Metadata-Version: 2.1
Name: maestrowf
Version: 1.1.8
Summary: A tool to easily orchestrate general computational workflows both locally and on supercomputers.
Home-page: https://github.com/llnl/maestrowf
Author: Francesco Di Natale
Author-email: dinatale3@llnl.gov
Maintainer: Francesco Di Natale
License: MIT License
Download-URL: https://pypi.org/project/maestrowf/
Description: ![](https://github.com/LLNL/maestrowf/raw/develop/assets/logo.png?raw=true "Orchestrate your workflows with ease!")
        
        # Maestro Workflow Conductor ([maestrowf](https://pypi.org/project/maestrowf/))
        
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        Maestro can be installed via [pip](https://pip.pypa.io/):
        
            pip install maestrowf
        
        ## Documentation
        
        * [Maestro Documentation](https://maestrowf.readthedocs.io)
        * [Maestro Samples](/samples)
        
        ## Getting Started is Quick and Easy
        
        Create a `YAML` file named `study.yaml` and paste the following content into the file:
        
        ``` yaml
        description:
            name: hello_world
            description: A simple 'Hello World' study.
        
        study:
            - name: say-hello
              description: Say hello to the world!
              run:
                  cmd: |
                    echo "Hello, World!" > hello_world.txt
        ```
        
        > *PHILOSOPHY*: 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 the `hello_world` study is as simple as...
        
            maestro run study.yaml
        
        ## Creating a Parameter Study is just as Easy
        
        With the addition of the `global.parameters` block, and a few simple tweaks to your `study` block, the complete specification should look like this:
        
        ``` yaml
        description:
            name: hello_planet
            description: A simple study to say hello to planets (and Pluto)
        
        study:
            - name: say-hello
              description: Say hello to a planet!
              run:
                  cmd: |
                    echo "Hello, $(PLANET)!" > hello_$(PLANET).txt
        
        global.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:
        
        ``` bash
            maestro run study.yaml
        ```
        
        ## Scheduling Made Simple
        
        But 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 an [HPC](https://en.wikipedia.org/wiki/Supercomputer) system.
        
        ``` yaml
        description:
            name: hello_planet
            description: A simple study to say hello to planets (and Pluto)
        
        batch:
            type:  slurm
            queue: pbatch
            host:  quartz
            bank:  science
        
        study:
            - name: say-hello
              description: Say hello to a planet!
              run:
                  cmd: |
                    echo "Hello, $(PLANET)!" > hello_$(PLANET).txt
                  nodes: 1
                  procs: 1
                  walltime: "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 the `batch` header, 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 the [samples](/samples) subfolder. To continue with our Hello World example, see the [Basics of Study Construction](https://maestrowf.readthedocs.io/en/latest/hello_world.html) in our [documentation](https://maestrowf.readthedocs.io/en/latest/index.html).
        
        ## An Example Study using LULESH
        
        Maestro comes packed with a basic example using [LULESH](https://github.com/LLNL/LULESH), a proxy application provided by LLNL. You can find the example [here](https://maestrowf.readthedocs.io/en/latest/quick_start.html#).
        
        ## 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 Concepts
        
        There are many definitions of workflow, so we try to keep it simple and define the term as follows:
        
        ``` text
        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:
        
        ##### Repeatability
        
        A 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.
        
        ##### Consistent
        
        Studies 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-documenting
        
        Documentation 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 Environment
        
        To get started, we recommend using virtual environments. If you do not have the
        Python `virtualenv` package installed, take a look at their official [documentation](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) to get started.
        
        To create a new virtual environment:
        
            python -m virtualenv maestro_venv
            source maestro_venv/bin/activate
        
        ### Getting Started for Contributors
        
        If you plan to develop on Maestro, install the repository directly using:
        
            pip install -r requirements.txt
            pip install -e .
        
        Once set up, test the environment. The paths should point to a virtual environment folder.
        
            which python
            which pip
        
        ## Contributors
        
        Many thanks go to MaestroWF's [contributors](https://github.com/LLNL/maestrowf/graphs/contributors).
        
        If you have any questions or to submit feature requests please [open a ticket](https://github.com/llnl/maestrowf/issues).
        
        ----------------
        
        ## Release
        MaestroWF is released under an MIT license.  For more details see the
        NOTICE and LICENSE files.
        
        ``LLNL-CODE-734340``
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: System :: Distributed Computing
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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