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. 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
Running a basic workflow
A Toil workflow can be run with just two steps:
1. |
Copy and paste the following code block into a new file called helloWorld.py: |
from toil.common import Toil from toil.job import Job def helloWorld(message, memory="1G", cores=1, disk="1G"): return "Hello, world!, here's a message: %s" % message if __name__ == "__main__": parser = Job.Runner.getDefaultArgumentParser() options = parser.parse_args() 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 wdlSupport 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 |
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2. |
Run it with the default settings: |
$ python sort.py file:jobStore
The workflow created a file called sortedFile.txt in your current directory. Have a look at it and notice that it contains a whole lot of sorted lines!
This workflow does a smart merge sort on a file it generates, fileToSort.txt. The sort is smart because each step of the process−−−splitting the file into separate chunks, sorting these chunks, and merging them back together−−−is compartmentalized into a job. Each job can specify its own resource requirements and will only be run after the jobs it depends upon have run. Jobs without dependencies will be run in parallel.
NOTE:
Delete fileToSort.txt before moving on to #3. This example introduces options that specify dimensions for fileToSort.txt, if it does not already exist. If it exists, this workflow will use the existing file and the results will be the same as #2.
3. |
Run with custom options: |
$ python sort.py file:jobStore −−numLines=5000 −−lineLength=10 −−overwriteOutput=True −−workDir=/tmp/
Here we see that we can add our own options to a Toil script. As noted above, the first two options, −−numLines and −−lineLength, determine the number of lines and how many characters are in each line. −−overwriteOutput causes the current contents of sortedFile.txt to be overwritten, if it already exists. The last option, −−workDir, is an option built into Toil to specify where temporary files unique to a job are kept.
Describing the source code
To understand the details of what's going on inside. Let's start with the main() function. It looks like a lot of code, but don't worry−−−we'll break it down piece by piece.
def main(options=None): if not options: # deal with command line arguments parser = ArgumentParser() Job.Runner.addToilOptions(parser) parser.add_argument('−−numLines', default=defaultLines, help='Number of lines in file to sort.', type=int) parser.add_argument('−−lineLength', default=defaultLineLen, help='Length of lines in file to sort.', type=int) parser.add_argument("−−fileToSort", help="The file you wish to sort") parser.add_argument("−−outputFile", help="Where the sorted output will go") parser.add_argument("−−overwriteOutput", help="Write over the output file if it already exists.", default=True) parser.add_argument("−−N", dest="N", help="The threshold below which a serial sort function is used to sort file. " "All lines must of length less than or equal to N or program will fail", default=10000) parser.add_argument('−−downCheckpoints', action='store_true', help='If this option is set, the workflow will make checkpoints on its way through' 'the recursive "down" part of the sort') parser.add_argument("−−sortMemory", dest="sortMemory", help="Memory for jobs that sort chunks of the file.", default=None) parser.add_argument("−−mergeMemory", dest="mergeMemory", help="Memory for jobs that collate results.", default=None) options = parser.parse_args() if not hasattr(options, "sortMemory") or not options.sortMemory: options.sortMemory = sortMemory if not hasattr(options, "mergeMemory") or not options.mergeMemory: options.mergeMemory = sortMemory # do some input verification sortedFileName = options.outputFile or "sortedFile.txt" if not options.overwriteOutput and os.path.exists(sortedFileName): print("the output file {} already exists. Delete it to run the sort example again or use −−overwriteOutput=True".format(sortedFileName)) exit() fileName = options.fileToSort if options.fileToSort is None: # make the file ourselves fileName = 'fileToSort.txt' if os.path.exists(fileName): print("Sorting existing file: {}".format(fileName)) else: print('No sort file specified. Generating one automatically called: {}.'.format(fileName)) makeFileToSort(fileName=fileName, lines=options.numLines, lineLen=options.lineLength) else: if not os.path.exists(options.fileToSort): raise RuntimeError("File to sort does not exist: %s" % options.fileToSort) if int(options.N) <= 0: raise RuntimeError("Invalid value of N: %s" % options.N) # Now we are ready to run with Toil(options) as workflow: sortedFileURL = 'file://' + os.path.abspath(sortedFileName) if not workflow.options.restart: sortFileURL = 'file://' + os.path.abspath(fileName) sortFileID = workflow.importFile(sortFileURL) sortedFileID = workflow.start(Job.wrapJobFn(setup, sortFileID, int(options.N), options.downCheckpoints, options=options, memory=sortMemory)) else: sortedFileID = workflow.restart() workflow.exportFile(sortedFileID, sortedFileURL)
First we make a parser to process command line arguments using the argparse module. It's important that we add the call to Job.Runner.addToilOptions() to initialize our parser with all of Toil's default options. Then we add the command line arguments unique to this workflow, and parse the input. The help message listed with the arguments should give you a pretty good idea of what they can do.
Next we do a little bit of verification of the input arguments. The option −−fileToSort allows you to specify a file that needs to be sorted. If this option isn't given, it's here that we make our own file with the call to makeFileToSort().
Finally we come to the context manager that initializes the workflow. We create a path to the input file prepended with 'file://' as per the documentation for toil.common.Toil() when staging a file that is stored locally. Notice that we have to check whether or not the workflow is restarting so that we don't import the file more than once. Finally we can kick off the workflow by calling toil.common.Toil.start() on the job setup. When the workflow ends we capture its output (the sorted file's fileID) and use that in toil.common.Toil.exportFile() to move the sorted file from the job store back into "userland".
Next let's look at the job that begins the actual workflow, setup.
def setup(job, inputFile, N, downCheckpoints, options): """ Sets up the sort. Returns the FileID of the sorted file """ job.fileStore.logToMaster("Starting the merge sort") return job.addChildJobFn(down, inputFile, N, 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, downCheckpoints, options, memory=sortMemory): """ Input is a file and a range into that file to sort and an output location in which to write the sorted file. 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. """ # Read the file inputFile = job.fileStore.readGlobalFile(inputFileStoreID, cache=False) length = os.path.getsize(inputFile) if length > N: # We will subdivide the file job.fileStore.logToMaster("Splitting file: %s of size: %s" % (inputFileStoreID, length), level=logging.CRITICAL) # Split the file into two copies midPoint = getMidPoint(inputFile, 0, length) t1 = job.fileStore.getLocalTempFile() with open(t1, 'w') as fH: copySubRangeOfFile(inputFile, 0, midPoint+1, fH) t2 = job.fileStore.getLocalTempFile() with open(t2, 'w') as fH: copySubRangeOfFile(inputFile, midPoint+1, length, fH) # 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. return job.addFollowOnJobFn(up, job.addChildJobFn(down, job.fileStore.writeGlobalFile(t1), N, downCheckpoints, checkpoint=downCheckpoints, options=options, preemptable=True, memory=options.sortMemory).rv(), job.addChildJobFn(down, job.fileStore.writeGlobalFile(t2), N, downCheckpoints, checkpoint=downCheckpoints, options=options, preemptable=True, memory=options.mergeMemory).rv(), preemptable=True, options=options, memory=options.sortMemory).rv() else: # We can sort this bit of the file job.fileStore.logToMaster("Sorting file: %s of size: %s" % (inputFileStoreID, length), level=logging.CRITICAL) # Sort the copy and write back to the fileStore shutil.copyfile(inputFile, inputFile + '.sort') sort(inputFile + '.sort') return job.fileStore.writeGlobalFile(inputFile + '.sort')
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, options, memory=sortMemory): """ Merges the two files and places them in the output. """ 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) job.fileStore.logToMaster("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) 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 loggingRef.
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 −−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 −−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: |
(venv) $ toil launch−cluster <cluster−name> −−keyPairName <AWS−key−pair−name> −−leaderNodeType t2.medium −−zone us−west−2a
The arguments keyPairName, leaderNodeType, and zone are required to launch a cluster.
2. |
Copy helloWorld.py to the /tmp directory on the leader node using the rsyncCluster command: |
(venv) $ toil rsync−cluster −−zone us−west−2a <cluster−name> helloWorld.py :/tmp
Note that the command requires defining the file to copy as well as the target location on the cluster leader node.
3. |
Login to the cluster leader node using the sshCluster command: |
(venv) $ toil ssh−cluster −−zone us−west−2a <cluster−name>
Note that this command will log you in as the root user.
4. |
Run the Toil script in the cluster: |
$ python /tmp/helloWorld.py aws:us−west−2:my−S3−bucket
In this particular case, we create an S3 bucket called my−S3−bucket in the us−west−2 availability zone to store intermediate job results.
Along with some other INFO log messages, you should get the following output in your terminal window: Hello, world!, here's a message: You did it!.
5. |
Exit from the SSH connection. |
$ exit
6. |
Use the 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: |
(venv) $ toil launch−cluster <cluster−name> −−keyPairName <AWS−key−pair−name> −−leaderNodeType t2.medium −−zone us−west−2a
2. |
Copy example.cwl and example−job.yaml from the CWL example to the node using the rsyncCluster command: |
(venv) $ toil rsync−cluster −−zone us−west−2a <cluster−name> example.cwl :/tmp (venv) $ toil rsync−cluster −−zone us−west−2a <cluster−name> example−job.yaml :/tmp
3. |
SSH into the cluster's leader node using the sshCluster utility: |
(venv) $ toil ssh−cluster −−zone us−west−2a <cluster−name>
4. |
Once on the leader node, it's a good idea to update and install the following: |
sudo apt−get update sudo apt−get −y upgrade sudo apt−get −y dist−upgrade sudo apt−get −y install git sudo pip install mesos.cli
5. |
Now create a new virtualenv with the −−system−site−packages option and activate: |
virtualenv −−system−site−packages venv source venv/bin/activate
6. |
Now run the CWL workflow: |
(venv) $ toil−cwl−runner −−provisioner aws −−jobStore aws:us−west−2a:any−name /tmp/example.cwl /tmp/example−job.yaml
TIP:
When running a CWL workflow on AWS, input files can be provided either on the local file system or in S3 buckets using s3:// URI references. Final output files will be copied to the local file system of the leader node.
7. |
Finally, log out of the leader node and from your local computer, destroy the cluster: |
(venv) $ toil destroy−cluster −−zone us−west−2a <cluster−name>
Running a Workflow with Autoscaling − Cactus
Cactus is a reference−free, whole−genome multiple alignment program that can be run on any of the cloud platforms Toil supports.
NOTE:
Cloud Independence:
This example provides a "cloud agnostic" view of running Cactus with Toil. Most options will not change between cloud providers. However, each provisioner has unique inputs for −−leaderNodeType, −−nodeType and −−zone. We recommend the following:
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 |
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2. |
Launch a leader node using the launchCluster command: |
(venv) $ toil launch−cluster <cluster−name> −−provisioner <aws, gce, azure> −−keyPairName <key−pair−name> −−leaderNodeType <type> −−zone <zone>
NOTE:
A Helpful Tip
When using AWS or Azure, setting the environment variable eliminates having to specify the −−zone option for each command. This will be supported for GCE in the future.
(venv) $ export TOIL_AWS_ZONE=us−west−2c
3. |
Create appropriate directory for uploading files: |
(venv) $ toil ssh−cluster −−provisioner <aws, gce, azure> <cluster−name> $ mkdir /root/cact_ex $ exit
4. |
Copy the required files, i.e., seqFile.txt (a text file containing the locations of the input sequences as well as their phylogenetic tree, see here), organisms' genome sequence files in FASTA format, and configuration files (e.g. blockTrim1.xml, if desired), up to the leader node: |
(venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> pestis−short−aws−seqFile.txt :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> GCF_000169655.1_ASM16965v1_genomic.fna :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> GCF_000006645.1_ASM664v1_genomic.fna :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> GCF_000182485.1_ASM18248v1_genomic.fna :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> GCF_000013805.1_ASM1380v1_genomic.fna :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> setup_leaderNode.sh :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> blockTrim1.xml :/root/cact_ex (venv) $ toil rsync−cluster −−provisioner <aws, gce, azure> <cluster−name> blockTrim3.xml :/root/cact_ex
5. |
Log in to the leader node: |
(venv) $ toil ssh−cluster −−provisioner <aws, gce, azure> <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, azure> −−nodeType <type> −−maxNodes 2 −−minNodes 0 −−retry 10 −−batchSystem mesos −−disableCaching −−logDebug −−logFile /logFile_pestis3 −−configFile /root/cact_ex/blockTrim3.xml <aws, google, azure>:<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, azure>:<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, azure> <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, azure> <cluster−name>
Toil runs in various environments, including locally and in the cloud (Amazon Web Services, Google Compute Engine, and Microsoft Azure). 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
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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). |
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batchSystemInterface: Specifies either a local single−machine or a currently supported HPC environment (lsf, parasol, mesos, slurm, torque, htcondor, or gridengine). Mesos is a special case, and is launched for cloud environments. |
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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:
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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. |
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azureJobStore: Experimental. |
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googleJobStore: A Google Cloud Storage bucket formatted as "gce:<zone>:<bucketname>" where only numbers, letters, and dashes are allowed in the bucket name. Example: gce:us−west2−a:my−google−jobstore−name. |
These use cloud buckets to house all of the files. This is useful if there are several different worker machines all running jobs that need to access the job store.
Batch System
A Toil batch system is either a local single−machine (one computer) or a currently supported HPC cluster of computers (lsf, parasol, mesos, slurm, torque, htcondor, or gridengine). Mesos is a special case, and is launched for cloud environments. These environments manage individual worker nodes under a leader node to process the work required in a workflow. The leader and its workers all coordinate their tasks and files through a centralized job store location.
See batchSystemInterface for a more detailed description of different batch systems.
Provisioner
The Toil provisioner provides a tool set for running a Toil workflow on a particular cloud platform.
The clusterRef are command line tools used to provision nodes in your desired cloud platform. They allows you to launch nodes, ssh to the leader, and rsync files back and forth.
For detailed instructions for using the provisioner see runningAWS or runningGCE. An Azure provisioner is in the works and coming soon. For more details see the Azure provisioner github ticket.
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
Azure: azure:account−name−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 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.
−−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 options only applies when using scalable batch systems.
−−restart
If −\−restart is specified then will attempt to restart existing workflow at the location pointed to by the −\−jobStore option. Will raise an exception if the workflow does not exist
Logging Options
Toil hides stdout and stderr by default except in case of job failure. Log levels in toil are based on priority from the logging module:
−−logOff
Only CRITICAL log levels are shown. Equivalent to −−logLevel=OFF or −−logLevel=CRITICAL.
−−logCritical
Only CRITICAL log levels are shown. Equivalent to −−logLevel=OFF or −−logLevel=CRITICAL.
