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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 bellow 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+https://github.com/Project-MONAI/MONAI#egg=monai

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

BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=monai

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+https://github.com/Project-MONAI/MONAI#egg=monai

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 https://github.com/Project-MONAI/MONAI.git

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

cd MONAI/
python setup.py develop

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

cd MONAI/
BUILD_MONAI=1 python setup.py develop
# for MacOS
BUILD_MONAI=1 CC=clang CXX=clang++ python setup.py develop

To uninstall the package please run:

cd MONAI/
python setup.py develop --uninstall

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

Installing the recommended dependencies

By default, the installation steps will only download and install the minimal requirements of MONAI. Optional dependencies can be installed using the extras syntax to support additional features.

For example, to install MONAI with Nibabel and Scikit-image support:

git clone https://github.com/Project-MONAI/MONAI.git
cd MONAI/
pip install -e '.[nibabel,skimage]'

Alternatively, to install all optional dependencies:

git clone https://github.com/Project-MONAI/MONAI.git
cd MONAI/
pip install -e '.[all]'

To install all optional dependencies with pip based on MONAI development environment settings:

git clone https://github.com/Project-MONAI/MONAI.git
cd MONAI/
pip install -r requirements-dev.txt

To install all optional dependencies with conda based on MONAI development environment settings (environment-dev.yml; this will install PyTorch as well as pytorch-cuda, please follow https://pytorch.org/get-started/locally/#start-locally for more details about installing PyTorch):

git clone https://github.com/Project-MONAI/MONAI.git
cd MONAI/
conda create -n <name> python=<ver>  # eg 3.9
conda env update -n <name> -f environment-dev.yml

Since MONAI v0.2.0, the extras syntax such as pip install 'monai[nibabel]' is available via PyPI.

  • The options are
[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime]

which correspond to nibabel, scikit-image, pillow, tensorboard, gdown, pytorch-ignite, torchvision, itk, tqdm, lmdb, psutil, cucim, openslide-python, pandas, einops, transformers, mlflow, clearml, matplotlib, tensorboardX, tifffile, imagecodecs, pyyaml, fire, jsonschema, ninja, pynrrd, pydicom, h5py, nni, optuna, onnx, onnxruntime, respectively.

  • pip install 'monai[all]' installs all the optional dependencies.