Makeflow User's Manual


Makeflow is a workflow engine for large scale distributed computing. It accepts a specification of a large amount of work to be performed, and runs it on remote machines in parallel where possible. In addition, Makeflow is fault-tolerant, so you can use it to coordinate very large tasks that may run for days or weeks in the face of failures. Makeflow is designed to be similar to Make , so if you can write a Makefile, then you can write a Makeflow.

Makeflow makes it easy to move a large amount of work from one facility to another. After writing a workflow, you can test it out on your local laptop, then run it at your university computing center, move it over to a national computing facility like ACCESS, and then again to a commercial cloud system. Using the (bundled) TaskVine system, you can even run across multiple systems simultaneously. No matter where you run your tasks, the workflow language stays the same.

Makeflow is used in production to support large scale problems in science and engineering. Researchers in fields such as bioinformatics, biometrics, geography, and high energy physics all use Makeflow to compose workflows from existing applications.

Makeflow can send your jobs to a wide variety of services, such as batch systems (HTCondor, SGE, PBS, LSF, Torque), task executors (TaskVine, Work Queue), cluster managers (Mesos and Kubernetes), cloud services (Amazon EC2 or Lambda) and container environments like Docker and Singularity. Details for each of those systems are given in the Batch System Support section.

Getting Started


See the Installation Instructions for the Cooperative Computing Tools package. Then, Make sure to set your PATH appropriately.

Basic Usage

A Makeflow workflow consists of a set of rules. Each rule specifies a set of output files to create, a set of input files needed to create them, and a command that generates the target files from the source files.

Makeflow attempts to generate all of the output files in a workflow. It examines all of the rules and determines which rules must run before others. Where possible, it runs commands in parallel to reduce the execution time.

Makeflow suppose two ways of writing a workflow: classic Make and JX. Classic Make is very easy to learn and get started, but but can very verbose when writing large workflows. The JX workflow language is a little more complex, but allows for more programmable construction of workflows.

Here is an example workflow written in the classic Make language. It uses the convert utility to make an animation. It downloads an image from the web, creates four variations of the image, and then combines them back together into an animation. The first and the last task are marked as LOCAL to force them to run on the controlling machine.


capitol.anim.gif: capitol.jpg capitol.90.jpg capitol.180.jpg capitol.270.jpg capitol.360.jpg
    LOCAL $(CONVERT) -delay 10 -loop 0 capitol.jpg capitol.90.jpg capitol.180.jpg capitol.270.jpg capitol.360.jpg capitol.270.jpg capitol.180.jpg capitol.90.jpg capitol.anim.gif

capitol.90.jpg: capitol.jpg
    $(CONVERT) -swirl 90 capitol.jpg capitol.90.jpg

capitol.180.jpg: capitol.jpg
    $(CONVERT) -swirl 180 capitol.jpg capitol.180.jpg

capitol.270.jpg: capitol.jpg
    $(CONVERT) -swirl 270 capitol.jpg capitol.270.jpg

capitol.360.jpg: capitol.jpg
    $(CONVERT) -swirl 360 capitol.jpg capitol.360.jpg

    LOCAL $(CURL) -o capitol.jpg $(URL)

(Note that Makeflow differs from Make in a few subtle ways, you can learn about those in the Language Reference below.)

To try out the example above, copy and paste it into a file named example.makeflow. To run it on your local machine:

$ makeflow example.makeflow

Note that if you run it a second time you will get the message:

makeflow: nothing left to do

as all of the files are already built.

Use the --clean option to clean everything up before trying it again:

$ makeflow --clean example.makeflow

If you have access to a batch system like Condor, SGE, or Torque, or a cloud service provider like Amazon, you can direct Makeflow to run your jobs there by using the -T option:

$ makeflow -T condor example.makeflow

$ makeflow -T sge example.makeflow

$ makeflow -T torque example.makeflow

$ makeflow -T amazon example.makeflow

To learn more about the various batch system options, see the Batch System Support section.


You may have to slightly adapt the makeflow workflow file to work across different batch systems. This is because different batch systems have different expectations on whether the underlying filesystem is shared (e.g., Slurm and Torque), or not (e.g., HTCondor, TaskVine, and Work Queue).

JX Workflow Language

The classic make language is easy to learn and suitable for many purposes, but it can get rather verbose for complex workflows. Makeflow also supports the JX workflow language for expressing workflows in a more programmable way. To give you an idea, here is how to quickly generate one thousand simulation jobs using JX:

    "rules": [
            "command": "./ -n "+N+" > output."+N+".txt",
            "inputs": [ "" ], "outputs": [ "output."+N+".txt" ],
        } for N in range(1,1000)

You can use the JX language with Makeflow by simply using the --jx argument to any invocation. For example: makeflow --jx example.jx -T condor

Learn more about JX here.


Most batch systems require information about what resources each job needs, so as to schedule them appropriately. You can convey this by setting the variables CORES, MEMORY (in MB), and DISK (in MB), ahead of each job. Makeflow will translate this information as needed to the underlying batch system. For example:


output.txt: input.dat
    analyze input.dat > output.txt


A variety of tools are available to help you monitor the progress of a workflow as it runs. Makeflow itself creates a transaction log (example.makeflow.makeflowlog) which contains details of each task as it runs, tracking how many are idle, running, complete, and so forth. These tools can read the transaction log and summarize the workflow:

  • makeflow_monitor reads the transaction log and produces a continuous display that shows the overall time and progress through the workflow:

    $ makeflow example.makeflow
    $ makeflow_monitor example.makeflow.makeflowlog

  • makeflow_graph_log will read the transaction log, and produce a timeline graph showing the number of jobs ready, running, and complete over time:

    $ makeflow example.makeflow
    $ makeflow_graph_log example.makeflow.makeflowlog example.png

  • makeflow_viz will display the workflow in graphical form, so that you can more easily understand the structure and dependencies. Read more about Visualization.