−−logError
Only ERROR, and CRITICAL log levels are shown. Equivalent to −−logLevel=ERROR.
−−logWarning
Only WARN, ERROR, and CRITICAL log levels are shown. Equivalent to −−logLevel=WARNING.
−−logInfo
All log statements are shown, except DEBUG. Equivalent to −−logLevel=INFO.
−−logDebug
All log statements are shown. Equivalent to −−logLevel=DEBUG.
−−logLevel=LOGLEVEL
May be set to: OFF (or CRITICAL), ERROR, WARN (or WARNING), INFO, or DEBUG.
−−logFile FILEPATH
Specifies a file path to write the logging output to.
−−rotatingLogging
Turn on rotating logging, which prevents log files from getting too big (set using −−maxLogFileSize BYTESIZE).
−−maxLogFileSize BYTESIZE
Sets the maximum log file size in bytes (−−rotatingLogging must be active).
Batch System Options
−−batchSystem BATCHSYSTEM
The type of batch system to run the job(s) with, currently can be one of LSF, Mesos, Slurm, Torque, HTCondor, singleMachine, parasol, gridEngine'. (default: singleMachine)
−−parasolCommand PARASOLCOMMAND
The name or path of the parasol program. Will be looked up on PATH unless it starts with a slash. (default: parasol)
−−parasolMaxBatches PARASOLMAXBATCHES
Maximum number of job batches the Parasol batch is allowed to create. One batch is created for jobs with a unique set of resource requirements. (default: 1000)
−−scale SCALE
A scaling factor to change the value of all submitted tasks' submitted cores. Used in singleMachine batch system. (default: 1)
−−linkImports
When using Toil's importFile function for staging, input files are copied to the job store. Specifying this option saves space by sym−linking imported files. As long as caching is enabled Toil will protect the file automatically by changing the permissions to read−only.
−−mesosMaster MESOSMASTERADDRESS
The host and port of the Mesos master separated by a colon. (default: 169.233.147.202:5050)
Autoscaling Options
−−provisioner CLOUDPROVIDER
The provisioner for cluster auto−scaling. The currently supported choices are 'aws' or 'gce'. The default is None.
−−nodeTypes NODETYPES
List of node types separated by commas. The syntax for each node type depends on the provisioner used. For the cgcloud and AWS provisioners this is the name of an EC2 instance type, optionally followed by a colon and the price in dollars to bid for a spot instance of that type, for example 'c3.8xlarge:0.42'. If no spot bid is specified, nodes of this type will be non−preemptable. It is acceptable to specify an instance as both preemptable and non−preemptable, including it twice in the list. In that case, preemptable nodes of that type will be preferred when creating new nodes once the maximum number of preemptable−nodes have been reached.
−−nodeOptions NODEOPTIONS
Options for provisioning the nodes. The syntax depends on the provisioner used. Neither the CGCloud nor the AWS provisioner support any node options.
−−minNodes MINNODES
Minimum number of nodes of each type in the cluster, if using auto−scaling. This should be provided as a comma−separated list of the same length as the list of node types. default=0
−−maxNodes MAXNODES
Maximum number of nodes of each type in the cluster, if using autoscaling, provided as a comma−separated list. The first value is used as a default if the list length is less than the number of nodeTypes. default=10
−−preemptableCompensation PREEMPTABLECOMPENSATION
The preference of the autoscaler to replace preemptable nodes with non−preemptable nodes, when preemptable nodes cannot be started for some reason. Defaults to 0.0. This value must be between 0.0 and 1.0, inclusive. A value of 0.0 disables such compensation, a value of 0.5 compensates two missing preemptable nodes with a non−preemptable one. A value of 1.0 replaces every missing pre−emptable node with a non−preemptable one.
−−nodeStorage NODESTORAGE
Specify the size of the root volume of worker nodes when they are launched in gigabytes. You may want to set this if your jobs require a lot of disk space. The default value is 50.
−−metrics
Enable the prometheus/grafana dashboard for monitoring CPU/RAM usage, queue size, and issued jobs.
−−defaultMemory INT
The default amount of memory to request for a job. Only applicable to jobs that do not specify an explicit value for this requirement. Standard suffixes like K, Ki, M, Mi, G or Gi are supported. Default is 2.0G
−−defaultCores FLOAT
The default number of CPU cores to dedicate a job. Only applicable to jobs that do not specify an explicit value for this requirement. Fractions of a core (for example 0.1) are supported on some batch systems, namely Mesos and singleMachine. Default is 1.0
−−defaultDisk INT
The default amount of disk space to dedicate a job. Only applicable to jobs that do not specify an explicit value for this requirement. Standard suffixes like K, Ki, M, Mi, G or Gi are supported. Default is 2.0G
−−maxCores INT
The maximum number of CPU cores to request from the batch system at any one time. Standard suffixes like K, Ki, M, Mi, G or Gi are supported.
−−maxMemory INT
The maximum amount of memory to request from the batch system at any one time. Standard suffixes like K, Ki, M, Mi, G or Gi are supported.
−−maxDisk INT
The maximum amount of disk space to request from the batch system at any one time. Standard suffixes like K, Ki, M, Mi, G or Gi are supported.
−−retryCount RETRYCOUNT
Number of times to retry a failing job before giving up and labeling job failed. default=1
−−maxJobDuration MAXJOBDURATION
Maximum runtime of a job (in seconds) before we kill it (this is a lower bound, and the actual time before killing the job may be longer).
−−rescueJobsFrequency RESCUEJOBSFREQUENCY
Period of time to wait (in seconds) between checking for missing/overlong jobs, that is jobs which get lost by the batch system.
−−maxServiceJobs MAXSERVICEJOBS
The maximum number of service jobs that can be run concurrently, excluding service jobs running on preemptable nodes. default=9223372036854775807
−−maxPreemptableServiceJobs MAXPREEMPTABLESERVICEJOBS
The maximum number of service jobs that can run concurrently on preemptable nodes. default=9223372036854775807
−−deadlockWait DEADLOCKWAIT
The minimum number of seconds to observe the cluster stuck running only the same service jobs before throwing a deadlock exception. default=60
−−statePollingWait STATEPOLLINGWAIT
Time, in seconds, to wait before doing a scheduler query for job state. Return cached results if within the waiting period.
Miscellaneous Options
−−disableCaching
Disables caching in the file store. This flag must be set to use a batch system that does not support caching such as Grid Engine, Parasol, LSF, or Slurm
−−disableChaining
Disables chaining of jobs (chaining uses one job's resource allocation for its successor job if possible).
−−maxLogFileSize MAXLOGFILESIZE
The maximum size of a job log file to keep (in bytes), log files larger than this will be truncated to the last X bytes. Setting this option to zero will prevent any truncation. Setting this option to a negative value will truncate from the beginning. Default=62.5 K
−−writeLogs FILEPATH
Write worker logs received by the leader into their own files at the specified path. The current working directory will be used if a path is not specified explicitly. Note: By default only the logs of failed jobs are returned to leader. Set log level to 'debug' to get logs back from successful jobs, and adjust 'maxLogFileSize' to control the truncation limit for worker logs.
−−writeLogsGzip FILEPATH
Identical to −\−writeLogs except the logs files are gzipped on the leader.
−−realTimeLogging
Enable real−time logging from workers to masters
−−sseKey SSEKEY
Path to file containing 32 character key to be used for server−side encryption on awsJobStore or googleJobStore. SSE will not be used if this flag is not passed.
−−cseKey CSEKEY
Path to file containing 256−bit key to be used for client−side encryption on azureJobStore. By default, no encryption is used.
−−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)
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:
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−−maxServiceJobs: The maximum number of service jobs that can be run concurrently, excluding service jobs running on preemptable nodes. |
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−−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 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: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: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.
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 with Azure coming soon.
Toil uses Apache Mesos as the batchSystemOverview.
See here for instructions for runningAWS.
See here for instructions for runningGCE.
See here for the latest details on the development of the Azure provisioner.
Storage (Toil jobStore)
Toil can make use of cloud storage such as Azure, AWS, or Google buckets to take of 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:
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awsJobStore |
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azureJobStore |
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googleJobStore |
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:
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If necessary, create and activate an AWS account |
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Only needed once, but AWS requires that users "subscribe" to use the Container Linux by CoreOS AMI. You will encounter errors if this is not done. |
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Next, generate a key pair for AWS with the command (do NOT generate your key pair with the Amazon browser): |
$ ssh−keygen −t rsa
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This should prompt you to save your key. Please save it in |
~/.ssh/id_rsa
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Now move this to where your OS can see it as an authorized key: |
$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys $ eval `ssh−agent −s` $ ssh−add
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You'll also need to chmod your private key (good practice but also enforced by AWS): |
$ chmod 400 id_rsa
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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
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Now click on the "Import Key Pair" button to add your key: |
Adding an Amazon Key Pair.UNINDENT
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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
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The directions (transcribed from: https://docs.aws.amazon.com/general/latest/gr/managing−aws−access−keys.html ) are now: |
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On the IAM Dashboard page, choose your account name in the navigation bar, and then choose My Security Credentials. |
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Expand the Access keys (access key ID and secret access key) section. |
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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. <strong><em>After you close the dialog box, you can't retrieve this secret access key again. |
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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
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Now configure your AWS credentials with: |
$ aws configure
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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]: "
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Toil also relies on boto, and you'll need to create a boto file containing your credentials as well. To do this, run: |
$ nano ~/.boto
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Paste in the following (with your actual "AWS Access Key ID" and "AWS Secret Access Key"): |
[Credentials] aws_access_key_id = ****************Q65Q aws_secret_access_key = ****************G0ys
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If not done already, install toil (example uses version 3.12.0, but we recommend the latest release): |
$ virtualenv venv $ source venv/bin/activate $ pip install toil[all]==3.12.0
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Now that toil is installed and you are running a virtualenv, an example of launching a toil leader node would be the following (again, note that we set TOIL_APPLIANCE_SELF to toil version 3.12.0 in this example, but please set the version to the installed version that you are using if you're using a different version): |
$ TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:3.12.0 toil launch−cluster clustername −−leaderNodeType t2.medium −−zone us−west−1a −−keyPairName id_rsa
To further break down each of these commands:
TOIL_APPLIANCE_SELF=quay.io/ucsc_cgl/toil:latest −−− This is optional. It specifies a mesos docker image that we maintain with the latest version of toil installed on it. If you want to use a different version of toil, please specify the image tag you need from https://quay.io/repository/ucsc_cgl/toil?tag=latest&tab=tags.
toil launch−cluster −−− Base command in toil to launch a cluster.
clustername −−− Just choose a name for your cluster.
−−leaderNodeType t2.medium −−− Specify the leader node type. Make a t2.medium (2CPU; 4Gb RAM; $0.0464/Hour). List of available AWS instances: https://aws.amazon.com/ec2/pricing/on−demand/
−−zone us−west−1a −−− Specify the AWS zone you want to launch the instance in. Must have the same prefix as the zone in your awscli credentials (which, in the example of this tutorial is: "us−west−1").
−−keyPairName id_rsa −−− The name of your key pair, which should be "id_rsa" if you've followed this tutorial.
AWS Job Store
Using the AWS job store is straightforward after you've finished Preparing your AWS environment; all you need to do is specify the prefix for the job store name.
To run the sort example sort example with the AWS job store you would type
$ python sort.py aws:us−west−2:my−aws−sort−jobstore
Toil Provisioner
The Toil provisioner is included in Toil alongside the [aws] extra and allows us to spin up a cluster.
Getting started with the provisioner is simple:
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Make sure you have Toil installed with the AWS extras. For detailed instructions see extras. |
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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. In addition, the Toil provisioner will automatically tag your cluster with an Owner tag that corresponds to your keypair name to facilitate cost tracking.
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.
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.
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Download sort.py |
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2. |
Launch the leader node in AWS using the launchCluster command: |
(venv) $ toil launch−cluster <cluster−name> −−keyPairName <AWS−key−pair−name> −−leaderNodeType t2.medium −−zone us−west−2a
3. |
Copy the sort.py script up to the leader node: |
(venv) $ toil rsync−cluster <cluster−name> sort.py :/root
4. |
Login to the leader node: |
(venv) $ toil ssh−cluster <cluster−name>
5. |
Run the script as an autoscaling workflow: |
$ python /root/sort.py aws:us−west−2:<my−jobstore−name> −−provisioner aws −−nodeTypes c3.large −−maxNodes 2 −−batchSystem mesos
NOTE:
In this example, the autoscaling Toil code creates up to two instances of type c3.large and launches Mesos slave containers inside them. The containers are then available to run jobs defined by the sort.py script. Toil also creates a bucket in S3 called aws:us−west−2:autoscaling−sort−jobstore to store intermediate job results. The Toil autoscaler can also provision multiple different node types, which is useful for workflows that have jobs with varying resource requirements. For example, one could execute the script with −−nodeTypes c3.large,r3.xlarge −−maxNodes 5,1, which would allow the provisioner to create up to five c3.large nodes and one r3.xlarge node for memory−intensive jobs. In this situation, the autoscaler would avoid creating the more expensive r3.xlarge node until needed, running most jobs on the c3.large nodes.
1. |
View the generated file to sort: |
$ head fileToSort.txt
2. |
View the sorted file: |
$ head sortedFile.txt
For more information on other autoscaling (and other) options have a look at workflowOptions and/or run
$ python my−toil−script.py −−help
IMPORTANT:
Some important caveats about starting a toil run through an ssh session are explained in the sshCluster section.
Preemptability
Toil can run on a heterogeneous cluster of both preemptable and non−preemptable nodes. Being preemptable node simply means that the node may be shut down at any time, while jobs are running. These jobs can then be restarted later somewhere else.
A node type can be specified as preemptable by adding a spot bid to its entry in the list of node types provided with the −−nodeTypes flag. If spot instance prices rise above your bid, the preemptable node whill be shut down.
While individual jobs can each explicitly specify whether or not they should be run on preemptable nodes via the boolean preemptable resource requirement, the −−defaultPreemptable flag will allow jobs without a preemptable requirement to run on preemptable machines.
Specify Preemptability Carefully
Ensure that your choices for −−nodeTypes and −−maxNodes <> make sense for your workflow and won't cause it to hang. You should make sure the provisioner is able to create nodes large enough to run the largest job in the workflow, and that non−preemptable node types are allowed if there are non−preemptable jobs in the workflow.
Finally, the −−preemptableCompensation flag can be used to handle cases where preemptable nodes may not be available but are required for your workflow. With this flag enabled, the autoscaler will attempt to compensate for a shortage of preemptable nodes of a certain type by creating non−preemptable nodes of that type, if non−preemptable nodes of that type were specified in −−nodeTypes.
