Creating a Segmentation App Consuming a MONAI Bundle

This tutorial shows how to create an inference application with multiple models, focusing on model files organization, inferring with named model in the application, and packaging.

The models used in this example are trained with MONAI, and are packaged in the MONAI Bundle format.


# Create a virtual environment with Python 3.8.
# Skip if you are already in a virtual environment.
# (JupyterLab dropped its support for Python 3.6 since 2021-12-23.
#  See
conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
conda activate monai

# Launch JupyterLab if you want to work on Jupyter Notebook

Executing from Shell

# Clone the github project (the latest version of main branch only)
git clone --branch main --depth 1

cd monai-deploy-app-sdk

# Install monai-deploy-app-sdk package
pip install --upgrade monai-deploy-app-sdk

# Download the zip file containing both the model and test data
pip install gdown

# After downloading it using gdown, unzip the zip file saved by gdown
unzip -o

# Install necessary packages from the app; note that numpy-stl and trimesh are only
# needed if the application uses the STL Conversion Operator
pip install monai torch pydicom highdicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh

# Local execution of the app directly or using MONAI Deploy CLI
python examples/apps/examples/apps/ai_multi_ai_app/ -i dcm/ -o output -m multip_models
# or alternatively,
monai-deploy exec ../examples/apps/examples/apps/ai_multi_ai_app/ -i dcm/ -o output -m multip_models

# Package app (creating MAP docker image) using `-l DEBUG` option to see progress.
# This assumes that nvidia docker is installed in the local machine.
# Please see to install nvidia-docker2.
monai-deploy package -b examples/apps/ai_multi_ai_app --tag multi_model_app:latest --model multi_models -l DEBUG

# Run the app with docker image and input file locally
monai-deploy run multi_model_app:latest dcm/ output