In addition, if you give the workflow a "project name" with the -N option, it will report its status to the catalog server once per minute. The makeflow_status command will query the catalog and summarize your currently running workloads, like this:

OWNER      PROJECT              JOBS   WAIT    RUN   COMP   ABRT   FAIL   TYPE
alfred     simulation           2263   2258      1      4      0      0 condor
betty      analysis             2260      1      1   2258      0      0   vine

General Advice

A few key bits of advice address the most common problems encountered when using Makeflow:

First, Makeflow works best when it has accurate information about each task that you wish to run. Make sure that you are careful to indicate exactly which input files each task needs, and which output files it produces.

Second, if Makeflow is doing something unexpected, you may find it useful to turn on the debugging stream with the -d all option. This will emit all sorts of detail about how each job is constructed and sent to the underlying batch system.

When debugging failed tasks, it is often useful to examine any output produced. Makeflow automatically saves these files in a makeflow.failed.$ruleno directory for each failed rule. Only the specified outputs of a rule will be saved. If the rule is retried and later succeeds, the failed outputs will be automatically deleted.

Finally, Makeflow was created by the Cooperative Computing Lab at the University of Notre Dame. We are always happy to learn more about how Makeflow is used and assist you if you have questions or difficulty.

For the latest information about Makeflow, please visit our web site and subscribe to our mailing list for more information.

Batch System Support

Makeflow supports a wide variety of batch systems. Use makeflow --help to see the current list supported. Generally speaking, simply run Makeflow with the -T option to select your desired batch system. If no option is given, then -T local is assumed.


As mentioned before, different batch systems have different expectations on whether the underlying filesystem is shared. For example, one workflow may work with -Tslurm, but may fail with -Tcondor, etc. The most portable workflows read and write files from the working directory of each rule. If a rule depends on the creation of a directory, make this creation explicit, as the directory may not be present for the intermidate results on the site where the rule executes.

If you need to pass additional parameters to your batch system, such as specifying a specific queue or machine category, use the -B option to Makeflow, or set the BATCH_OPTIONS variable in your Makeflow file. The interpretation of these options is slightly different with each system, as noted below.

To avoid overwhelming a batch system with an enormous number of idle jobs, Makeflow will limit the number of jobs sent to a system at once. You can control this on the command line with the --max-remote option or the MAKEFLOW_MAX_REMOTE_JOBS environment variable. Likewise, local execution can be limited with --max-local and MAKEFLOW_MAX_LOCAL_JOBS.

Local Execution

By default, Makeflow executes on the local machine. It will measure the available cores, memory, and disk on the local machine, and then run as many jobs as fit in those resources. (Of course, you must label your jobs with CORES, MEMORY, and DISK appropriately. You can put an upper limit on the resources used with the --local-cores, --local-memory, and --local-disk options to Makeflow. Also, the total number of jobs running locally can be limited with --max-local.


If running Makeflow directly from the command line, simply use the -T condor option to submit jobs to the HTCondor batch system.

If you have a long-running workflow, we recommend using condor_submit_makeflow like this:

$ condor_submit_makeflow

Submitting as a background job to HTCondor...
Submitting job(s). 1 job(s) submitted to cluster 364.
Makeflow Output : makeflow.364.output
Makeflow Error : makeflow.364.error
Condor Log : makeflow.364.condorlog

This will cause Makeflow itself to be submitted as a batch job itself, so that it can be monitored by HTCondor, allowing you to log out and return later to check the status of the workflow. You will see both Makeflow and the jobs it runs in the HTCondor queue:

10.0 dthain 7/17 13:03  0+00:00:05  R   0   1.7  makeflow -T condor
11.0 dthain 7/17 13:03  0+00:00:00  I   0   0.0 myjob -p 1

Makeflow will automatically generate a submit file for each job, so you don't need to write one. However, if you would like to customize the Condor submit file, use the -B option or BATCH_OPTIONS variable to specify text to add to the submit file.

For example, if you want to add Requirements and Rank lines to your Condor submit files, add this to your Makeflow:

BATCH_OPTIONS="Requirements = (Memory>1024)"

UGE - Univa Grid Engine / OGE - Open Grid Engine / SGE - Sun Grid Engine

Use the -T sge option to submit jobs to Sun Grid Engine or systems derived from it like Open Grid Scheduler or Univa Grid Engine.

As above, Makeflow will automatically generate qsub commands. Use the -B option or BATCH_OPTIONS variable to specify text to add to the command line. For example, to specify that jobs should be submitted to the devel queue:

BATCH_OPTIONS="-q devel"

PBS - Portable Batch System

Use the -T pbs option to submit jobs to the Portable Batch System or compatible systems.

This will add the values for cores, memory, and disk. These values will be added onto qsub in this format:

-l nodes=1:ppn=${CORES},mem=${MEMORY}mb,file=${DISK}mb`

To remove resources specification at submission use the Makeflow option --safe-submit-mode.

LSF - Load Sharing Facility

Use the -T lsf option to submit jobs to the Load Sharing Facility, known previously as "Platform LSF" and now "IBM Spectrum LSF".

As above, Makeflow will automatically generate bsub commands to submit jobs. For example, to submit a job requiring 4 cores, 64MB of memory, and an expected runtime of 120 minutes, Makeflow will generate a command line like this:

bsub -J makeflow23 -n 4 -M 64MB -We 120

If you have additional options peculiar to your local facility, use the -B option to makeflow or the BATCH_OPTIONS variable to specify additional submission options.

Torque Batch System

Use the -T torque option to submit jobs to the Torque Resource Manager batch system.

This will any of the values for cores, memory, disk, and wall-time defined. These values will be added onto qsub in this format:

-l nodes=1:ppn=${CORES},mem=${MEMORY}mb,file=${DISK}mb

To remove resources specification at submission use Makeflow option --safe-submit-mode.

SLURM - Simple Linux Resource Manager

Use the -T slurm option to submit jobs to the SLURM batch system.

This will add the values for cores and memory. These values will be added onto sbatch in this format:

-N 1 -n 1 -c ${CORES} --mem=${MEMORY}M --time=${WALL_TIME_in_minutes}

Note that slurm expects this time to be in minutes, while makeflow expects WALL_TIME to be in seconds. You should specify .MAKEFLOW WALL_TIME in seconds, and makeflow will round-up this value to minutes when submiting the job.