Dashboard
Toil provides a dashboard for viewing the RAM and CPU usage of each node, the number of issued jobs of each type, the number of failed jobs, and the size of the jobs queue. To launch this dashboard for a toil workflow, include the −−metrics flag in the toil script command. The dashboard can then be viewed in your browser at localhost:3000 while connected to the leader node through toil ssh−cluster. On AWS, the dashboard keeps track of every node in the cluster to monitor CPU and RAM usage, but it can also be used while running a workflow on a single machine. The dashboard uses Grafana as the front end for displaying real−time plots, and Prometheus for tracking metrics exported by toil. In order to use the dashboard for a non−released toil version, you will have to build the containers locally with make docker, since the prometheus, grafana, and mtail containers used in the dashboard are tied to a specific toil version.
Running in Azure
This section describes how to deploy a leader node in Azure and launch a Toil cluster from the leader node to run workflows. You'll need an account in Azure prior to executing the steps in the document. To setup Azure, see Preparing your Azure environment.
NOTE:
Azure support in Toil is still experimental and in Beta!
Preparing your Azure environment
Follow the steps below to prepare your Azure environment for running a Toil workflow.
1. |
Create an Azure account and an Azure storage account |
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2. |
Locate your Azure storage account key and then store it in one of |
the following locations:
• |
AZURE_ACCOUNT_KEY_<account> environment variable |
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• |
AZURE_ACCOUNT_KEY environment variable |
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• |
or finally in ~/.toilAzureCredentials. with the format |
[AzureStorageCredentials] accountName1=ACCOUNTKEY1 accountName2=ACCOUNTKEY2
These locations are searched in the order above, which can be useful if you work with multiple accounts. Only one is needed however.
3. |
Create an Azure active directory and service principal to create the |
following credentials:
• |
Client ID (also known as an Application ID) |
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• |
Subscription ID (found via "All Services", then "Subscriptions") |
||
• |
secret (also known as the authentication key) |
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• |
Tenant ID (also known as a Directory ID) |
These Azure credentials need to be stored a location where the Ansible scripts can read them. There are multiple options for doing so as described here, one of which is to create a file called ~/.azure/credentials with the format
[default] subscription_id=22222222−2222−2222−2222−222222222222 client_id=1111111−111−1111−111−111111111 secret=abc123 tenant=33333333−3333−3333−3333−333333333333
4. |
Add the "app" you created as a user with appropriate permissions by going to "All Services", then "Permissions", then "Access Control (IAM)", then "+ Add", then selecting "Owner" from the "Role" drop−down menu on the right, then typing in the name of your app under "Select" and add permissions. |
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5. |
Create an SSH keypair if one doesn't exist (see: add SSH) |
NOTE:
You do not need to upload your key pair to Azure as with AWS.
Running a Workflow with Autoscaling
WARNING:
Azure Autoscaling is in beta!
The steps to run a Azure workflow are similar to those of AWS (Autoscaling), except you will need to explicitly specify the −−provisioner azure option which otherwise defaults to aws.
1. |
Download sort.py |
||
2. |
Launch the leader node in Azure using the launchCluster command. |
(venv) $ toil launch−cluster <CLUSTER−NAME> −−provisioner azure −−leaderNodeType Standard_A2 −−keyPairName <OWNER> −−zone westus
For Azure, the provisioner needs the ssh public key. It will read the file given by −−publicKeyFile. The default location is ~/.ssh/id_rsa.pub. The −−keyPairName option is used to indicate the instances owner. See add SSH.
3. |
Upload the sort example and ssh into the leader: |
(venv) $ toil rsync−cluster −−provisioner azure <CLUSTER−NAME> sort.py :/root (venv) $ toil ssh−cluster −−provisioner azure <CLUSTER−NAME>
4. |
Run the workflow: |
$ python /root/sort.py azure:<AZURE−STORAGE−ACCOUNT>:<JOBSTORE−NAME> −−provisioner azure −−batchSystem mesos −−nodeTypes Standard_A2 −−maxNodes 2
5. |
Clean up: |
$ exit # this exits the ssh from the leader node (venv) $ toil destroy−cluster −−provisioner azure <CLUSTER−NAME>
Azure Job Store
After Preparing your Azure environment all you will need to do is specify the job store name prefix for Azure.
For example to run the sort example with Azure job store you would run
$ python sort.py azure:<my−azure−account−name>:my−azure−jobstore
Details about Launching a Cluster in Azure
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 Standard_A2 leader in the westus zone, run
(venv) $ toil launch−cluster my−cluster −−provisioner azure −−leaderNodeType Standard_A2 −−zone westus −−keyPairName <your−Azure−key−pair−name>
The cluster name is used to uniquely identify your cluster and will be used to populate the instance's Name tag. In addition, the Toil provisioner will automatically tag your cluster with an Owner tag that corresponds to your keypair name to facilitate cost tracking.
The −−zone parameter specifies which availability zone to launch the cluster in. Alternatively, you can specify this option via the TOIL_AZURE_ZONE environment variable.
For more information on options try:
(venv) $ toil launch−cluster −−help
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 |
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2. |
Follow Google's Instructions to download credentials and set the GOOGLE_APPLICATION_CREDENTIALS environment variable |
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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 AT example DOT 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 AT example DOT 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 AT example DOT com, then your key pair name will be just jane.
3. |
Upload the sort example and ssh into the leader: |
(venv) $ toil rsync−cluster −−provisioner gce <CLUSTER−NAME> sort.py :/root (venv) $ toil ssh−cluster −−provisioner gce <CLUSTER−NAME>
4. |
Run the workflow: |
$ python /root/sort.py google:<PROJECT−ID>:<JOBSTORE−NAME> −−provisioner gce −−batchSystem mesos −−nodeTypes n1−standard−2 −−maxNodes 2
5. |
Clean up: |
$ exit # this exits the ssh from the leader node (venv) $ toil destroy−cluster −−provisioner gce <CLUSTER−NAME>
Cluster Utilities
There are several utilities used for starting and managing a Toil cluster using the AWS provisioner. They are installed via the [aws] or [google] extra. For installation details see installProvisioner. The cluster utilities are used for runningAWS and are comprised of toil launch−cluster, toil rsync−cluster, toil ssh−cluster, and toil destroy−cluster entry points.
Cluster commands specific to toil are:
status −−− Reports runtime and resource usage for all jobs in a specified jobstore (workflow must have originally been run using the −\−stats option).
stats −−− Inspects a job store to see which jobs have failed, run successfully, etc.
destroy−cluster −−− For autoscaling. Terminates the specified cluster and associated resources.
launch−cluster −−− For autoscaling. This is used to launch a toil leader instance with the specified provisioner.
rsync−cluster −−− For autoscaling. Used to transfer files to a cluster launched with toil launch−cluster.
ssh−cluster −−− SSHs into the toil appliance container running on the leader of the cluster.
clean −−− Delete the job store used by a previous Toil workflow invocation.
kill −−− Kills any running jobs in a rogue toil.
For information on a specific utility run:
toil launch−cluster −−help
for a full list of its options and functionality.
The cluster utilities can be used for runningGCE and runningAWS.
TIP:
By default, all of the cluster utilities expect to be running on AWS. To run with Google you will need to specify the −−provisioner gce option for each utility.
NOTE:
Boto must be configured with AWS credentials before using cluster utilities.
runningGCE contains instructions for
Stats Command
To use the stats command, a workflow must first be run using the −−stats option. Using this command makes certain that toil does not delete the job store, no matter what other options are specified (i.e. normally the option −−clean=always would delete the job, but −−stats will override this).
An example of this would be running the following:
python discoverfiles.py file:my−jobstore −−stats
Where discoverfiles.py is the following:
import subprocess from toil.common import Toil from toil.job import Job class discoverFiles(Job): """Views files at a specified path using ls.""" def __init__(self, path, *args, **kwargs): self.path = path super(discoverFiles, self).__init__(*args, **kwargs) def run(self, fileStore): subprocess.check_call(["ls", self.path]) def main(): options = Job.Runner.getDefaultArgumentParser().parse_args() job1 = discoverFiles(path="/", displayName='sysFiles') job2 = discoverFiles(path="/home/lifeisaboutfishtacos", displayName='userFiles') job3 = discoverFiles(path="/home/andbeeftacos") 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 stats 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 where temporary directory containing 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 google's 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_AZURE_ZONE, or TOIL_GCE_ZONE environment variables, or by the ec2_region_name parameter in your .boto file if using AWS, or derived from the instance metadata if using this utility on an existing EC2 instance.
−−leaderNodeType LEADERNODETYPE
Non−preemptable node type to use for the cluster leader.
−−keyPairName KEYPAIRNAME
The name of the AWS or ssh key pair to include on the instance
−−boto BOTOPATH
The path to the boto credentials directory. This is transferred to all nodes in order to access the AWS jobStore from non−AWS instances.
−−tag KEYVALUE
KEYVALUE is specified as KEY=VALUE. −t KEY=VALUE also accepted. Tags are added to the AWS cluster for this node and all of its children. Tags are of the form: −t key1=value1 −−tag key2=value2. Multiple tags are allowed and each tag needs its own flag. By default the cluster is tagged with: { "Name": clusterName, "Owner": IAM username }.
−−vpcSubnet VPCSUBNET
VPC subnet ID to launch cluster in. Uses default subnet if not specified. This subnet needs to have auto assign IPs turned on.
−−nodeTypes NODETYPES
Comma−separated list of node types to create while launching the leader. The syntax for each node type depends on the provisioner used. For the aws provisioner this is the name of an EC2 instance type followed by a colon and the price in dollar to bid for a spot instance, for example 'c3.8xlarge:0.42'. Must also provide the −−workers argument to specify how many workers of each node type to create
−−workers WORKERS
−w WORKERS also accepted. Comma−separated list of the number of workers of each node type to launch alongside the leader when the cluster is created. This can be useful if running toil without auto−scaling but with need of more hardware support.
−−leaderStorage LEADERSTORAGE
Specify the size (in gigabytes) of the root volume for the leader instance. This is an EBS volume.
−−nodeStorage NODESTORAGE
Specify the size (in gigabytes) of the root volume for any worker instances created when using the −w flag. This is an EBS volume.
Logging Options
−−logOff
Same as −\−logCritical
−−logCritical
Turn on logging at level CRITICAL and above. (default is INFO)
−−logError
Turn on logging at level ERROR and above. (default is INFO)
−−logWarning
Turn on logging at level WARNING and above. (default is INFO)
−−logInfo
Turn on logging at level INFO and above. (default is INFO)
−−logDebug
Turn on logging at level DEBUG and above. (default is INFO)
−−logLevel LOGLEVEL
Log at given level (may be either OFF (or CRITICAL), ERROR, WARN (or WARNING), INFO or DEBUG). (default is INFO)
−−logFile LOGFILE
File to log in
−−rotatingLogging
Turn on rotating logging, which prevents log files getting too big.
Ssh−Cluster Command
Toil provides the ability to ssh into the leader of the cluster. This can be done as follows:
$ toil ssh−cluster CLUSTER−NAME−HERE
This will open a shell on the Toil leader and is used to start an Autoscaling run. Issues with docker prevent using screen and tmux when sshing the cluster (The shell doesn't know that it is a TTY which prevents it from allocating a new screen session). This can be worked around via
$ script $ screen
Simply running screen within script will get things working properly again.
Finally, you can execute remote commands with the following syntax:
$ toil ssh−cluster CLUSTER−NAME−HERE remoteCommand
It is not advised that you run your Toil workflow using remote execution like this unless a tool like nohup is used to insure 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 node, volumes, and security groups etc. are deleted. If a node or cluster in 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
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 schedulars used in these environments. 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 schedulars we currently consider these schedulars 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.
The Common Workflow Language (CWL) is an emerging standard for writing workflows that are portable across multiple workflow engines and platforms. Toil has full support for the CWL v1.0.1 specification.
Running CWL Locally
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.
Running CWL in the Cloud
To run in cloud and HPC configurations, you may need to provide additional command line parameters to select and configure the batch system to use.
To run a CWL workflow in AWS with toil see awscwl.
Running CWL within Toil Scripts
A CWL workflow can be run indirectly in a native Toil script. However, this is not the standard way to run CWL workflows with Toil and doing so comes at the cost of job efficiency. For some use cases, such as running one process on multiple files, it may be useful. For example, if you want to run a CWL workflow with 3 YML files specifying different samples inputs, it could look something like:
from toil.job import Job from toil.common import Toil import subprocess import os def initialize_jobs(job): job.fileStore.logToMaster('initialize_jobs') def runQC(job, cwl_file, cwl_filename, yml_file, yml_filename, outputs_dir, output_num): job.fileStore.logToMaster("runQC") tempDir = job.fileStore.getLocalTempDir() cwl = job.fileStore.readGlobalFile(cwl_file, userPath=os.path.join(tempDir, cwl_filename)) yml = job.fileStore.readGlobalFile(yml_file, userPath=os.path.join(tempDir, yml_filename)) subprocess.check_call(["cwltool", 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" with Toil(options) as toil: # specify the folder where the cwl and yml files live inputs_dir = "/tmp/" # specify where you wish the outputs to be written outputs_dir = "/tmp/" job0 = Job.wrapJobFn(initialize_jobs) cwl_filename = "hello.cwl" cwl_file = toil.importFile("file://" + 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.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)
Support is still in the alpha phase and should be able to handle basic wdl files. See the specification below for more details.
How to Run a WDL file in Toil
Recommended best practice when running wdl files is to first use the Broad's wdltool for syntax validation and generating the needed json input file. Full documentation can be found on the repository, and a precompiled jar binary can be downloaded here: wdltool (this requires java7).
That means two steps. First, make sure your wdl file is valid and devoid of syntax errors by running
java −jar wdltool.jar validate example_wdlfile.wdl
Second, generate a complementary json file if your wdl file needs one. This json will contain keys for every necessary input that your wdl file needs to run:
java −jar wdltool.jar inputs example_wdlfile.wdl
When this json template is generated, open the file, and fill in values as necessary by hand. WDL files all require json files to accompany them. If no variable inputs are needed, a json file containing only '{}' may be required.
Once a wdl file is validated and has an appropriate json file, workflows can be run in toil using:
toil−wdl−runner example_wdlfile.wdl example_jsonfile.json
See options below for more parameters.
ENCODE Example from ENCODE−DCC
To follow this example, you will need docker installed. The original workflow can be found here: https://github.com/ENCODE−DCC/pipeline−container
We've included the wdl file and data files in the toil repository needed to run this example. First, download the example code and unzip. The file needed is "testENCODE/encode_mapping_workflow.wdl".
