generated by the parser. Also saves the compiled toil python workflow file for debugging.
Any number of arbitrary options may also be specified. These options will not be parsed
immediately, but passed down as toil options once the wdl/json files are processed. For
valid toil options, see the documentation:
http://toil.readthedocs.io/en/latest/running/cliOptions.html
Running WDL within Toil Scripts
NOTE:
A cromwell.jar file is needed in order to run a WDL workflow.
A WDL workflow can be run indirectly in a native Toil script. However, this is not the
standard way to run WDL workflows with Toil and doing so comes at the cost of job
efficiency. For some use cases, such as running one process on multiple files, it may be
useful. For example, if you want to run a WDL workflow with 3 JSON files specifying
different samples inputs, it could look something like:
import os
import subprocess
from toil.common import Toil
from toil.job import Job
def initialize_jobs(job):
job.fileStore.logToMaster("initialize_jobs")
def runQC(job, wdl_file, wdl_filename, json_file, json_filename, outputs_dir, jar_loc,output_num):
job.fileStore.logToMaster("runQC")
tempDir = job.fileStore.getLocalTempDir()
wdl = job.fileStore.readGlobalFile(wdl_file, userPath=os.path.join(tempDir, wdl_filename))
json = job.fileStore.readGlobalFile(json_file, userPath=os.path.join(tempDir, json_filename))
subprocess.check_call(["java","-jar",jar_loc,"run",wdl,"--inputs",json])
output_filename = "output.txt"
output_file = job.fileStore.writeGlobalFile(outputs_dir + output_filename)
job.fileStore.readGlobalFile(output_file, userPath=os.path.join(outputs_dir, "sample_" + output_num + "_" + output_filename))
return output_file
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
# specify the folder where the wdl and json files live
inputs_dir = "wdlExampleFiles/"
# specify where you wish the outputs to be written
outputs_dir = "wdlExampleFiles/"
# specify the location of your cromwell jar
jar_loc = os.path.abspath("wdlExampleFiles/cromwell-35.jar")
job0 = Job.wrapJobFn(initialize_jobs)
wdl_filename = "hello.wdl"
wdl_file = toil.importFile("file://" + os.path.abspath(os.path.join(inputs_dir, wdl_filename)))
# add list of yml config inputs here or import and construct from file
json_files = ["hello1.json", "hello2.json", "hello3.json"]
i = 0
for json in json_files:
i = i + 1
json_file = toil.importFile("file://" + os.path.join(inputs_dir, json))
json_filename = json
job = Job.wrapJobFn(runQC, wdl_file, wdl_filename, json_file, json_filename, outputs_dir, jar_loc, output_num=str(i))
job0.addChild(job)
toil.start(job0)
WDL Specifications
WDL language specifications can be found here:
https://github.com/broadinstitute/wdl/blob/develop/SPEC.md
Implementing support for more features is currently underway, but a basic roadmap so far
is:
CURRENTLY IMPLEMENTED:
· Scatter
· Many Built-In Functions
· Docker Calls
· Handles Priority, and Output File Wrangling
· Currently Handles Primitives and Arrays
TO BE IMPLEMENTED:
· Integrate Cloud Autoscaling Capacity More Robustly
· WDL Files That "Import" Other WDL Files (Including URI Handling for 'http://' and
'https://')
WORKFLOW EXECUTION SERVICE (WES)
The GA4GH Workflow Execution Service (WES) is a standardized API for submitting and
monitoring workflows. Toil has experimental support for setting up a WES server and
executing CWL, WDL, and Toil workflows using the WES API. More information about the WES
API specification can be found here.
To get started with the Toil WES server, make sure that the server extra (extras) is
installed.
Preparing your WES environment
The WES server requires Celery to distribute and execute workflows. To set up Celery:
1. Start RabbitMQ, which is the broker between the WES server and Celery workers:
docker run -d --name wes-rabbitmq -p 5672:5672 rabbitmq:3.9.5
2. Start Celery workers:
celery -A toil.server.celery_app worker --loglevel=INFO
Starting a WES server
To start a WES server on the default port 8080, run the Toil command:
$ toil server
The WES API will be hosted on the following URL:
http://localhost:8080/ga4gh/wes/v1
To use another port, e.g.: 3000, you can specify the --port argument:
$ toil server --port 3000
There are many other command line options. Help information can be found by using this
command:
$ toil server --help
Below is a detailed summary of all available options:
--debug
Enable debug mode.
--host HOST
The host interface that the Toil server binds on. (default: "127.0.0.1").
--port PORT
The port that the Toil server listens on. (default: 8080).
--swagger_ui
Enable the swagger UI on the ga4gh/wes/v1/ui endpoint. (default: False).
--cors Enable Cross Origin Resource Sharing (CORS). This should only be turned on if the
server is intended to be used by a website or domain. (default: False).
--cors_origins ORIGIN
Ignored if --cors is False. This sets the allowed origins for CORS. For details
about CORS and its security risks, see the GA4GH docs on CORS. (default: "*").
--workers WORKERS
Ignored if debug mode is on. The number of worker processes launched by the
production WSGI server. (default: 2).
--work_dir WORKDIR
The directory where workflows should be stored. This directory should be empty or
only contain previous workflows. (default: './workflows').
--opt ENGINE_OPTION
Specify the default parameters to be sent to the workflow engine for each run.
Accepts multiple values.
Example: toil server --opt=--logLevel=CRITICAL --opt=--workDir=/tmp.
Running the Server with docker-compose
Instead of manually setting up the server components (toil server, RabbitMQ, and Celery),
you can use the following docker-compose.yml file to orchestrate and link them together.
Make sure to change /tmp/toil-workflows if you want Toil workflows to live somewhere else,
and create the directory before starting the server.
Also make sure to run it behind a firewall; it opens up the Toil server on port 8080 to
anyone who connects.
# Dockerfile
FROM python:3.8
WORKDIR /app
RUN pip install -e git+https://github.com/DataBiosphere/toil.git@9f9a83048344aa31583ba52ccae5f8f6ec23c8de#egg=toil[cwl,wdl,server]
# docker-compose.yml
version: "3.8"
services:
rabbitmq:
image: rabbitmq:3.9.5
hostname: rabbitmq
container_name: rabbitmq
celery-worker:
build:
context: .
dockerfile: Dockerfile
container_name: celery-worker
volumes:
- /tmp/toil-workflows:/app/toil-workflows
command: celery --broker=amqp://guest:guest@rabbitmq:5672// -A toil.server.celery_app worker --loglevel=INFO
depends_on:
- rabbitmq
wes-server:
build:
context: .
dockerfile: Dockerfile
container_name: wes-server
volumes:
- /tmp/toil-workflows:/app/toil-workflows
environment:
- TOIL_WES_BROKER_URL=amqp://guest:guest@rabbitmq:5672//
command: toil server --host 0.0.0.0
ports:
- "127.0.0.1:8080:8080"
depends_on:
- rabbitmq
- celery-worker
Once everything is configured, simply run docker compose up to start the containers. Run
docker compose down to stop and remove all containers.
Note that this method only works if docker-compose is installed, which does not work on
the Toil appliance.
Running on a Toil cluster
To run the server on a Toil leader instance on EC2:
1. Launch a Toil cluster with the toil launch-cluster command with the AWS provisioner
2. SSH into your cluster with the --sshOption=-L8080:localhost:8080 option to forward port
8080
3. Set up Celery workers required to run WES workflows (Preparing your WES environment)
4. Now, you can run the WES server with toil server on the Toil appliance.
NOTE:
To run the server in the background, run "nohup toil server &".
WES API Endpoints
As defined by the GA4GH WES API specification, the following endpoints with base path
ga4gh/wes/v1/ are supported by Toil:
┌───────────────────────────┬──────────────────────────────────┐
│GET /service-info │ Get information about the │
│ │ Workflow Execution Service. │
├───────────────────────────┼──────────────────────────────────┤
│GET /runs │ List the workflow runs. │
├───────────────────────────┼──────────────────────────────────┤
│POST /runs │ Run a workflow. This endpoint │
│ │ creates a new workflow run and │
│ │ returns a run_id to monitor its │
│ │ progress. │
├───────────────────────────┼──────────────────────────────────┤
│GET /runs/{run_id} │ Get detailed info about a │
│ │ workflow run. │
├───────────────────────────┼──────────────────────────────────┤
│POST /runs/{run_id}/cancel │ Cancel a running workflow. │
├───────────────────────────┼──────────────────────────────────┤
│GET /runs/{run_id}/status │ Get the status (overall state) │
│ │ of a workflow run. │
└───────────────────────────┴──────────────────────────────────┘
Submitting a Workflow
Now that the WES API is up and running, we can submit and monitor workflows remotely using
the WES API endpoints. A workflow can be submitted for execution using the POST /runs
endpoint.
As a quick example, we can submit the example CWL workflow from cwlquickstart to our WES
API:
# example.cwl
cwlVersion: v1.0
class: CommandLineTool
baseCommand: echo
stdout: output.txt
inputs:
message:
type: string
inputBinding:
position: 1
outputs:
output:
type: stdout
using cURL:
$ curl --location --request POST 'http://localhost:8080/ga4gh/wes/v1/runs' \
--form 'workflow_url="example.cwl"' \
--form 'workflow_type="cwl"' \
--form 'workflow_type_version="v1.0"' \
--form 'workflow_params="{\"message\": \"Hello world!\"}"' \
--form 'workflow_attachment=@"./toil_test_files/example.cwl"'
{
"run_id": "4deb8beb24894e9eb7c74b0f010305d1"
}
If the workflow is submitted successfully, a JSON object containing a run_id will be
returned. The run_id is a unique identifier of your requested workflow, which can be used
to monitor or cancel the run.
There are a few required parameters that have to be set for all workflow submissions,
which are the following:
┌──────────────────────┬──────────────────────────────────┐
│workflow_url │ The URL of the workflow to run. │
│ │ This can refer to a file from │
│ │ workflow_attachment. │
├──────────────────────┼──────────────────────────────────┤
│workflow_type │ The type of workflow language. │
│ │ Toil currently supports one of │
│ │ the following: "CWL", "WDL", or │
│ │ "py". To run a Toil native │
│ │ python script, set this to "py". │
├──────────────────────┼──────────────────────────────────┤
│workflow_type_version │ The version of the workflow │
│ │ language. Supported versions can │
│ │ be found by accessing the GET │
│ │ /service-info endpoint of your │
│ │ WES server. │
├──────────────────────┼──────────────────────────────────┤
│workflow_params │ A JSON object that specifies the │
│ │ inputs of the workflow. │
└──────────────────────┴──────────────────────────────────┘
Additionally, the following optional parameters are also available:
┌───────────────────────────┬──────────────────────────────────┐
│workflow_attachment │ A list of files associated with │
│ │ the workflow run. │
├───────────────────────────┼──────────────────────────────────┤
│workflow_engine_parameters │ A JSON key-value map of workflow │
│ │ engine parameters to send to the │
│ │ runner. │
│ │ │
│ │ Example: {"--logLevel": "INFO", │
│ │ "--workDir": "/tmp/"} │
├───────────────────────────┼──────────────────────────────────┤
│tags │ A JSON key-value map of metadata │
│ │ associated with the workflow. │
└───────────────────────────┴──────────────────────────────────┘
For more details about these parameters, refer to the Run Workflow section in the WES API
spec.
Upload multiple files
Looking at the body of the request of the previous example, note that the workflow_url is
a relative URL that refers to the example.cwl file uploaded from the local path
./toil_test_files/example.cwl.
To specify the file name (or subdirectory) of the remote destination file, set the
filename field in the Content-Disposition header. You could also upload more than one file
by providing the workflow_attachment parameter multiple times with different files.
This can be shown by the following example:
$ curl --location --request POST 'http://localhost:8080/ga4gh/wes/v1/runs' \
--form 'workflow_url="example.cwl"' \
--form 'workflow_type="cwl"' \
--form 'workflow_type_version="v1.0"' \
--form 'workflow_params="{\"message\": \"Hello world!\"}"' \
--form 'workflow_attachment=@"./toil_test_files/example.cwl"' \
--form 'workflow_attachment=@"./toil_test_files/2.fasta";filename=inputs/test.fasta' \
--form 'workflow_attachment=@"./toil_test_files/2.fastq";filename=inputs/test.fastq'
On the server, the execution directory would have the following structure from the above
request:
execution/
├── example.cwl
├── inputs
│ ├── test.fasta
| └── test.fastq
└── wes_inputs.json
Specify Toil options
To pass Toil specific parameters to the workflow, you can include the
workflow_engine_parameters parameter along with your request.
For example, to set the logging level to INFO, and change the working directory of the
workflow, simply include the following as workflow_engine_parameters:
{"--logLevel": "INFO", "--workDir": "/tmp/"}
These options would be appended at the end of existing parameters during command
construction, which would override the default parameters if provided. (Default parameters
that can be passed multiple times would not be overridden).
Monitoring a Workflow
With the run_id returned when submitting the workflow, we can check the status or get the
full logs of the workflow run.
Checking the state
The GET /runs/{run_id}/status endpoint can be used to get a simple result with the overall
state of your run:
$ curl http://localhost:8080/ga4gh/wes/v1/runs/4deb8beb24894e9eb7c74b0f010305d1/status
{
"run_id": "4deb8beb24894e9eb7c74b0f010305d1",
"state": "RUNNING"
}
The possible states here are: QUEUED, INITIALIZING, RUNNING, COMPLETE, EXECUTOR_ERROR,
SYSTEM_ERROR, CANCELING, and CANCELED.
Getting the full logs
To get the detailed information about a workflow run, use the GET /runs/{run_id} endpoint:
$ curl http://localhost:8080/ga4gh/wes/v1/runs/4deb8beb24894e9eb7c74b0f010305d1
{
"run_id": "4deb8beb24894e9eb7c74b0f010305d1",
"request": {
"workflow_attachment": [
"example.cwl"
],
"workflow_url": "example.cwl",
"workflow_type": "cwl",
"workflow_type_version": "v1.0",
"workflow_params": {
"message": "Hello world!"
}
},
"state": "RUNNING",
"run_log": {
"cmd": [
"toil-cwl-runner --outdir=/home/toil/workflows/4deb8beb24894e9eb7c74b0f010305d1/outputs --jobStore=file:/home/toil/workflows/4deb8beb24894e9eb7c74b0f010305d1/toil_job_store /home/toil/workflows/4deb8beb24894e9eb7c74b0f010305d1/execution/example.cwl /home/workflows/4deb8beb24894e9eb7c74b0f010305d1/execution/wes_inputs.json"
],
"start_time": "2021-08-30T17:35:50Z",
"end_time": null,
"stdout": null,
"stderr": null,
"exit_code": null
},
"task_logs": [],
"outputs": {}
}
Canceling a run
To cancel a workflow run, use the POST /runs/{run_id}/cancel endpoint:
$ curl --location --request POST 'http://localhost:8080/ga4gh/wes/v1/runs/4deb8beb24894e9eb7c74b0f010305d1/cancel'
{
"run_id": "4deb8beb24894e9eb7c74b0f010305d1"
}
DEVELOPING A WORKFLOW
This tutorial walks through the features of Toil necessary for developing a workflow using
the Toil Python API.
NOTE:
"script" and "workflow" will be used interchangeably
Scripting Quick Start
To begin, consider this short toil script which illustrates defining a workflow:
from toil.common import Toil
from toil.job import Job
def helloWorld(message, memory="2G", cores=2, disk="3G"):
return f"Hello, world!, here's a message: {message}"
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "OFF"
options.clean = "always"
hello_job = Job.wrapFn(helloWorld, "Woot")
with Toil(options) as toil:
print(toil.start(hello_job)) # prints "Hello, world!, ..."
The workflow consists of a single job. The resource requirements for that job are
(optionally) specified by keyword arguments (memory, cores, disk). The script is run using
toil.job.Job.Runner.getDefaultOptions(). Below we explain the components of this code in
detail.
