.. _installation:

Installation Guide
==================

This guide presents different installation paths so you can
choose the one that best fits your use case.

.. contents::
   :local:
   :depth: 1

.. note::

    **System Requirements**

    - **Training**: Linux + NVIDIA GPU (CUDA 12.4+ recommended)
    - **Evaluation**: Linux, macOS, or Windows (WSL)
    - **Python**: 3.10 or higher

    See :ref:`faq` for more details on what is exactly supported.


How to choose an installation method?
-------------------------------------

Select the card that best matches how you plan to use ``mjlab``.

.. grid:: 2
   :gutter: 2

   .. grid-item-card:: Method 1 - Use mjlab as a dependency (uv)
      :link: install-uv-dependency
      :link-type: ref

      You are **using mjlab as a dependency** in your own project managed by ``uv``. **(Recommended for most users)**

   .. grid-item-card:: Method 2 - Develop / contribute (uv)
      :link: install-uv-develop
      :link-type: ref

      You are **trying mjlab** or **contributing to mjlab itself** directly from inside the mjlab repository, with ``uv`` managing the environment.

   .. grid-item-card:: Method 3 - Classic pip / venv / conda
      :link: install-pip
      :link-type: ref

      You are using **classic tools** (``pip`` / ``venv`` / ``conda``) and **do not use uv**.

   .. grid-item-card:: Method 4 - Docker / clusters
      :link: install-docker
      :link-type: ref

      You are **running in containers or on clusters** and prefer a **Docker-based** setup.


.. _install-uv-dependency:

Method 1 - Use mjlab as a dependency (uv)
-----------------------------------------

This is our recommended way to use ``mjlab``. You have
your own project and want to use ``mjlab`` as a dependency
using ``uv``.

1. Install uv
^^^^^^^^^^^^^

If you do not have ``uv`` installed, run:

.. code-block:: bash

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Initialize your project
^^^^^^^^^^^^^^^^^^^^^^^^^^

Initialize a managed Python project:

.. code-block:: bash

   # Create a new package-based project
   uv init --package my_mjlab_project
   cd my_mjlab_project

3. Add mjlab dependencies
^^^^^^^^^^^^^^^^^^^^^^^^^

There are different options to add ``mjlab`` as a dependency.
We recommend using the latest stable version from PyPI. If you need
the latest features, use the direct GitHub installation. Finally, if you
need to use a feature you have developed locally, use the local editable
install. These options are interchangeable: you can switch at any time.

.. tab-set::

   .. tab-item:: PyPI

      Once in your project, install the latest snapshot from PyPI:

      .. code:: bash

         uv add mjlab

   .. tab-item:: Source

      Once in your project, install directly from GitHub without cloning:

      .. code:: bash

         uv add "mjlab @ git+https://github.com/mujocolab/mjlab"

   .. tab-item:: Local

      Clone the repository:

      .. code:: bash

         git clone https://github.com/mujocolab/mjlab.git

      Once in your project, add it as an editable dependency:

      .. code:: bash

         uv add --editable /path/to/cloned/mjlab

.. tip::

   For a complete example of how to structure a project that integrates a custom robot
   with an existing ``mjlab`` task, check out the
   `ANYmal C Velocity Tracking <https://github.com/mujocolab/anymal_c_velocity>`_ repository.

Verification
^^^^^^^^^^^^

After installation, verify that ``mjlab`` is working by running the demo:

.. code-block:: bash

   uv run demo


.. _install-uv-develop:

Method 2 - Develop / contribute (uv)
------------------------------------

This method is for developing ``mjlab`` itself or contributing to the project.

.. code:: bash

   git clone https://github.com/mujocolab/mjlab.git && cd mjlab
   uv sync

Verification
^^^^^^^^^^^^

After installation, verify that ``mjlab`` is working by running the demo:

.. code-block:: bash

   uv run demo


.. _install-pip:

Method 3 - Classic pip / venv / conda
-------------------------------------

Activate your virtual environment (``venv``, ``conda``, etc.), then install:

.. code:: bash

   pip install mjlab


Verification
^^^^^^^^^^^^

After installation, verify that ``mjlab`` is working by running the demo:

.. code-block:: bash

   demo


.. _install-docker:

Method 4 - Docker / clusters
----------------------------

Prerequisites:

- Install Docker: `Docker installation guide <https://docs.docker.com/engine/install/>`_.
- Install an appropriate NVIDIA driver for your system and the
  `NVIDIA Container Toolkit <https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html>`_.

  - Be sure to register the container runtime with Docker and restart,
    as described in the Docker configuration section of the NVIDIA
    install guide.

.. tab-set::

   .. tab-item:: Pre-built image (recommended)

      Pull and run the latest image from the GitHub Container Registry:

      .. code-block:: bash

         docker run --rm --runtime=nvidia --gpus all \
           ghcr.io/mujocolab/mjlab uv run demo

      The image is rebuilt on every push to ``main``.

   .. tab-item:: Local build

      Build from source and run:

      .. code-block:: bash

         ./scripts/run_docker.sh uv run demo


Having some troubles?
---------------------

1. **Check the FAQ**

    Consult the mjlab :ref:`faq` for answers to common installation and runtime issues

2. **Still stuck?**

    Open an issue on GitHub: https://github.com/mujocolab/mjlab/issues