To remove resources specification at submission use Makeflow option --safe-submit-mode.

Disk is not specifies as a shared file system is assumed. The only available disk command line option is --tmp, which governs temporary disk space. This may have unintended limitations on available machines as temporary disk is highly dependent on host site.

Note: Some SLURM sites disallow specifying memory (example Stampede2). To avoid specification errors the Makeflow option --ignore-memory-spec removes memory from sbatch.

SLURM and MPI jobs

If your rules should run as an MPI jobs, you can set the environment variables "MPI_PROCESSES" and "CORES", or alternatively, use the makeflow directives ".MAKEFLOW MPI_PROCESSES" and ".MAKEFLOW CORES". The value of MPI_PROCESSES sets the number of MPI processes the rule should spawn, and it should exactly divide the value of CORES. The value of sbatch used is something similar to:

-N 1 -n ${MPI_PROCESSES} -c ${CORES_divided_by_MPI_PROCESSES} --mem=${MEMORY}M

Further, the commands in your rules should be prepended with the srun command appropiate to your prefered MPI API, for example, here is a rule that uses 4 MPI process, with 2 CORES each, using the pmi2 API:


output: input
    srun --mpi=pmi2 -- ./my-mpi-job -i input -o output

Some installations may require a queue and time limit specifications. Since both sbatch (used internally by makeflow), and srun require these options, the environment variable can be used in the following way:

# Setting queue and time limit needed as in xsede stampede2:
BATCH_OPTIONS=-pnormal -t900

output: input
    srun $(BATCH_OPTIONS) --mpi=pmi2 -- ./my-mpi-job -i input -o output

Moab Scheduler

Use the -T moab option to submit jobs to the Moab scheduler or compatible systems.

MPI Makeflow

An alternate method of using Makeflow on an HPC system is to submit Makeflow itself as one large parallel job. Makeflow will then use the MPI (Message Passing Interface) to communicate between nodes and dispatch each task in the workflow to nodes as needed. The entire "job" will return when the workflow is complete. This mode assumes that there exists a shared parallel filesystem mounted across all nodes in the system.

To use this option, Makeflow must be built from source to take advantage of the specialized MPI compilers and libraries available on your machine. First, consult your local documentation on how to set up access to your MPI compiler, typically named mpicc. Then, configure and build Makeflow using the --with- mpi-path option, like this:

$ git clone cctools-src
$ cd cctools-src
$ ./configure --with-mpi-path `which mpicc` --prefix $HOME/cctools
$ make clean
$ make install
$ export PATH=$HOME/cctools/bin:$PATH

You can test this configuration locally at small scale using mpirun. For example, to run makeflow on four MPI nodes:

$ mpirun -np 4 makeflow -T mpi example.makeflow

Then, consult your local documentation on how to submit an MPI job to your cluster. This varies considerably between sites, but here is a typical example of a script for a system that uses a qsub style interface and modules for loading software:

#$ -pe mpi 48
module load ompi
mpirun -np $NSLOTS makeflow -T mpi example.makeflow


Makeflow can be used with Apache Mesos. To run Makeflow with Mesos, give the batch mode via -T mesos and pass the hostname and port number of Mesos manager to Makeflow with the --mesos-master option. Since the Makeflow-Mesos Scheduler is based on Mesos Python2 API, the path to Mesos Python2 library should be included in the $PATH, or one can specify a preferred Mesos Python2 API via --mesos-path option. To successfully import the Python library, you may need to indicate dependencies through --mesos-preload option.

For example, here is the command to run Makeflow on a Mesos cluster that has the manager listening on port 5050 of localhost, with a user specified python library:

$ makeflow -T mesos --mesos-master localhost:5050 --mesos-path /path/to/mesos-0.26.0/lib/python2.6/site-packages example.makeflow ...


Makeflow can be run on Kubernetes cluster. To run Makeflow with Kubernetes, give the batch mode via -T k8s and indicate the desired Docker image with the --k8s-image option. The Kubernetes mode is depend on kubectl, before starting Makeflow, make sure kubectl has been installed and correctly set up to access the desired Kubernetes cluster.

Following is an example of running Makeflow on a Kubernetes cluster using centos image.

$ makeflow -T k8s --k8s-image "centos" example.makeflow

Amazon Web Services

Makeflow can be configured to run jobs remotely on Amazon Web Services. For each job in the workflow, a new virtual machine will be created, the job will run on the virtual machine, and then the virtual machine will be destroyed.

You will need to do some one-time setup first:

  1. Create an account with Amazon Web Services, and test it out by creating and deleting some virtual machines manually.

  2. Install the AWS Command Line Interace on the machine where you will run Makeflow.

  3. Add the aws command to your PATH.

  4. Run aws configure and enter your AWS Access Key ID, your AWS Secret Access Key, your preferred region name and json output format. (The keys can be obtained from the AWS web page by going to your profile menu, selecting "My Security Credentials" and then "Access Keys". You may need to create a new access key.)

  5. Test that the command-line tool is working by entering: aws ec2 describe-hosts Which should display: { "Hosts": [] }

Now you are ready to use Makeflow with Amazon. Before running a workflow, use the makeflow_ec2_setup command, which will setup a virtual private cluster, security group, keypairs, and other necessary details, and store the necessary information into my.config.

Give the name of the default Amazon Machine Image (AMI) you wish to use as the second argument:

$ makeflow_ec2_setup my.config ami-343a694f

and then

$ makeflow -T amazon --amazon-config my.config ...

to use the configuration you just created. You can run multiple workflows using a single configuration. Intances are reused between makeflow jobs.

When you are done, terminate created instances and destroy the configuration with this command: makeflow_ec2_cleanup my.config

Makeflow selects the virtual machine instance type automatically by translating the job resources into an appropriate instance type of the c4 or m4 category. For example, a job requiring CORES=2 and MEMORY=8000 will be placed into an m4.large instance type. If you do not specify any resources for a job, then it will be placed into a t2.micro instance, which has very limited performance. To get good performance, you should label your jobs with the appropriate CORES and MEMORY.