Job Basics
The atomic unit of work in a Toil workflow is a Job. User scripts inherit from this base
class to define units of work. For example, here is a more long-winded class-based version
of the job in the quick start example:
from toil.job import Job
class HelloWorld(Job):
def __init__(self, message):
Job.__init__(self, memory="2G", cores=2, disk="3G")
self.message = message
def run(self, fileStore):
return f"Hello, world! Here's a message: {self.message}"
In the example a class, HelloWorld, is defined. The constructor requests 2 gigabytes of
memory, 2 cores and 3 gigabytes of local disk to complete the work.
The toil.job.Job.run() method is the function the user overrides to get work done. Here it
just returns a message.
It is also possible to log a message using toil.job.Job.log(), which will be registered in
the log output of the leader process of the workflow:
...
def run(self, fileStore):
self.log(f"Hello, world! Here's a message: {self.message}")
Invoking a Workflow
We can add to the previous example to turn it into a complete workflow by adding the
necessary function calls to create an instance of HelloWorld and to run this as a workflow
containing a single job. For example:
from toil.common import Toil
from toil.job import Job
class HelloWorld(Job):
def __init__(self, message):
Job.__init__(self, memory="2G", cores=2, disk="3G")
self.message = message
def run(self, fileStore):
return f"Hello, world!, here's a message: {self.message}"
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "OFF"
options.clean = "always"
hello_job = HelloWorld("Woot")
with Toil(options) as toil:
print(toil.start(hello_job))
NOTE:
Do not include a . in the name of your python script (besides .py at the end). This is
to allow toil to import the types and functions defined in your file while starting a
new process.
This uses the toil.common.Toil class, which is used to run and resume Toil workflows. It
is used as a context manager and allows for preliminary setup, such as staging of files
into the job store on the leader node. An instance of the class is initialized by
specifying an options object. The actual workflow is then invoked by calling the
toil.common.Toil.start() method, passing the root job of the workflow, or, if a workflow
is being restarted, toil.common.Toil.restart() should be used. Note that the context
manager should have explicit if else branches addressing restart and non restart cases.
The boolean value for these if else blocks is toil.options.restart.
For example:
from toil.common import Toil
from toil.job import Job
class HelloWorld(Job):
def __init__(self, message):
Job.__init__(self, memory="2G", cores=2, disk="3G")
self.message = message
def run(self, fileStore):
return f"Hello, world!, I have a message: {self.message}"
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
if not toil.options.restart:
job = HelloWorld("Woot!")
output = toil.start(job)
else:
output = toil.restart()
print(output)
The call to toil.job.Job.Runner.getDefaultOptions() creates a set of default options for
the workflow. The only argument is a description of how to store the workflow's state in
what we call a job-store. Here the job-store is contained in a directory within the
current working directory called "toilWorkflowRun". Alternatively this string can encode
other ways to store the necessary state, e.g. an S3 bucket object store location. By
default the job-store is deleted if the workflow completes successfully.
The workflow is executed in the final line, which creates an instance of HelloWorld and
runs it as a workflow. Note all Toil workflows start from a single starting job, referred
to as the root job. The return value of the root job is returned as the result of the
completed workflow (see promises below to see how this is a useful feature!).
Specifying Commandline Arguments
To allow command line control of the options we can use the
toil.job.Job.Runner.getDefaultArgumentParser() method to create a argparse.ArgumentParser
object which can be used to parse command line options for a Toil script. For example:
from toil.common import Toil
from toil.job import Job
class HelloWorld(Job):
def __init__(self, message):
Job.__init__(self, memory="2G", cores=2, disk="3G")
self.message = message
def run(self, fileStore):
return "Hello, world!, here's a message: %s" % self.message
if __name__ == "__main__":
parser = Job.Runner.getDefaultArgumentParser()
options = parser.parse_args()
options.logLevel = "OFF"
options.clean = "always"
hello_job = HelloWorld("Woot")
with Toil(options) as toil:
print(toil.start(hello_job))
Creates a fully fledged script with all the options Toil exposed as command line
arguments. Running this script with "--help" will print the full list of options.
Alternatively an existing argparse.ArgumentParser or optparse.OptionParser object can have
Toil script command line options added to it with the toil.job.Job.Runner.addToilOptions()
method.
Resuming a Workflow
In the event that a workflow fails, either because of programmatic error within the jobs
being run, or because of node failure, the workflow can be resumed. Workflows can only
not be reliably resumed if the job-store itself becomes corrupt.
Critical to resumption is that jobs can be rerun, even if they have apparently completed
successfully. Put succinctly, a user defined job should not corrupt its input arguments.
That way, regardless of node, network or leader failure the job can be restarted and the
workflow resumed.
To resume a workflow specify the "restart" option in the options object passed to
toil.common.Toil.start(). If node failures are expected it can also be useful to use the
integer "retryCount" option, which will attempt to rerun a job retryCount number of times
before marking it fully failed.
In the common scenario that a small subset of jobs fail (including retry attempts) within
a workflow Toil will continue to run other jobs until it can do no more, at which point
toil.common.Toil.start() will raise a toil.leader.FailedJobsException exception. Typically
at this point the user can decide to fix the script and resume the workflow or delete the
job-store manually and rerun the complete workflow.
Functions and Job Functions
Defining jobs by creating class definitions generally involves the boilerplate of creating
a constructor. To avoid this the classes toil.job.FunctionWrappingJob and
toil.job.JobFunctionWrappingTarget allow functions to be directly converted to jobs. For
example, the quick start example (repeated here):
from toil.common import Toil
from toil.job import Job
def helloWorld(message, memory="2G", cores=2, disk="3G"):
return f"Hello, world!, here's a message: {message}"
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "OFF"
options.clean = "always"
hello_job = Job.wrapFn(helloWorld, "Woot")
with Toil(options) as toil:
print(toil.start(hello_job)) # prints "Hello, world!, ..."
Is equivalent to the previous example, but using a function to define the job.
The function call:
Job.wrapFn(helloWorld, "Woot")
Creates the instance of the toil.job.FunctionWrappingTarget that wraps the function.
The keyword arguments memory, cores and disk allow resource requirements to be specified
as before. Even if they are not included as keyword arguments within a function header
they can be passed as arguments when wrapping a function as a job and will be used to
specify resource requirements.
We can also use the function wrapping syntax to a job function, a function whose first
argument is a reference to the wrapping job. Just like a self argument in a class, this
allows access to the methods of the wrapping job, see toil.job.JobFunctionWrappingTarget.
For example:
from toil.common import Toil
from toil.job import Job
def helloWorld(job, message):
job.log(f"Hello world, I have a message: {message}")
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
hello_job = Job.wrapJobFn(helloWorld, "Woot!")
with Toil(options) as toil:
toil.start(hello_job)
Here helloWorld() is a job function. It uses the toil.job.Job.log() to log a message that
will be printed to the output console. Here the only subtle difference to note is the
line:
hello_job = Job.wrapJobFn(helloWorld, "Woot")
Which uses the function toil.job.Job.wrapJobFn() to wrap the job function instead of
toil.job.Job.wrapFn() which wraps a vanilla function.
Workflows with Multiple Jobs
A parent job can have child jobs and follow-on jobs. These relationships are specified by
methods of the job class, e.g. toil.job.Job.addChild() and toil.job.Job.addFollowOn().
Considering a set of jobs the nodes in a job graph and the child and follow-on
relationships the directed edges of the graph, we say that a job B that is on a directed
path of child/follow-on edges from a job A in the job graph is a successor of A, similarly
A is a predecessor of B.
A parent job's child jobs are run directly after the parent job has completed, and in
parallel. The follow-on jobs of a job are run after its child jobs and their successors
have completed. They are also run in parallel. Follow-ons allow the easy specification of
cleanup tasks that happen after a set of parallel child tasks. The following shows a
simple example that uses the earlier helloWorld() job function:
from toil.common import Toil
from toil.job import Job
def helloWorld(job, message, memory="2G", cores=2, disk="3G"):
job.log(f"Hello world, I have a message: {message}")
if __name__ == "__main__":
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(f"Hello world, I have a message: {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(f"Hello world, I have a message: {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(f"Binary string: {message}")
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
toil.start(Job.wrapJobFn(binaryStringFn, depth=5))
The job function binaryStringFn logs all possible binary strings of length n (here n=5),
creating a total of 2^(n+2) - 1 jobs dynamically and recursively. Static and dynamic
creation of jobs can be mixed in a Toil workflow, with jobs defined within a job or job
function being created at run time.
Promises
The previous example of dynamic job creation shows variables from a parent job being
passed to a child job. Such forward variable passing is naturally specified by recursive
invocation of successor jobs within parent jobs. This can also be achieved statically by
passing around references to the return variables of jobs. In Toil this is achieved with
promises, as illustrated in the following example:
from toil.common import Toil
from toil.job import Job
def fn(job, i):
job.log("i is: %s" % i, level=100)
return i + 1
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
j1 = Job.wrapJobFn(fn, 1)
j2 = j1.addChildJobFn(fn, j1.rv())
j3 = j1.addFollowOnJobFn(fn, j2.rv())
with Toil(options) as toil:
toil.start(j1)
Running this workflow results in three log messages from the jobs: i is 1 from j1, i is 2
from j2 and i is 3 from j3.
The return value from the first job is promised to the second job by the call to
toil.job.Job.rv() in the following line:
j2 = j1.addChildFn(fn, j1.rv())
The value of j1.rv() is a promise, rather than the actual return value of the function,
because j1 for the given input has at that point not been evaluated. A promise
(toil.job.Promise) is essentially a pointer to for the return value that is replaced by
the actual return value once it has been evaluated. Therefore, when j2 is run the promise
becomes 2.
Promises also support indexing of return values:
def parent(job):
indexable = Job.wrapJobFn(fn)
job.addChild(indexable)
job.addFollowOnFn(raiseWrap, indexable.rv(2))
def raiseWrap(arg):
raise RuntimeError(arg) # raises "2"
def fn(job):
return (0, 1, 2, 3)
Promises can be quite useful. For example, we can combine dynamic job creation with
promises to achieve a job creation process that mimics the functional patterns possible in
many programming languages:
from toil.common import Toil
from toil.job import Job
def binaryStrings(job, depth, message=""):
if depth > 0:
s = [job.addChildJobFn(binaryStrings, depth - 1, message + "0").rv(),
job.addChildJobFn(binaryStrings, depth - 1, message + "1").rv()]
return job.addFollowOnFn(merge, s).rv()
return [message]
def merge(strings):
return strings[0] + strings[1]
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.loglevel = "OFF"
options.clean = "always"
with Toil(options) as toil:
print(toil.start(Job.wrapJobFn(binaryStrings, depth=5)))
The return value l of the workflow is a list of all binary strings of length 10, computed
recursively. Although a toy example, it demonstrates how closely Toil workflows can mimic
typical programming patterns.
Promised Requirements
Promised requirements are a special case of Promises that allow a job's return value to be
used as another job's resource requirements.
This is useful when, for example, a job's storage requirement is determined by a file
staged to the job store by an earlier job:
import os
from toil.common import Toil
from toil.job import Job, PromisedRequirement
def parentJob(job):
downloadJob = Job.wrapJobFn(stageFn, "file://" + os.path.realpath(__file__), cores=0.1, memory='32M', disk='1M')
job.addChild(downloadJob)
analysis = Job.wrapJobFn(analysisJob,
fileStoreID=downloadJob.rv(0),
disk=PromisedRequirement(downloadJob.rv(1)))
job.addFollowOn(analysis)
def stageFn(job, url, cores=1):
importedFile = job.fileStore.import_file(url)
return importedFile, importedFile.size
def analysisJob(job, fileStoreID, cores=2):
# now do some analysis on the file
pass
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
toil.start(Job.wrapJobFn(parentJob))
Note that this also makes use of the size attribute of the FileID object. This promised
requirements mechanism can also be used in combination with an aggregator for multiple
jobs' output values:
def parentJob(job):
aggregator = []
for fileNum in range(0, 10):
downloadJob = Job.wrapJobFn(stageFn, "file://" + os.path.realpath(__file__), cores=0.1, memory='32M', disk='1M')
job.addChild(downloadJob)
aggregator.append(downloadJob)
analysis = Job.wrapJobFn(analysisJob,
fileStoreID=downloadJob.rv(0),
disk=PromisedRequirement(lambda xs: sum(xs), [j.rv(1) for j in aggregator]))
job.addFollowOn(analysis)
Limitations
Just like regular promises, the return value must be determined prior to
scheduling any job that depends on the return value. In our example above,
notice how the dependent jobs were follow ons to the parent while promising jobs
are children of the parent. This ordering ensures that all promises are properly
fulfilled.
FileID
The toil.fileStore.FileID class is a small wrapper around Python's builtin string class.
It is used to represent a file's ID in the file store, and has a size attribute that is
the file's size in bytes. This object is returned by importFile and writeGlobalFile.
Managing files within a workflow
It is frequently the case that a workflow will want to create files, both persistent and
temporary, during its run. The toil.fileStores.abstractFileStore.AbstractFileStore class
is used by jobs to manage these files in a manner that guarantees cleanup and resumption
on failure.
The toil.job.Job.run() method has a file store instance as an argument. The following
example shows how this can be used to create temporary files that persist for the length
of the job, be placed in a specified local disk of the node and that will be cleaned up,
regardless of failure, when the job finishes:
from toil.common import Toil
from toil.job import Job
class LocalFileStoreJob(Job):
def run(self, fileStore):
# self.tempDir will always contain the name of a directory within the allocated disk space reserved for the job
scratchDir = self.tempDir
# Similarly create a temporary file.
scratchFile = fileStore.getLocalTempFile()
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
# Create an instance of FooJob which will have at least 2 gigabytes of storage space.
j = LocalFileStoreJob(disk="2G")
# Run the workflow
with Toil(options) as toil:
toil.start(j)
Job functions can also access the file store for the job. The equivalent of the
LocalFileStoreJob class is
def localFileStoreJobFn(job):
scratchDir = job.tempDir
scratchFile = job.fileStore.getLocalTempFile()
Note that the fileStore attribute is accessed as an attribute of the job argument.
In addition to temporary files that exist for the duration of a job, the file store allows
the creation of files in a global store, which persists during the workflow and are
globally accessible (hence the name) between jobs. For example:
import os
from toil.common import Toil
from toil.job import Job
def globalFileStoreJobFn(job):
job.log("The following example exercises all the methods provided "
"by the toil.fileStores.abstractFileStore.AbstractFileStore class")
# Create a local temporary file.
scratchFile = job.fileStore.getLocalTempFile()
# Write something in the scratch file.
with open(scratchFile, 'w') as fH:
fH.write("What a tangled web we weave")
# Write a copy of the file into the file-store; fileID is the key that can be used to retrieve the file.
# This write is asynchronous by default
fileID = job.fileStore.writeGlobalFile(scratchFile)
# Write another file using a stream; fileID2 is the
# key for this second file.
with job.fileStore.writeGlobalFileStream(cleanup=True) as (fH, fileID2):
fH.write(b"Out brief candle")
# Now read the first file; scratchFile2 is a local copy of the file that is read-only by default.
scratchFile2 = job.fileStore.readGlobalFile(fileID)
# Read the second file to a desired location: scratchFile3.
scratchFile3 = os.path.join(job.tempDir, "foo.txt")
job.fileStore.readGlobalFile(fileID2, userPath=scratchFile3)
# Read the second file again using a stream.
with job.fileStore.readGlobalFileStream(fileID2) as fH:
print(fH.read()) # This prints "Out brief candle"
# Delete the first file from the global file-store.
job.fileStore.deleteGlobalFile(fileID)
# It is unnecessary to delete the file keyed by fileID2 because we used the cleanup flag,
# which removes the file after this job and all its successors have run (if the file still exists)
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
toil.start(Job.wrapJobFn(globalFileStoreJobFn))
The example demonstrates the global read, write and delete functionality of the
file-store, using both local copies of the files and streams to read and write the files.
It covers all the methods provided by the file store interface.
What is obvious is that the file-store provides no functionality to update an existing
"global" file, meaning that files are, barring deletion, immutable. Also worth noting is
that there is no file system hierarchy for files in the global file store. These
limitations allow us to fairly easily support different object stores and to use caching
to limit the amount of network file transfer between jobs.
