Package: maestrowf
Maestro Workflow Conductor (maestrowf)Maestro can be installed viapip:pip install maestrowfDocumentationMaestro Documentation- Official Maestro documentation.Maestro Sheetmusic- A collection of sample and user contributed Maestro study examples.Maestro Samples- Maestro sample studies.Getting Started is Quick and EasyCreate aYAMLfile namedstudy.yamland paste the following content into the file:description:name:hello_worlddescription:A simple 'Hello World' study.study:-name:say-hellodescription:Say hello to the world!run:cmd:|echo "Hello, World!" > hello_world.txtPHILOSOPHY: Maestro believes in the principle of a clearly defined process, specified as a list of tasks, that are self-documenting and clear in their intent.Running thehello_worldstudy is as simple as...maestro run study.yamlCreating a Parameter Study is just as EasyWith the addition of theglobal.parametersblock, and a few simple tweaks to yourstudyblock, the complete specification should look like this:description:name:hello_planetdescription:A simple study to say hello to planets (and Pluto)study:-name:say-hellodescription:Say hello to a planet!run:cmd:|echo "Hello, $(PLANET)!" > hello_$(PLANET).txtglobal.parameters:PLANET:values:[Mercury,Venus,Earth,Mars,Jupiter,Saturn,Uranus,Neptune,Pluto]label:PLANET.%%PHILOSOPHY: Maestro believes that a workflow should be easily parameterized with minimal modifications to the core process.Maestro will automatically expand each parameter into its own isolated workspace, generate a script for each parameter, and automatically monitor execution of each task.And, running the study is still as simple as:maestrorunstudy.yamlScheduling Made SimpleBut wait there's more! If you want to schedule a study, it's just as simple. With some minor modifications, you are able to run on anHPCsystem.description:name:hello_planetdescription:A simple study to say hello to planets (and Pluto)batch:type:slurmqueue:pbatchhost:quartzbank:sciencestudy:-name:say-hellodescription:Say hello to a planet!run:cmd:|echo "Hello, $(PLANET)!" > hello_$(PLANET).txtnodes:1procs:1walltime:"00:02:00"global.parameters:PLANET:values:[Mercury,Venus,Earth,Mars,Jupiter,Saturn,Uranus,Neptune,Pluto]label:PLANET.%%NOTE: This specification is configured to run on LLNL's quartz cluster. Under thebatchheader, you will need to make the necessary changes to schedule onto other HPC resources.PHILOSOPHY: Maestro believes that how a workflow is defined should be decoupled from how it's run. We achieve this capability by providing a seamless interface to multiple schedulers that allows Maestro to readily port workflows to multiple platforms.For other samples, see thesamplessubfolder. To continue with our Hello World example, see theBasics of Study Constructionin ourdocumentation.An Example Study using LULESHMaestro comes packed with a basic example usingLULESH, a proxy application provided by LLNL. You can find the examplehere.What is Maestro?Maestro is an open-source HPC software tool that defines a YAML-based study specification for defining multistep workflows and automates execution of software flows on HPC resources. The core design tenants of Maestro focus on encouraging clear workflow communication and documentation, while making consistent execution easier to allow users to focus on science. Maestro's study specification helps users think about complex workflows in a step-wise, intent-oriented, manner that encourages modularity and tool reuse. These principles are becoming increasingly important as computational science is continuously more present in scientific fields and has started to require a similar rigor to physical experiment. Maestro is currently in use for multiple projects at Lawrence Livermore National Laboratory and has been used to run existing codes including MFEM, and other simulation codes. It has also been used in other areas including in the training of machine-learned models and more.Maestro's Foundation and Core ConceptsThere are many definitions of workflow, so we try to keep it simple and define the term as follows:A set of high level tasks to be executed in some order, with or without dependencies on each other.We have designed Maestro around the core concept of what we call a "study". A study is defined as a set of steps that are executed (a workflow) over a set of parameters. A study in Maestro's context is analogous to an actual tangible scientific experiment, which has a set of clearly defined and repeatable steps which are repeated over multiple specimen.Maestro's core tenets are defined as follows:RepeatabilityA study should be easily repeatable. Like any well-planned and implemented science experiment, the steps themselves should be executed the exact same way each time a study is run over each set of parameters or over different runs of the study itself.ConsistentStudies should be consistently documented and able to be run in a consistent fashion. The removal of variation in the process means less mistakes when executing studies, ease of picking up studies created by others, and uniformity in defining new studies.Self-documentingDocumentation is important in computational studies as much as it is in physical science. The YAML specification defined by Maestro provides a few required key encouraging human-readable documentation. Even further, the specification itself is a documentation of a complete workflow.Setting up your Python EnvironmentTo get started, we recommend using virtual environments. If you do not have the
Pythonvirtualenvpackage installed, take a look at their officialdocumentationto get started.To create a new virtual environment:python -m virtualenv maestro_venv
source maestro_venv/bin/activateGetting Started for ContributorsIf you plan to develop on Maestro, install the repository directly using:pip install poetry
poetry installOnce set up, test the environment. The paths should point to a virtual environment folder.which python
which pipUsing Maestro DockerfilesMaestro comes packaged with a set of Docker files for testing things out. The two primary files
are:A standardDockerfilein the root of the Maestro repository. This file is a standard install
of Maestro meant to try out Maestro on the demo samples provided with this repository. In order
to try Maestro locally, withDockerinstalled run:docker build -t maestrowf .
docker run -ti maestrowfFrom within the container run the following:maestro run ./maestrowf/samples/lulesh/lulesh_sample1_unix.yamlIn order to try out Flux 0.19.0 integration, from the root of the Maestro repository run the
following:docker build -t flux_0190 -f ./docker/flux/0.19.0/Dockerfile .
docker run -ti flux_0190From within the container run the following:maestro run ./maestrowf/samples/lulesh/lulesh_sample1_unix_flux.yamlContributorsMany thanks go to MaestroWF'scontributors.If you have any questions or to submit feature requests pleaseopen a ticket.ReleaseMaestroWF is released under an MIT license.  For more details see the
NOTICE and LICENSE files.LLNL-CODE-734340