Next, use wdltool (this requires java7) to validate this file:
java −jar wdltool.jar validate encode_mapping_workflow.wdl
Next, use wdltool to generate a json file for this wdl file:
java −jar wdltool.jar inputs encode_mapping_workflow.wdl
This json file once opened should look like this:
{ "encode_mapping_workflow.fastqs": "Array[File]", "encode_mapping_workflow.trimming_parameter": "String", "encode_mapping_workflow.reference": "File" }
The trimming_parameter should be set to 'native'. Download the example code and unzip. Inside are two data files required for the run
ENCODE_data/reference/GRCh38_chr21_bwa.tar.gz ENCODE_data/ENCFF000VOL_chr21.fq.gz
Editing the json to include these as inputs, the json should now look something like this:
{ "encode_mapping_workflow.fastqs": ["/path/to/unzipped/ENCODE_data/ENCFF000VOL_chr21.fq.gz"], "encode_mapping_workflow.trimming_parameter": "native", "encode_mapping_workflow.reference": "/path/to/unzipped/ENCODE_data/reference/GRCh38_chr21_bwa.tar.gz" }
The wdl and json files can now be run using the command
toil−wdl−runner encode_mapping_workflow.wdl encode_mapping_workflow.json
This should deposit the output files in the user's current working directory (to change this, specify a new directory with the '−o' option).
GATK Examples from the Broad
Simple examples of WDL can be found on the Broad's website as tutorials: https://software.broadinstitute.org/wdl/documentation/topic?name=wdl−tutorials.
One can follow along with these tutorials, write their own wdl files following the directions and run them using either cromwell or toil. For example, in tutorial 1, if you've followed along and named your wdl file 'helloHaplotypeCall.wdl', then once you've validated your wdl file using wdltool (this requires java7) using
java −jar wdltool.jar validate helloHaplotypeCaller.wdl
and generated a json file (and subsequently typed in appropriate filepaths* and variables) using
java −jar wdltool.jar inputs helloHaplotypeCaller.wdl
• |
Absolute filepath inputs are recommended for local testing. |
then the wdl script can be run using
toil−wdl−runner helloHaplotypeCaller.wdl helloHaplotypeCaller_inputs.json
toilwdl.py Options
'−o' or '−\−output_directory': Specifies the output folder, and defaults to the current working directory if not specified by the user.
'−\−gen_parse_files': Creates "AST.out", which holds a printed AST of the wdl file and "mappings.out", which holds the printed task, workflow, csv, and tsv dictionaries generated by the parser.
'−\−dont_delete_compiled': Saves the compiled toil python workflow file for debugging.
Any number of arbitrary options may also be specified. These options will not be parsed immediately, but passed down as toil options once the wdl/json files are processed. For valid toil options, see the documentation: http://toil.readthedocs.io/en/3.12.0/running/cli.html
Running WDL within Toil Scripts
NOTE:
A cromwell.jar file is needed in order to run a WDL workflow.
A WDL workflow can be run indirectly in a native Toil script. However, this is not the standard way to run WDL workflows with Toil and doing so comes at the cost of job efficiency. For some use cases, such as running one process on multiple files, it may be useful. For example, if you want to run a WDL workflow with 3 JSON files specifying different samples inputs, it could look something like:
from toil.job import Job from toil.common import Toil import subprocess import os def initialize_jobs(job): job.fileStore.logToMaster("initialize_jobs") def runQC(job, wdl_file, wdl_filename, json_file, json_filename, outputs_dir, jar_loc,output_num): job.fileStore.logToMaster("runQC") tempDir = job.fileStore.getLocalTempDir() wdl = job.fileStore.readGlobalFile(wdl_file, userPath=os.path.join(tempDir, wdl_filename)) json = job.fileStore.readGlobalFile(json_file, userPath=os.path.join(tempDir, json_filename)) subprocess.check_call(["java","−jar",jar_loc,"run",wdl,"−−inputs",json]) output_filename = "output.txt" output_file = job.fileStore.writeGlobalFile(outputs_dir + output_filename) job.fileStore.readGlobalFile(output_file, userPath=os.path.join(outputs_dir, "sample_" + output_num + "_" + output_filename)) return output_file if __name__ == "__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" with Toil(options) as toil: # specify the folder where the cwl and yml files live inputs_dir = "/tmp/" # specify where you wish the outputs to be written outputs_dir = "/tmp/" # specify the location of your cromwell jar jar_loc = "cromwell.jar" job0 = Job.wrapJobFn(initialize_jobs) wdl_filename = "hello.wdl" wdl_file = toil.importFile("file://" + 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:
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scatter |
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read_tsv, read_csv |
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docker calls |
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handles priority, and output file wrangling |
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• |
currently handles primitives and arrays |
TO BE IMPLEMENTED SOON:
• |
implement type: $type_postfix_quantifier |
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"default" values inside variables |
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$map_types & $object_types |
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wdl files that "import" other wdl files (including URI handling for 'http://' and 'https://') |
This tutorial walks through the features of Toil necessary for developing a workflow using the Toil Python API.
NOTE:
"script" and "workflow" will be used interchangeably
Scripting Quick Start
To begin, consider this short toil script which illustrates defining a workflow:
from toil.common import Toil from toil.job import Job def helloWorld(message, memory="2G", cores=2, disk="3G"): return "Hello, world!, here's a message: %s" % message if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" hello_job = Job.wrapFn(helloWorld, "Woot") with Toil(options) as toil: print toil.start(hello_job) #Prints Hello, world!, ...
The workflow consists of a single job. The resource requirements for that job are (optionally) specified by keyword arguments (memory, cores, disk). The script is run using toil.job.Job.Runner.getDefaultOptions(). Below we explain the components of this code in detail.
Job Basics
The atomic unit of work in a Toil workflow is a Job. User scripts inherit from this base class to define units of work. For example, here is a more long−winded class−based version of the job in the quick start example:
from toil.job import Job class HelloWorld(Job): def __init__(self, message): Job.__init__(self, memory="2G", cores=2, disk="3G") self.message = message def run(self, fileStore): return "Hello, world!, here's a message: %s" % self.message
In the example a class, HelloWorld, is defined. The constructor requests 2 gigabytes of memory, 2 cores and 3 gigabytes of local disk to complete the work.
The toil.job.Job.run() method is the function the user overrides to get work done. Here it just logs a message using toil.job.Job.log(), which will be registered in the log output of the leader process of the workflow.
Invoking a Workflow
We can add to the previous example to turn it into a complete workflow by adding the necessary function calls to create an instance of HelloWorld and to run this as a workflow containing a single job. This uses the toil.job.Job.Runner class, which is used to start and resume Toil workflows. For example:
from toil.common import Toil from toil.job import Job class HelloWorld(Job): def __init__(self, message): Job.__init__(self, memory="2G", cores=2, disk="3G") self.message = message def run(self, fileStore): return "Hello, world!, here's a message: %s" % self.message if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" hello_job = HelloWorld("Woot") with Toil(options) as toil: print(toil.start(hello_job))
Alternatively, the more powerful toil.common.Toil class can be used to run and resume workflows. It is used as a context manager and allows for preliminary setup, such as staging of files into the job store on the leader node. An instance of the class is initialized by specifying an options object. The actual workflow is then invoked by calling the toil.common.Toil.start() method, passing the root job of the workflow, or, if a workflow is being restarted, toil.common.Toil.restart() should be used. Note that the context manager should have explicit if else branches addressing restart and non restart cases. The boolean value for these if else blocks is toil.options.restart.
For example:
from toil.job import Job from toil.common import Toil class HelloWorld(Job): def __init__(self, message): Job.__init__(self, memory="2G", cores=2, disk="3G") self.message = message def run(self, fileStore): self.log("Hello, world!, I have a message: {}".format(self.message)) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" with Toil(options) as toil: if not toil.options.restart: job = HelloWorld("Woot!") toil.start(job) else: toil.restart()
The call to toil.job.Job.Runner.getDefaultOptions() creates a set of default options for the workflow. The only argument is a description of how to store the workflow's state in what we call a job−store. Here the job−store is contained in a directory within the current working directory called "toilWorkflowRun". Alternatively this string can encode other ways to store the necessary state, e.g. an S3 bucket or Azure 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 = "INFO" options.clean = "always" hello_job = HelloWorld("Woot") with Toil(options) as toil: print(toil.start(hello_job))
Creates a fully fledged script with all the options Toil exposed as command line arguments. Running this script with "−−help" will print the full list of options.
Alternatively an existing argparse.ArgumentParser or optparse.OptionParser object can have Toil script command line options added to it with the toil.job.Job.Runner.addToilOptions() method.
Resuming a Workflow
In the event that a workflow fails, either because of programmatic error within the jobs being run, or because of node failure, the workflow can be resumed. Workflows can only not be reliably resumed if the job−store itself becomes corrupt.
Critical to resumption is that jobs can be rerun, even if they have apparently completed successfully. Put succinctly, a user defined job should not corrupt its input arguments. That way, regardless of node, network or leader failure the job can be restarted and the workflow resumed.
To resume a workflow specify the "restart" option in the options object passed to toil.common.Toil.start(). If node failures are expected it can also be useful to use the integer "retryCount" option, which will attempt to rerun a job retryCount number of times before marking it fully failed.
In the common scenario that a small subset of jobs fail (including retry attempts) within a workflow Toil will continue to run other jobs until it can do no more, at which point toil.common.Toil.start() will raise a toil.leader.FailedJobsException exception. Typically at this point the user can decide to fix the script and resume the workflow or delete the job−store manually and rerun the complete workflow.
Functions and Job Functions
Defining jobs by creating class definitions generally involves the boilerplate of creating a constructor. To avoid this the classes toil.job.FunctionWrappingJob and toil.job.JobFunctionWrappingTarget allow functions to be directly converted to jobs. For example, the quick start example (repeated here):
from toil.common import Toil from toil.job import Job def helloWorld(message, memory="2G", cores=2, disk="3G"): return "Hello, world!, here's a message: %s" % message if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" hello_job = Job.wrapFn(helloWorld, "Woot!") with Toil(options) as toil: print(toil.start(hello_job))
Is equivalent to the previous example, but using a function to define the job.
The function call:
Job.wrapFn(helloWorld, "Woot")
Creates the instance of the toil.job.FunctionWrappingTarget that wraps the function.
The keyword arguments memory, cores and disk allow resource requirements to be specified as before. Even if they are not included as keyword arguments within a function header they can be passed as arguments when wrapping a function as a job and will be used to specify resource requirements.
We can also use the function wrapping syntax to a job function, a function whose first argument is a reference to the wrapping job. Just like a self argument in a class, this allows access to the methods of the wrapping job, see toil.job.JobFunctionWrappingTarget. For example:
from toil.common import Toil from toil.job import Job def helloWorld(job, message): job.log("Hello world, I have a message: {}".format(message)) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" hello_job = Job.wrapJobFn(helloWorld, "Woot!") with Toil(options) as toil: toil.start(hello_job)
Here helloWorld() is a job function. It uses the toil.job.Job.log() to log a message that will be printed to the output console. Here the only subtle difference to note is the line:
hello_job = Job.wrapJobFn(helloWorld, "Woot")
Which uses the function toil.job.Job.wrapJobFn() to wrap the job function instead of toil.job.Job.wrapFn() which wraps a vanilla function.
Workflows with Multiple Jobs
A parent job can have child jobs and follow−on jobs. These relationships are specified by methods of the job class, e.g. toil.job.Job.addChild() and toil.job.Job.addFollowOn().
Considering a set of jobs the nodes in a job graph and the child and follow−on relationships the directed edges of the graph, we say that a job B that is on a directed path of child/follow−on edges from a job A in the job graph is a successor of A, similarly A is a predecessor of B.
A parent job's child jobs are run directly after the parent job has completed, and in parallel. The follow−on jobs of a job are run after its child jobs and their successors have completed. They are also run in parallel. Follow−ons allow the easy specification of cleanup tasks that happen after a set of parallel child tasks. The following shows a simple example that uses the earlier helloWorld() job function:
from toil.common import Toil from toil.job import Job def helloWorld(job, message, memory="2G", cores=2, disk="3G"): job.log("Hello world, I have a message: {}".format(message)) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" j1 = Job.wrapJobFn(helloWorld, "first") j2 = Job.wrapJobFn(helloWorld, "second or third") j3 = Job.wrapJobFn(helloWorld, "second or third") j4 = Job.wrapJobFn(helloWorld, "last") j1.addChild(j2) j1.addChild(j3) j1.addFollowOn(j4) with Toil(options) as toil: toil.start(j1)
In the example four jobs are created, first j1 is run, then j2 and j3 are run in parallel as children of j1, finally j4 is run as a follow−on of j1.
There are multiple short hand functions to achieve the same workflow, for example:
from toil.common import Toil from toil.job import Job def helloWorld(job, message, memory="2G", cores=2, disk="3G"): job.log("Hello world, I have a message: {}".format(message)) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" j1 = Job.wrapJobFn(helloWorld, "first") j2 = j1.addChildJobFn(helloWorld, "second or third") j3 = j1.addChildJobFn(helloWorld, "second or third") j4 = j1.addFollowOnJobFn(helloWorld, "last") with Toil(options) as toil: toil.start(j1)
Equivalently defines the workflow, where the functions toil.job.Job.addChildJobFn() and toil.job.Job.addFollowOnJobFn() are used to create job functions as children or follow−ons of an earlier job.
Jobs graphs are not limited to trees, and can express arbitrary directed acylic graphs. For a precise definition of legal graphs see toil.job.Job.checkJobGraphForDeadlocks(). The previous example could be specified as a DAG as follows:
from toil.common import Toil from toil.job import Job def helloWorld(job, message, memory="2G", cores=2, disk="3G"): job.log("Hello world, I have a message: {}".format(message)) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" j1 = Job.wrapJobFn(helloWorld, "first") j2 = j1.addChildJobFn(helloWorld, "second or third") j3 = j1.addChildJobFn(helloWorld, "second or third") j4 = j2.addChildJobFn(helloWorld, "last") j3.addChild(j4) with Toil(options) as toil: toil.start(j1)
Note the use of an extra child edge to make j4 a child of both j2 and j3.
Dynamic Job Creation
The previous examples show a workflow being defined outside of a job. However, Toil also allows jobs to be created dynamically within jobs. For example:
from toil.common import Toil from toil.job import Job def binaryStringFn(job, depth, message=""): if depth > 0: job.addChildJobFn(binaryStringFn, depth−1, message + "0") job.addChildJobFn(binaryStringFn, depth−1, message + "1") else: job.log("Binary string: {}".format(message)) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" with Toil(options) as toil: toil.start(Job.wrapJobFn(binaryStringFn, depth=5))
The job function binaryStringFn logs all possible binary strings of length n (here n=5), creating a total of 2^(n+2) − 1 jobs dynamically and recursively. Static and dynamic creation of jobs can be mixed in a Toil workflow, with jobs defined within a job or job function being created at run time.