Staging of Files into the Job Store
External files can be imported into or exported out of the job store prior to running a
workflow when the toil.common.Toil context manager is used on the leader. The context
manager provides methods toil.common.Toil.importFile(), and toil.common.Toil.exportFile()
for this purpose. The destination and source locations of such files are described with
URLs passed to the two methods. Local files can be imported and exported as relative
paths, and should be relative to the directory where the toil workflow is initially run
from.
Using absolute paths and appropriate schema where possible (prefixing with "file://" or
"s3:/" for example), make imports and exports less ambiguous and is recommended.
A list of the currently supported URLs can be found at
toil.jobStores.abstractJobStore.AbstractJobStore.importFile(). To import an external file
into the job store as a shared file, pass the optional sharedFileName parameter to that
method.
If a workflow fails for any reason an imported file acts as any other file in the job
store. If the workflow was configured such that it not be cleaned up on a failed run, the
file will persist in the job store and needs not be staged again when the workflow is
resumed.
Example:
import os
from toil.common import Toil
from toil.job import Job
class HelloWorld(Job):
def __init__(self, id):
Job.__init__(self, memory="2G", cores=2, disk="3G")
self.inputFileID = id
def run(self, fileStore):
with fileStore.readGlobalFileStream(self.inputFileID, encoding='utf-8') as fi:
with fileStore.writeGlobalFileStream(encoding='utf-8') as (fo, outputFileID):
fo.write(fi.read() + 'World!')
return outputFileID
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
if not toil.options.restart:
ioFileDirectory = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stagingExampleFiles")
inputFileID = toil.importFile("file://" + os.path.abspath(os.path.join(ioFileDirectory, "in.txt")))
outputFileID = toil.start(HelloWorld(inputFileID))
else:
outputFileID = toil.restart()
toil.exportFile(outputFileID, "file://" + os.path.abspath(os.path.join(ioFileDirectory, "out.txt")))
Using Docker Containers in Toil
Docker containers are commonly used with Toil. The combination of Toil and Docker allows
for pipelines to be fully portable between any platform that has both Toil and Docker
installed. Docker eliminates the need for the user to do any other tool installation or
environment setup.
In order to use Docker containers with Toil, Docker must be installed on all workers of
the cluster. Instructions for installing Docker can be found on the Docker website.
When using Toil-based autoscaling, Docker will be automatically set up on the cluster's
worker nodes, so no additional installation steps are necessary. Further information on
using Toil-based autoscaling can be found in the Autoscaling documentation.
In order to use docker containers in a Toil workflow, the container can be built locally
or downloaded in real time from an online docker repository like Quay. If the container is
not in a repository, the container's layers must be accessible on each node of the
cluster.
When invoking docker containers from within a Toil workflow, it is strongly recommended
that you use dockerCall(), a toil job function provided in toil.lib.docker. dockerCall
leverages docker's own python API, and provides container cleanup on job failure. When
docker containers are run without this feature, failed jobs can result in resource leaks.
Docker's API can be found at docker-py.
In order to use dockerCall, your installation of Docker must be set up to run without
sudo. Instructions for setting this up can be found here.
An example of a basic dockerCall is below:
dockerCall(job=job,
tool='quay.io/ucsc_cgl/bwa',
workDir=job.tempDir,
parameters=['index', '/data/reference.fa'])
Note the assumption that reference.fa file is located in /data. This is Toil's standard
convention as a mount location to reduce boilerplate when calling dockerCall. Users can
choose their own mount locations by supplying a volumes kwarg to dockerCall, such as:
volumes={working_dir: {'bind': '/data', 'mode': 'rw'}}, where working_dir is an absolute
path on the user's filesystem.
dockerCall can also be added to workflows like any other job function:
import os
from toil.common import Toil
from toil.job import Job
from toil.lib.docker import apiDockerCall
align = Job.wrapJobFn(apiDockerCall,
image='ubuntu',
working_dir=os.getcwd(),
parameters=['ls', '-lha'])
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
toil.start(align)
cgl-docker-lib contains dockerCall-compatible Dockerized tools that are commonly used in
bioinformatics analysis.
The documentation provides guidelines for developing your own Docker containers that can
be used with Toil and dockerCall. In order for a container to be compatible with
dockerCall, it must have an ENTRYPOINT set to a wrapper script, as described in
cgl-docker-lib containerization standards. This can be set by passing in the optional
keyword argument, 'entrypoint'. Example:
entrypoint=["/bin/bash","-c"]
dockerCall supports currently the 75 keyword arguments found in the python Docker API,
under the 'run' command.
Services
It is sometimes desirable to run services, such as a database or server, concurrently with
a workflow. The toil.job.Job.Service class provides a simple mechanism for spawning such a
service within a Toil workflow, allowing precise specification of the start and end time
of the service, and providing start and end methods to use for initialization and cleanup.
The following simple, conceptual example illustrates how services work:
from toil.common import Toil
from toil.job import Job
class DemoService(Job.Service):
def start(self, fileStore):
# Start up a database/service here
# Return a value that enables another process to connect to the database
return "loginCredentials"
def check(self):
# A function that if it returns False causes the service to quit
# If it raises an exception the service is killed and an error is reported
return True
def stop(self, fileStore):
# Cleanup the database here
pass
j = Job()
s = DemoService()
loginCredentialsPromise = j.addService(s)
def dbFn(loginCredentials):
# Use the login credentials returned from the service's start method to connect to the service
pass
j.addChildFn(dbFn, loginCredentialsPromise)
if __name__ == "__main__":
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
toil.start(j)
In this example the DemoService starts a database in the start method, returning an object
from the start method indicating how a client job would access the database. The service's
stop method cleans up the database, while the service's check method is polled
periodically to check the service is alive.
A DemoService instance is added as a service of the root job j, with resource requirements
specified. The return value from toil.job.Job.addService() is a promise to the return
value of the service's start method. When the promised is fulfilled it will represent how
to connect to the database. The promise is passed to a child job of j, which uses it to
make a database connection. The services of a job are started before any of its successors
have been run and stopped after all the successors of the job have completed successfully.
Multiple services can be created per job, all run in parallel. Additionally, services can
define sub-services using toil.job.Job.Service.addChild(). This allows complex networks
of services to be created, e.g. Apache Spark clusters, within a workflow.
Checkpoints
Services complicate resuming a workflow after failure, because they can create complex
dependencies between jobs. For example, consider a service that provides a database that
multiple jobs update. If the database service fails and loses state, it is not clear that
just restarting the service will allow the workflow to be resumed, because jobs that
created that state may have already finished. To get around this problem Toil supports
checkpoint jobs, specified as the boolean keyword argument checkpoint to a job or wrapped
function, e.g.:
j = Job(checkpoint=True)
A checkpoint job is rerun if one or more of its successors fails its retry attempts, until
it itself has exhausted its retry attempts. Upon restarting a checkpoint job all its
existing successors are first deleted, and then the job is rerun to define new successors.
By checkpointing a job that defines a service, upon failure of the service the database
and the jobs that access the service can be redefined and rerun.
To make the implementation of checkpoint jobs simple, a job can only be a checkpoint if
when first defined it has no successors, i.e. it can only define successors within its run
method.
Encapsulation
Let A be a root job potentially with children and follow-ons. Without an encapsulated job
the simplest way to specify a job B which runs after A and all its successors is to create
a parent of A, call it Ap, and then make B a follow-on of Ap. e.g.:
from toil.common import Toil
from toil.job import Job
if __name__ == "__main__":
# A is a job with children and follow-ons, for example:
A = Job()
A.addChild(Job())
A.addFollowOn(Job())
# B is a job which needs to run after A and its successors
B = Job()
# The way to do this without encapsulation is to make a parent of A, Ap, and make B a follow-on of Ap.
Ap = Job()
Ap.addChild(A)
Ap.addFollowOn(B)
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
print(toil.start(Ap))
An encapsulated job E(A) of A saves making Ap, instead we can write:
from toil.common import Toil
from toil.job import Job
if __name__ == "__main__":
# A
A = Job()
A.addChild(Job())
A.addFollowOn(Job())
# Encapsulate A
A = A.encapsulate()
# B is a job which needs to run after A and its successors
B = Job()
# With encapsulation A and its successor subgraph appear to be a single job, hence:
A.addChild(B)
options = Job.Runner.getDefaultOptions("./toilWorkflowRun")
options.logLevel = "INFO"
options.clean = "always"
with Toil(options) as toil:
print(toil.start(A))
Note the call to toil.job.Job.encapsulate() creates the toil.job.Job.EncapsulatedJob.
Depending on Toil
If you are packing your workflow(s) as a pip-installable distribution on PyPI, you might
be tempted to declare Toil as a dependency in your setup.py, via the install_requires
keyword argument to setup(). Unfortunately, this does not work, for two reasons: For one,
Toil uses Setuptools' extra mechanism to manage its own optional dependencies. If you
explicitly declared a dependency on Toil, you would have to hard-code a particular
combination of extras (or no extras at all), robbing the user of the choice what Toil
extras to install. Secondly, and more importantly, declaring a dependency on Toil would
only lead to Toil being installed on the leader node of a cluster, but not the worker
nodes. Auto-deployment does not work here because Toil cannot auto-deploy itself, the
classic "Which came first, chicken or egg?" problem.
In other words, you shouldn't explicitly depend on Toil. Document the dependency instead
(as in "This workflow needs Toil version X.Y.Z to be installed") and optionally add a
version check to your setup.py. Refer to the check_version() function in the toil-lib
project's setup.py for an example. Alternatively, you can also just depend on toil-lib and
you'll get that check for free.
If your workflow depends on a dependency of Toil, consider not making that dependency
explicit either. If you do, you risk a version conflict between your project and Toil. The
pip utility may silently ignore that conflict, breaking either Toil or your workflow. It
is safest to simply assume that Toil installs that dependency for you. The only downside
is that you are locked into the exact version of that dependency that Toil declares. But
such is life with Python, which, unlike Java, has no means of dependencies belonging to
different software components within the same process, and whose favored software
distribution utility is incapable of properly resolving overlapping dependencies and
detecting conflicts.
Best Practices for Dockerizing Toil Workflows
Computational Genomics Lab's Dockstore based production system provides workflow authors a
way to run Dockerized versions of their pipeline in an automated, scalable fashion. To be
compatible with this system of a workflow should meet the following requirements. In
addition to the Docker container, a common workflow language descriptor file is needed.
For inputs:
· Only command line arguments should be used for configuring the workflow. If the workflow
relies on a configuration file, like Toil-RNAseq or ProTECT, a wrapper script inside the
Docker container can be used to parse the CLI and generate the necessary configuration
file.
· All inputs to the pipeline should be explicitly enumerated rather than implicit. For
example, don't rely on one FASTQ read's path to discover the location of its pair. This
is necessary since all inputs are mapped to their own isolated directories when the
Docker is called via Dockstore.
· All inputs must be documented in the CWL descriptor file. Examples of this file can be
seen in both Toil-RNAseq and ProTECT.
For outputs:
· All outputs should be written to a local path rather than S3.
· Take care to package outputs in a local and user-friendly way. For example, don't tar up
all output if there are specific files that will care to see individually.
· All output file names should be deterministic and predictable. For example, don't
prepend the name of an output file with PASS/FAIL depending on the outcome of the
pipeline.
· All outputs must be documented in the CWL descriptor file. Examples of this file can be
seen in both Toil-RNAseq and ProTECT.
TOIL CLASS API
The Toil class configures and starts a Toil run.
class toil.common.Toil(options: argparse.Namespace)
A context manager that represents a Toil workflow, specifically the batch system,
job store, and its configuration.
__init__(options: argparse.Namespace) -> None
Initialize a Toil object from the given options.
Note that this is very light-weight and that the bulk of the work is done
when the context is entered.
Parameters
options -- command line options specified by the user
config
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: str) -> AbstractJobStore
Create an instance of the concrete job store implementation that matches the
given locator.
Parameters
locator (str) -- The location of the job store to be represent by the
instance
Returns
an instance of a concrete subclass of AbstractJobStore
static createBatchSystem(config: toil.common.Config) -> AbstractBatchSystem
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
import_file(src_uri: str, shared_file_name: Optional[str] = None, symlink: bool =
False) -> Optional[Union[toil.fileStores.FileID, str]]
Imports the file at the given URL into job store.
See toil.jobStores.abstractJobStore.AbstractJobStore.importFile() for a full
description
export_file(file_id: Union[toil.fileStores.FileID, str], dst_uri: str) -> None
Exports file to destination pointed at by the destination URL.
See toil.jobStores.abstractJobStore.AbstractJobStore.exportFile() for a full
description
static normalize_uri(uri: str, check_existence: bool = False) -> str
Given a URI, if it has no scheme, prepend "file:".
Parameters
check_existence -- If set, raise an error if a URI points to a local
file that does not exist.
static getToilWorkDir(configWorkDir: Optional[str] = None) -> str
Returns a path to a writable directory under which per-workflow directories
exist. This directory is always required to exist on a machine, even if the
Toil worker has not run yet. If your workers and leader have different temp
directories, you may need to set TOIL_WORKDIR.
Parameters
configWorkDir (str) -- Value passed to the program using the
--workDir flag
Returns
Path to the Toil work directory, constant across all machines
Return type
str
classmethod getLocalWorkflowDir(workflowID: str, configWorkDir: Optional[str] =
None) -> str
Returns a path to the directory where worker directories and the cache will
be located for this workflow on this machine.
Parameters
configWorkDir -- Value passed to the program using the --workDir flag
Returns
Path to the local workflow directory on this machine
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(locator: str)
Represents the physical storage for the jobs and files in a Toil workflow.
JobStores are responsible for storing toil.job.JobDescription (which relate jobs to
each other) and files.
Actual toil.job.Job objects are stored in files, referenced by JobDescriptions. All
the non-file CRUD methods the JobStore provides deal in JobDescriptions and not
full, executable Jobs.
To actually get ahold of a toil.job.Job, use toil.job.Job.loadJob() with a JobStore
and the relevant JobDescription.
__init__(locator: str) -> None
Create an instance of the job store.
The instance will not be fully functional until either initialize() or
resume() is invoked. Note that the destroy() method may be invoked on the
object with or without prior invocation of either of these two methods.
Takes and stores the locator string for the job store, which will be
accessible via self.locator.
initialize(config: toil.common.Config) -> None
Initialize this job store.
Create the physical storage for this job store, allocate a workflow ID and
persist the given Toil configuration to the store.
Parameters
config -- the Toil configuration to initialize this job store with.
The given configuration will be updated with the newly allocated
workflow ID.
Raises JobStoreExistsException -- if the physical storage for this job store
already exists
write_config() -> None
Persists the value of the AbstractJobStore.config attribute to the job
store, so that it can be retrieved later by other instances of this class.
resume() -> None
Connect this instance to the physical storage it represents and load the
Toil configuration into the AbstractJobStore.config attribute.
Raises NoSuchJobStoreException -- if the physical storage for this job store
doesn't exist
property config: toil.common.Config
Return the Toil configuration associated with this job store.
property locator: str
Get the locator that defines the job store, which can be used to connect to
it.
setRootJob(rootJobStoreID: toil.fileStores.FileID) -> None
Set the root job of the workflow backed by this job store.
set_root_job(job_id: toil.fileStores.FileID) -> None
Set the root job of the workflow backed by this job store.
Parameters
job_id -- The ID of the job to set as root
load_root_job() -> toil.job.JobDescription
Loads the JobDescription for the root job in the current job store.
Raises toil.job.JobException -- If no root job is set or if the root job
doesn't exist in this job store
Returns
The root job.
create_root_job(job_description: toil.job.JobDescription) ->
toil.job.JobDescription
Create the given JobDescription and set it as the root job in this job
store.
Parameters
job_description -- JobDescription to save and make the root job.
get_root_job_return_value() -> Any
Parse the return value from the root job.
Raises an exception if the root job hasn't fulfilled its promise yet.
import_file(src_uri: str, shared_file_name: Optional[str] = None, hardlink: bool =
False, symlink: bool = False) -> Optional[toil.fileStores.FileID]
Imports the file at the given URL into job store. The ID of the newly
imported file is returned. If the name of a shared file name is provided,
the file will be imported as such and None is returned. If an executable
file on the local filesystem is uploaded, its executability will be
preserved when it is downloaded.
