Installation Guide#

Table of Contents#

MONAI’s core functionality is written in Python 3 (>= 3.8) and only requires Numpy and Pytorch.

The package is currently distributed via Github as the primary source code repository, and the Python package index (PyPI). The pre-built Docker images are made available on DockerHub.

To install optional features such as handling the NIfTI files using Nibabel, or building workflows using Pytorch Ignite, please follow the instructions:

The installation commands below usually end up installing CPU variant of PyTorch. To install GPU-enabled PyTorch:

  1. Install the latest NVIDIA driver.

  2. Check PyTorch Official Guide for the recommended CUDA versions. For Pip package, the user needs to download the CUDA manually, install it on the system, and ensure CUDA_PATH is set properly.

  3. Continue to follow the guide and install PyTorch.

  4. Install MONAI using one the ways described below.

From PyPI#

Milestone release#

To install the current milestone release:

pip install monai

Weekly preview release#

To install the weekly preview release:

pip install monai-weekly

The weekly build is released to PyPI every Sunday with a pre-release build number dev[%y%U]. To report any issues on the weekly preview, please include the version and commit information:

python -c "import monai; print(monai.__version__); print(monai.__commit_id__)"

Coexistence of package monai and monai-weekly in a system may cause namespace conflicts and ImportError. This is usually a result of running both pip install monai and pip install monai-weekly without uninstalling the existing one first. To address this issue, please uninstall both packages, and retry the installation.

Uninstall the packages#

The packages installed using pip install could be removed by:

pip uninstall -y monai
pip uninstall -y monai-weekly

From conda-forge#

To install the current milestone release:

conda install -c conda-forge monai

From GitHub#

(If you have installed the PyPI release version using pip install monai, please run pip uninstall monai before using the commands from this section. Because pip by default prefers the milestone release.)

The milestone versions are currently planned and released every few months. As the codebase is under active development, you may want to install MONAI from GitHub for the latest features:

Option 1 (as a part of your system-wide module):#

pip install git+

or, to build with MONAI C++/CUDA extensions:

BUILD_MONAI=1 pip install git+

To build the extensions, if the system environment already has a version of Pytorch installed, --no-build-isolation might be preferred:

BUILD_MONAI=1 pip install --no-build-isolation git+

this command will download and install the current dev branch of MONAI from GitHub.

This documentation website by default shows the information for the latest version.

Option 2 (editable installation):#

To install an editable version of MONAI, it is recommended to clone the codebase directly:

git clone

This command will create a MONAI/ folder in your current directory. You can install it by running:

python develop

or, to build with MONAI C++/CUDA extensions and install:

BUILD_MONAI=1 python develop
# for MacOS
BUILD_MONAI=1 CC=clang CXX=clang++ python develop

To uninstall the package please run:

python develop --uninstall

# to further clean up the MONAI/ folder (Bash script)
./ --clean

Alternatively, simply adding the root directory of the cloned source code (e.g., /workspace/Documents/MONAI) to your $PYTHONPATH and the codebase is ready to use (without the additional features of MONAI C++/CUDA extensions).

The C++/CUDA extension features are currently experimental, a pre-compiled version is made available via the recent docker image releases. Building the extensions from source may require Ninja and CUDA Toolkit. By default, CUDA extension is built if torch.cuda.is_available(). It’s possible to force building by setting FORCE_CUDA=1 environment variable.

Validating the install#

You can verify the installation by:

python -c "import monai; monai.config.print_config()"

If the installation is successful, this command will print out the MONAI version information, and this confirms the core modules of MONAI are ready-to-use.

MONAI version string#

The MONAI version string shows the current status of your local installation. For example:

MONAI version: 0.1.0+144.g52c763d.dirty
  • 0.1.0 indicates that your installation is based on the 0.1.0 milestone release.

  • +144 indicates that your installation is 144 git commits ahead of the milestone release.

  • g52c763d indicates that your installation corresponds to the git commit hash 52c763d.

  • dirty indicates that you have modified the codebase locally, and the codebase is inconsistent with 52c763d.

From DockerHub#

Make sure you have installed the NVIDIA driver and Docker 19.03+ for your Linux distribution. Note that you do not need to install the CUDA toolkit on the host, but the driver needs to be installed. Please find out more information on nvidia-docker.

Assuming that you have the Nvidia driver and Docker 19.03+ installed, running the following command will download and start a container with the latest version of MONAI. The latest dev branch of MONAI from GitHub is included in the image.

docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest

You can also run a milestone release docker image by specifying the image tag, for example:

docker run --gpus all --rm -ti --ipc=host projectmonai/monai:0.1.0