You can override the choice of virtual machine instance type, as well as the choice of virtual machine image (AMI) within the Makeflow on a job-by-job basis by setting the AMAZON_INSTANCE_TYPE and AMAZON_AMI environment variables within the Makeflow file. For example:

$ export AMAZON_INSTANCE_TYPE=m4.4xlarge export AMAZON_AMI=ami-343a694f

Amazon Lambda

To use Amazon Lambda, first set up an AWS account and install the aws command line tools as noted above.

Then, use the makeflow_lambda_setup command, which will establish the appropriate roles, buckets, and functions needed to use Amazon Lambda. The names of these items will be stored in a config file named on the command line:

$ makeflow_lambda_setup my.config

Then, you may use makeflow normally, indicating lambda as the batch type, and passing in the configuration file with the --lambda-config option:

$ makeflow -T lambda --lambda-config my.config example.makeflow ...

You may run multiple workflows in this fashion. When you are ready to clean up the associated state in Amazon, run makeflow_lambda_cleanup and pass in the configuration file:

$ makeflow_lambda_cleanup my.config

Generic Cluster Submission

For clusters that are not directly supported by Makeflow we strongly suggest using the TaskVine system and submitting workers via the cluster's normal submission mechanism.

However, if you have a system similar to Torque, SGE, or PBS which submits jobs with commands like "qsub", you can inform Makeflow of those commands and use the cluster driver. For this to work, it is assumed there is a distributed filesystem shared (like NFS) shared across all nodes of the cluster.

To configure a custom driver, set the following environment variables:

  • BATCH_QUEUE_CLUSTER_NAME The name of the cluster, used in debugging messages and as the name for the wrapper script.
  • BATCH_QUEUE_CLUSTER_SUBMIT_COMMAND The submit command for the cluster (such as qsub or msub)
  • BATCH_QUEUE_CLUSTER_SUBMIT_OPTIONS The command-line arguments that must be passed to the submit command. This string should end with the argument used to set the name of the task (usually -N).
  • BATCH_QUEUE_CLUSTER_REMOVE_COMMAND The delete command for the cluster (such as qdel or mdel)

These will be used to construct a task submission for each makeflow rule that consists of:

$SUBMIT_COMMAND $SUBMIT_OPTIONS $CLUSTER_NAME.wrapper "<rule commandline>"

The wrapper script is a shell script that reads the command to be run as an argument and handles bookkeeping operations necessary for Makeflow.

Using TaskVine


As described so far, Makeflow translates each job in the workflow into a single submission to the target batch system. This works well as long as each job is relatively long running and does not perform a large amount of I/O. However, if each job is relatively short, the workflow may run very slowly, because it can take 30 seconds or more for the batch system to start an individual job. If common files are needed by each job, they may end up being transferred to the same node multiple times.

To get around these limitations, we provide the TaskVine system. The basic idea is to submit a number of persistent "worker" processes to an existing batch system. Makeflow communicates directly with the workers to quickly dispatch jobs and cache data, rather than interacting with the underlying batch system. This accelerates short jobs and exploits common data dependencies between jobs.

To begin, let's assume that you are logged into the head node of your cluster, called Run Makeflow in TaskVine mode like this:

$ makeflow -T vine example.makeflow

Then, submit 10 worker processes to Condor like this:

$ vine_submit_workers -T condor 9123 10

10 Submitting job(s)..........
Logging submit event(s)..........
10 job(s) submitted to cluster 298.

Or, submit 10 worker processes to SGE like this:

$ vine_submit_workers -T sge 9123 10

Or, you can start workers manually on any other machine you can log into:

$ vine_worker 9123

Once the workers begin running, Makeflow will dispatch multiple tasks to each one very quickly. If a worker should fail, Makeflow will retry the work elsewhere, so it is safe to submit many workers to an unreliable system.

When the workflow completes, your workers will still be available, so you can either run another Makeflow with the same workers, remove them from the batch system, or wait for them to expire. If you do nothing for 15 minutes, they will automatically exit.

Note that vine_submit_workers is a simple shell script, so you can edit it directly if you would like to change batch options or other details. Please refer to the TaskVine manual for more details.

Port Numbers

Makeflow listens on a port which the remote workers would connect to. The default port number is 9123. Sometimes, however, the port number might be not available on your system. You can change the default port via the -p option. For example, if you want the manager to listen on port 9567 by default, you can run the following command:

$ makeflow -T vine -p 9567 example.makeflow

Project Names

If you do not like using port numbers, an easier way to match workers to managers is to use a project name. You can give each manager a project name with the -N option.

$ makeflow -T vine -N MyProject example.makeflow

The -N option gives the manager a project name called MyProject, and will cause it to advertise its information such as the project name, running status, hostname and port number, to a default globally available catalog server. Then a worker can simply identify the workload by its project name.

To start a worker that automatically finds the manager named MyProject via the default catalog server:

$ vine_worker -N MyProject

You can also give multiple -N options to a worker. The worker will find out which ones of the specified projects are running from the catalog server and randomly select one to work for. When one project is done, the worker would repeat this process. Thus, the worker can work for a different manager without being stopped and given the different hostname and port of the manager. An example of specifying multiple projects:

$ vine_worker -N proj1 -N proj2 -N proj3

In addition to creating a project name using the -N option, this will also trigger automatic reporting to the designated catalog server. The Port and Server address are taken from the environment variables CATALOG_HOST and CATALOG_PORT. If either variable is not set, then the addresses, and port 9097 will be used.

It is also easy to run your own catalog server, if you prefer. For more details, see the Catalog Server Manual.

Setting a Password

We recommend that anyone using the catalog server also set a password. To do this, select any password and write it to a file called mypwfile. Then, run Makeflow and each worker with the --password option to indicate the password file:

$ makeflow --password mypwfile ...
$ vine_worker --password mypwfile ...