Currently supported schemes are:
·
's3' for objects in Amazon S3
e.g. s3://bucket/key
·
'file' for local files
e.g. file:///local/file/path
·
'http' e.g. http://someurl.com/path
·
'gs' e.g. gs://bucket/file
Parameters
· src_uri (str) -- URL that points to a file or object in the storage
mechanism of a supported URL scheme e.g. a blob in an AWS s3
bucket.
· shared_file_name (str) -- Optional name to assign to the imported
file within the job store
Returns
The jobStoreFileID of the imported file or None if sharedFileName was
given
Return type
toil.fileStores.FileID or None
export_file(file_id: toil.fileStores.FileID, dst_uri: str) -> None
Exports file to destination pointed at by the destination URL. The exported
file will be executable if and only if it was originally uploaded from an
executable file on the local filesystem.
Refer to AbstractJobStore.import_file() documentation for currently
supported URL schemes.
Note that the helper method _exportFile is used to read from the source and
write to destination. To implement any optimizations that circumvent this,
the _exportFile method should be overridden by subclasses of
AbstractJobStore.
Parameters
· file_id (str) -- The id of the file in the job store that should be
exported.
· dst_uri (str) -- URL that points to a file or object in the storage
mechanism of a supported URL scheme e.g. a blob in an AWS s3
bucket.
abstract classmethod get_size(url: urllib.parse.ParseResult) -> None
Get the size in bytes of the file at the given URL, or None if it cannot be
obtained.
Parameters
url (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.
abstract destroy() -> None
The inverse of initialize(), this method deletes the physical storage
represented by this instance. While not being atomic, this method is at
least idempotent, as a means to counteract potential issues with eventual
consistency exhibited by the underlying storage mechanisms. This means that
if the method fails (raises an exception), it may (and should be) invoked
again. If the underlying storage mechanism is eventually consistent, even a
successful invocation is not an ironclad guarantee that the physical storage
vanished completely and immediately. A successful invocation only guarantees
that the deletion will eventually happen. It is therefore recommended to not
immediately reuse the same job store location for a new Toil workflow.
get_env() -> Dict[str, str]
Returns a dictionary of environment variables that this job store requires
to be set in order to function properly on a worker.
Return type
dict[str,str]
clean(jobCache: Optional[Dict[str, toil.job.JobDescription]] = None) ->
toil.job.JobDescription
Function to cleanup the state of a job store after a restart.
Fixes jobs that might have been partially updated. Resets the try counts and
removes jobs that are not successors of the current root job.
Parameters
jobCache -- if a value it must be a dict from job ID keys to
JobDescription object values. Jobs will be loaded from the cache
(which can be downloaded from the job store in a batch) instead of
piecemeal when recursed into.
abstract assign_job_id(job_description: toil.job.JobDescription) -> None
Get a new jobStoreID to be used by the described job, and assigns it to the
JobDescription.
Files associated with the assigned ID will be accepted even if the
JobDescription has never been created or updated.
Parameters
job_description (toil.job.JobDescription) -- The JobDescription to
give an ID to
batch() -> Iterator[None]
If supported by the batch system, calls to create() with this context
manager active will be performed in a batch after the context manager is
released.
abstract create_job(job_description: toil.job.JobDescription) ->
toil.job.JobDescription
Writes the given JobDescription to the job store. The job must have an ID
assigned already.
Must call jobDescription.pre_update_hook()
Returns
The JobDescription passed.
Return type
toil.job.JobDescription
abstract job_exists(job_id: str) -> bool
Indicates whether a description of the job with the specified jobStoreID
exists in the job store
Return type
bool
abstract get_public_url(file_name: str) -> str
Returns a publicly accessible URL to the given file in the job store. The
returned URL may expire as early as 1h after its been returned. Throw an
exception if the file does not exist.
Parameters
file_name (str) -- the jobStoreFileID of the file to generate a URL
for
Raises NoSuchFileException -- if the specified file does not exist in this
job store
Return type
str
abstract get_shared_public_url(shared_file_name: str) -> str
Differs from getPublicUrl() in that this method is for generating URLs for
shared files written by writeSharedFileStream().
Returns a publicly accessible URL to the given file in the job store. The
returned URL starts with 'http:', 'https:' or 'file:'. The returned URL may
expire as early as 1h after its been returned. Throw an exception if the
file does not exist.
Parameters
shared_file_name (str) -- The name of the shared file to generate a
publically accessible url for.
Raises NoSuchFileException -- raised if the specified file does not exist in
the store
Return type
str
abstract load_job(job_id: str) -> toil.job.JobDescription
Loads the description of the job referenced by the given ID, assigns it the
job store's config, and returns it.
May declare the job to have failed (see
toil.job.JobDescription.setupJobAfterFailure()) if there is evidence of a
failed update attempt.
Parameters
job_id -- the ID of the job to load
Raises NoSuchJobException -- if there is no job with the given ID
abstract update_job(job_description: toil.job.JobDescription) -> None
Persists changes to the state of the given JobDescription in this store
atomically.
Must call jobDescription.pre_update_hook()
Parameters
job (toil.job.JobDescription) -- the job to write to this job store
abstract delete_job(job_id: str) -> None
Removes the JobDescription from the store atomically. You may not then
subsequently call load(), write(), update(), etc. with the same jobStoreID
or any JobDescription bearing it.
This operation is idempotent, i.e. deleting a job twice or deleting a
non-existent job will succeed silently.
Parameters
job_id (str) -- the ID of the job to delete from this job store
jobs() -> Iterator[toil.job.JobDescription]
Best effort attempt to return iterator on JobDescriptions for all jobs in
the store. The iterator may not return all jobs and may also contain
orphaned jobs that have already finished successfully and should not be
rerun. To guarantee you get any and all jobs that can be run instead
construct a more expensive ToilState object
Returns
Returns iterator on jobs in the store. The iterator may or may not
contain all jobs and may contain invalid jobs
Return type
Iterator[toil.job.jobDescription]
abstract write_file(local_path: str, job_id: Optional[str] = None, cleanup: bool =
False) -> str
Takes a file (as a path) and places it in this job store. Returns an ID that
can be used to retrieve the file at a later time. The file is written in a
atomic manner. It will not appear in the jobStore until the write has
successfully completed.
Parameters
· local_path (str) -- the path to the local file that will be
uploaded to the job store. The last path component (basename of
the file) will remain associated with the file in the file store,
if supported, so that the file can be searched for by name or name
glob.
· job_id (str) -- the id of a job, or None. If specified, the may be
associated with that job in a job-store-specific way. This may
influence the returned ID.
· cleanup (bool) -- Whether to attempt to delete the file when the
job whose jobStoreID was given as jobStoreID is deleted with
jobStore.delete(job). If jobStoreID was not given, does nothing.
Raises
· ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
· NoSuchJobException -- if the job specified via jobStoreID does not
exist
FIXME: some implementations may not raise this
Returns
an ID referencing the newly created file and can be used to read the
file in the future.
Return type
str
abstract write_file_stream(job_id: Optional[str] = None, cleanup: bool = False,
basename: Optional[str] = None, encoding: Optional[str] = None, errors:
Optional[str] = None) -> Iterator[Tuple[IO[bytes], str]]
Similar to writeFile, but returns a context manager yielding a tuple of 1) a
file handle which can be written to and 2) the ID of the resulting file in
the job store. The yielded file handle does not need to and should not be
closed explicitly. The file is written in a atomic manner. It will not
appear in the jobStore until the write has successfully completed.
Parameters
· job_id (str) -- the id of a job, or None. If specified, the may be
associated with that job in a job-store-specific way. This may
influence the returned ID.
· cleanup (bool) -- Whether to attempt to delete the file when the
job whose jobStoreID was given as jobStoreID is deleted with
jobStore.delete(job). If jobStoreID was not given, does nothing.
· basename (str) -- If supported by the implementation, use the given
file basename so that when searching the job store with a query
matching that basename, the file will be detected.
· encoding (str) -- the name of the encoding used to encode the file.
Encodings are the same as for encode(). Defaults to None which
represents binary mode.
· errors (str) -- an optional string that specifies how encoding
errors are to be handled. Errors are the same as for open().
Defaults to 'strict' when an encoding is specified.
Raises
· ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
· NoSuchJobException -- if the job specified via jobStoreID does not
exist
FIXME: some implementations may not raise this
Returns
a context manager yielding a file handle which can be written to and
an ID that references the newly created file and can be used to read
the file in the future.
Return type
Iterator[Tuple[IO[bytes], str]]
abstract get_empty_file_store_id(job_id: Optional[str] = None, cleanup: bool =
False, basename: Optional[str] = None) -> str
Creates an empty file in the job store and returns its ID. Call to
fileExists(getEmptyFileStoreID(jobStoreID)) will return True.
Parameters
· job_id (str) -- the id of a job, or None. If specified, the may be
associated with that job in a job-store-specific way. This may
influence the returned ID.
· cleanup (bool) -- Whether to attempt to delete the file when the
job whose jobStoreID was given as jobStoreID is deleted with
jobStore.delete(job). If jobStoreID was not given, does nothing.
· basename (str) -- If supported by the implementation, use the given
file basename so that when searching the job store with a query
matching that basename, the file will be detected.
Returns
a jobStoreFileID that references the newly created file and can be
used to reference the file in the future.
Return type
str
abstract read_file(file_id: str, local_path: str, symlink: bool = False) -> None
Copies or hard links the file referenced by jobStoreFileID to the given
local file path. The version will be consistent with the last copy of the
file written/updated. If the file in the job store is later modified via
updateFile or updateFileStream, it is implementation-defined whether those
writes will be visible at localFilePath. The file is copied in an atomic
manner. It will not appear in the local file system until the copy has
completed.
The file at the given local path may not be modified after this method
returns!
Note! Implementations of readFile need to respect/provide the executable
attribute on FileIDs.
Parameters
· file_id (str) -- ID of the file to be copied
· local_path (str) -- the local path indicating where to place the
contents of the given file in the job store
· symlink (bool) -- whether the reader can tolerate a symlink. If set
to true, the job store may create a symlink instead of a full copy
of the file or a hard link.
abstract read_file_stream(file_id: Union[toil.fileStores.FileID, str], encoding:
Optional[str] = None, errors: Optional[str] = None) ->
Union[ContextManager[_io.BytesIO], ContextManager[TextIO]]
Similar to readFile, but returns a context manager yielding a file handle
which can be read from. The yielded file handle does not need to and should
not be closed explicitly.
Parameters
· file_id (str) -- ID of the file to get a readable file handle for
· encoding (str) -- the name of the encoding used to decode the file.
Encodings are the same as for decode(). Defaults to None which
represents binary mode.
· errors (str) -- an optional string that specifies how encoding
errors are to be handled. Errors are the same as for open().
Defaults to 'strict' when an encoding is specified.
Returns
a context manager yielding a file handle which can be read from
Return type
Iterator[Union[BytesIO, TextIO]]
abstract delete_file(file_id: str) -> None
Deletes the file with the given ID from this job store. This operation is
idempotent, i.e. deleting a file twice or deleting a non-existent file will
succeed silently.
Parameters
file_id (str) -- ID of the file to delete
fileExists(jobStoreFileID: str) -> bool
Determine whether a file exists in this job store.
abstract file_exists(file_id: str) -> bool
Determine whether a file exists in this job store.
Parameters
file_id -- an ID referencing the file to be checked
getFileSize(jobStoreFileID: str) -> int
Get the size of the given file in bytes.
abstract get_file_size(file_id: str) -> int
Get the size of the given file in bytes, or 0 if it does not exist when
queried.
Note that job stores which encrypt files might return overestimates of file
sizes, since the encrypted file may have been padded to the nearest block,
augmented with an initialization vector, etc.
Parameters
file_id (str) -- an ID referencing the file to be checked
Return type
int
updateFile(jobStoreFileID: str, localFilePath: str) -> None
Replaces the existing version of a file in the job store.
abstract update_file(file_id: str, local_path: str) -> None
Replaces the existing version of a file in the job store.
Throws an exception if the file does not exist.
Parameters
· file_id -- the ID of the file in the job store to be updated
· local_path -- the local path to a file that will overwrite the
current version in the job store
Raises
· ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
· NoSuchFileException -- if the specified file does not exist
abstract update_file_stream(file_id: str, encoding: Optional[str] = None, errors:
Optional[str] = None) -> Iterator[IO[Any]]
Replaces the existing version of a file in the job store. Similar to
writeFile, but returns a context manager yielding a file handle which can be
written to. The yielded file handle does not need to and should not be
closed explicitly.
Parameters
· file_id (str) -- the ID of the file in the job store to be updated
· encoding (str) -- the name of the encoding used to encode the file.
Encodings are the same as for encode(). Defaults to None which
represents binary mode.
· errors (str) -- an optional string that specifies how encoding
errors are to be handled. Errors are the same as for open().
Defaults to 'strict' when an encoding is specified.
Raises
· ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
· NoSuchFileException -- if the specified file does not exist
abstract write_shared_file_stream(shared_file_name: str, encrypted: Optional[bool]
= None, encoding: Optional[str] = None, errors: Optional[str] = None) ->
Iterator[IO[bytes]]
Returns a context manager yielding a writable file handle to the global file
referenced by the given name. File will be created in an atomic manner.
Parameters
· shared_file_name (str) -- A file name matching
AbstractJobStore.fileNameRegex, unique within this job store
· encrypted (bool) -- True if the file must be encrypted, None if it
may be encrypted or False if it must be stored in the clear.
· encoding (str) -- the name of the encoding used to encode the file.
Encodings are the same as for encode(). Defaults to None which
represents binary mode.
· errors (str) -- an optional string that specifies how encoding
errors are to be handled. Errors are the same as for open().
Defaults to 'strict' when an encoding is specified.
Raises ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
Returns
a context manager yielding a writable file handle
Return type
Iterator[IO[bytes]]
abstract read_shared_file_stream(shared_file_name: str, encoding: Optional[str] =
None, errors: Optional[str] = None) -> Iterator[_io.BytesIO]
Returns a context manager yielding a readable file handle to the global file
referenced by the given name.
Parameters
· shared_file_name (str) -- A file name matching
AbstractJobStore.fileNameRegex, unique within this job store
· encoding (str) -- the name of the encoding used to decode the file.
Encodings are the same as for decode(). Defaults to None which
represents binary mode.
· errors (str) -- an optional string that specifies how encoding
errors are to be handled. Errors are the same as for open().
Defaults to 'strict' when an encoding is specified.
Returns
a context manager yielding a readable file handle
Return type
Iterator[BytesIO]
abstract write_logs(msg: str) -> None
Stores a message as a log in the jobstore.
Parameters
msg (str) -- the string to be written
Raises ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
abstract read_logs(callback: Callable[[...], Any], read_all: bool = False) -> int
Reads logs accumulated by the write_logs() method. For each log this method
calls the given callback function with the message as an argument (rather
than returning logs directly, this method must be supplied with a callback
which will process log messages).
Only unread logs will be read unless the read_all parameter is set.
Parameters
· callback (Callable) -- a function to be applied to each of the
stats file handles found
· read_all (bool) -- a boolean indicating whether to read the already
processed stats files in addition to the unread stats files
Raises ConcurrentFileModificationException -- if the file was modified
concurrently during an invocation of this method
Returns
the number of stats files processed
Return type
int
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 -- 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 -- Used to create local and globally sharable temporary
files and to send log messages to the leader process.
Returns
The return value of the function can be passed to other jobs by means
of toil.job.Job.rv().
EncapsulatedJob
The subclass of Job for encapsulating a job, allowing a subgraph of jobs to be treated as
a single job.
class toil.job.EncapsulatedJob(job, unitName=None)
A convenience Job class used to make a job subgraph appear to be a single job.
Let A be the root job of a job subgraph and B be another job we'd like to run after
A and all its successors have completed, for this use encapsulate:
# Job A and subgraph, Job B
A, B = A(), B()
Aprime = A.encapsulate()
Aprime.addChild(B)
# B will run after A and all its successors have completed, A and its subgraph of
# successors in effect appear to be just one job.
If the job being encapsulated has predecessors (e.g. is not the root job), then the
encapsulated job will inherit these predecessors. If predecessors are added to the
job being encapsulated after the encapsulated job is created then the encapsulating
job will NOT inherit these predecessors automatically. Care should be exercised to
ensure the encapsulated job has the proper set of predecessors.
