Installation¶
Prerequisites¶
MONAI Label supports following OS with GPU/CUDA enabled.
Windows¶
Make sure you have python 3.x version environment with PyTorch and CUDA installed.
Install Python
Install the following Python libraries
python -m pip install --upgrade pip setuptools wheel
# Install latest stable version for pytorch
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
# Check if cuda enabled
python -c "import torch; print(torch.cuda.is_available())"
Install From PyPI¶
Milestone release¶
To install the current milestone release:
pip install monailabel
Weekly preview release¶
To install the weekly preview release:
pip install monailabel-weekly
The weekly build is released to PyPI every Sunday with a pre-release build number dev[%y%U].
From GitHub¶
To install latest from github main branch
pip install git+https://github.com/Project-MONAI/MONAILabel#egg=monailabel
Note
If you have installed the
PyPI release version using pip install monailabel
, please run pip uninstall
monailabel
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
From DockerHub¶
To install latest from DockerHub:
docker run -it --rm --gpus all --ipc=host --net=host -v ~:/workspace/ projectmonai/monailabel:latest bash
MONAI Label CLI¶
Simple monailabel command will help user to download sample apps, datasets and run server.
monailabel --help
Downloading Sample Apps or Datasets¶
You can download sample apps and datasets from monailabel CLI.
# Download Sample Apps
monailabel apps # List sample apps
monailabel apps --download --name deepedit --output apps
# Download MSD Datasets
monailabel datasets # List sample datasets
monailabel datasets --download --name Task09_Spleen --output datasets
Starting Server¶
You can start server using monailabel CLI
monailabel start_server --app apps\deepedit --studies datasets\Task09_Spleen\imagesTr
Note
Once you start the MONAI Label Server, by default it will be up and serving at http://127.0.0.1:8000/. Open the serving URL in browser. It will provide you the list of Rest APIs available.
Deployment¶
MONAI Label Server uses Uvicorn which is a lightning-fast ASGI server implementation. However user can deploy the application in any server that supports ASGI specification
There are multiple choices available for Uvicorn to run as Development Server vs Standalone Server vs Production.
Deploying MONAI Label server for production use is out of project scope. However for basic production deployment, you might need to run Uvicorn independently. In such cases, you can following these simple steps.
# dryrun the MONAI Label CLI for pre-init and dump the env variables to .env or env.bat
monailabel start_server --app apps\deepedit --studies datasets\Task09_Spleen\imagesTr --host 0.0.0.0 --port 8000 --dryrun
# Linux/Ubuntu
source .env
uvicorn monailabel.app:app \
--host 0.0.0.0 \
--port 8000 \
--log-config apps\deepedit\logs\logging.json \
--no-access-log
# Windows
call env.bat
uvicorn monailabel.app:app ^
--host 0.0.0.0 ^
--port 8000 ^
--log-config apps\deepedit\logs\logging.json ^
--no-access-log
For more options about Uvicorn (concurrency, SSL etc..) refer: https://www.uvicorn.org/#command-line-options
3D Slicer Plugin¶
Download Preview Release from https://download.slicer.org/ and install MONAI Label plugin from Slicer Extension Manager.
Refer 3D Slicer plugin for other options to install and run MONAI Label plugin in 3D Slicer.
Note
To avoid accidentally using an older Slicer version, you may want to uninstall any previously installed 3D Slicer package.
OHIF Plugin¶
MONAI Label comes with pre-built plugin for OHIF Viewer. To use OHIF Viewer, you need to provide DICOMWeb instead of FileSystem as studies when you start the server.
monailabel start_server --app apps\deepedit --studies http://127.0.0.1:8042/dicom-web
Note
Please install Orthanc before using OHIF Viewer.
For Ubuntu 20.x, Orthanc can be installed as apt-get install orthanc orthanc-dicomweb. However, you have to upgrade to latest version by following steps mentioned here
You can use PlastiMatch to convert NIFTI to DICOM
OHIF Viewer will be accessible at http://127.0.0.1:8000/ohif/