Installation

Library and CLI

From the repository root or PyPI:

pip install -e .

Development tooling:

pip install -e ".[test,dev]"

Documentation build (local)

Install documentation dependencies and serve the site:

pip install -e ".[docs]"
mkdocs serve

The site configuration is mkdocs.yml at the repository root. Read the Docs builds the same site using .readthedocs.yaml; the hosted site is lrdbench.readthedocs.io.

Optional extras

Extra	Purpose
reports	Jinja2 / tabulate helpers for richer reporting
parquet	Parquet export via PyArrow
ml	scikit-learn baselines: MLRandomForest, MLSVR
nn	PyTorch baselines: MLCNN, MLLSTM
data-driven	All ML and NN baseline dependencies
docs	MkDocs + Material + mkdocstrings
test	pytest, coverage, Hypothesis
dev	Ruff, mypy, build, twine, pre-commit
all	Reports, parquet, docs, notebooks, tests, dev tools, and all data-driven dependencies

Example: pip install -e ".[docs,test]".

For a full local development environment, including the heavier scikit-learn and PyTorch data-driven dependencies, use:

pip install -e ".[all]"

For a lighter development environment that avoids PyTorch, prefer .[test,dev,docs,reports].

Because the repository's default pytest configuration enables coverage reports, install the test extra before running python -m pytest. For a quick no-coverage smoke check in a minimal environment, run python -m pytest -q -o addopts=''.

Data-driven smoke benchmarks need the feature-based ML extra and reporting support:

pip install -e ".[ml,reports]"
lrdbench run configs/suites/smoke_data_driven.yaml

The neural-network extra may install a large PyTorch distribution depending on your platform. Use ml for the packaged RF/SVR smoke suite, and install nn only when running CNN/LSTM benchmarks.