The return value of an encapsulated job (as accessed by the toil.job.Job.rv()
function) is the return value of the root job, e.g. A().encapsulate().rv() and
A().rv() will resolve to the same value after A or A.encapsulate() has been run.
__init__(job, unitName=None)
Parameters
· job (toil.job.Job) -- the job to encapsulate.
· unitName (str) -- human-readable name to identify this job
instance.
addChild(childJob)
Add a childJob to be run as child of this job.
Child jobs will be run directly after this job's toil.job.Job.run() method
has completed.
Returns
childJob: for call chaining
addService(service, parentService=None)
Add a service.
The toil.job.Job.Service.start() method of the service will be called after
the run method has completed but before any successors are run. The
service's toil.job.Job.Service.stop() method will be called once the
successors of the job have been run.
Services allow things like databases and servers to be started and accessed
by jobs in a workflow.
Raises toil.job.JobException -- If service has already been made the child
of a job or another service.
Parameters
· service -- Service to add.
· parentService -- Service that will be started before 'service' is
started. Allows trees of services to be established. parentService
must be a service of this job.
Returns
a promise that will be replaced with the return value from
toil.job.Job.Service.start() of service in any successor of the job.
addFollowOn(followOnJob)
Add a follow-on job.
Follow-on jobs will be run after the child jobs and their successors have
been run.
Returns
followOnJob for call chaining
rv(*path) -> toil.job.Promise
Create a promise (toil.job.Promise).
The "promise" representing a return value of the job's run method, or, in
case of a function-wrapping job, the wrapped function's return value.
Parameters
path ((Any)) -- Optional path for selecting a component of the
promised return value. If absent or empty, the entire return value
will be used. Otherwise, the first element of the path is used to
select an individual item of the return value. For that to work, the
return value must be a list, dictionary or of any other type
implementing the __getitem__() magic method. If the selected item is
yet another composite value, the second element of the path can be
used to select an item from it, and so on. For example, if the return
value is [6,{'a':42}], .rv(0) would select 6 , rv(1) would select
{'a':3} while rv(1,'a') would select 3. To select a slice from a
return value that is slicable, e.g. tuple or list, the path element
should be a slice object. For example, assuming that the return value
is [6, 7, 8, 9] then .rv(slice(1, 3)) would select [7, 8]. Note that
slicing really only makes sense at the end of path.
Returns
A promise representing the return value of this jobs
toil.job.Job.run() method.
Return type
toil.job.Promise
prepareForPromiseRegistration(jobStore)
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(*args)
References a return value from a method as a promise before the method itself is
run.
References a return value from a toil.job.Job.run() or toil.job.Job.Service.start()
method as a promise before the method itself is run.
Let T be a job. Instances of Promise (termed a promise) are returned by T.rv(),
which is used to reference the return value of T's run function. When the promise
is passed to the constructor (or as an argument to a wrapped function) of a
different, successor job the promise will be replaced by the actual referenced
return value. This mechanism allows a return values from one job's run method to be
input argument to job before the former job's run function has been executed.
filesToDelete = {}
A set of IDs of files containing promised values when we know we won't need
them anymore
__init__(job: toil.job.Job, path: Any)
Initialize this promise.
Parameters
· job (Job) -- the job whose return value this promise references
· path -- see Job.rv()
class toil.job.PromisedRequirement(valueOrCallable, *args)
Class for dynamically allocating job function resource requirements.
(involving toil.job.Promise instances.)
Use when resource requirements depend on the return value of a parent function.
PromisedRequirements can be modified by passing a function that takes the Promise
as input.
For example, let f, g, and h be functions. Then a Toil workflow can be defined as
follows:: A = Job.wrapFn(f) B = A.addChildFn(g, cores=PromisedRequirement(A.rv()) C
= B.addChildFn(h, cores=PromisedRequirement(lambda x: 2*x, B.rv()))
__init__(valueOrCallable, *args)
Initialize this Promised Requirement.
Parameters
· valueOrCallable -- A single Promise instance or a function that
takes args as input parameters.
· args (int or Promise) -- variable length argument list
getValue()
Return PromisedRequirement value.
static convertPromises(kwargs: Dict[str, Any]) -> bool
Return True if reserved resource keyword is a Promise or PromisedRequirement
instance.
Converts Promise instance to PromisedRequirement.
Parameters
kwargs -- function keyword arguments
JOB METHODS API
Jobs are the units of work in Toil which are composed into workflows.
class toil.job.Job(memory: Optional[Union[int, str]] = None, cores: Optional[Union[int,
float, str]] = None, disk: Optional[Union[int, str]] = None, preemptable:
Optional[Union[bool, int, str]] = None, unitName: Optional[str] = '', checkpoint:
Optional[bool] = False, displayName: Optional[str] = '', descriptionClass: Optional[str] =
None)
Class represents a unit of work in toil.
__init__(memory: Optional[Union[int, str]] = None, cores: Optional[Union[int,
float, str]] = None, disk: Optional[Union[int, str]] = None, preemptable:
Optional[Union[bool, int, str]] = None, unitName: Optional[str] = '', checkpoint:
Optional[bool] = False, displayName: Optional[str] = '', descriptionClass:
Optional[str] = None) -> None
Job initializer.
This method must be called by any overriding constructor.
Parameters
· memory (int or string convertible by
toil.lib.conversions.human2bytes to an int) -- the maximum number
of bytes of memory the job will require to run.
· cores (float, int, or string convertible by
toil.lib.conversions.human2bytes to an int) -- the number of CPU
cores required.
· disk (int or string convertible by toil.lib.conversions.human2bytes
to an int) -- the amount of local disk space required by the job,
expressed in bytes.
· preemptable (bool, int in {0, 1}, or string in {'false', 'true'} in
any case) -- if the job can be run on a preemptable node.
· unitName (str) -- Human-readable name for this instance of the job.
· checkpoint (bool) -- if any of this job's successor jobs completely
fails, exhausting all their retries, remove any successor jobs and
rerun this job to restart the subtree. Job must be a leaf vertex in
the job graph when initially defined, see
toil.job.Job.checkNewCheckpointsAreCutVertices().
· displayName (str) -- Human-readable job type display name.
· descriptionClass (class) -- Override for the JobDescription class
used to describe the job.
property jobStoreID
Get the ID of this Job.
Return type
str|toil.job.TemporaryID
property description
Expose the JobDescription that describes this job.
Return type
toil.job.JobDescription
property disk: int
The maximum number of bytes of disk the job will require to run.
Return type
int
property memory
The maximum number of bytes of memory the job will require to run.
Return type
int
property cores
The number of CPU cores required.
Return type
int|float
property preemptable
Whether the job can be run on a preemptable node.
Return type
bool
property checkpoint
Determine if the job is a checkpoint job or not.
Return type
bool
assignConfig(config: toil.common.Config)
Assign the given config object.
It will be used by various actions implemented inside the Job class.
Parameters
config -- Config object to query
run(fileStore: AbstractFileStore) -> Any
Override this function to perform work and dynamically create successor
jobs.
Parameters
fileStore -- Used to create local and globally sharable temporary
files and to send log messages to the leader process.
Returns
The return value of the function can be passed to other jobs by means
of toil.job.Job.rv().
addChild(childJob: toil.job.Job) -> toil.job.Job
Add a childJob to be run as child of this job.
Child jobs will be run directly after this job's toil.job.Job.run() method
has completed.
Returns
childJob: for call chaining
hasChild(childJob: toil.job.Job) -> bool
Check if childJob is already a child of this job.
Returns
True if childJob is a child of the job, else False.
addFollowOn(followOnJob: toil.job.Job) -> toil.job.Job
Add a follow-on job.
Follow-on jobs will be run after the child jobs and their successors have
been run.
Returns
followOnJob for call chaining
hasPredecessor(job: toil.job.Job) -> bool
Check if a given job is already a predecessor of this job.
hasFollowOn(followOnJob: toil.job.Job) -> bool
Check if given job is already a follow-on of this job.
Returns
True if the followOnJob is a follow-on of this job, else False.
addService(service: Service, parentService: Optional[Service] = None) -> Promise
Add a service.
The toil.job.Job.Service.start() method of the service will be called after
the run method has completed but before any successors are run. The
service's toil.job.Job.Service.stop() method will be called once the
successors of the job have been run.
Services allow things like databases and servers to be started and accessed
by jobs in a workflow.
Raises toil.job.JobException -- If service has already been made the child
of a job or another service.
Parameters
· service -- Service to add.
· parentService -- Service that will be started before 'service' is
started. Allows trees of services to be established. parentService
must be a service of this job.
Returns
a promise that will be replaced with the return value from
toil.job.Job.Service.start() of service in any successor of the job.
hasService(service: Service) -> bool
Return True if the given Service is a service of this job, and False
otherwise.
addChildFn(fn: Callable, *args, **kwargs) -> toil.job.FunctionWrappingJob
Add a function as a child job.
Parameters
fn -- Function to be run as a child job with *args and **kwargs as
arguments to this function. See toil.job.FunctionWrappingJob for
reserved keyword arguments used to specify resource
requirements.
Returns
The new child job that wraps fn.
addFollowOnFn(fn: Callable, *args, **kwargs) -> toil.job.FunctionWrappingJob
Add a function as a follow-on job.
Parameters
fn -- Function to be run as a follow-on job with *args and **kwargs
as arguments to this function. See
toil.job.FunctionWrappingJob for reserved keyword arguments
used to specify resource requirements.
Returns
The new follow-on job that wraps fn.
addChildJobFn(fn: Callable, *args, **kwargs) -> toil.job.FunctionWrappingJob
Add a job function as a child job.
See toil.job.JobFunctionWrappingJob for a definition of a job function.
Parameters
fn -- Job function to be run as a child job with *args and **kwargs
as arguments to this function. See
toil.job.JobFunctionWrappingJob for reserved keyword
arguments used to specify resource requirements.
Returns
The new child job that wraps fn.
addFollowOnJobFn(fn: Callable, *args, **kwargs) -> toil.job.FunctionWrappingJob
Add a follow-on job function.
See toil.job.JobFunctionWrappingJob for a definition of a job function.
Parameters
fn -- Job function to be run as a follow-on job with *args and
**kwargs as arguments to this function. See
toil.job.JobFunctionWrappingJob for reserved keyword
arguments used to specify resource requirements.
Returns
The new follow-on job that wraps fn.
property tempDir: str
Shortcut to calling job.fileStore.getLocalTempDir().
Temp dir is created on first call and will be returned for first and future
calls :return: Path to tempDir. See job.fileStore.getLocalTempDir
log(text: str, level=20) -> None
Convenience wrapper for fileStore.logToMaster().
static wrapFn(fn, *args, **kwargs)
Makes a Job out of a function. Convenience function for constructor
of toil.job.FunctionWrappingJob.
Parameters
fn -- Function to be run with *args and **kwargs as arguments.
See toil.job.JobFunctionWrappingJob for reserved keyword arguments
used to specify resource requirements.
Returns
The new function that wraps fn.
Return type
toil.job.FunctionWrappingJob
static wrapJobFn(fn, *args, **kwargs)
Makes a Job out of a job function. Convenience function for
constructor of toil.job.JobFunctionWrappingJob.
Parameters
fn -- Job function to be run with *args and **kwargs as arguments.
See toil.job.JobFunctionWrappingJob for reserved keyword arguments
used to specify resource requirements.
Returns
The new job function that wraps fn.
Return type
toil.job.JobFunctionWrappingJob
encapsulate(name=None)
Encapsulates the job, see toil.job.EncapsulatedJob. Convenience function
for constructor of toil.job.EncapsulatedJob.
Parameters
name (str) -- Human-readable name for the encapsulated job.
Returns
an encapsulated version of this job.
Return type
toil.job.EncapsulatedJob
rv(*path) -> Any
Create a promise (toil.job.Promise).
The "promise" representing a return value of the job's run method, or, in
case of a function-wrapping job, the wrapped function's return value.
Parameters
path ((Any)) -- Optional path for selecting a component of the
promised return value. If absent or empty, the entire return value
will be used. Otherwise, the first element of the path is used to
select an individual item of the return value. For that to work, the
return value must be a list, dictionary or of any other type
implementing the __getitem__() magic method. If the selected item is
yet another composite value, the second element of the path can be
used to select an item from it, and so on. For example, if the return
value is [6,{'a':42}], .rv(0) would select 6 , rv(1) would select
{'a':3} while rv(1,'a') would select 3. To select a slice from a
return value that is slicable, e.g. tuple or list, the path element
should be a slice object. For example, assuming that the return value
is [6, 7, 8, 9] then .rv(slice(1, 3)) would select [7, 8]. Note that
slicing really only makes sense at the end of path.
Returns
A promise representing the return value of this jobs
toil.job.Job.run() method.
Return type
toil.job.Promise
prepareForPromiseRegistration(jobStore)
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()
Ensures that a graph of Jobs (that hasn't yet been saved to the JobStore)
doesn't contain any pathological relationships between jobs that would
result in deadlocks if we tried to run the jobs.
See toil.job.Job.checkJobGraphConnected(),
toil.job.Job.checkJobGraphAcyclic() and
toil.job.Job.checkNewCheckpointsAreLeafVertices() for more info.
Raises toil.job.JobGraphDeadlockException -- if the job graph is cyclic,
contains multiple roots or contains checkpoint jobs that are not leaf
vertices when defined (see
toil.job.Job.checkNewCheckpointsAreLeaves()).
getRootJobs() -> Set[toil.job.Job]
Returns the set of root job objects that contain this job. A root job is a
job with no predecessors (i.e. which are not children, follow-ons, or
services).
Only deals with jobs created here, rather than loaded from the job store.
checkJobGraphConnected()
Raises toil.job.JobGraphDeadlockException -- if toil.job.Job.getRootJobs()
does not contain exactly one root job.
As execution always starts from one root job, having multiple root jobs will
cause a deadlock to occur.
Only deals with jobs created here, rather than loaded from the job store.
checkJobGraphAcylic()
Raises toil.job.JobGraphDeadlockException -- if the connected component
of jobs containing this job contains any cycles of child/followOn
dependencies in the augmented job graph (see below). Such
cycles are not allowed in valid job graphs.
A follow-on edge (A, B) between two jobs A and B is equivalent to
adding a child edge to B from (1) A, (2) from each child of A, and
(3) from the successors of each child of A. We call each such edge
an edge an "implied" edge. The augmented job graph is a job graph including
all the implied edges.
For a job graph G = (V, E) the algorithm is O(|V|^2). It is O(|V| + |E|) for
a graph with no follow-ons. The former follow-on case could be improved!
Only deals with jobs created here, rather than loaded from the job store.
checkNewCheckpointsAreLeafVertices()
A checkpoint job is a job that is restarted if either it fails, or if any of
its successors completely fails, exhausting their retries.
A job is a leaf it is has no successors.
A checkpoint job must be a leaf when initially added to the job graph. When
its run method is invoked it can then create direct successors. This
restriction is made to simplify implementation.
Only works on connected components of jobs not yet added to the JobStore.
Raises toil.job.JobGraphDeadlockException -- if there exists a job being
added to the graph for which checkpoint=True and which is not
a leaf.
defer(function, *args, **kwargs)
Register a deferred function, i.e. a callable that will be invoked after the
current attempt at running this job concludes. A job attempt is said to
conclude when the job function (or the toil.job.Job.run() method for
class-based jobs) returns, raises an exception or after the process running
it terminates abnormally. A deferred function will be called on the node
that attempted to run the job, even if a subsequent attempt is made on
another node. A deferred function should be idempotent because it may be
called multiple times on the same node or even in the same process. More
than one deferred function may be registered per job attempt by calling this
method repeatedly with different arguments. If the same function is
registered twice with the same or different arguments, it will be called
twice per job attempt.
Examples for deferred functions are ones that handle cleanup of resources
external to Toil, like Docker containers, files outside the work directory,
etc.
Parameters
· function (callable) -- The function to be called after this job
concludes.