Container Environments

Makeflow can interoperate with a variety of container technologies, including Docker, Singularity, and Umbrella. In each case, you name the container image on the command line, and then Makeflow will apply it to each job by creating the container, loading (or linking) the input files into the container, running the job, extracting the output files, and deleting the container.

Note that container specification is independent of batch system selection. For example, if you select HTCondor execution with -T condor and Docker support with --docker, then Makeflow will submit HTCondor batch jobs, each consisting of an invocation of docker to run the job. You can switch to any combination of batch system and container technology that you like.


If you have the Docker container enviornment installed on your cluster, Makeflow can be set up to place each job in your workflow into a Docker container. Invoke Makeflow with the --docker argument followed by the container image to use. Makeflow will ensure that the named image is pulled into each Docker node, and then execute the task within that container. For example, --docker debian will cause all tasks to be run in the container name debian.

Alternatively, if you have an exported container image, you can use the exported image via the --docker-tar option. Makeflow will load the container into each execution node as needed. This allows you to use a container without pushing it to a remote repository.


In a similar way, Makeflow can be used with the Singularity container system. When using this mode, Singularity will take in an image, set up the container, and runs the command inside of the container. Any needed input files will be read in from Makeflow, and created files will be delivered by Makeflow.

Invoke Makeflow with the --singularity argument, followed by the path to the desired image file. Makeflow will ensure that the named image will be transferred to each job, using the appropriate mechanism for that batch system


Makeflow allows the user to specify the execution environment for each rule via its --umbrella-spec and --umbrella-binary options. The --umbrella- spec option allows the user to specify an Umbrella specification, the --umbrella-binary option allows the user to specify the path to an Umbrella binary. Using this mode, each rule will be converted into an Umbrella job, and the specified Umbrella specification and binary will be added into the input file list of a job.

To test makeflow with umbrella using local execution engine:

$ makeflow --umbrella-binary
$ $(which umbrella) --umbrella-spec convert_S.umbrella example.makeflow

To test makeflow with umbrella using vine execution engine:

$ makeflow -T vine --umbrella-binary
$ $(which umbrella) --umbrella-spec convert_S.umbrella example.makeflow

To run each makeflow rule as an Umbrella job, the --umbrella-spec must be specified. However, the --umbrella-binary option is optional: when it is specified, the specified umbrella binary will be sent to the execution node; when it is not specified, the execution node is expected to have an umbrella binary available through the $PATH environment variable.

Makeflow also allows the user to specify the umbrella log file prefix via its --umbrella-log-prefix option. The umbrella log file is in the format of ".". The default value for the --umbrella-log-prefix option is ".umbrella.log".

Makeflow also allows the user to specify the umbrella execution mode via its --umbrella-mode option. Currently, this option can be set to the following three modes: local, parrot, and docker. The default value of the --umbrella- mode option is local, which first tries to utilize the docker mode, and tries to utilize the parrot mode if the docker mode is not available.

You can also specify an Umbrella specification for a group of rule(s) in the Makefile by putting the following directives before the rule(s) you want to apply the Umbrella spec to:


In this case, the specified Umbrella spec will be applied to all the following rules until a new .MAKEFLOW CATEGORY ... directive is declared. All the rules before the first .MAKEFLOW CATEGORY ... directive will use the Umbrella spec specified by the --umbrella-spec option. If the --umbrella- spec option is not specified, these rules will run without being wrapped by Umbrella.

Wrapper Commands

Makeflow allows a global wrapper command to be applied to every rule in the workflow. This is particularly useful for applying troubleshooting tools, or for setting up a global environment without rewriting the entire workflow. The --wrapper option will prefix a command in front of every rule, while the --wrapper-input and --wrapper-output options will specify input and output files related to the wrapper.

A few special characters are supported by wrappers. If the wrapper command or wrapper files contain two percents (%%), then the number of the current rule will be substituted there. If the command contains curly braces ({}) the original command will be substituted at that point. Square brackets ([]) are the same as curly braces, except that the command is quoted and escaped before substitution. If neither specifier is given, Makeflow appends /bin/sh -c [] to the wrapper command.

For example, suppose that you wish to shell builtin command time to every rule in the workflow. Instead of modifying the workflow, run it like this:

$ makeflow --wrapper 'time -p' example.makeflow

Since the preceding wrapper did not specify where to substitute the command, it is equivalent to

$ makeflow --wrapper 'time -p /bin/sh -c []' example.makeflow

This way, if a single rule specifies multiple commands, the wrapper will time all of them.

The square brackets and the default behavior of running commands in a shell were added because Makeflow allows a rule to run multiple commands. The curly braces simply perform text substitution, so for example

$ makeflow --wrapper 'env -i {}' example.makeflow

does not work correctly if multiple commands are specified. For example:

target_1: source_1
    command_1; command_2; command_3

will be executed as env -i command_1; command_2; command_3, which it is likely not what you want. Notice that only command_1's environment will be cleared; subsequent commands are not affected. Thus this wrapper should be given as

$ makeflow --wrapper 'env -i /bin/sh -c []' example.makeflow

or more succinctly as

$ makeflow --wrapper 'env -i' example.makeflow

Suppose you want to apply strace to every rule, to obtain system call traces. Since every rule would have to have its own output file for the trace, you could indicate output files like this:

$ makeflow --wrapper 'strace -o trace.%%' --wrapper-output 'trace.%%' example.makeflow

Suppose you want to wrap every command with a script that would set up an appropriate Java environment. You might write a script called like this:

#!/bin/sh export JAVA_HOME=/opt/java- export
PATH=${JAVA_HOME}/bin:$PATH echo "using java in $JAVA_HOME" exec "$@"

And then invoke Makeflow like this:

$ makeflow --wrapper ./ --wrapper-input example.makeflow

Advanced Features

Shared File Systems

By default, Makeflow does not assume that your cluster has a shared filesystem like NFS or HDFS, and so, to be safe, it copies all necessary dependencies for each job. However, if you do have a shared filesystem, it can be used to efficiently access files without making remote copies. Makeflow must be told where the shared filesystem is located, so that it can take advantage of it. To enable this, invoke Makeflow with the --shared-fs option, indicating the path where the shared filesystem is mounted. (This option can be given multiple times.) Makeflow will then verify the existence of input and output files in these locations, but will not cause them to be transferred.