Promises
The previous example of dynamic job creation shows variables from a parent job being passed to a child job. Such forward variable passing is naturally specified by recursive invocation of successor jobs within parent jobs. This can also be achieved statically by passing around references to the return variables of jobs. In Toil this is achieved with promises, as illustrated in the following example:
from toil.common import Toil from toil.job import Job def fn(job, i): job.log("i is: %s" % i, level=100) return i+1 if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" j1 = Job.wrapJobFn(fn, 1) j2 = j1.addChildJobFn(fn, j1.rv()) j3 = j1.addFollowOnJobFn(fn, j2.rv()) with Toil(options) as toil: toil.start(j1)
Running this workflow results in three log messages from the jobs: i is 1 from j1, i is 2 from j2 and i is 3 from j3.
The return value from the first job is promised to the second job by the call to toil.job.Job.rv() in the following line:
j2 = j1.addChildFn(fn, j1.rv())
The value of j1.rv() is a promise, rather than the actual return value of the function, because j1 for the given input has at that point not been evaluated. A promise (toil.job.Promise) is essentially a pointer to for the return value that is replaced by the actual return value once it has been evaluated. Therefore, when j2 is run the promise becomes 2.
Promises also support indexing of return values:
def parent(job): indexable = Job.wrapJobFn(fn) job.addChild(indexable) job.addFollowOnFn(raiseWrap, indexable.rv(2)) def raiseWrap(arg): raise RuntimeError(arg) # raises "2" def fn(job): return (0, 1, 2, 3)
Promises can be quite useful. For example, we can combine dynamic job creation with promises to achieve a job creation process that mimics the functional patterns possible in many programming languages:
from toil.common import Toil from toil.job import Job def binaryStrings(job, depth, message=""): if depth > 0: s = [ job.addChildJobFn(binaryStrings, depth−1, message + "0").rv(), job.addChildJobFn(binaryStrings, depth−1, message + "1").rv() ] return job.addFollowOnFn(merge, s).rv() return [message] def merge(strings): return strings[0] + strings[1] if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.loglevel = "INFO" options.clean = "always" with Toil(options) as toil: print toil.start(Job.wrapJobFn(binaryStrings, depth=5))
The return value l of the workflow is a list of all binary strings of length 10, computed recursively. Although a toy example, it demonstrates how closely Toil workflows can mimic typical programming patterns.
Promised Requirements
Promised requirements are a special case of Promises that allow a job's return value to be used as another job's resource requirements.
This is useful when, for example, a job's storage requirement is determined by a file staged to the job store by an earlier job:
from toil.common import Toil from toil.job import Job, PromisedRequirement import os def parentJob(job): downloadJob = Job.wrapJobFn(stageFn, "File://"+os.path.realpath(__file__), cores=0.1, memory='32M', disk='1M') job.addChild(downloadJob) analysis = Job.wrapJobFn(analysisJob, fileStoreID=downloadJob.rv(0), disk=PromisedRequirement(downloadJob.rv(1))) job.addFollowOn(analysis) def stageFn(job, url, cores=1): importedFile = job.fileStore.importFile(url) return importedFile, importedFile.size def analysisJob(job, fileStoreID, cores=2): # now do some analysis on the file pass if __name__ == "__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" with Toil(options) as toil: toil.start(Job.wrapJobFn(parentJob))
Note that this also makes use of the size attribute of the FileID object. This promised requirements mechanism can also be used in combination with an aggregator for multiple jobs' output values:
def parentJob(job): aggregator = [] for fileNum in range(0,10): downloadJob = Job.wrapJobFn(stageFn, "File://"+os.path.realpath(__file__), cores=0.1, memory='32M', disk='1M') job.addChild(downloadJob) aggregator.append(downloadJob) analysis = Job.wrapJobFn(analysisJob, fileStoreID=downloadJob.rv(0), disk=PromisedRequirement(lambda xs: sum(xs), [j.rv(1) for j in aggregator])) job.addFollowOn(analysis)
Limitations
Just like regular promises, the return value must be determined prior to scheduling any job that depends on the return value. In our example above, notice how the dependent jobs were follow ons to the parent while promising jobs are children of the parent. This ordering ensures that all promises are properly fulfilled.
FileID
The toil.fileStore.FileID class is a small wrapper around Python's builtin string class. It is used to represent a file's ID in the file store, and has a size attribute that is the file's size in bytes. This object is returned by importFile and writeGlobalFile.
Managing files within a workflow
It is frequently the case that a workflow will want to create files, both persistent and temporary, during its run. The toil.fileStore.FileStore 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 10 gigabytes of storage space. j = LocalFileStoreJob(disk="10G") #Run the workflow with Toil(options) as toil: toil.start(j)
Job functions can also access the file store for the job. The equivalent of the LocalFileStoreJob class is
def localFileStoreJobFn(job): scratchDir = job.tempDir scratchFile = job.fileStore.getLocalTempFile()
Note that the fileStore attribute is accessed as an attribute of the job argument.
In addition to temporary files that exist for the duration of a job, the file store allows the creation of files in a global store, which persists during the workflow and are globally accessible (hence the name) between jobs. For example:
from toil.common import Toil from toil.job import Job import os def globalFileStoreJobFn(job): job.log("The following example exercises all the methods provided" " by the toil.fileStore.FileStore 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("Out brief candle") # Now read the first file; scratchFile2 is a local copy of the file that is read−only by default. scratchFile2 = job.fileStore.readGlobalFile(fileID) # Read the second file to a desired location: scratchFile3. scratchFile3 = os.path.join(job.tempDir, "foo.txt") job.fileStore.readGlobalFile(fileID2, userPath=scratchFile3) # Read the second file again using a stream. with job.fileStore.readGlobalFileStream(fileID2) as fH: print fH.read() #This prints "Out brief candle" # Delete the first file from the global file−store. job.fileStore.deleteGlobalFile(fileID) # It is unnecessary to delete the file keyed by fileID2 because we used the cleanup flag, # which removes the file after this job and all its successors have run (if the file still exists) if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" with Toil(options) as toil: toil.start(Job.wrapJobFn(globalFileStoreJobFn))
The example demonstrates the global read, write and delete functionality of the file−store, using both local copies of the files and streams to read and write the files. It covers all the methods provided by the file store interface.
What is obvious is that the file−store provides no functionality to update an existing "global" file, meaning that files are, barring deletion, immutable. Also worth noting is that there is no file system hierarchy for files in the global file store. These limitations allow us to fairly easily support different object stores and to use caching to limit the amount of network file transfer between jobs.
Staging of Files into the Job Store
External files can be imported into or exported out of the job store prior to running a workflow when the toil.common.Toil context manager is used on the leader. The context manager provides methods toil.common.Toil.importFile(), and toil.common.Toil.exportFile() for this purpose. The destination and source locations of such files are described with URLs passed to the two methods. A list of the currently supported URLs can be found at toil.jobStores.abstractJobStore.AbstractJobStore.importFile(). To import an external file into the job store as a shared file, pass the optional sharedFileName parameter to that method.
If a workflow fails for any reason an imported file acts as any other file in the job store. If the workflow was configured such that it not be cleaned up on a failed run, the file will persist in the job store and needs not be staged again when the workflow is resumed.
Example:
from toil.common import Toil from toil.job import Job class HelloWorld(Job): def __init__(self, inputFileID): Job.__init__(self, memory="2G", cores=2, disk="3G") self.inputFileID = inputFileID with fileStore.readGlobalFileStream(self.inputFileID) as fi: with fileStore.writeGlobalFileStream() as (fo, outputFileID): fo.write(fi.read() + 'World!') return outputFileID if __name__=="__main__": options = Job.Runner.getDefaultOptions("./toilWorkflowRun") options.logLevel = "INFO" options.clean = "always" with Toil(options) as toil: if not toil.options.restart: inputFileID = toil.importFile('file:///some/local/path') outputFileID = toil.start(HelloWorld(inputFileID)) else: outputFileID = toil.restart() toil.exportFile(outputFileID, 'file:///some/other/local/path')
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'])
dockerCall can also be added to workflows like any other job function:
from toil.job import Job align = Job.wrapJobFn(dockerCall, tool='quay.io/ucsc_cgl/bwa', workDir=job.tempDir, parameters=['index', '/data/reference.fa'])) 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, e.g. bd2k−python−lib, 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. |
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• |
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. |
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• |
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. |
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• |
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. |
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• |
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. |
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• |
All outputs must be documented in the CWL descriptor file. Examples of this file can be seen in both Toil−RNAseq and ProTECT. |
The Toil class configures and starts a Toil run.
class toil.common.Toil(options)
A context manager that represents a Toil workflow, specifically the batch system, job store, and its configuration.
__init__(options)
Initialize a Toil object from the given options. Note that this is very light−weight and that the bulk of the work is done when the context is entered.
Parameters
options (argparse.Namespace) −− command line options specified by the user
config = None
Type |
toil.common.Config |
start(rootJob)
Invoke a Toil workflow with the given job as the root for an initial run. This method must be called in the body of a with Toil(...) as toil: statement. This method should not be called more than once for a workflow that has not finished.
Parameters
rootJob (toil.job.Job) −− The root job of the workflow
Returns
The root job's return value
restart()
Restarts a workflow that has been interrupted.
Returns
The root job's return value
classmethod getJobStore(locator)
Create an instance of the concrete job store implementation that matches the given locator.
Parameters
locator (str) −− The location of the job store to be represent by the instance
Returns
an instance of a concrete subclass of AbstractJobStore
Return type
toil.jobStores.abstractJobStore.AbstractJobStore
static createBatchSystem(config)
Creates an instance of the batch system specified in the given config.
Parameters
config (toil.common.Config) −− the current configuration
Return type
batchSystems.abstractBatchSystem.AbstractBatchSystem
Returns
an instance of a concrete subclass of AbstractBatchSystem
importFile(srcUrl, sharedFileName=None)
Imports the file at the given URL into job store.
See toil.jobStores.abstractJobStore.AbstractJobStore.importFile() for a full description
exportFile(jobStoreFileID, dstUrl)
Exports file to destination pointed at by the destination URL.
See toil.jobStores.abstractJobStore.AbstractJobStore.exportFile() for a full description
static getWorkflowDir(workflowID, configWorkDir=None)
Returns a path to the directory where worker directories and the cache will be located for this workflow.
Parameters
• |
workflowID (str) −− Unique identifier for the workflow |
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• |
configWorkDir (str) −− Value passed to the program using the −−workDir flag |
Returns
Path to the workflow directory
Return type
str
writePIDFile()
Write a the pid of this process to a file in the jobstore.
Overwriting the current contents of pid.log is a feature, not a bug of this method. Other methods will rely on always having the most current pid available. So far there is no reason to store any old pids.
The job store interface is an abstraction layer that that hides the specific details of file storage, for example standard file systems, S3, etc. The AbstractJobStore API is implemented to support a give file store, e.g. S3. Implement this API to support a new file store.
class toil.jobStores.abstractJobStore.AbstractJobStore
Represents the physical storage for the jobs and files in a Toil workflow.
__init__()
Create an instance of the job store. The instance will not be fully functional until either initialize() or resume() is invoked. Note that the destroy() method may be invoked on the object with or without prior invocation of either of these two methods.
initialize(config)
Create the physical storage for this job store, allocate a workflow ID and persist the given Toil configuration to the store.
Parameters
config (toil.common.Config) −− the Toil configuration to initialize this job store with. The given configuration will be updated with the newly allocated workflow ID.
Raises |
JobStoreExistsException −− if the physical storage for this job store already exists |
writeConfig()
Persists the value of the AbstractJobStore.config attribute to the job store, so that it can be retrieved later by other instances of this class.
resume()
Connect this instance to the physical storage it represents and load the Toil configuration into the AbstractJobStore.config attribute.
Raises |
NoSuchJobStoreException −− if the physical storage for this job store doesn't exist |
||
config |
The Toil configuration associated with this job store.
Return type
toil.common.Config
setRootJob(rootJobStoreID)
Set the root job of the workflow backed by this job store
Parameters
rootJobStoreID (str) −− The ID of the job to set as root
loadRootJob()
Loads the root job in the current job store.
Raises |
toil.job.JobException −− If no root job is set or if the root job doesn't exist in this job store |
Returns
The root job.
Return type
toil.jobGraph.JobGraph
createRootJob(*args, **kwargs)
Create a new job and set it as the root job in this job store
Return type
toil.jobGraph.JobGraph
getRootJobReturnValue()
Parse the return value from the root job.
Raises an exception if the root job hasn't fulfilled its promise yet.
importFile(srcUrl, sharedFileName=None)
Imports the file at the given URL into job store. The ID of the newly imported file is returned. If the name of a shared file name is provided, the file will be imported as such and None is returned.
Currently supported schemes are:
• |
's3' for objects in Amazon S3 |
e.g. s3://bucket/key
• |
'wasb' for blobs in Azure Blob Storage |
e.g. wasb://container/blob
• |
'file' for local files |
e.g. file:///local/file/path
• |
||||
'http' |
e.g. http://someurl.com/path |
|||
• |
||||
'gs' |
e.g. gs://bucket/file |
Parameters
• |
srcUrl (str) −− URL that points to a file or object in the storage mechanism of a supported URL scheme e.g. a blob in an Azure Blob Storage container. |
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• |
sharedFileName (str) −− Optional name to assign to the imported file within the job store |
Returns
The jobStoreFileId of the imported file or None if sharedFileName was given
Return type
toil.fileStore.FileID or None
exportFile(jobStoreFileID, dstUrl)
Exports file to destination pointed at by the destination URL.
Refer to AbstractJobStore.importFile() documentation for currently supported URL schemes.
Note that the helper method _exportFile is used to read from the source and write to destination. To implement any optimizations that circumvent this, the _exportFile method should be overridden by subclasses of AbstractJobStore.