· args (list) -- The arguments to the function
· kwargs (dict) -- The keyword arguments to the function
getTopologicalOrderingOfJobs()
Returns
a list of jobs such that for all pairs of indices i, j for which i <
j, the job at index i can be run before the job at index j.
Only considers jobs in this job's subgraph that are newly added, not loaded
from the job store.
Ignores service jobs.
Return type
list[Job]
saveBody(jobStore)
Save the execution data for just this job to the JobStore, and fill in the
JobDescription with the information needed to retrieve it.
The Job's JobDescription must have already had a real jobStoreID assigned to
it.
Does not save the JobDescription.
Parameters
jobStore (toil.jobStores.abstractJobStore.AbstractJobStore) -- The
job store to save the job body into.
saveAsRootJob(jobStore)
Save this job to the given jobStore as the root job of the workflow.
Parameters
jobStore (toil.jobStores.abstractJobStore.AbstractJobStore) --
Returns
the JobDescription describing this job.
classmethod loadJob(jobStore: AbstractJobStore, jobDescription:
toil.job.JobDescription) -> Job
Retrieves a toil.job.Job instance from a JobStore
Parameters
· jobStore -- The job store.
· jobDescription -- the JobDescription of the job to retrieve.
Returns
The job referenced by the JobDescription.
JobDescription
The class used to store all the information that the Toil Leader ever needs to know about
a Job.
class toil.job.JobDescription(requirements: Mapping[str, Union[int, str, bool]], jobName:
str, unitName: str = '', displayName: str = '', command: Optional[str] = None)
Stores all the information that the Toil Leader ever needs to know about a Job.
(requirements information, dependency information, commands to issue, etc.)
Can be obtained from an actual (i.e. executable) Job object, and can be used to
obtain the Job object from the JobStore.
Never contains other Jobs or JobDescriptions: all reference is by ID.
Subclassed into variants for checkpoint jobs and service jobs that have their
specific parameters.
__init__(requirements: Mapping[str, Union[int, str, bool]], jobName: str, unitName:
str = '', displayName: str = '', command: Optional[str] = None) -> None
Create a new JobDescription.
Parameters
· requirements -- Dict from string to number, string, or bool
describing the resource requirements of the job. 'cores', 'memory',
'disk', and 'preemptable' fields, if set, are parsed and broken out
into properties. If unset, the relevant property will be
unspecified, and will be pulled from the assigned Config object if
queried (see toil.job.Requirer.assignConfig()).
· jobName -- Name of the kind of job this is. May be used in job
store IDs and logging. Also used to let the cluster scaler learn a
model for how long the job will take. Ought to be the job class's
name if no real user-defined name is available.
· unitName -- Name of this instance of this kind of job. May appear
with jobName in logging.
· displayName -- A human-readable name to identify this particular
job instance. Ought to be the job class's name if no real
user-defined name is available.
serviceHostIDsInBatches() -> Iterator[List[str]]
Find all batches of service host job IDs that can be started at the same
time.
(in the order they need to start in)
successorsAndServiceHosts() -> Iterator[str]
Get an iterator over all child, follow-on, and service job IDs.
allSuccessors()
Get an iterator over all child and follow-on job IDs.
property services
Get a collection of the IDs of service host jobs for this job, in arbitrary
order.
Will be empty if the job has no unfinished services.
nextSuccessors() -> List[str]
Return the collection of job IDs for the successors of this job that are
ready to run.
If those jobs have multiple predecessor relationships, they may still be
blocked on other jobs.
Returns None when at the final phase (all successors done), and an empty
collection if there are more phases but they can't be entered yet (e.g.
because we are waiting for the job itself to run).
property stack: Tuple[Tuple[str, ...], ...]
Get IDs of successors that need to run still.
Batches of successors are in reverse order of the order they need to run in.
Some successors in each batch may have already been finished. Batches may be
empty.
Exists so that code that used the old stack list immutably can work still.
New development should use nextSuccessors(), and all mutations should use
filterSuccessors() (which automatically removes completed phases).
Returns
Batches of successors that still need to run, in reverse order. An
empty batch may exist under a non-empty batch, or at the top when the
job itself is not done.
Return type
tuple(tuple(str))
filterSuccessors(predicate: Callable[[str], bool]) -> None
Keep only successor jobs for which the given predicate function approves.
The predicate function is called with the job's ID.
Treats all other successors as complete and forgets them.
filterServiceHosts(predicate: Callable[[str], bool]) -> None
Keep only services for which the given predicate approves.
The predicate function is called with the service host job's ID.
Treats all other services as complete and forgets them.
clearSuccessorsAndServiceHosts() -> None
Remove all references to child, follow-on, and associated service jobs.
replace(other: toil.job.JobDescription) -> None
Take on the ID of another JobDescription, retaining our own state and type.
When updated in the JobStore, we will save over the other JobDescription.
Useful for chaining jobs: the chained-to job can replace the parent job.
Merges cleanup state from the job being replaced into this one.
Parameters
other -- Job description to replace.
addChild(childID: str) -> None
Make the job with the given ID a child of the described job.
addFollowOn(followOnID: str) -> None
Make the job with the given ID a follow-on of the described job.
addServiceHostJob(serviceID, parentServiceID=None)
Make the ServiceHostJob with the given ID a service of the described job.
If a parent ServiceHostJob ID is given, that parent service will be started
first, and must have already been added.
hasChild(childID: str) -> bool
Return True if the job with the given ID is a child of the described job.
hasFollowOn(followOnID: str) -> bool
Test if the job with the given ID is a follow-on of the described job.
hasServiceHostJob(serviceID) -> bool
Test if the ServiceHostJob is a service of the described job.
renameReferences(renames: Dict[toil.job.TemporaryID, str]) -> None
Apply the given dict of ID renames to all references to jobs.
Does not modify our own ID or those of finished predecessors. IDs not
present in the renames dict are left as-is.
Parameters
renames -- Rename operations to apply.
addPredecessor() -> None
Notify the JobDescription that a predecessor has been added to its Job.
onRegistration(jobStore: AbstractJobStore) -> None
Called by the Job saving logic when this JobDescription meets the JobStore
and has its ID assigned.
Overridden to perform setup work (like hooking up flag files for service
jobs) that requires the JobStore.
Parameters
jobStore -- The job store we are being placed into
setupJobAfterFailure(exitStatus=None)
Reduce the remainingTryCount if greater than zero and set the memory to be
at least as big as the default memory (in case of exhaustion of memory,
which is common).
Requires a configuration to have been assigned (see
toil.job.Requirer.assignConfig()).
Parameters
exitReason (toil.batchSystems.abstractBatchSystem.BatchJobExitReason)
-- The configuration for the current workflow run.
getLogFileHandle(jobStore)
Returns a context manager that yields a file handle to the log file.
Assumes logJobStoreFileID is set.
property remainingTryCount
The try count set on the JobDescription, or the default based on the retry
count from the config if none is set.
clearRemainingTryCount() -> bool
Clear remainingTryCount and set it back to its default value.
Returns
True if a modification to the JobDescription was made, and False
otherwise.
pre_update_hook() -> None
Called by the job store before pickling and saving a created or updated
version of a job.
JOB.RUNNER API
The Runner contains the methods needed to configure and start a Toil run.
class Job.Runner
Used to setup and run Toil workflow.
static getDefaultArgumentParser() -> argparse.ArgumentParser
Get argument parser with added toil workflow options.
Returns
The argument parser used by a toil workflow with added Toil options.
Return type
argparse.ArgumentParser
static getDefaultOptions(jobStore: str) -> argparse.Namespace
Get default options for a toil workflow.
Parameters
jobStore (string) -- A string describing the jobStore for
the workflow.
Returns
The options used by a toil workflow.
Return type
argparse.ArgumentParser values object
static addToilOptions(parser)
Adds the default toil options to an optparse or argparse parser object.
Parameters
parser (optparse.OptionParser or argparse.ArgumentParser) -- Options
object to add toil options to.
static startToil(job, options)
Run the toil workflow using the given options.
Deprecated by toil.common.Toil.start.
(see Job.Runner.getDefaultOptions and Job.Runner.addToilOptions) starting
with this job. :param toil.job.Job job: root job of the workflow :raises:
toil.leader.FailedJobsException if at the end of function their
remain failed jobs. :return: The return value of the root job's run
function. :rtype: Any
JOB.FILESTORE API
The AbstractFileStore is an abstraction of a Toil run's shared storage.
class toil.fileStores.abstractFileStore.AbstractFileStore(jobStore:
toil.jobStores.abstractJobStore.AbstractJobStore, jobDesc: toil.job.JobDescription,
localTempDir: str, waitForPreviousCommit: Callable[[], Any])
Interface used to allow user code run by Toil to read and write files.
Also provides the interface to other Toil facilities used by user code, including:
· normal (non-real-time) logging
· finding the correct temporary directory for scratch work
· importing and exporting files into and out of the workflow
Stores user files in the jobStore, but keeps them separate from actual jobs.
May implement caching.
Passed as argument to the toil.job.Job.run() method.
Access to files is only permitted inside the context manager provided by
toil.fileStores.abstractFileStore.AbstractFileStore.open().
Also responsible for committing completed jobs back to the job store with an update
operation, and allowing that commit operation to be waited for.
__init__(jobStore: toil.jobStores.abstractJobStore.AbstractJobStore, jobDesc:
toil.job.JobDescription, localTempDir: str, waitForPreviousCommit: Callable[[],
Any]) -> None
Create a new file store object.
Parameters
· jobStore -- the job store in use for the current Toil run.
· jobDesc -- the JobDescription object for the currently running job.
· localTempDir -- the per-worker local temporary directory, under
which per-job directories will be created. Assumed to be inside the
workflow directory, which is assumed to be inside the work
directory.
· waitForPreviousCommit -- the waitForCommit method of the previous
job's file store, when jobs are running in sequence on the same
worker. Used to prevent this file store's startCommit and the
previous job's startCommit methods from running at the same time
and racing. If they did race, it might be possible for the later
job to be fully marked as completed in the job store before the
eralier job was.
static createFileStore(jobStore: toil.jobStores.abstractJobStore.AbstractJobStore,
jobDesc: toil.job.JobDescription, localTempDir: str, waitForPreviousCommit:
Callable[[], Any], caching: bool) -> Union[NonCachingFileStore, CachingFileStore]
Create a concreate FileStore.
static shutdownFileStore(workflowDir: str, workflowID: str) -> None
Carry out any necessary filestore-specific cleanup.
This is a destructive operation and it is important to ensure that there are
no other running processes on the system that are modifying or using the
file store for this workflow.
This is the intended to be the last call to the file store in a Toil run,
called by the batch system cleanup function upon batch system shutdown.
Parameters
· workflowDir -- The path to the cache directory
· workflowID -- The workflow ID for this invocation of the workflow
open(job: toil.job.Job) -> Generator[None, None, None]
Create the context manager around tasks prior and after a job has been run.
File operations are only permitted inside the context manager.
Implementations must only yield from within with super().open(job):.
Parameters
job -- The job instance of the toil job to run.
getLocalTempDir() -> str
Get a new local temporary directory in which to write files.
The directory will only persist for the duration of the job.
Returns
The absolute path to a new local temporary directory. This directory
will exist for the duration of the job only, and is guaranteed to be
deleted once the job terminates, removing all files it contains
recursively.
getLocalTempFile() -> str
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.
getLocalTempFileName() -> str
Get a valid name for a new local file. Don't actually create a file at the
path.
Returns
Path to valid file
abstract writeGlobalFile(localFileName: str, cleanup: bool = False) ->
toil.fileStores.FileID
Upload a file (as a path) to the job store.
If the file is in a FileStore-managed temporary directory (i.e. from
toil.fileStores.abstractFileStore.AbstractFileStore.getLocalTempDir()), it
will become a local copy of the file, eligible for deletion by
toil.fileStores.abstractFileStore.AbstractFileStore.deleteLocalFile().
If an executable file on the local filesystem is uploaded, its executability
will be preserved when it is downloaded again.
Parameters
· localFileName -- The path to the local file to upload. The last
path component (basename of the file) will remain associated with
the file in the file store, if supported by the backing JobStore,
so that the file can be searched for by name or name glob.
· cleanup -- if True then the copy of the global file will be deleted
once the job and all its successors have completed running. If not
the global file must be deleted manually.
Returns
an ID that can be used to retrieve the file.
writeGlobalFileStream(cleanup: bool = False, basename: Optional[str] = None,
encoding: Optional[str] = None, errors: Optional[str] = None) ->
Iterator[Tuple[toil.lib.io.WriteWatchingStream, toil.fileStores.FileID]]
Similar to writeGlobalFile, but allows the writing of a stream to the job
store. The yielded file handle does not need to and should not be closed
explicitly.
Parameters
· encoding -- The name of the encoding used to decode the file.
Encodings are the same as for decode(). Defaults to None which
represents binary mode.
· errors -- Specifies how encoding errors are to be handled. Errors
are the same as for open(). Defaults to 'strict' when an encoding
is specified.
· cleanup -- is as in
toil.fileStores.abstractFileStore.AbstractFileStore.writeGlobalFile().
· basename -- If supported by the backing JobStore, use the given
file basename so that when searching the job store with a query
matching that basename, the file will be detected.
Returns
A context manager yielding a tuple of 1) a file handle which can be
written to and 2) the toil.fileStores.FileID of the resulting file in
the job store.
logAccess(fileStoreID: Union[toil.fileStores.FileID, str], destination:
Optional[str] = None) -> None
Record that the given file was read by the job.
(to be announced if the job fails)
If destination is not None, it gives the path that the file was downloaded
to. Otherwise, assumes that the file was streamed.
Must be called by readGlobalFile() and readGlobalFileStream()
implementations.
abstract readGlobalFile(fileStoreID: str, userPath: Optional[str] = None, cache:
bool = True, mutable: bool = False, symlink: bool = False) -> str
Make the file associated with fileStoreID available locally.
If mutable is True, then a copy of the file will be created locally so that
the original is not modified and does not change the file for other jobs. If
mutable is False, then a link can be created to the file, saving disk
resources. The file that is downloaded will be executable if and only if it
was originally uploaded from an executable file on the local filesystem.
If a user path is specified, it is used as the destination. If a user path
isn't specified, the file is stored in the local temp directory with an
encoded name.
The destination file must not be deleted by the user; it can only be deleted
through deleteLocalFile.
Implementations must call logAccess() to report the download.
Parameters
· fileStoreID -- job store id for the file
· userPath -- a path to the name of file to which the global file
will be copied or hard-linked (see below).
· cache -- Described in
toil.fileStores.CachingFileStore.readGlobalFile()
· mutable -- Described in
toil.fileStores.CachingFileStore.readGlobalFile()
Returns
An absolute path to a local, temporary copy of the file keyed by
fileStoreID.
abstract readGlobalFileStream(fileStoreID: str, encoding: Optional[str] = None,
errors: Optional[str] = None) -> ContextManager[Union[BinaryIO, TextIO]]
Read a stream from the job store; similar to readGlobalFile.
The yielded file handle does not need to and should not be closed
explicitly.
Parameters
· encoding -- the name of the encoding used to decode the file.
Encodings are the same as for decode(). Defaults to None which
represents binary mode.
· errors -- an optional string that specifies how encoding errors are
to be handled. Errors are the same as for open(). Defaults to
'strict' when an encoding is specified.
Implementations must call logAccess() to report the download.
Returns
a context manager yielding a file handle which can be read from.
getGlobalFileSize(fileStoreID: Union[toil.fileStores.FileID, str]) -> int
Get the size of the file pointed to by the given ID, in bytes.
If a FileID or something else with a non-None 'size' field, gets that.
Otherwise, asks the job store to poll the file's size.
Note that the job store may overestimate the file's size, for example if it
is encrypted and had to be augmented with an IV or other encryption framing.
Parameters
fileStoreID -- File ID for the file
Returns
File's size in bytes, as stored in the job store
abstract deleteLocalFile(fileStoreID: Union[toil.fileStores.FileID, str]) -> None
Delete local copies of files associated with the provided job store ID.
Raises an OSError with an errno of errno.ENOENT if no such local copies
exist. Thus, cannot be called multiple times in succession.