For example, if you use NFS to access the /home and /software directories on your cluster, then invoke makeflow like this:

$ makeflow --shared-fs /home --shared-fs /software example.makeflow ...

NFS Consistency Delay

After a job completes, Makeflow checks that the expected output files were actually created. However, if the output files were written via NFS (or another shared filesystem), it is possible that the outputs may not be visible for a few seconds. This is due to caching and buffering effects in many filesystems.

If you experience this problem, you can instruct Makeflow to retry the output file check for a limited amount of time, before concluding that the files are not there. Use the --wait-for-files-upto option to specify the number of seconds to wait.

Mounting Remote Files

It is often convenient to separate the logical purpose of a file from it's physical location. For example, you may have a workflow which makes use of a large reference database called ref.db which is a standard file downloaded from a common data repository whenever needed.

Makeflow allows you to map logical file names within the workflow to arbitrary locations on disk, or downloaded from URLs. This is accomplished by writing a "mount file" in which each line lists a logical file name and its physical location.

Here is an example of a mountfile:

curl        /usr/bin/curl
convert     ../bin/convert
data/file1  /home/bob/input1

Then, simply execute Makeflow with the --mounts option to apply the desred mountfile:

$ makeflow --mounts my.mountfile example.makeflow ...

Before execution, Makeflow first parses each line of the mountfile when the --mounts option is set, and copies the specified dependency from the location specified by source field into a local cache, and then links the target to the item under the cache. Makeflow also records the location of the local cache and the info (source, target, filename under the cache dir, and so on) of each dependencies specified in the mountfile into its log.

To cleanup a makeflow together with the local cache and all the links created due to the mountfile:

$ makeflow -c example.makeflow

By default, makeflow creates a unique directory under the current working directory to hold all the dependencies introduced by the mountfile. This location can be adjusted with the --cache-dir option.

To only cleanup the local cache and all the links created due to the mountfile:

$ makeflow -ccache example.makeflow

To limit the behavoir of a makeflow inside the current working directory, the target field should satisfy the following requirements:

  • the target path should not exist;
  • the target path should be relative;
  • the target path should not include any symbolic links; (For example, if the target path is a/b/c/d, then a, b, c should not be symbolic links.) This avoids the makeflow leaving the current working directory through symbolic links.
  • the target path should not include doubledots (..) to limit makeflow to the current working directory. For example, a/../b is an invalid target, however, a/b..c/d is a valid target.
  • the target path should be the same with its usage in Makeflow makefiles. For example, the target path for the path ./mycurl from a makefile must also be ./mycurl, can not be mycurl.

The source field can be a local file path or a http URL. When a local file path is specified, the following requirements should be satisfied:

  • the source path should exist;
  • the source path can be relative or absolute;
  • the source path can include doubledots (..);
  • the source path should only be regular files, directories, and symbolic links;
  • the source path should not be any ancestor directory of the target to avoid infinite loop;

To limit the behavoir of a makeflow inside the current working directory, the cache_dir should satisfy the following requirements:

  • the cache_dir path should be a relative path;
  • the cache_dir path should not include any symbolic link; (For example, if the cache_dir path is a/b/c/d, then a, b, c should not be symbolic link.) This limitation aims to avoid the makeflow leaving the current working directory through symbolic links.
  • the cache_dir path should not include doubledots (..) to limit the behavoir of the makeflow within the current working directory. For example, a/../b is an invalid cache_dir, however, a/b..c/d is a valid cache_dir.

Garbage Collection

As the workflow execution progresses, Makeflow can automatically delete intermediate files that are no longer needed. In this context, an intermediate file is an input of some rule that is the target of another rule. Therefore, by default, garbage collection does not delete the original input files, nor final target files.

Which files are deleted can be tailored from the default by appending files to the Makeflow variables MAKEFLOW_INPUTS and MAKEFLOW_OUTPUTS. Files added to MAKEFLOW_INPUTS augment the original inputs files that should not be deleted. MAKEFLOW_OUTPUTS marks final target files that should not be deleted. However, different from MAKEFLOW_INPUTS, files specified in MAKEFLOW_OUTPUTS does not include all output files. If MAKEFLOW_OUTPUTS is not specified, then all files not used in subsequent rules are considered outputs. It is considered best practice to always specify MAKEFLOW_INPUTS/OUTPUTS to clearly specify which files are considered inputs and outputs and allow for better space management if garbage collection is used.

Makeflow offers two modes for garbage collection: reference count, and on demand. With the reference count mode, intermediate files are deleted as soon as no rule has them listed as input. The on-demand mode is similar to reference count, only that files are deleted until the space on the local file system is below a given threshold.

To activate reference count garbage collection:

$ makeflow -gref_cnt

To activate on-demand garbage collection, with a threshold of 500MB:

$ makeflow -gon_demand -G500000000


There are several ways to visualize both the structure of a Makeflow as well as its progress over time. makeflow_viz can be used to convert a Makeflow into a file that can be displayed by Graphviz DOT tools like this:

$ makeflow_viz -D dot example.makeflow >
$ dot -Tgif < > example.gif

Or use a similar command to generate a Cytoscape input file. (This will also create a Cytoscape style.xml file.)

$ makeflow_viz -D cyto example.makeflow > example.xgmml

To observe how a makeflow runs over time, use makeflow_graph_log to convert a log file into a timeline that shows the number of tasks ready, running, and complete over time:

$ makeflow_graph_log example.makeflowlog example.png

Archiving Jobs

Makeflow allows for users to archive the results of each job within a specified archive directory. This is done using the --archive option, which by default creates an archiving directory at /tmp/makeflow.archive.$UID. Both files and jobs are stored as the workflow executes. Makeflow will also check to see if a job has already been archived into the archiving directory, and if so the outputs of the job will be copied to the working directory and the job will skip execution.