Parameters
• |
jobStoreFileID (str) −− The id of the file in the job store that should be exported. |
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• |
dstUrl (str) −− URL that points to a file or object in the storage mechanism of a supported URL scheme e.g. a blob in an Azure Blob Storage container. |
classmethod getSize(url)
returns the size in bytes of the file at the given URL
Parameters
url (urlparse.ParseResult) −− URL that points to a file or object in the storage mechanism of a supported URL scheme e.g. a blob in an Azure Blob Storage container.
destroy()
The inverse of initialize(), this method deletes the physical storage represented by this instance. While not being atomic, this method is at least idempotent, as a means to counteract potential issues with eventual consistency exhibited by the underlying storage mechanisms. This means that if the method fails (raises an exception), it may (and should be) invoked again. If the underlying storage mechanism is eventually consistent, even a successful invocation is not an ironclad guarantee that the physical storage vanished completely and immediately. A successful invocation only guarantees that the deletion will eventually happen. It is therefore recommended to not immediately reuse the same job store location for a new Toil workflow.
getEnv()
Returns a dictionary of environment variables that this job store requires to be set in order to function properly on a worker.
Return type
dict[str,str]
clean(jobCache=None)
Function to cleanup the state of a job store after a restart. Fixes jobs that might have been partially updated. Resets the try counts and removes jobs that are not successors of the current root job.
Parameters
jobCache (dict[str,toil.jobGraph.JobGraph]) −− if a value it must be a dict from job ID keys to JobGraph object values. Jobs will be loaded from the cache (which can be downloaded from the job store in a batch) instead of piecemeal when recursed into.
batch()
All calls to create() with this context manager active will be performed in a batch after the context manager is released.
Return type
None
create(jobNode)
Creates a job graph from the given job node & writes it to the job store.
Return type
toil.jobGraph.JobGraph
exists(jobStoreID)
Indicates whether the job with the specified jobStoreID exists in the job store
Return type
bool
getPublicUrl(fileName)
Returns a publicly accessible URL to the given file in the job store. The returned URL may expire as early as 1h after its been returned. Throw an exception if the file does not exist.
Parameters
fileName (str) −− the jobStoreFileID of the file to generate a URL for
Raises |
NoSuchFileException −− if the specified file does not exist in this job store |
Return type
str
getSharedPublicUrl(sharedFileName)
Differs from getPublicUrl() in that this method is for generating URLs for shared files written by writeSharedFileStream().
Returns a publicly accessible URL to the given file in the job store. The returned URL starts with 'http:', 'https:' or 'file:'. The returned URL may expire as early as 1h after its been returned. Throw an exception if the file does not exist.
Parameters
sharedFileName (str) −− The name of the shared file to generate a publically accessible url for.
Raises |
NoSuchFileException −− raised if the specified file does not exist in the store |
Return type
str
load(jobStoreID)
Loads the job referenced by the given ID and returns it.
Parameters
jobStoreID (str) −− the ID of the job to load
Raises |
NoSuchJobException −− if there is no job with the given ID |
Return type
toil.jobGraph.JobGraph
update(job)
Persists the job in this store atomically.
Parameters
job (toil.jobGraph.JobGraph) −− the job to write to this job store
delete(jobStoreID)
Removes from store atomically, can not then subsequently call load(), write(), update(), etc. with the job.
This operation is idempotent, i.e. deleting a job twice or deleting a non−existent job will succeed silently.
Parameters
jobStoreID (str) −− the ID of the job to delete from this job store
jobs() |
Best effort attempt to return iterator on all jobs in the store. The iterator may not return all jobs and may also contain orphaned jobs that have already finished successfully and should not be rerun. To guarantee you get any and all jobs that can be run instead construct a more expensive ToilState object |
Returns
Returns iterator on jobs in the store. The iterator may or may not contain all jobs and may contain invalid jobs
Return type
Iterator[toil.jobGraph.JobGraph]
writeFile(localFilePath, jobStoreID=None)
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.
Parameters
• |
localFilePath (str) −− the path to the local file that will be uploaded to the job store. |
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• |
jobStoreID (str or None) −− If specified the file will be associated with that job and when jobStore.delete(job) is called all files written with the given job.jobStoreID will be removed from the job store. |
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Raises |
|||
• |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method |
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• |
NoSuchJobException −− if the job specified via jobStoreID does not exist |
FIXME: some implementations may not raise this
Returns
an ID referencing the newly created file and can be used to read the file in the future.
Return type
str
writeFileStream(jobStoreID=None)
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.
Parameters
jobStoreID (str) −− the id of a job, or None. If specified, the file will be associated with that job and when when jobStore.delete(job) is called all files written with the given job.jobStoreID will be removed from the job store.
Raises |
|||
• |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method |
||
• |
NoSuchJobException −− if the job specified via jobStoreID does not exist |
FIXME: some implementations may not raise this
Returns
an ID that references the newly created file and can be used to read the file in the future.
Return type
str
getEmptyFileStoreID(jobStoreID=None)
Creates an empty file in the job store and returns its ID. Call to fileExists(getEmptyFileStoreID(jobStoreID)) will return True.
Parameters
jobStoreID (str) −− the id of a job, or None. If specified, the file will be associated with that job and when jobStore.delete(job) is called a best effort attempt is made to delete all files written with the given job.jobStoreID
Returns
a jobStoreFileID that references the newly created file and can be used to reference the file in the future.
Return type
str
readFile(jobStoreFileID, localFilePath, symlink=False)
Copies 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.
The file at the given local path may not be modified after this method returns!
Parameters
• |
jobStoreFileID (str) −− ID of the file to be copied |
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• |
localFilePath (str) −− the local path indicating where to place the contents of the given file in the job store |
readFileStream(jobStoreFileID)
Similar to readFile, but returns a context manager yielding a file handle which can be read from. The yielded file handle does not need to and should not be closed explicitly.
Parameters
jobStoreFileID (str) −− ID of the file to get a readable file handle for
deleteFile(jobStoreFileID)
Deletes the file with the given ID from this job store. This operation is idempotent, i.e. deleting a file twice or deleting a non−existent file will succeed silently.
Parameters
jobStoreFileID (str) −− ID of the file to delete
fileExists(jobStoreFileID)
Determine whether a file exists in this job store.
Parameters
jobStoreFileID (str) −− an ID referencing the file to be checked
Return type
bool
updateFile(jobStoreFileID, localFilePath)
Replaces the existing version of a file in the job store. Throws an exception if the file does not exist.
Parameters
• |
jobStoreFileID (str) −− the ID of the file in the job store to be updated |
||
• |
localFilePath (str) −− the local path to a file that will overwrite the current version in the job store |
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Raises |
|||
• |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method |
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• |
NoSuchFileException −− if the specified file does not exist |
updateFileStream(jobStoreFileID)
Replaces the existing version of a file in the job store. Similar to writeFile, but returns a context manager yielding a file handle which can be written to. The yielded file handle does not need to and should not be closed explicitly.
Parameters
jobStoreFileID (str) −− the ID of the file in the job store to be updated
Raises |
|||
• |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method |
||
• |
NoSuchFileException −− if the specified file does not exist |
writeSharedFileStream(sharedFileName, isProtected=None)
Returns a context manager yielding a writable file handle to the global file referenced by the given name.
Parameters
• |
sharedFileName (str) −− A file name matching AbstractJobStore.fileNameRegex, unique within this job store |
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• |
isProtected (bool) −− True if the file must be encrypted, None if it may be encrypted or False if it must be stored in the clear. |
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Raises |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method
readSharedFileStream(sharedFileName)
Returns a context manager yielding a readable file handle to the global file referenced by the given name.
Parameters
sharedFileName (str) −− A file name matching AbstractJobStore.fileNameRegex, unique within this job store
writeStatsAndLogging(statsAndLoggingString)
Adds the given statistics/logging string to the store of statistics info.
Parameters
statsAndLoggingString (str) −− the string to be written to the stats file
Raises |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method |
readStatsAndLogging(callback, readAll=False)
Reads stats/logging strings accumulated by the writeStatsAndLogging() method. For each stats/logging string this method calls the given callback function with an open, readable file handle from which the stats string can be read. Returns the number of stats/logging strings processed. Each stats/logging string is only processed once unless the readAll parameter is set, in which case the given callback will be invoked for all existing stats/logging strings, including the ones from a previous invocation of this method.
Parameters
• |
callback (Callable) −− a function to be applied to each of the stats file handles found |
||
• |
readAll (bool) −− a boolean indicating whether to read the already processed stats files in addition to the unread stats files |
||
Raises |
ConcurrentFileModificationException −− if the file was modified concurrently during an invocation of this method
Returns
the number of stats files processed
Return type
int
Functions to wrap jobs and return values (promises).
FunctionWrappingJob
The subclass of Job for wrapping user functions.
class toil.job.FunctionWrappingJob(userFunction, *args, **kwargs)
Job used to wrap a function. In its run method the wrapped function is called.
__init__(userFunction, *args, **kwargs)
Parameters
userFunction (callable) −− The function to wrap. It will be called with *args and **kwargs as arguments.
The keywords memory, cores, disk, preemptable and checkpoint are reserved keyword arguments that if specified will be used to determine the resources required for the job, as toil.job.Job.__init__(). If they are keyword arguments to the function they will be extracted from the function definition, but may be overridden by the user (as you would expect).
run(fileStore)
Override this function to perform work and dynamically create successor jobs.
Parameters
fileStore (toil.fileStore.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.fileStore.FileStore 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 |
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• |
disk |
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• |
cores |
For example to wrap a function into a job we would call:
Job.wrapJobFn(myJob, memory='100k', disk='1M', cores=0.1)
run(fileStore)
Override this function to perform work and dynamically create successor jobs.
Parameters
fileStore (toil.fileStore.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)
A convenience Job class used to make a job subgraph appear to be a single job.
Let A be the root job of a job subgraph and B be another job we'd like to run after A and all its successors have completed, for this use encapsulate:
# Job A and subgraph, Job B A, B = A(), B() A' = A.encapsulate() A'.addChild(B) # B will run after A and all its successors have completed, A and its subgraph of # successors in effect appear to be just one job.
If the job being encapsulated has predecessors (e.g. is not the root job), then the encapsulated job will inherit these predecessors. If predecessors are added to the job being encapsulated after the encapsulated job is created then the encapsulating job will NOT inherit these predecessors automatically. Care should be exercised to ensure the encapsulated job has the proper set of predecessors.
The return value of an encapsulatd job (as accessed by the toil.job.Job.rv() function) is the return value of the root job, e.g. A().encapsulate().rv() and A().rv() will resolve to the same value after A or A.encapsulate() has been run.
__init__(job)
Parameters
job (toil.job.Job) −− the job to encapsulate.
addChild(childJob)
Adds childJob to be run as child of this job. Child jobs will be run directly after this job's toil.job.Job.run() method has completed.
Parameters
childJob (toil.job.Job) −−
Returns
childJob
Return type
toil.job.Job
addService(service, parentService=None)
Add a service.
The toil.job.Job.Service.start() method of the service will be called after the run method has completed but before any successors are run. The service's toil.job.Job.Service.stop() method will be called once the successors of the job have been run.
Services allow things like databases and servers to be started and accessed by jobs in a workflow.
Raises |
toil.job.JobException −− If service has already been made the child of a job or another service. |
Parameters
• |
service (toil.job.Job.Service) −− Service to add. |
||
• |
parentService (toil.job.Job.Service) −− Service that will be started before 'service' is started. Allows trees of services to be established. parentService must be a service of this job. |
Returns
a promise that will be replaced with the return value from toil.job.Job.Service.start() of service in any successor of the job.
Return type
toil.job.Promise
addFollowOn(followOnJob)
Adds a follow−on job, follow−on jobs will be run after the child jobs and their successors have been run.
Parameters
followOnJob (toil.job.Job) −−
Returns
followOnJob
Return type
toil.job.Job
rv(*path)
Creates a promise (toil.job.Promise) representing a return value of the job's run method, or, in case of a function−wrapping job, the wrapped function's return value.
Parameters
path ((Any)) −− Optional path for selecting a component of the promised return value. If absent or empty, the entire return value will be used. Otherwise, the first element of the path is used to select an individual item of the return value. For that to work, the return value must be a list, dictionary or of any other type implementing the __getitem__() magic method. If the selected item is yet another composite value, the second element of the path can be used to select an item from it, and so on. For example, if the return value is [6,{'a':42}], .rv(0) would select 6 , rv(1) would select {'a':3} while rv(1,'a') would select 3. To select a slice from a return value that is slicable, e.g. tuple or list, the path element should be a slice object. For example, assuming that the return value is [6, 7, 8, 9] then .rv(slice(1, 3)) would select [7, 8]. Note that slicing really only makes sense at the end of path.
Returns
A promise representing the return value of this jobs toil.job.Job.run() method.
Return type
toil.job.Promise
prepareForPromiseRegistration(jobStore)
Ensure that a promise by this job (the promissor) can register with the promissor when another job referring to the promise (the promissee) is being serialized. The promissee holds the reference to the promise (usually as part of the the job arguments) and when it is being pickled, so will the promises it refers to. Pickling a promise triggers it to be registered with the promissor.
Returns
Promise
The class used to reference return values of jobs/services not yet run/started.
class toil.job.Promise(job, path)
References a return value from a toil.job.Job.run() or toil.job.Job.Service.start() method as a promise before the method itself is run.
Let T be a job. Instances of Promise (termed a promise) are returned by T.rv(), which is used to reference the return value of T's run function. When the promise is passed to the constructor (or as an argument to a wrapped function) of a different, successor job the promise will be replaced by the actual referenced return value. This mechanism allows a return values from one job's run method to be input argument to job before the former job's run function has been executed.
filesToDelete = {}
A set of IDs of files containing promised values when we know we won't need them anymore
__init__(job, path)
Parameters
• |
job (Job) −− the job whose return value this promise references |
||
• |
path −− see Job.rv() |
class toil.job.PromisedRequirement(valueOrCallable, *args)
__init__(valueOrCallable, *args)
Class for dynamically allocating job function resource requirements involving toil.job.Promise instances.
Use when resource requirements depend on the return value of a parent function. PromisedRequirements can be modified by passing a function that takes the Promise as input.
For example, let f, g, and h be functions. Then a Toil workflow can be defined as follows:: A = Job.wrapFn(f) B = A.addChildFn(g, cores=PromisedRequirement(A.rv()) C = B.addChildFn(h, cores=PromisedRequirement(lambda x: 2*x, B.rv()))
Parameters
• |
valueOrCallable −− A single Promise instance or a function that takes *args as input parameters. |
||
• |
*args (int or Promise) −− variable length argument list |
getValue()
Returns PromisedRequirement value
static convertPromises(kwargs)
Returns True if reserved resource keyword is a Promise or PromisedRequirement instance. Converts Promise instance to PromisedRequirement.
Parameters
kwargs −− function keyword arguments
Returns
bool
Jobs are the units of work in Toil which are composed into workflows.
class toil.job.Job(memory=None, cores=None, disk=None,
preemptable=None, unitName=None, checkpoint=False, displayName=None)
Class represents a unit of work in toil.