The files deleted are all those previously read from this file ID via
readGlobalFile by the current job into the job's file-store-provided temp
directory, plus the file that was written to create the given file ID, if it
was written by the current job from the job's file-store-provided temp
directory.
Parameters
fileStoreID -- File Store ID of the file to be deleted.
abstract deleteGlobalFile(fileStoreID: Union[toil.fileStores.FileID, str]) -> None
Delete local files and then permanently deletes them from the job store.
To ensure that the job can be restarted if necessary, the delete will not
happen until after the job's run method has completed.
Parameters
fileStoreID -- the File Store ID of the file to be deleted.
logToMaster(text: str, level: int = 20) -> None
Send a logging message to the leader. The message will also be
logged by the worker at the same level.
Parameters
· text -- The string to log.
· level -- The logging level.
abstract startCommit(jobState: bool = False) -> None
Update the status of the job on the disk.
May start an asynchronous process. Call waitForCommit() to wait on that
process.
Parameters
jobState -- If True, commit the state of the FileStore's job, and
file deletes. Otherwise, commit only file creates/updates.
abstract waitForCommit() -> bool
Blocks while startCommit is running.
This function is called by this job's successor to ensure that it does not
begin modifying the job store until after this job has finished doing so.
Might be called when startCommit is never called on a particular instance,
in which case it does not block.
Returns
Always returns True
abstract classmethod shutdown(dir_: str) -> None
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: str, *args: Any)
A small wrapper around Python's builtin string class.
It is used to represent a file's ID in the file store, and has a size attribute
that is the file's size in bytes. This object is returned by importFile and
writeGlobalFile.
Calls into the file store can use bare strings; size will be queried from the job
store if unavailable in the ID.
__init__(fileStoreID: str, size: int, executable: bool = False) -> None
pack() -> str
Pack the FileID into a string so it can be passed through external code.
classmethod unpack(packedFileStoreID: str) -> toil.fileStores.FileID
Unpack the result of pack() into a FileID object.
BATCH SYSTEM API
The batch system interface is used by Toil to abstract over different ways of running
batches of jobs, for example Slurm, GridEngine, Mesos, Parasol and a single node. The
toil.batchSystems.abstractBatchSystem.AbstractBatchSystem API is implemented to run jobs
using a given job management system, e.g. Mesos.
Batch System Enivronmental Variables
Environmental variables allow passing of scheduler specific parameters.
For SLURM:
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.
abstract classmethod supportsAutoDeployment() -> bool
Whether this batch system supports auto-deployment of the user script
itself. If it does, the setUserScript() can be invoked to set the resource
object representing the user script.
Note to implementors: If your implementation returns True here, it should
also override
abstract classmethod supportsWorkerCleanup() -> bool
Indicates whether this batch system invokes
BatchSystemSupport.workerCleanup() after the last job for a particular
workflow invocation finishes. Note that the term worker refers to an entire
node, not just a worker process. A worker process may run more than one job
sequentially, and more than one concurrent worker process may exist on a
worker node, for the same workflow. The batch system is said to shut down
after the last worker process terminates.
setUserScript(userScript: toil.resource.Resource) -> None
Set the user script for this workflow. This method must be called before the
first job is issued to this batch system, and only if
supportsAutoDeployment() returns True, otherwise it will raise an exception.
Parameters
userScript -- the resource object representing the user script or
module and the modules it depends on.
abstract issueBatchJob(jobDesc: toil.job.JobDescription, job_environment:
Optional[Dict[str, str]] = None) -> int
Issues a job with the specified command to the batch system and returns a
unique jobID.
:param jobDesc a toil.job.JobDescription :param job_environment: a
collection of job-specific environment variables
to be set on the worker.
Returns
a unique jobID that can be used to reference the newly issued job
abstract killBatchJobs(jobIDs: List[int]) -> None
Kills the given job IDs. After returning, the killed jobs will not appear in
the results of getRunningBatchJobIDs. The killed job will not be returned
from getUpdatedBatchJob.
Parameters
jobIDs -- list of IDs of jobs to kill
abstract getIssuedBatchJobIDs() -> List[int]
Gets all currently issued jobs
Returns
A list of jobs (as jobIDs) currently issued (may be running, or may
be waiting to be run). Despite the result being a list, the ordering
should not be depended upon.
abstract getRunningBatchJobIDs() -> Dict[int, float]
Gets a map of jobs as jobIDs that are currently running (not just waiting)
and how long they have been running, in seconds.
Returns
dictionary with currently running jobID keys and how many seconds
they have been running as the value
abstract getUpdatedBatchJob(maxWait: int) ->
Optional[toil.batchSystems.abstractBatchSystem.UpdatedBatchJobInfo]
Returns information about job that has updated its status (i.e. ceased
running, either successfully or with an error). Each such job will be
returned exactly once.
Does not return info for jobs killed by killBatchJobs, although they may
cause None to be returned earlier than maxWait.
Parameters
maxWait -- the number of seconds to block, waiting for a result
Returns
If a result is available, returns UpdatedBatchJobInfo. Otherwise it
returns None. wallTime is the number of seconds (a strictly positive
float) in wall-clock time the job ran for, or None if this batch
system does not support tracking wall time.
getSchedulingStatusMessage() -> Optional[str]
Get a log message fragment for the user about anything that might be going
wrong in the batch system, if available.
If no useful message is available, return None.
This can be used to report what resource is the limiting factor when
scheduling jobs, for example. If the leader thinks the workflow is stuck,
the message can be displayed to the user to help them diagnose why it might
be stuck.
Returns
User-directed message about scheduling state.
abstract shutdown() -> None
Called at the completion of a toil invocation. Should cleanly terminate all
worker threads.
setEnv(name: str, value: Optional[str] = None) -> None
Set an environment variable for the worker process before it is launched.
The worker process will typically inherit the environment of the machine it
is running on but this method makes it possible to override specific
variables in that inherited environment before the worker is launched. Note
that this mechanism is different to the one used by the worker internally to
set up the environment of a job. A call to this method affects all jobs
issued after this method returns. Note to implementors: This means that you
would typically need to copy the variables before enqueuing a job.
If no value is provided it will be looked up from the current environment.
classmethod add_options(parser: Union[argparse.ArgumentParser,
argparse._ArgumentGroup]) -> None
If this batch system provides any command line options, add them to the
given parser.
classmethod setOptions(setOption: Callable[[str, Optional[Callable[[Any],
toil.batchSystems.abstractBatchSystem.OptionType]],
Optional[Callable[[toil.batchSystems.abstractBatchSystem.OptionType], None]],
Optional[toil.batchSystems.abstractBatchSystem.OptionType], Optional[List[str]]],
None]) -> None
Process command line or configuration options relevant to this batch system.
Parameters
setOption -- A function with signature setOption(option_name,
parsing_function=None, check_function=None, default=None, env=None)
returning nothing, used to update run configuration as a side effect.
getWorkerContexts() -> List[ContextManager[Any]]
Get a list of picklable context manager objects to wrap worker work in, in
order.
Can be used to ask the Toil worker to do things in-process (such as
configuring environment variables, hot-deploying user scripts, or cleaning
up a node) that would otherwise require a wrapping "executor" process.
JOB.SERVICE API
The Service class allows databases and servers to be spawned within a Toil workflow.
class Job.Service(memory=None, cores=None, disk=None, preemptable=None, unitName=None)
Abstract class used to define the interface to a service.
Should be subclassed by the user to define services.
Is not executed as a job; runs within a ServiceHostJob.
__init__(memory=None, cores=None, disk=None, preemptable=None, unitName=None)
Memory, core and disk requirements are specified identically to as in
toil.job.Job.__init__().
abstract start(job)
Start the service.
Parameters
job (toil.job.Job) -- The underlying host job that the service is
being run in. Can be used to register deferred functions, or to
access the fileStore for creating temporary files.
Returns
An object describing how to access the service. The object must be
pickleable and will be used by jobs to access the service (see
toil.job.Job.addService()).
abstract stop(job)
Stops the service. Function can block until complete.
Parameters
job (toil.job.Job) -- The underlying host job that the service is
being run in. Can be used to register deferred functions, or to
access the fileStore for creating temporary files.
check()
Checks the service is still running.
Raises exceptions.RuntimeError -- If the service failed, this will cause the
service job to be labeled failed.
Returns
True if the service is still running, else False. If False then the
service job will be terminated, and considered a success. Important
point: if the service job exits due to a failure, it should raise a
RuntimeError, not return False!
EXCEPTIONS API
Toil specific exceptions.
exception toil.job.JobException(message: str)
General job exception.
__init__(message: str) -> None
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)
exception
toil.jobStores.abstractJobStore.ConcurrentFileModificationException(jobStoreFileID:
toil.fileStores.FileID)
Indicates that the file was attempted to be modified by multiple processes at once.
__init__(jobStoreFileID: toil.fileStores.FileID)
Parameters
jobStoreFileID -- the ID of the file that was modified by multiple
workers or processes concurrently
exception toil.jobStores.abstractJobStore.JobStoreExistsException(locator: str)
Indicates that the specified job store already exists.
__init__(locator: str)
Parameters
locator (str) -- The location of the job store
exception toil.jobStores.abstractJobStore.NoSuchFileException(jobStoreFileID:
toil.fileStores.FileID, customName: Optional[str] = None, *extra: Any)
Indicates that the specified file does not exist.
__init__(jobStoreFileID: toil.fileStores.FileID, customName: Optional[str] = None,
*extra: Any)
Parameters
· jobStoreFileID -- the ID of the file that was mistakenly assumed to
exist
· customName -- optionally, an alternate name for the nonexistent
file
· extra (list) -- optional extra information to add to the error
message
exception toil.jobStores.abstractJobStore.NoSuchJobException(jobStoreID:
toil.fileStores.FileID)
Indicates that the specified job does not exist.
__init__(jobStoreID: toil.fileStores.FileID)
Parameters
jobStoreID (str) -- the jobStoreID that was mistakenly assumed to
exist
exception toil.jobStores.abstractJobStore.NoSuchJobStoreException(locator: str)
Indicates that the specified job store does not exist.
__init__(locator: str)
Parameters
locator (str) -- The location of the job store
RUNNING TESTS
Test make targets, invoked as $ make <target>, subject to which environment variables are
set (see Running Integration Tests).
┌───────────────────────┬──────────────────────────────────┐
│TARGET │ DESCRIPTION │
├───────────────────────┼──────────────────────────────────┤
│test │ Invokes all tests. │
├───────────────────────┼──────────────────────────────────┤
│integration_test │ Invokes only the integration │
│ │ tests. │
├───────────────────────┼──────────────────────────────────┤
│test_offline │ Skips building the Docker │
│ │ appliance and only invokes tests │
│ │ that have no docker │
│ │ dependencies. │
├───────────────────────┼──────────────────────────────────┤
│integration_test_local │ Makes integration tests easier │
│ │ to debug locally by running the │
│ │ integration tests serially and │
│ │ doesn't redirect output. This │
│ │ makes it appears on the terminal │
│ │ as expected. │
└───────────────────────┴──────────────────────────────────┘
Before running tests for the first time, initialize your virtual environment following the
steps in 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. Review
the PR by following the reviewingPRs checklist.
· Modified pull requests must be re-reviewed before merging. Note that Github does not
enforce this!
· Merge pull requests by following the mergingPRs checklist.
· When merging a pull request, make sure to update the Draft Changelog on the Github wiki,
which we will use to produce the changelog for the next release. The PR template tells
you to do this, so don't forget. New entries should go at the bottom.
· Pull requests will not be merged unless CI tests pass. Gitlab tests are only run on
code in the main Toil repository on some branch, so it is the responsibility of the
approving reviewer to make sure that pull requests from outside repositories are copied
to branches in the main repository. This can be accomplished with (from a Toil clone):
./contrib/admin/test-pr theirusername their-branch issues/123-fix-description-here
This must be repeated every time the PR submitter updates their PR, after checking to
see that the update is not malicious.
If there is no issue corresponding to the PR, after which the branch can be named, the
reviewer of the PR should first create the issue.
Developers who have push access to the main Toil repository are encouraged to make their
pull requests from within the repository, to avoid this step.
· Prefer using "Squash and marge" when merging pull requests to master especially when the
PR contains a "single unit" of work (i.e. if one were to rewrite the PR from scratch
with all the fixes included, they would have one commit for the entire PR). This makes
the commit history on master more readable and easier to debug in case of a breakage.
When squashing a PR from multiple authors, please add Co-authored-by to give credit to
all contributing authors.
See Issue #2816 for more details.
Publishing a Release
These are the steps to take to publish a Toil release:
· Determine the release version X.Y.Z. This should follow semantic versioning; if
user-workflow-breaking changes are made, X should be incremented, and Y and Z should be
zero. If non-breaking changes are made but new functionality is added, X should remain
the same as the last release, Y should be incremented, and Z should be zero. If only
patches are released, X and Y should be the same as the last release and Z should be
incremented.
· If it does not exist already, create a release branch in the Toil repo named X.Y.x,
where x is a literal lower-case "x". For patch releases, find the existing branch and
make sure it is up to date with the patch commits that are to be released. They may be
cherry-picked over from master.
· On the release branch, edit version_template.py in the root of the repository. Find the
line that looks like this (slightly different for patch releases):
baseVersion = 'X.Y.0a1'
Make it look like this instead:
baseVersion = 'X.Y.Z'
Commit your change to the branch.
· Tag the current state of the release branch as releases/X.Y.Z.
· Make the Github release here, referencing that tag. For a non-patch release, fill in the
description with the changelog from the wiki page, which you should clear. For a patch
release, just describe the patch.
· For a non-patch release, set up the main branch so that development builds will declare
themselves to be alpha versions of what the next release will probably be. Edit
version_template.py in the root of the repository on the main branch to set baseVersion
like this:
baseVersion = 'X.Y+1.0a1'
Make sure to replace X and Y+1 with actual numbers.
Adding Retries to a Function
See toil.lib.retry .
retry() can be used to decorate any function based on the list of errors one wishes to
retry on.
This list of errors can contain normal Exception objects, and/or RetryCondition objects
wrapping Exceptions to include additional conditions.
For example, retrying on a one Exception (HTTPError):
from requests import get
from requests.exceptions import HTTPError
@retry(errors=[HTTPError])
def update_my_wallpaper():
return get('https://www.deviantart.com/')
Or:
from requests import get
from requests.exceptions import HTTPError
@retry(errors=[HTTPError, ValueError])
def update_my_wallpaper():
return get('https://www.deviantart.com/')
The examples above will retry for the default interval on any errors specified the
"errors=" arg list.
To retry on specifically 500/502/503/504 errors, you could specify an ErrorCondition
object instead, for example:
from requests import get
from requests.exceptions import HTTPError
@retry(errors=[
ErrorCondition(
error=HTTPError,
error_codes=[500, 502, 503, 504]
)])
def update_my_wallpaper():
return requests.get('https://www.deviantart.com/')
To retry on specifically errors containing the phrase "NotFound":
from requests import get
from requests.exceptions import HTTPError
@retry(errors=[
ErrorCondition(
error=HTTPError,
error_message_must_include="NotFound"
)])
def update_my_wallpaper():
return requests.get('https://www.deviantart.com/')
To retry on all HTTPError errors EXCEPT an HTTPError containing the phrase "NotFound":
from requests import get
from requests.exceptions import HTTPError
@retry(errors=[
HTTPError,
ErrorCondition(
error=HTTPError,
error_message_must_include="NotFound",
retry_on_this_condition=False
)])
def update_my_wallpaper():
return requests.get('https://www.deviantart.com/')
To retry on boto3's specific status errors, an example of the implementation is:
import boto3
from botocore.exceptions import ClientError
@retry(errors=[
ErrorCondition(
error=ClientError,
boto_error_codes=["BucketNotFound"]
)])
def boto_bucket(bucket_name):
boto_session = boto3.session.Session()
s3_resource = boto_session.resource('s3')
return s3_resource.Bucket(bucket_name)
Any combination of these will also work, provided the codes are matched to the correct
exceptions. A ValueError will not return a 404, for example.
The retry function as a decorator should make retrying functions easier and clearer. It
also encourages smaller independent functions, as opposed to lumping many different things
that may need to be retried on different conditions in the same function.
