.. _quick_start:

###############
Quick start
###############

`bde` implements Bayesian Deep Ensembles that plug directly into scikit-learn
pipelines while running training and sampling in JAX. This page walks through
installing the package, setting up the environment and information regarding JAX device count.

Installation
============

To install the latest release from PyPI, run:

.. prompt:: bash $

  pip install sklearn-contrib-bde

To install the latest development version from GitHub, run:

.. prompt:: bash $

  pip install git+https://github.com/scikit-learn-contrib/bde.git

If you prefer reproducible environments with GPU or multi-device CPU support,
consider using `pixi <https://prefix.dev/docs/pixi/overview>`__ as described in
:ref:`user_guide`. The repo already ships with a ``pixi.toml`` that pins the
required versions of JAX, scikit-learn, and the CUDA toolchain when available.

Set the JAX device count
========================

When you run the estimators outside of the examples shipped in ``examples/``,
ensure JAX can see enough host devices. The environment variable below makes
CPU-only runs allocate eight virtual devices; tweak the value to match your
hardware:

.. prompt:: bash $

  export XLA_FLAGS="--xla_force_host_platform_device_count=<desired_devices_to_be_used>"

You can set it once per shell session or inject it programmatically before
importing JAX in your Python scripts.

Work with the development environment
=====================================

Once you clone the repository, bootstrap the dev dependencies and tooling with:

.. prompt:: bash $

  pixi install

You can then run the standard tasks:

* ``pixi run lint`` for style checks (``ruff`` and ``black``)
* ``pixi run test`` for the pytest suite, including scikit-learn compatibility
  checks in ``tests/test_common.py``
* ``pixi run build-doc`` to render this documentation locally with Sphinx and
  sphinx-gallery examples

If you prefer calling Python tooling directly, enter the environment shell:

.. prompt:: bash $

  pixi shell -e dev

From there ``pytest``, ``ruff`` and ``sphinx-build`` are available on ``PATH``.

Next steps
==========

* Dive into :ref:`user_guide` for a walkthrough of the package internals and
  configuration knobs.
* Explore the auto-generated API reference in :ref:`api` to see every public
  class, function, and dataclass.
* Run the gallery in :ref:`general_examples` to compare regression and
  classification behaviours on real-world datasets!