$ makeflow --archive example.makeflow

To only write to the archiving directory (and ensure that all nodes will be executed instead), pass --archive-write. To only read from the archive and use the outputs of any archived job, pass --archive-read. To specify a directory to use for the archiving directory, give an optional argument as shown below

$ makeflow --archive=/path/to/directory/ example.makeflow

The archive also has an option to upload and download workflow contents from and Amazon Web Services S3 bucket. This is done using the --archive-s3 option, which by default uploads/downloads from the S3 bucket name makeflows3archive. Makeflow will also check to see if files have already been uploaded to the S3 bucket so files are not uploaded twice, wasting time. Archiving with Amazon S3 utilizes the same functionality that the regular archive tool uses locally with copying files to and from /tmp/makeflow.archive.$UID. The user will need to make sure that their aws key id and secret key are stored within the credentials file that is provided when you configure AWS.

$ makeflow --archive-s3= Amazon S3 Bucket Name

If you do not want to check to see if files exist when uploading you can use the other option --archive-s3-no-check, which has the same default S3 bucket and options to change the bucket name.

$ makeflow --archive-s3-no-check= Amazon S3 Bucket Name

Linking Dependencies

Makeflow provides a tool to collect all of the dependencies for a given workflow into one directory. By collecting all of the input files and programs contained in a workflow it is possible to run the workflow on other machines.

Currently, Makeflow copies all of the files specified as dependencies by the rules in the makeflow file, including scripts and data files. Some of the files not collected are dynamically linked libraries, executables not listed as dependencies (python, perl), and configuration files (mail.rc).

To avoid naming conflicts, files which would otherwise have an identical path are renamed when copied into the bundle:

  • All file paths are relative to the top level directory.
  • The makeflow file is rewritten with the new file locations and placed in the top level directory.
  • Files originally specified with an absolute path are placed into the top level directory.
  • Files with the same path are appended with a unique number

Example usage:

$ makeflow_analyze -b some_output_directory example.makeflow

Technical Reference

Language Reference

The Makeflow language is very similar to Make, but it does have a few important differences that you should be aware of.

Get the Dependencies Right

You must be careful to accurately specify all of the files that a rule requires and creates , including any custom executables. This is because Makeflow requires all these information to construct the environment for a remote job. For example, suppose that you have written a simulation program called mysim.exe that reads and then produces and output file. The following rule won't work, because it doesn't inform Makeflow what input files are neded to execute the simulation:

# This is an incorrect rule:
    ./mysim.exe -c -o output.txt

However, the following is correct, because the rule states all of the files needed to run the simulation. Makeflow will use this information to construct a batch job that consists of mysim.exe and and uses it to produce output.txt:

# This is a correct rule.
output.txt: mysim.exe
    ./mysim.exe -c -o output.txt

Note that when a directory is specified as an input dependency, it means that the command relies on the directory and all of its contents. So, if you have a large collection of input data, you can place it in a single directory, and then simply give the name of that directory.

No Phony Rules

For a similar reason, you cannot have "phony" rules that do not actually create the specified files. For example, it is common practice to define a clean rule in Make that deletes all derived files. This does not make sense in Makeflow, because such a rule does not actually create a file named clean. Instead use the -c option as shown above.

Just Plain Rules

Makeflow does not support all of the syntax that you find in various versions of Make. Each rule must have exactly one command to execute. If you have multiple commands, simply join them together with semicolons. Makeflow allows you to define and use variables, but it does not support pattern rules, wildcards, or special variables like $< or $@. You simply have to write out the rules longhand, or write a script in your favorite language to generate a large Makeflow.

Local Job Execution

Certain jobs don't make much sense to distribute. For example, if you have a very fast running job that consumes a large amount of data, then it should simply run on the same machine as Makeflow. To force this, simply add the word LOCAL to the beginning of the command line in the rule.

Rule Lexical Scope

Variables in Makeflow have global scope, that is, once defined, their value can be accessed from any rule. Sometimes it is useful to define a variable locally inside a rule, without affecting the global value. In Makeflow, this can be achieved by defining the variables after the rule's requirements, but before the rule's command, and prepending the name of the variable with @, as follows:

target_1: source_1

target_2: source_2
    command_2 target_3: source_3 command_3

In this example, SOME_VARIABLE has the value original_value for rules 1 and 3, and the value local_value_for_2 for rule 2.

Environment Variables

Environment variables can be defined with the export keyword inside a workflow. Makeflow will communicate explicitly named environment variables to remote batch systems, where they will override whatever local setting is present. For example, suppose you want to modify the PATH for every job in the makeflow: export PATH=/opt/tools/bin:${PATH} If no value is given, then the current value of the environment variable is passed along to the job, as in:

export USER

Remote File Renaming⇗

When the underlying batch system supports it, Makeflow supports "Remote Renaming" where the name of a file in the execution sandbox is different than the name of the same file in the DAG. This is currently supported by the TaskVine, Work Queue, HTCondor, and Amazon batch system.

For example, local_name->remote_name indicates that the file local_name is called remote_name in the remote system. Consider the following example:

b: a myprog
    LOCAL myprog a > b

c->out: a->in1 b myprog->prog
    prog in1 b > out

The first rule runs locally, using the executable myprog and the local file a to locally create b. The second rule runs remotely, but the remote system expects a to be named in1, c, to be named out and so on. Note we did not need to rename the file b. Without remote file renaming, we would have to create either a symbolic link, or a copy of the files with the expected correct names.

Nested Makeflows

One Makeflow can be nested inside of another by writing a rule with the following syntax: output-files: input-files MAKEFLOW makeflow-file [working- dir] The input and output files are specified as usual, describing the files consumed and created by the child makeflow as a whole. Then, the MAKEFLOW keyword introduces the child makeflow specification, and an optional working directory into which the makeflow will be executed. If not given, the current working directory is assumed.

Resources and Categories

Makeflow has the capability of automatically setting the cores, memory, and disk space requirements to the underlying batch system. (This works with TaskVine, WorkQueue, and HTCondor.) Jobs are grouped into job categories , and jobs in the same category have the same cores, memory, and disk requirements.