The ErrorCondition object tries to take some of the heavy lifting of writing specific
retry conditions and boil it down to an API that covers all common use-cases without the
user having to write any new bespoke functions.
Use-cases covered currently:
1. Retrying on a normal error, like a KeyError.
2. Retrying on HTTP error codes (use ErrorCondition).
3. Retrying on boto's specific status errors, like "BucketNotFound" (use ErrorCondition).
4. Retrying when an error message contains a certain phrase (use ErrorCondition).
5. Explicitly NOT retrying on a condition (use ErrorCondition).
If new functionality is needed, it's currently best practice in Toil to add functionality
to the ErrorCondition itself rather than making a new custom retry method.
PULL REQUEST CHECKLISTS
This document contains checklists for dealing with PRs. More general PR information is
available at PRs.
Reviewing Pull Requests
This checklist is to be kept in sync with the checklist in the pull request template.
When reviewing a PR, do the following:
·
Make sure it is coming from issues/XXXX-fix-the-thing in the Toil repo, or from an
external repo.
·
If it is coming from an external repo, make sure to pull it in for CI with:
contrib/admin/test-pr otheruser theirbranchname issues/XXXX-fix-the-thing
·
If there is no associated issue, create one.
·
Read through the code changes. Make sure that it doesn't have:
·
Addition of trailing whitespace.
·
New variable or member names in camelCase that want to be in snake_case.
·
New functions without type hints.
·
New functions or classes without informative docstrings.
·
Changes to semantics not reflected in the relevant docstrings.
·
New or changed command line options for Toil workflows that are not reflected
in docs/running/cliOptions.rst
·
New features without tests.
·
Comment on the lines of code where problems exist with a review comment. You can
shift-click the line numbers in the diff to select multiple lines.
·
Finish the review with an overall description of your opinion.
Merging Pull Requests
This checklist is to be kept in sync with the checklist in the pull request template.
When merging a PR, do the following:
·
Make sure the PR passes tests.
·
Make sure the PR has been reviewed since its last modification. If not, review it.
·
Merge with the Github Squash and merge feature.
·
If there are multiple authors' commits, add Co-authored-by to give credit to
all contributing authors.
·
Copy its recommended changelog entry to the Draft Changelog.
·
Append the issue number in parentheses to the changelog entry.
TOIL ARCHITECTURE
The following diagram layouts out the software architecture of Toil.
[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.
Jobs and JobDescriptions
As noted in jobBasics, a job is the atomic unit of work in a Toil workflow. User scripts
inherit from the Job class to define units of work. These jobs are pickled and stored in
the job-store by the leader, and are retrieved and un-pickled by the worker when they are
scheduled to run.
During scheduling, Toil does not work with the actual Job objects. Instead, JobDescription
objects are used to store all the information that the Toil Leader ever needs to know
about the Job. This includes requirements information, dependency information, commands to
issue, etc.
Internally, the JobDescription object is referenced by its jobStoreID, which is often not
human readable. However, the Job and JobDescription objects contain several human-readable
names that are useful for logging and identification:
┌────────────┬──────────────────────────────────┐
│jobName │ Name of the kind of job this is. │
│ │ This may be used in job store │
│ │ IDs and logging. Also used to │
│ │ let the cluster scaler learn a │
│ │ model for how long the job will │
│ │ take. Defaults to the job │
│ │ class's name if no real │
│ │ user-defined name is available. │
│ │ │
│ │ For a FunctionWrappingJob, the │
│ │ jobName is replaced by the │
│ │ wrapped function's name. │
│ │ │
│ │ For a CWL workflow, the jobName │
│ │ is the class name of the │
│ │ internal job that is running the │
│ │ CWL workflow, such as "CWLJob". │
├────────────┼──────────────────────────────────┤
│unitName │ Name of this instance of this │
│ │ kind of job. If set by the user, │
│ │ it will appear with the jobName │
│ │ in logging. │
│ │ │
│ │ For a CWL workflow, the unitName │
│ │ is set to a descriptive name │
│ │ that includes the CWL file name │
│ │ and the ID in the file if set. │
├────────────┼──────────────────────────────────┤
│displayName │ A human-readable name to │
│ │ identify this particular job │
│ │ instance. Used as an identifier │
│ │ of the job class in the stats │
│ │ report. Defaults to the job │
│ │ class's name if no real │
│ │ user-defined name is available. │
│ │ │
│ │ For a CWL workflow, the │
│ │ displayName is the absolute │
│ │ workflow URI. │
└────────────┴──────────────────────────────────┘
Optimizations
Toil implements lots of optimizations designed for scalability. Here we detail some of
the key optimizations.
Read-only leader
The leader process is currently implemented as a single thread. Most of the leader's tasks
revolve around processing the state of jobs, each stored as a file within the job-store.
To minimise the load on this thread, each worker does as much work as possible to manage
the state of the job it is running. As a result, with a couple of minor exceptions, the
leader process never needs to write or update the state of a job within the job-store.
For example, when a job is complete and has no further successors the responsible worker
deletes the job from the job-store, marking it complete. The leader then only has to check
for the existence of the file when it receives a signal from the batch-system to know that
the job is complete. This off-loading of state management is orthogonal to future
parallelization of the leader.
Job chaining
The scheduling of successor jobs is partially managed by the worker, reducing the number
of individual jobs the leader needs to process. Currently this is very simple: if the
there is a single next successor job to run and its resources fit within the resources of
the current job and closely match the resources of the current job then the job is run
immediately on the worker without returning to the leader. Further extensions of this
strategy are possible, but for many workflows which define a series of serial successors
(e.g. map sequencing reads, post-process mapped reads, etc.) this pattern is very
effective at reducing leader workload.
Preemptable node support
Critical to running at large-scale is dealing with intermittent node failures. Toil is
therefore designed to always be resumable providing the job-store does not become corrupt.
This robustness allows Toil to run on preemptible nodes, which are only available when
others are not willing to pay more to use them. Designing workflows that divide into many
short individual jobs that can use preemptable nodes allows for workflows to be
efficiently scheduled and executed.
Caching
Running bioinformatic pipelines often require the passing of large datasets between jobs.
Toil caches the results from jobs such that child jobs running on the same node can
directly use the same file objects, thereby eliminating the need for an intermediary
transfer to the job store. Caching also reduces the burden on the local disks, because
multiple jobs can share a single file. The resulting drop in I/O allows pipelines to run
faster, and, by the sharing of files, allows users to run more jobs in parallel by
reducing overall disk requirements.
To demonstrate the efficiency of caching, we ran an experimental internal pipeline on 3
samples from the TCGA Lung Squamous Carcinoma (LUSC) dataset. The pipeline takes the tumor
and normal exome fastqs, and the tumor rna fastq and input, and predicts MHC presented
neoepitopes in the patient that are potential targets for T-cell based immunotherapies.
The pipeline was run individually on the samples on c3.8xlarge machines on AWS (60GB
RAM,600GB SSD storage, 32 cores). The pipeline aligns the data to hg19-based references,
predicts MHC haplotypes using PHLAT, calls mutations using 2 callers (MuTect and RADIA)
and annotates them using SnpEff, then predicts MHC:peptide binding using the IEDB suite of
tools before running an in-house rank boosting algorithm on the final calls.
To optimize time taken, The pipeline is written such that mutations are called on a
per-chromosome basis from the whole-exome bams and are merged into a complete vcf. Running
mutect in parallel on whole exome bams requires each mutect job to download the complete
Tumor and Normal Bams to their working directories -- An operation that quickly fills the
disk and limits the parallelizability of jobs. The script was run in Toil, with and
without caching, and Figure 2 shows that the workflow finishes faster in the cached case
while using less disk on average than the uncached run. We believe that benefits of
caching arising from file transfers will be much higher on magnetic disk-based storage
systems as compared to the SSD systems we tested this on.
[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. When using the │
│ │ Toil docker container, such as │
│ │ on Kubernetes, this defaults to │
│ │ /var/lib/toil. When using Toil │
│ │ autoscaling with Mesos, this is │
│ │ somewhere inside the Mesos │
│ │ sandbox. In all other cases, the │
│ │ system's standard temporary │
│ │ directory is used. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_WORKDIR_OVERRIDE │ An absolute path to a directory │
│ │ where Toil will write its │
│ │ temporary files. This overrides │
│ │ TOIL_WORKDIR and the --workDir │
│ │ command line option. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_KUBERNETES_HOST_PATH │ A path on Kubernetes hosts that │
│ │ will be mounted as the Toil work │
│ │ directory in the workers, to │
│ │ allow for shared caching. │
└────────────────────────────────┴─────────────────────────────────────┘
│TOIL_KUBERNETES_OWNER │ A name prefix for easy │
│ │ identification of Kubernetes │
│ │ jobs. If not set, Toil will use │
│ │ the current user name. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_KUBERNETES_SERVICE_ACCOUNT │ A service account name to apply │
│ │ when creating Kubernetes pods. │
├────────────────────────────────┼─────────────────────────────────────┤
│KUBE_WATCH_ENABLED │ A boolean variable that allows │
│ │ for users to utilize kubernetes │
│ │ watch stream feature instead of │
│ │ polling for running jobs. │
│ │ Default value is set to False. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_TES_ENDPOINT │ URL to the TES server to run │
│ │ against when using the tes batch │
│ │ system. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_TES_USER │ Username to use with HTTP Basic │
│ │ Authentication to log into the │
│ │ TES server. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_TES_PASSWORD │ Password to use with HTTP Basic │
│ │ Authentication to log into the │
│ │ TES server. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_TES_BEARER_TOKEN │ Token to use to authenticate to │
│ │ the TES server. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_APPLIANCE_SELF │ The fully qualified reference │
│ │ for the Toil Appliance you wish │
│ │ to use, in the form │
│ │ REPO/IMAGE:TAG. │
│ │ quay.io/ucsc_cgl/toil:3.6.0 and │
│ │ cket/toil:3.5.0 are both │
│ │ examples of valid options. Note │
│ │ that since Docker defaults to │
│ │ Dockerhub repos, only quay.io │
│ │ repos need to specify their │
│ │ registry. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_DOCKER_REGISTRY │ The URL of the registry of the │
│ │ Toil Appliance image you wish to │
│ │ use. Docker will use Dockerhub │
│ │ by default, but the quay.io │
│ │ registry is also very popular │
│ │ and easily specifiable by │
│ │ setting this option to quay.io. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_DOCKER_NAME │ The name of the Toil Appliance │
│ │ image you wish to use. Generally │
│ │ this is simply toil but this │
│ │ option is provided to override │
│ │ this, since the image can be │
│ │ built with arbitrary names. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_AWS_SECRET_NAME │ For the Kubernetes batch system, │
│ │ the name of a Kubernetes secret │
│ │ which contains a credentials │
│ │ file granting access to AWS │
│ │ resources. Will be mounted as │
│ │ ~/.aws inside Kubernetes-managed │
│ │ Toil containers. Enables the │
│ │ AWSJobStore to be used with the │
│ │ Kubernetes batch system, if the │
│ │ credentials allow access to S3 │
│ │ and SimpleDB. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_AWS_ZONE │ 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_GOOGLE_PROJECTID │ The Google project ID to use │
│ │ when generating Google job store │
│ │ names for tests or CWL │
│ │ workflows. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_SLURM_ARGS │ Arguments for sbatch for the │
│ │ slurm batch system. Do not pass │
│ │ CPU or memory specifications │
│ │ here. Instead, define resource │
│ │ requirements for the job. There │
│ │ is no default value for this │
│ │ variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_GRIDENGINE_ARGS │ Arguments for qsub for the │
│ │ gridengine batch system. Do not │
│ │ pass CPU or memory │
│ │ specifications here. Instead, │
│ │ define resource requirements for │
│ │ the job. There is no default │
│ │ value for this variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_GRIDENGINE_PE │ Parallel environment arguments │
│ │ for qsub and for the gridengine │
│ │ batch system. There is no │
│ │ default value for this variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_TORQUE_ARGS │ Arguments for qsub for the │
│ │ Torque batch system. Do not │
│ │ pass CPU or memory │
│ │ specifications here. Instead, │
│ │ define extra parameters for the │
│ │ job such as queue. Example: -q │
│ │ medium Use TOIL_TORQUE_REQS to │
│ │ pass extra values for the -l │
│ │ resource requirements parameter. │
│ │ There is no default value for │
│ │ this variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_TORQUE_REQS │ Arguments for the resource │
│ │ requirements for Torque batch │
│ │ system. Do not pass CPU or │
│ │ memory specifications here. │
│ │ Instead, define extra resource │
│ │ requirements as a string that │
│ │ goes after the -l argument to │
│ │ qsub. Example: │
│ │ walltime=2:00:00,file=50gb There │
│ │ is no default value for this │
│ │ variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_LSF_ARGS │ Additional arguments for the │
│ │ LSF's bsub command. Instead, │
│ │ define extra parameters for the │
│ │ job such as queue. Example: -q │
│ │ medium. There is no default │
│ │ value for this variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_HTCONDOR_PARAMS │ Additional parameters to include │
│ │ in the HTCondor submit file │
│ │ passed to condor_submit. Do not │
│ │ pass CPU or memory │
│ │ specifications here. Instead │
│ │ define extra parameters which │
│ │ may be required by HTCondor. │
│ │ This variable is parsed as a │
│ │ semicolon-separated string of │
│ │ parameter = value pairs. │
│ │ Example: requirements = │
│ │ TARGET.has_sse4_2 == true; │
│ │ accounting_group = test. There │
│ │ is no default value for this │
│ │ variable. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_CUSTOM_DOCKER_INIT_COMMAND │ Any custom bash command to run │
│ │ in the Toil docker container │
│ │ prior to running the Toil │
│ │ services. Can be used for any │
│ │ custom initialization in the │
│ │ worker and/or primary nodes such │
│ │ as private docker docker │
│ │ authentication. Example for AWS │
│ │ ECR: pip install awscli && eval │
│ │ $(aws ecr get-login │
│ │ --no-include-email --region │
│ │ us-east-1). │
└────────────────────────────────┴─────────────────────────────────────┘
│TOIL_CUSTOM_INIT_COMMAND │ Any custom bash command to run │
│ │ prior to starting the Toil │
│ │ appliance. Can be used for any │
│ │ custom initialization in the │
│ │ worker and/or primary nodes such │
│ │ as private docker authentication │
│ │ for the Toil appliance itself │
│ │ (i.e. from TOIL_APPLIANCE_SELF). │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_S3_HOST │ the IP address or hostname to │
│ │ use for connecting to S3. │
│ │ Example: TOIL_S3_HOST=127.0.0.1 │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_S3_PORT │ a port number to use for │
│ │ connecting to S3. Example: │
│ │ TOIL_S3_PORT=9001 │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_S3_USE_SSL │ enable or disable the usage of │
│ │ SSL for connecting to S3 (True │
│ │ by default). Example: │
│ │ TOIL_S3_USE_SSL=False │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_WES_BROKER_URL │ An optional broker URL to use to │
│ │ communicate between the WES │
│ │ server and Celery task queue. If │
│ │ unset, │
│ │ amqp://guest:guest@localhost:5672// │
│ │ is used. │
├────────────────────────────────┼─────────────────────────────────────┤
│TOIL_OWNER_TAG │ This will tag cloud resources with │
│ │ a tag reading: "Owner: │
│ │ $TOIL_OWNER_TAG". This is used │
│ │ internally at UCSC to stop a bot we │
│ │ have that terminates untagged │
│ │ resources. │
├────────────────────────────────┼─────────────────────────────────────┤
│SINGULARITY_DOCKER_HUB_MIRROR │ An http or https URL for the │
│ │ Singularity wrapper in the Toil │
│ │ Docker container to use as a mirror │
│ │ for Docker Hub. │
├────────────────────────────────┼─────────────────────────────────────┤
│OMP_NUM_THREADS │ The number of cores set for OpenMP │
│ │ applications in the workers. If not │
│ │ set, Toil will use the number of │
│ │ job threads. │
└────────────────────────────────┴─────────────────────────────────────┘
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AUTHOR
UCSC Computational Genomics Lab
COPYRIGHT
2022 – 2022 UCSC Computational Genomics Lab