__init__(memory=None, cores=None, disk=None, preemptable=None,
unitName=None, checkpoint=False, displayName=None)
This method must be called by any overriding constructor.
Parameters
• |
memory (int or string convertible by toil.lib.humanize.human2bytes to an int) −− the maximum number of bytes of memory the job will require to run. |
||
• |
cores (int or string convertible by toil.lib.humanize.human2bytes to an int) −− the number of CPU cores required. |
||
• |
disk (int or string convertible by toil.lib.humanize.human2bytes to an int) −− the amount of local disk space required by the job, expressed in bytes. |
||
• |
preemptable (bool) −− if the job can be run on a preemptable node. |
||
• |
checkpoint −− if any of this job's successor jobs completely fails, exhausting all their retries, remove any successor jobs and rerun this job to restart the subtree. Job must be a leaf vertex in the job graph when initially defined, see toil.job.Job.checkNewCheckpointsAreCutVertices(). |
run(fileStore)
Override this function to perform work and dynamically create successor jobs.
Parameters
fileStore (toil.fileStore.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)
Adds childJob to be run as child of this job. Child jobs will be run directly after this job's toil.job.Job.run() method has completed.
Parameters
childJob (toil.job.Job) −−
Returns
childJob
Return type
toil.job.Job
hasChild(childJob)
Check if childJob is already a child of this job.
Parameters
childJob (toil.job.Job) −−
Returns
True if childJob is a child of the job, else False.
Return type
bool
addFollowOn(followOnJob)
Adds a follow−on job, follow−on jobs will be run after the child jobs and their successors have been run.
Parameters
followOnJob (toil.job.Job) −−
Returns
followOnJob
Return type
toil.job.Job
hasFollowOn(followOnJob)
Check if given job is already a follow−on of this job.
Parameters
followOnJob (toil.job.Job) −−
Returns
True if the followOnJob is a follow−on of this job, else False.
Return type
bool
addService(service, parentService=None)
Add a service.
The toil.job.Job.Service.start() method of the service will be called after the run method has completed but before any successors are run. The service's toil.job.Job.Service.stop() method will be called once the successors of the job have been run.
Services allow things like databases and servers to be started and accessed by jobs in a workflow.
Raises |
toil.job.JobException −− If service has already been made the child of a job or another service. |
Parameters
• |
service (toil.job.Job.Service) −− Service to add. |
||
• |
parentService (toil.job.Job.Service) −− Service that will be started before 'service' is started. Allows trees of services to be established. parentService must be a service of this job. |
Returns
a promise that will be replaced with the return value from toil.job.Job.Service.start() of service in any successor of the job.
Return type
toil.job.Promise
addChildFn(fn, *args, **kwargs)
Adds a function as a child job.
Parameters
fn −− Function to be run as a child job with *args and **kwargs as arguments to this function. See toil.job.FunctionWrappingJob for reserved keyword arguments used to specify resource requirements.
Returns
The new child job that wraps fn.
Return type
toil.job.FunctionWrappingJob
addFollowOnFn(fn, *args, **kwargs)
Adds a function as a follow−on job.
Parameters
fn −− Function to be run as a follow−on job with *args and **kwargs as arguments to this function. See toil.job.FunctionWrappingJob for reserved keyword arguments used to specify resource requirements.
Returns
The new follow−on job that wraps fn.
Return type
toil.job.FunctionWrappingJob
addChildJobFn(fn, *args, **kwargs)
Adds a job function as a child job. See toil.job.JobFunctionWrappingJob for a definition of a job function.
Parameters
fn −− Job function to be run as a child job with *args and **kwargs as arguments to this function. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements.
Returns
The new child job that wraps fn.
Return type
toil.job.JobFunctionWrappingJob
addFollowOnJobFn(fn, *args, **kwargs)
Add a follow−on job function. See toil.job.JobFunctionWrappingJob for a definition of a job function.
Parameters
fn −− Job function to be run as a follow−on job with *args and **kwargs as arguments to this function. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements.
Returns
The new follow−on job that wraps fn.
Return type
toil.job.JobFunctionWrappingJob
tempDir
Shortcut to calling job.fileStore.getLocalTempDir(). Temp dir is created on first call and will be returned for first and future calls :return: Path to tempDir. See job.fileStore.getLocalTempDir :rtype: str
log(text, level=20)
convenience wrapper for fileStore.logToMaster()
static wrapFn(fn, *args, **kwargs)
Makes a Job out of a function. Convenience function for constructor of toil.job.FunctionWrappingJob.
Parameters
fn −− Function to be run with *args and **kwargs as arguments. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements.
Returns
The new function that wraps fn.
Return type
toil.job.FunctionWrappingJob
static wrapJobFn(fn, *args, **kwargs)
Makes a Job out of a job function. Convenience function for constructor of toil.job.JobFunctionWrappingJob.
Parameters
fn −− Job function to be run with *args and **kwargs as arguments. See toil.job.JobFunctionWrappingJob for reserved keyword arguments used to specify resource requirements.
Returns
The new job function that wraps fn.
Return type
toil.job.JobFunctionWrappingJob
encapsulate()
Encapsulates the job, see toil.job.EncapsulatedJob. Convenience function for constructor of toil.job.EncapsulatedJob.
Returns
an encapsulated version of this job.
Return type
toil.job.EncapsulatedJob
rv(*path)
Creates a promise (toil.job.Promise) representing a return value of the job's run method, or, in case of a function−wrapping job, the wrapped function's return value.
Parameters
path ((Any)) −− Optional path for selecting a component of the promised return value. If absent or empty, the entire return value will be used. Otherwise, the first element of the path is used to select an individual item of the return value. For that to work, the return value must be a list, dictionary or of any other type implementing the __getitem__() magic method. If the selected item is yet another composite value, the second element of the path can be used to select an item from it, and so on. For example, if the return value is [6,{'a':42}], .rv(0) would select 6 , rv(1) would select {'a':3} while rv(1,'a') would select 3. To select a slice from a return value that is slicable, e.g. tuple or list, the path element should be a slice object. For example, assuming that the return value is [6, 7, 8, 9] then .rv(slice(1, 3)) would select [7, 8]. Note that slicing really only makes sense at the end of path.
Returns
A promise representing the return value of this jobs toil.job.Job.run() method.
Return type
toil.job.Promise
prepareForPromiseRegistration(jobStore)
Ensure that a promise by this job (the promissor) can register with the promissor when another job referring to the promise (the promissee) is being serialized. The promissee holds the reference to the promise (usually as part of the the job arguments) and when it is being pickled, so will the promises it refers to. Pickling a promise triggers it to be registered with the promissor.
Returns
checkJobGraphForDeadlocks()
See toil.job.Job.checkJobGraphConnected(), toil.job.Job.checkJobGraphAcyclic() and toil.job.Job.checkNewCheckpointsAreLeafVertices() for more info.
Raises |
toil.job.JobGraphDeadlockException −− if the job graph is cyclic, contains multiple roots or contains checkpoint jobs that are not leaf vertices when defined (see toil.job.Job.checkNewCheckpointsAreLeaves()). |
getRootJobs()
Returns
The roots of the connected component of jobs that contains this job. A root is a job with no predecessors.
:rtype : set of toil.job.Job instances
checkJobGraphConnected()
Raises |
toil.job.JobGraphDeadlockException −− if toil.job.Job.getRootJobs() does not contain exactly one root job. |
As execution always starts from one root job, having multiple root jobs will cause a deadlock to occur.
checkJobGraphAcylic()
Raises |
toil.job.JobGraphDeadlockException −− if the connected component of jobs containing this job contains any cycles of child/followOn dependencies in the augmented job graph (see below). Such cycles are not allowed in valid job graphs. |
A follow−on edge (A, B) between two jobs A and B is equivalent to adding a child edge to B from (1) A, (2) from each child of A, and (3) from the successors of each child of A. We call each such edge an edge an "implied" edge. The augmented job graph is a job graph including all the implied edges.
For a job graph G = (V, E) the algorithm is O(|V|^2). It is O(|V| + |E|) for a graph with no follow−ons. The former follow−on case could be improved!
checkNewCheckpointsAreLeafVertices()
A checkpoint job is a job that is restarted if either it fails, or if any of its successors completely fails, exhausting their retries.
A job is a leaf it is has no successors.
A checkpoint job must be a leaf when initially added to the job graph. When its run method is invoked it can then create direct successors. This restriction is made to simplify implementation.
Raises |
toil.job.JobGraphDeadlockException −− if there exists a job being added to the graph for which checkpoint=True and which is not a leaf. |
defer(function, *args, **kwargs)
Register a deferred function, i.e. a callable that will be invoked after the current attempt at running this job concludes. A job attempt is said to conclude when the job function (or the toil.job.Job.run() method for class−based jobs) returns, raises an exception or after the process running it terminates abnormally. A deferred function will be called on the node that attempted to run the job, even if a subsequent attempt is made on another node. A deferred function should be idempotent because it may be called multiple times on the same node or even in the same process. More than one deferred function may be registered per job attempt by calling this method repeatedly with different arguments. If the same function is registered twice with the same or different arguments, it will be called twice per job attempt.
Examples for deferred functions are ones that handle cleanup of resources external to Toil, like Docker containers, files outside the work directory, etc.
Parameters
• |
function (callable) −− The function to be called after this job concludes. |
||
• |
args (list) −− The arguments to the function |
||
• |
kwargs (dict) −− The keyword arguments to the function |
getTopologicalOrderingOfJobs()
Returns
a list of jobs such that for all pairs of indices i, j for which i < j, the job at index i can be run before the job at index j.
Return type
list
The Runner contains the methods needed to configure and start a Toil run.
class Job.Runner
Used to setup and run Toil workflow.
static getDefaultArgumentParser()
Get argument parser with added toil workflow options.
Returns
The argument parser used by a toil workflow with added Toil options.
Return type
argparse.ArgumentParser
static getDefaultOptions(jobStore)
Get default options for a toil workflow.
Parameters
jobStore (string) −− A string describing the jobStore for the workflow.
Returns
The options used by a toil workflow.
Return type
argparse.ArgumentParser values object
static addToilOptions(parser)
Adds the default toil options to an optparse or argparse parser object.
Parameters
parser (optparse.OptionParser or argparse.ArgumentParser) −− Options object to add toil options to.
static startToil(job, options)
Deprecated by toil.common.Toil.start. Runs the toil workflow using the given options (see Job.Runner.getDefaultOptions and Job.Runner.addToilOptions) starting with this job. :param toil.job.Job job: root job of the workflow :raises: toil.leader.FailedJobsException if at the end of function their remain failed jobs. :return: The return value of the root job's run function. :rtype: Any
The FileStore is an abstraction of a Toil run's shared storage.
class toil.fileStore.FileStore(jobStore, jobGraph, localTempDir,
inputBlockFn)
An abstract base class to represent the interface between a worker and the job store. Concrete subclasses will be used to manage temporary files, read and write files from the job store and log messages, passed as argument to the toil.job.Job.run() method.
__init__(jobStore, jobGraph, localTempDir, inputBlockFn)
Initialize self. See help(type(self)) for accurate signature.
open(job)
The context manager used to conduct tasks prior−to, and after a job has been run.
Parameters
job (toil.job.Job) −− The job instance of the toil job to run.
getLocalTempDir()
Get a new local temporary directory in which to write files that persist for the duration of the job.
Returns
The absolute path to a new local temporary directory. This directory will exist for the duration of the job only, and is guaranteed to be deleted once the job terminates, removing all files it contains recursively.
Return type
str
getLocalTempFile()
Get a new local temporary file that will persist for the duration of the job.
Returns
The absolute path to a local temporary file. This file will exist for the duration of the job only, and is guaranteed to be deleted once the job terminates.
Return type
str
getLocalTempFileName()
Get a valid name for a new local file. Don't actually create a file at the path.
Returns
Path to valid file
Return type
str
writeGlobalFile(localFileName, cleanup=False)
Takes a file (as a path) and uploads it to the job store.
Parameters
• |
localFileName (string) −− The path to the local file to upload. |
||
• |
cleanup (bool) −− if True then the copy of the global file will be deleted once the job and all its successors have completed running. If not the global file must be deleted manually. |
Returns
an ID that can be used to retrieve the file.
Return type
toil.fileStore.FileID
writeGlobalFileStream(cleanup=False)
Similar to writeGlobalFile, but allows the writing of a stream to the job store. The yielded file handle does not need to and should not be closed explicitly.
Parameters
cleanup (bool) −− is as in toil.fileStore.FileStore.writeGlobalFile().
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.
readGlobalFile(fileStoreID, userPath=None, cache=True,
mutable=False, symlink=False)
Makes the file associated with fileStoreID available locally. If mutable is True, then a copy of the file will be created locally so that the original is not modified and does not change the file for other jobs. If mutable is False, then a link can be created to the file, saving disk resources.
If a user path is specified, it is used as the destination. If a user path isn't specified, the file is stored in the local temp directory with an encoded name.
Parameters
• |
fileStoreID (toil.fileStore.FileID) −− job store id for the file |
||
• |
userPath (string) −− a path to the name of file to which the global file will be copied or hard−linked (see below). |
||
• |
cache (bool) −− Described in toil.fileStore.CachingFileStore.readGlobalFile() |
||
• |
mutable (bool) −− Described in toil.fileStore.CachingFileStore.readGlobalFile() |
Returns
An absolute path to a local, temporary copy of the file keyed by fileStoreID.
Return type
str
readGlobalFileStream(fileStoreID)
Similar to readGlobalFile, but allows a stream to be read from the job store. The yielded file handle does not need to and should not be closed explicitly.
Returns
a context manager yielding a file handle which can be read from.
deleteLocalFile(fileStoreID)
Deletes Local copies of files associated with the provided job store ID.
Parameters
fileStoreID (str) −− File Store ID of the file to be deleted.
deleteGlobalFile(fileStoreID)
Deletes local files with the provided job store ID and then permanently deletes them from the job store. To ensure that the job can be restarted if necessary, the delete will not happen until after the job's run method has completed.
Parameters
fileStoreID −− the job store ID of the file to be deleted.
classmethod findAndHandleDeadJobs(nodeInfo,
batchSystemShutdown=False)
This function looks at the state of all jobs registered on the node and will handle them (clean up their presence ont he node, and run any registered defer functions)
Parameters
• |
nodeInfo −− Information regarding the node required for identifying dead jobs. |
||
• |
batchSystemShutdown (bool) −− Is the batch system in the process of shutting down? |
logToMaster(text, level=20)
Send a logging message to the leader. The message will also be logged by the worker at the same level.
