Installation ============ DeepPavlov supports **Linux**, **Windows 10+** (through WSL/WSL2), **MacOS** (Big Sur+) platforms, **Python 3.6-3.11**. Depending on the model used, you may need from 4 to 16 GB RAM. Install with pip ~~~~~~~~~~~~~~~~ You should install DeepPavlov in a `virtual environment `_. If you’re unfamiliar with Python virtual environments, take a look at this `guide `_. A virtual environment makes it easier to manage different projects, and avoid compatibility issues between dependencies. #. Create a virtual environment: .. code:: bash python -m venv env #. Activate the virtual environment on Linux (`source` could be replaced with `.`): .. code:: bash source env/bin/activate #. Install DeepPavlov inside this virtual environment: .. code:: bash pip install deeppavlov Install from source ~~~~~~~~~~~~~~~~~~~ Install DeepPavlov **dev** branch from source with the following command: .. code:: bash pip install git+http://github.com/deeppavlov/DeepPavlov@dev This command installs the bleeding edge dev version rather than the latest release version. The dev version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last release but a new release hasn’t been rolled out yet. However, this means the dev version may not always be stable. Editable install ~~~~~~~~~~~~~~~~ You will need an editable install if you want to make changes in the DeepPavlov source code that immediately take place without requiring a new installation. Clone the repository and install DeepPavlov with the following commands: .. code:: bash git clone http://github.com/deeppavlov/DeepPavlov.git pip install -e DeepPavlov Docker Images ~~~~~~~~~~~~~ We have built several DeepPavlov based Docker images, which include: * DeepPavlov based Jupyter notebook Docker image; * Docker images which serve some of our models and allow to access them via REST API (:doc:`riseapi ` mode). Here is our `DockerHub repository `_ with images and deployment instructions.