Metadata-Version: 2.1
Name: lapis-sim
Version: 0.3.0
Summary: Lapis is an adaptable, performant, and interactive scheduling (Lapis) simulator
Home-page: https://github.com/MatterMiners/lapis
License: UNKNOWN
Keywords: htcondor simulation python cobald tardis opportunistic scheduling scheduler
Author: Eileen Kuehn, Max Fischer
Author-email: mainekuehn@gmail.com
Description-Content-Type: text/x-rst
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: System Administrators
Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Office/Business :: Scheduling
Classifier: Topic :: System :: Distributed Computing
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: cobald
Requires-Dist: usim == 0.4
Requires-Dist: click
Requires-Dist: pre-commit; extra == "dev"
Requires-Dist: sphinx; extra == "doc"
Requires-Dist: sphinx_rtd_theme; extra == "doc"
Requires-Dist: pytest >=4.3.0; extra == "test"
Requires-Dist: flake8; extra == "test"
Requires-Dist: flake8-bugbear; extra == "test"
Requires-Dist: black; extra == "test"
Project-URL: Documentation, https://lapis-sim.readthedocs.io/en/latest/
Provides-Extra: dev
Provides-Extra: doc
Provides-Extra: test

===============================================================================
Lapis is an adaptable, performant, and interactive scheduling (Lapis) simulator
===============================================================================

The ``lapis`` library provides a framework and runtime for simulating the scheduling and usage of opportunistic
and static resources.

Command Line Interface
----------------------

Currently the library provides a simple command line interface that allows three modes of operation:

* static provisioning of resources,
* dynamic provisioning of resources, and
* hybrid provisioning of resources.

In the most simple case you can apply a given workload, e.g. downloaded from the parallel workload archive to a
static resource configuration:


.. code:: bash

    python3 simulate.py --log-file - static --job-file <path-to-workload> swf --pool-file <path-to-pool-definition> htcondor

The output of simulation is given to stdout. You have further options you can explore via

.. code:: bash

    python3 simulate.py --help

and more specifically for the different operation modes with

.. code:: bash

    python3 simulate.py static --help

