This repository is a collection of example Maestro studies and code that can be used within workflows. The goal of these examples is to serve as a starting point for starting new Maestro examples and to illustrate some of the cases where Maestro can be used.

Some of the items in this repository:

• Sample specifications of complete multistep workflows
• Parameter generator (pgen) examples for executing various sampling techniques
• Both local and remote examples for running on super computers

Maestro is a general purpose software tool that defines a YAML-based study specification for defining multistep workflows and automates execution of software flows on HPC resources. For more information, see the Maestro Workflow Conductor repository.

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 to get started.

To create a new virtual environment:

python -m virtualenv maestro_venv
source maestro_venv/bin/activate

Once set up, test the environment. The paths should point to a virtual environment folder.

which python
which pip

Now, within your new environment, install the latest version of Maestro run

pip install maestrowf

If you'd like to contribute to these examples, please feel free to post a pull request. In order to contribute an example, please provide the following in a new directory for your example:

• a README that describes your example and provides installation instructions
• any standalone code needed to run your example
• whenever possible, a Maestro specification to run your example
• if your example provides code for pgen, specification generating, etc. please describe the process and what the code does and how it's used.

If you have any questions about Maestro itself or want to submit a feature request please open a ticket on the Maestro Workflow Conductor repository.

If you find that an example in this repository does not work or has a bug, please file an issue here.

MaestroWF is released under an MIT license. For more details see the NOTICE and LICENSE files.

LLNL-CODE-734340