Parameters
• |
text −− The string to log. |
|||
• |
level (int) −− The logging level. |
classmethod shutdown(dir_)
Shutdown the filestore on this node.
This is intended to be called on batch system shutdown.
Parameters
dir −− The jeystone directory containing the required information for fixing the state of failed workers on the node before cleaning up.
class toil.fileStore.FileID(fileStoreID, size)
A small wrapper around Python's builtin string class. It is used to represent a file's ID in the file store, and has a size attribute that is the file's size in bytes. This object is returned by importFile and writeGlobalFile.
__init__(fileStoreID, size)
Initialize self. See help(type(self)) for accurate signature.
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'
Batch System API
class toil.batchSystems.abstractBatchSystem.AbstractBatchSystem
An abstract (as far as Python currently allows) base class to represent the interface the batch system must provide to Toil.
classmethod supportsAutoDeployment()
Whether this batch system supports auto−deployment of the user script itself. If it does, the setUserScript() can be invoked to set the resource object representing the user script.
Note to implementors: If your implementation returns True here, it should also override
Return type
bool
classmethod supportsWorkerCleanup()
Indicates whether this batch system invokes workerCleanup() after the last job for a particular workflow invocation finishes. Note that the term worker refers to an entire node, not just a worker process. A worker process may run more than one job sequentially, and more than one concurrent worker process may exist on a worker node, for the same workflow. The batch system is said to shut down after the last worker process terminates.
Return type
bool
setUserScript(userScript)
Set the user script for this workflow. This method must be called before the first job is issued to this batch system, and only if supportsAutoDeployment() returns True, otherwise it will raise an exception.
Parameters
userScript (toil.resource.Resource) −− the resource object representing the user script or module and the modules it depends on.
issueBatchJob(jobNode)
Issues a job with the specified command to the batch system and returns a unique jobID.
:param jobNode a toil.job.JobNode
Returns
a unique jobID that can be used to reference the newly issued job
Return type
int
killBatchJobs(jobIDs)
Kills the given job IDs.
Parameters
jobIDs (list[int]) −− list of IDs of jobs to kill
getIssuedBatchJobIDs()
Gets all currently issued jobs
Returns
A list of jobs (as jobIDs) currently issued (may be running, or may be waiting to be run). Despite the result being a list, the ordering should not be depended upon.
Return type
list[str]
getRunningBatchJobIDs()
Gets a map of jobs as jobIDs that are currently running (not just waiting) and how long they have been running, in seconds.
Returns
dictionary with currently running jobID keys and how many seconds they have been running as the value
Return type
dict[str,float]
getUpdatedBatchJob(maxWait)
Returns a job that has updated its status.
Parameters
maxWait (float) −− the number of seconds to block, waiting for a result
Return type
tuple(str, int) or None
Returns
If a result is available, returns a tuple (jobID, exitValue, wallTime). Otherwise it returns None. wallTime is the number of seconds (a float) in wall−clock time the job ran for or None if this batch system does not support tracking wall time. Returns None for jobs that were killed.
shutdown()
Called at the completion of a toil invocation. Should cleanly terminate all worker threads.
setEnv(name, value=None)
Set an environment variable for the worker process before it is launched. The worker process will typically inherit the environment of the machine it is running on but this method makes it possible to override specific variables in that inherited environment before the worker is launched. Note that this mechanism is different to the one used by the worker internally to set up the environment of a job. A call to this method affects all jobs issued after this method returns. Note to implementors: This means that you would typically need to copy the variables before enqueuing a job.
If no value is provided it will be looked up from the current environment.
classmethod setOptions(setOption)
Process command line or configuration options relevant to this batch system. The
Parameters
setOption −− A function with signature setOption(varName, parsingFn=None, checkFn=None, default=None) used to update run configuration
The Service class allows databases and servers to be spawned within a Toil workflow.
class Job.Service(memory=None, cores=None, disk=None, preemptable=None,
unitName=None)
Abstract class used to define the interface to a service.
__init__(memory=None, cores=None, disk=None, preemptable=None,
unitName=None)
Memory, core and disk requirements are specified identically to as in toil.job.Job.__init__().
start(job)
Start the service.
Parameters
job (toil.job.Job) −− The underlying job that is being run. Can be used to register deferred functions, or to access the fileStore for creating temporary files.
Returns
An object describing how to access the service. The object must be pickleable and will be used by jobs to access the service (see toil.job.Job.addService()).
stop(job)
Stops the service. Function can block until complete.
Parameters
job (toil.job.Job) −− The underlying job that is being run. Can be used to register deferred functions, or to access the fileStore for creating temporary files.
check()
Checks the service is still running.
Raises |
exceptions.RuntimeError −− If the service failed, this will cause the service job to be labeled failed. |
Returns
True if the service is still running, else False. If False then the service job will be terminated, and considered a success. Important point: if the service job exits due to a failure, it should raise a RuntimeError, not return False!
Toil specific exceptions.
exception toil.job.JobException(message)
General job exception.
__init__(message)
Initialize self. See help(type(self)) for accurate signature.
exception toil.job.JobGraphDeadlockException(string)
An exception raised in the event that a workflow contains an unresolvable dependency, such as a cycle. See toil.job.Job.checkJobGraphForDeadlocks().
__init__(string)
Initialize self. See help(type(self)) for accurate signature.
exception
toil.jobStores.abstractJobStore.ConcurrentFileModificationException(jobStoreFileID)
Indicates that the file was attempted to be modified by multiple processes at once.
__init__(jobStoreFileID)
Parameters
jobStoreFileID (str) −− the ID of the file that was modified by multiple workers or processes concurrently
exception
toil.jobStores.abstractJobStore.JobStoreExistsException(locator)
Indicates that the specified job store already exists.
__init__(locator)
Initialize self. See help(type(self)) for accurate signature.
exception
toil.jobStores.abstractJobStore.NoSuchFileException(jobStoreFileID,
customName=None)
Indicates that the specified file does not exist.
__init__(jobStoreFileID, customName=None)
Parameters
• |
jobStoreFileID (str) −− the ID of the file that was mistakenly assumed to exist |
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• |
customName (str) −− optionally, an alternate name for the nonexistent file |
exception
toil.jobStores.abstractJobStore.NoSuchJobException(jobStoreID)
Indicates that the specified job does not exist.
__init__(jobStoreID)
Parameters
jobStoreID (str) −− the jobStoreID that was mistakenly assumed to exist
exception
toil.jobStores.abstractJobStore.NoSuchJobStoreException(locator)
Indicates that the specified job store does not exist.
__init__(locator)
Initialize self. See help(type(self)) for accurate signature.
Test make targets, invoked as $ make <target>, subject to which environment variables are set (see Running Integration Tests).
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 azure' src"
This will run only the tests that don't depend on the azure 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 Azure extra, use the following:
$ make test tests="−m 'not azure 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 (AZURE, GCE, AWS).
• |
For example, to prepare for running Azure related integration tests in the westus region: |
$ export TOIL_TEST_INTEGRATIVE=True $ export TOIL_AZURE_KEYNAME=[Your keyname] $ export TOIL_AZURE_ZONE=westus
• |
See the above sections for guidance on running tests. |
Test Environment Variables
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
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 |
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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. |
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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. |
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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.
In general, as developers and maintainers of the code, we adhere to the following guidelines:
• |
We strive to never break the build on master. |
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• |
Pull requests should be used for any and all changes (except truly trivial ones). |
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The commit message of direct commits to master must end in (resolves # followed by the issue number followed by ). |
Naming Conventions
• |
The branch name for a pull request starts with issues/ followed by the issue number (or numbers, separated by a dash), followed by a short snake−case 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.) |
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• |
The commit message of the first commit in a pull request needs to end in (resolves # followed by the issue number, followed by ). See here for details about writing properly−formatted and informative commit messages. |
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• |
The title of the pull request needs to have the same (resolves #...) suffix as the commit message. This lets Waffle stack the pull request and the associated issue. (Fortunately, Github automatically prepopulates the title of the PR with the message of the first commit in the PR, so this isn't any additional work.) |
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 that address multiple issues use the (resolves #602, resolves #214) suffix in the request's title. These pull requests can and should contain multiple commits, with each commit message referencing the specific issue(s) it addresses. We may or may not squash the commits in those PRs. |
Pull Requests
• |
All pull requests must be reviewed by a person other than the request's author. |
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Only the reviewer of a pull request can merge it. |
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Until the pull request is merged, it should be continually rebased by the author on top of master. |
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Pull requests are built automatically by Jenkins and won't be merged unless all tests pass. |
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• |
Ideally, a pull request should contain a single commit that addresses a single, specific issue. Rebasing and squashing can be used to achieve that goal (see Multi−Author Pull Requests). |
Multi−Author Pull Requests
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A pull request starts off as single−author and can be changed to multi−author upon request via comment (typically by the reviewer) in the PR. The author of a single−author PR has to explicitly grant the request. |
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Multi−author pull requests can have more than one commit. They must not be rebased as doing so would create havoc for other contributors. |
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To keep a multi−author pull request up to date with master, merge from master instead of rebasing on top of master. |
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• |
Before the PR is merged, it may transition back to single−author mode, again via comment request in the PR. Every contributor to the PR has to acknowledge the request after making sure they don't have any unpushed changes they care about. This is necessary because a single−author PR can be reabsed and rebasing would make it hard to integrate these pushed commits. |
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, Azure file store, etc.
• |
workers: |
The workers are temporary processes responsible for running jobs, one at a time per worker. Each worker process is invoked with a job argument that it is responsible for running. The worker monitors this job and reports back success or failure to the leader by editing the job's state in the file−store. If the job defines successor jobs the worker may choose to immediately run them (see Job Chaining below).
• |
the batch−system: |
Responsible for scheduling the jobs given to it by the leader, creating a worker command for each job. The batch−system is defined by the AbstractBatchSystem class. Toil uses multiple existing batch systems to schedule jobs, including Apache Mesos, GridEngine and a multi−process single node implementation that allows workflows to be run without any of these frameworks. Toil can therefore fairly easily be made to run a workflow using an existing cluster.
• |
the node provisioner: |
Creates worker nodes in which the batch system schedules workers. It is defined by the AbstractProvisioner class.
• |
the statistics and logging monitor: |
Monitors logging and statistics produced by the workers and reports them. Uses the job−store to gather this information.
Optimizations
Toil implements lots of optimizations designed for scalability. Here we detail some of the key optimizations.
Read−only leader
The leader process is currently implemented as a single thread. Most of the leader's tasks revolve around processing the state of jobs, each stored as a file within the job−store. To minimise the load on this thread, each worker does as much work as possible to manage the state of the job it is running. As a result, with a couple of minor exceptions, the leader process never needs to write or update the state of a job within the job−store. For example, when a job is complete and has no further successors the responsible worker deletes the job from the job−store, marking it complete. The leader then only has to check for the existence of the file when it receives a signal from the batch−system to know that the job is complete. This off−loading of state management is orthogonal to future parallelization of the leader.
Job chaining
The scheduling of successor jobs is partially managed by the worker, reducing the number of individual jobs the leader needs to process. Currently this is very simple: if the there is a single next successor job to run and it's 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 againsted 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 FileStore token), 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.
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.
There are several environment variables that affect the way Toil runs.
This section describes how to launch Azure virtual machines to create a static (non−autoscaling) cluster.
Launch a leader node in Azure
Toil comes with a cluster template to facilitate easy deployment of clusters running Toil on Microsoft Azure. The template allows these clusters to be created and managed through the Azure portal.
To use the template to set up a Toil Mesos cluster on Azure, follow these steps. Microsoft Azure deploy button.INDENT 0.0
1. |
Click on the blue deploy button above, or open the deploy link in your browser. If necessary, sign into the Microsoft account that you use for Azure. |
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2. |
You should be presented with a screen resembling the following: |
[image: Screenshot of the Azure deployment wizard.] [image]
Fill out the form on the far right (marked 1 in the image) with the following information, then click OK (marked 2 in the image). Important fields for which you will want to override the defaults are in bold:
1. |
Choose a subscription and select or create a Resource Group (marked 3 in the screenshot). If creating a Resource Group, select a region in which to place it. It is recommended to create a new Resource Group for every cluster; the template creates a large number of Azure entitites besides just the VMs (like virtual networks), and if they are organized into their own Resource Group they can all be cleaned up at once when you are done with the cluster by deleting the Resource Group. |
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2. |
Read the Azure Terms of Service (by clicking on the item marked "4" in the screenshot) and accept them by clicking the "Create" button on the right (not shown). This is the contract that you are accepting with Microsoft, under which you are purchasing the cluster. |
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3. |
Click the main "Create" button (marked "5" in the screenshot). This will kick off the process of creating the cluster. |
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4. |
Eventually, you'll receive a notification (Bell icon on the top bar of the Azure UI) letting you know that your cluster has been created. At this point, you should be able to connect to it; however, note that it will not be ready to run any Toil jobs until it is finished setting itself up. |
Launch a Toil Mesos Cluster
1. |
SSH into the first (and by default only) leader node. For this, you need to know the values AdminUsername and DnsNameForMastersPublicIp were set to above, and the name of the region you placed your cluster in. If you named your user phoebe and named your cluster toilisgreat, and placed it in the centralus region, the hostname of the cluster would be toilisgreat.centralus.cloudapp.azure.com, and you would want to connect as phoebe. SSH is forwarded through the cluster's load balancer to the first leader node on port 2211, so you would run ssh phoebe AT toilisgreat DOT centralus DOT cloudapp DOT azure DOT com −p 2211. |
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Wait for the leader node to finish setting itself up. Run tail −f /var/log/azure/cluster−bootstrap.log and wait until the log reaches the line completed mesos cluster configuration. At that point, kill tail with a ctrl−c. Your leader node is now ready. |
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At this point, you can start running Toil jobs, using the Mesos batch system (by passing −−batchSystem mesos −−mesosMaster 10.0.0.5:5050) and the Azure job store (for which you will need a separate Azure Storage account set up, ideally in the same region as your cluster but in a different Resource Group). The nodes of the cluster may take a few more minutes to finish installing, but when they do they will report in to Mesos and begin running any scheduled jobs. |
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4. |
Whan you are done running your jobs, go back to the Azure portal, find the Resource Group you created for your cluster, and delete it. This will destroy all the VMs and any data stored on them, and stop Microsoft charging you money for keeping the cluster around. As long as you used a separate Asure Storage account in a different Resource Group, any information kept in the job stores and file stores you were using will be retained. |
For more information about how your new cluster is organized, for information on how to access the Mesos Web UI, or for troubleshooting advice, please see the template documentation.
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