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:
Install the latest NVIDIA driver.
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.
Continue to follow the guide and install PyTorch.
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 settingFORCE_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 the0.1.0
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 hash52c763d
.dirty
indicates that you have modified the codebase locally, and the codebase is inconsistent with52c763d
.
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, scipy, 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, zarr, lpips, pynvml, huggingface_hub]
which correspond to nibabel
, scikit-image
,scipy
, 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
, zarr
, lpips
, nvidia-ml-py
, and huggingface_hub
respectively.
pip install 'monai[all]'
installs all the optional dependencies.