hyper-shell 2.5.0

A command-line utility toolkit for Python.

HyperShell is an elegant, cross-platform, high-throughput computing utility for processing shell commands over a distributed, asynchronous queue. It is a highly scalable workflow automation tool for many-task scenarios.

Built on Python and tested on Linux, macOS, and Windows.

Several tools offer similar functionality but not all together in a single tool with the user ergonomics we provide. Novel design elements include but are not limited to

• Cross-platform: run on any platform where Python runs. In fact, the server and client can run on different platforms in the same cluster.

• Client-server: workloads do not need to be monolithic. Run the server as a stand-alone service with SQLite or Postgres as a persistent database and dynamically scale clients as needed.

• Staggered launch: At the largest scales (1000s of nodes, 100k+ of workers), the launch process can be challenging. Come up gradually to balance the workload.

• Database in-the-loop: run in-memory for quick, ad-hoc workloads. Otherwise, include a database for persistence, recovery when restarting, and search.

Documentation

Documentation is available at hyper-shell.readthedocs.io. For basic usage information on the command line use: hyper-shell --help. For a more comprehensive usage guide on the command line you can view the manual page with man hyper-shell.

Contributions

Contributions are welcome. If you find bugs or have questions, open an Issue here. We’ve added a Code of Conduct recently, adapted from the Contributor Covenant, version 2.0.

Citation

If HyperShell has helped in your research please consider citing us.

@inproceedings{lentner_2022,
    author = {Lentner, Geoffrey and Gorenstein, Lev},
    title = {HyperShell v2: Distributed Task Execution for HPC},
    year = {2022},
    isbn = {9781450391610},
    publisher = {Association for Computing Machinery},
    url = {https://doi.org/10.1145/3491418.3535138},
    doi = {10.1145/3491418.3535138},
    booktitle = {Practice and Experience in Advanced Research Computing},
    articleno = {80},
    numpages = {3},
    series = {PEARC '22}
}