Job categories and resources are specified with variables. Jobs are assigned to the category named in the value of the variable CATEGORY. Likewise, the values of the variables CORES, MEMORY (in MB), and DISK (in MB) describe the resources requirements for the category specified in CATEGORY.

Jobs without an explicit category are assigned to default. Jobs in the default category get their resource requirements from the value of the environment variables CORES, MEMORY, and DISK.

Consider the following example:

# These tasks are assigned to the category preprocessing.
# MEMORY and CORES are read from the environment, if defined.


one: src

two: src

# These tasks have the category "simulation". Note that now CORES, MEMORY, and
# DISK are specified.


three: src

four: src

# Another category switch. MEMORY is read from the environment.

five: src

Setting the MEMORY in the environment as

$ export MEMORY=800
$ makeflow ...

we get the following resources assignments per category:

Category Cores Memory (MB) Disk (MB)
preprocessing (unspecified) 800 (from environment) 500
simulation 1 400 400
analysis 4 800 (from environment) 600

Transaction Log

As Makeflow runs, it creates a transaction log that records the details of each job, where it was sent, how long it ran, and what resources it consumed. By default, this log is named X.makeflowlog where X is the name of the original makeflow file.

The transaction log serves several purposes:

  1. Recovery. The transaction log allows Makeflow to continue where it left off. If you must restart Makeflow after a crash, it will read the transaction log, determine what jobs are complete (or still running) and then continue forward from there.
  2. Cleanup. The --clean option relies on the transaction log to quickly determine exactly which files have been created and which jobs have been submitted, so that they can be quickly and precisely deleted and removed. (There is no need to create a clean rule by hand, as you would in traditional Make.)
  3. Monitoring. Tools like makeflow_monitor and makeflow_graph_log read the transaction log to determine the current state of the workflow and display it to the user.

Each line in the log file represents a single action taken on a single rule in the workflow. For simplicity, rules are numbered from the beginning of the Makeflow, starting with zero. Each line contains the following items:

  • timestamp - the unix time (in microseconds) when this line is written to the log file.
  • task id - the id of this n.
  • new state - a integer represents the new state this task (whose id is in the task_id column) has just entered. The value of the integer ranges from 0 to 4 and the states they are representing are:
    1. waiting
    2. running
    3. complete
    4. failed
    5. aborted
  • job id - the underline execution system is a batch system, such as Condor or SGE, the job id would be the job id assigned by the batch system when the task was sent to the batch system for execution.
  • tasks waiting - the number of tasks are waiting to be executed.
  • tasks running - the number of tasks are being executed.
  • tasks complete - the number of tasks has been completed.
  • tasks failed - the number of tasks has failed.
  • tasks aborted - the number of tasks has been aborted.
  • task id_counter - total number of tasks in this Makeflow

In addition, lines starting with a pound sign are comments and contain additional high-level information that can be safely ignored. The transaction log begins with a comment to indicate the starting time, and ends with a comment indicating whether the entire workflow completed, failed, or was aborted.

Aside from the high-level information, file states are also recorded in the log. This allows for tracking files throughout the workflow execution. This information is shown starting with the #:

# FILE timestamp filename state size

Each file state line records the state change and time:

  • timestamp - the unix time (in microseconds) when this line is written to the log file.
  • filename - the file name given in the rule specification of Makeflow.
  • state - the integer represents the new state this file has just entered. The value of the integer ranges from 0 to 4 and the states they are representing are:
    1. unknown
    2. expect
    3. exists
    4. complete
    5. delete
  • size - the file size in bytes, either on disk (states 2,3), or hinted (states 0,1).
# FILE 1559838621414397 example.makeflow.wqlog 1 0
# FILE 1559838621415289 1 0
# STARTED 1559838621415323
# FILE 1559838621416244 capitol.jpg 1 1073741824
1559838621416490 5 1 30122 5 1 0 0 0 6
# FILE 1559838621624487 capitol.jpg 2 51274
1559838621624514 5 2 30122 5 0 1 0 0 6
# FILE 1559838621624543 capitol.360.jpg 1 1073741824
1559838621624608 4 1 1 4 1 1 0 0 6
# FILE 1559838621624629 capitol.270.jpg 1 1073741824
1559838621624660 3 1 2 3 2 1 0 0 6
# FILE 1559838621624678 capitol.180.jpg 1 1073741824
1559838621624709 2 1 3 2 3 1 0 0 6
# FILE 1559838621624726 capitol.90.jpg 1 1073741824
1559838621624759 1 1 4 1 4 1 0 0 6
# FILE 1559838913507186 capitol.90.jpg 2 61932
1559838913508280 1 2 4 1 3 2 0 0 6
# FILE 1559838913520062 capitol.360.jpg 2 70522
1559838913520082 4 2 1 1 2 3 0 0 6
# FILE 1559838913530803 capitol.270.jpg 2 68097
1559838913530814 3 2 2 1 1 4 0 0 6
# FILE 1559838913541134 capitol.180.jpg 2 64756
1559838913541145 2 2 3 1 0 5 0 0 6
# FILE 1559838913541173 capitol.montage.gif 1 1073741824
1559838913541257 0 1 5 0 1 5 0 0 6
# FILE 1559838914959866 capitol.montage.gif 2 913771
# FILE 1559838914959878 capitol.360.jpg 3 70522
# FILE 1559838914959886 capitol.270.jpg 3 68097
# FILE 1559838914959893 capitol.180.jpg 3 64756
# FILE 1559838914959900 capitol.90.jpg 3 61932
# FILE 1559838914959907 capitol.jpg 3 51274
1559838914959916 0 2 5 0 0 6 0 0 6
# COMPLETED 1559838914959929

Further Information

For more information, please see Getting Help or visit the Cooperative Computing Lab website.

CCTools is Copyright (C) 2022 The University of Notre Dame. This software is distributed under the GNU General Public License Version 2. See the file COPYING for details.