---
metadata:
  - name: generator
    content: Diplodoc Platform v5.50.3
alternate:
  - https://catboost.ai/docs/en/installation/python-installation-method-pip-install.md
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# pip install

<!-- source: en/_includes/work_src/reusage-installation/python__supported-versions.md -->
{% note info %}

CatBoost Python package supports only [CPython Python implementation](https://en.wikipedia.org/wiki/CPython).

{% endnote %}
<!-- endsource: en/_includes/work_src/reusage-installation/python__supported-versions.md -->

To install CatBoost from PyPI with pip:

1. Run the following command:

    ```no-highlight
    pip install catboost
    ```

    {% note info %}

    [PyPI](https://pypi.org/) contains precompiled wheels for most commonly used platform configurations:

    |Operating system|CPU architectures|GPU support using [CUDA](https://developer.nvidia.com/cuda-zone)|
    |--------|-----------------|------------|
    | macOS (versions currently supported by Apple) | x86_64 and arm64 |no|
    | Linux (compatible with [manylinux2014 platform tag](https://peps.python.org/pep-0599/) ) | x86_64 and aarch64 |yes|
    | Windows (versions 10, 11, Server 2022, Server 2025) | x86_64 |yes|

    If the platform where the installation is performed is incompatible with platform tags of the available precompiled wheels then `pip` will try to build CatBoost python package from source. This approach requires certain [build dependencies and requirements](https://catboost.ai/docs/en/installation/python-installation-method-build-from-source.md#dependencies-and-requirements) to be set up before the installation.

    {% endnote %}

    {% note info %}

    Release native binaries for x86_64 CPU architectures are built with SIMD extensions SSE2, SSE3, SSSE3, SSE4 enabled. If you need to run CatBoost on older CPUs that do not support these instruction sets [build CatBoost package from source](https://catboost.ai/docs/en/installation/python-installation-method-build-from-source.md)

    {% endnote %}

1. Install visualization tools:
    1. <!-- source: en/_includes/work_src/reusage-installation/install-apywidgets-short.md -->
       Install the `ipywidgets` Python package (version 7.x or higher is required):
       <!-- endsource: en/_includes/work_src/reusage-installation/install-apywidgets-short.md -->

    ```no-highlight
    pip install ipywidgets
    ```

    1. <!-- source: en/_includes/work_src/reusage-installation/turn-on-the-widgets-extension-intro.md -->
       Turn on the widgets extension:
       <!-- endsource: en/_includes/work_src/reusage-installation/turn-on-the-widgets-extension-intro.md -->

    ```no-highlight
    jupyter nbextension enable --py widgetsnbextension
    ```

    Refer to the following sections for details:
    - [Data visualization](https://catboost.ai/docs/en/features/visualization.md)
    - [Additional packages for data visualization support](https://catboost.ai/docs/en/installation/python-installation-additional-data-visualization-packages.md)

1. User-defined functions:

    <!-- source: en/_includes/work_src/reusage-installation/python__user-defined-functions-dependencies.md -->
    If you want to use [custom metrics or objectives implemented in your own python code](https://catboost.ai/docs/en/features/custom-loss-functions.md) you should install [`numba`](https://numba.pydata.org/) package to speed up the code execution using JIT compilation.

    If you want to use custom metrics or objectives on GPUs with CUDA support you must install [`numba`](https://numba.pydata.org/) package for JIT compilation of CUDA code.
    Installation of [`numba-cuda`](https://github.com/NVIDIA/numba-cuda) package is also encouraged.
    CUDA itself (not only drivers) must be installed on machines where this code is executed.
    See [`numba` CUDA support documentation](https://numba.readthedocs.io/en/stable/cuda/overview.html) for more details.

    These packages are not listed in package requirements that are installed automatically because they are not needed for other functionality.
    <!-- endsource: en/_includes/work_src/reusage-installation/python__user-defined-functions-dependencies.md -->