Metadata-Version: 2.1
Name: mlos-viz
Version: 0.4.2
Summary: MLOS Visualization Python interface for benchmark automation and optimization results.
Home-page: https://github.com/microsoft/MLOS
Author: Microsoft
Author-email: mlos-maintainers@service.microsoft.com
License: MIT
Project-URL: Documentation, https://microsoft.github.io/MLOS
Project-URL: Package Source, https://github.com/microsoft/MLOS/tree/main/mlos_viz/
Keywords: autotuning,benchmarking,optimization,systems
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: mlos-bench==0.4.2
Requires-Dist: dabl>=0.2.6
Provides-Extra: full
Provides-Extra: full-tests
Requires-Dist: pytest; extra == "full-tests"
Requires-Dist: pytest-forked; extra == "full-tests"
Requires-Dist: pytest-xdist; extra == "full-tests"
Requires-Dist: pytest-cov; extra == "full-tests"
Requires-Dist: pytest-local-badge; extra == "full-tests"

# mlos_viz

The [`mlos_viz`](https://github.com/microsoft/MLOS/tree/main/mlos_viz/./) module is an aid to visualizing experiment benchmarking and optimization results generated and stored by [`mlos_bench`](https://github.com/microsoft/MLOS/tree/main/mlos_viz/../mlos_bench/).

Its core API is `mlos_viz.plot(experiment)`, initially implemented as a wrapper around [`dabl`](https://github.com/dabl/dabl) to provide a basic visual overview of the results, where `experiment` is an [`ExperimentData`](https://github.com/microsoft/MLOS/tree/main/mlos_viz/../mlos_bench/mlos_bench/storage/base_experiment_data.py) objected returned from the [`mlos_bench.storage`](https://github.com/microsoft/MLOS/tree/main/mlos_viz/../mlos_bench/mlos_bench/storage/) layer APIs.

In the future, we plan to add more automatic visualizations, interactive visualizations, feedback to the `mlos_bench` experiment trial scheduler, etc.

It's available for `pip install` via the pypi repository at [mlos-viz](https://pypi.org/project/mlos-viz/).

See Also: <https://microsoft.github.io/MLOS> for full API details.
