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.

Setup

# 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 https://github.com/jupyterlab/jupyterlab/pull/11740)
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
jupyter-lab

Executing from Jupyter Notebook

• Creating a Deploy App for Multi-Ai with Multiple Models
  • Important Steps
  • Required Model File Organization
  • Example Model File Organization
  • Creating Operators and connecting them in Application class
    • Setup environment
    • Download/Extract input and model/bundle files from Google Drive
    • Setup imports
    • Determining the Model Name and IOfor the Model Bundle Inference Operator
    • Creating Application class
  • Executing app locally
    • app.py
    • __main__.py
  • Packaging app
  • Executing packaged app locally

Download 07_multi_model_app.ipynb

Executing from Shell

# Clone the github project (the latest version of main branch only)
git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-app-sdk.git

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
gdown https://drive.google.com/uc?id=1llJ4NGNTjY187RLX4MtlmHYhfGxBNWmd

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

# 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/app.py -i dcm/ -o output -m multip_models
# or alternatively,
monai-deploy exec ../examples/apps/examples/apps/ai_multi_ai_app/app.py -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 https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker to install nvidia-docker2.
monai-deploy package -b nvcr.io/nvidia/pytorch:22.08-py3 examples/apps/ai_multi_ai_app --tag multi_model_app:latest --model multi_models -l DEBUG

# For AMD GPUs, nvidia-docker is not required. Use --base [base image] option to override the docker base image.
# Please see https://hub.docker.com/r/rocm/pytorch for rocm/pytorch docker images.
monai-deploy package -b rocm/pytorch:rocm5.4.1_ubuntu20.04_py3.7_pytorch_1.12.1 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

Creating a Segmentation App Including Visualization with MONAI Deploy App SDK Creating a Deploy App for Multi-Ai with Multiple Models