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https://github.com/project-monai/monai-deploy-app-sdk
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
https://github.com/project-monai/monai-deploy-app-sdk
ai deep-learning deploy dicom healthcare image-processing machine-learning medical-imaging ml ml-infrastructure ml-platform mlops model-deployment model-serving monai pipeline python pytorch workflow
Last synced: about 21 hours ago
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MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
- Host: GitHub
- URL: https://github.com/project-monai/monai-deploy-app-sdk
- Owner: Project-MONAI
- License: apache-2.0
- Created: 2021-06-19T00:16:45.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-25T01:51:16.000Z (5 months ago)
- Last Synced: 2024-05-15T18:11:12.395Z (4 months ago)
- Topics: ai, deep-learning, deploy, dicom, healthcare, image-processing, machine-learning, medical-imaging, ml, ml-infrastructure, ml-platform, mlops, model-deployment, model-serving, monai, pipeline, python, pytorch, workflow
- Language: Jupyter Notebook
- Homepage:
- Size: 33.6 MB
- Stars: 83
- Watchers: 24
- Forks: 43
- Open Issues: 56
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
💡 If you want to know more about MONAI Deploy WG vision, overall structure, and guidelines, please read [MONAI Deploy](https://github.com/Project-MONAI/monai-deploy) main repo first.
# MONAI Deploy App SDK
[](LICENSE)
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
## Features
- Build medical imaging inference applications using a flexible, extensible & usable Pythonic API
- Easy management of inference applications via programmable Directed Acyclic Graphs (DAGs)
- Built-in operators to load DICOM data to be ingested in an inference app
- Out-of-the-box support for in-proc PyTorch based inference
- Easy incorporation of MONAI based pre and post transformations in the inference application
- Package inference application with a single command into a portable MONAI Application Package
- Locally run and debug your inference application using App Runner
## User Guide
User guide is available at [docs.monai.io](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/).
## Installation
To install [the current release](https://pypi.org/project/monai-deploy-app-sdk/), you can simply run:
```bash
pip install monai-deploy-app-sdk # '--pre' to install a pre-release version.
```
Please also note the following system requirements:
- Ubuntu 22.04 on X86-64 is required, as this is the only X86 platform that the underlying Holoscan SDK has been tested to support as of now.
- [CUDA 12](https://developer.nvidia.com/cuda-12-0-0-download-archive) is required along with a supported NVIDIA GPU with at least 8GB of video RAM. If AI inference is not used in the example application and a GPU is not installed, at least [CUDA 12 runtime](https://pypi.org/project/nvidia-cuda-runtime-cu12/) is required, as this is one of the requirements of Holoscan SDK, in addition, the `LIB_LIBRARY_PATH` must be set to include the installed shared library, e.g. in a Python 3.8 env, ```export LD_LIBRARY_PATH=`pwd`/.venv/lib/python3.8/site-packages/nvidia/cuda_runtime/lib:$LD_LIBRARY_PATH```
## Getting Started
Getting started guide is available at [here](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/index.html).
```bash
pip install monai-deploy-app-sdk # '--pre' to install a pre-release version.
# Clone monai-deploy-app-sdk repository for accessing examples.
git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git
cd monai-deploy-app-sdk
# Install necessary dependencies for simple_imaging_app
pip install matplotlib Pillow scikit-image
# Execute the app locally
python examples/apps/simple_imaging_app/app.py -i examples/apps/simple_imaging_app/brain_mr_input.jpg -o output
# Package app (creating MAP Docker image), using `-l DEBUG` option to see progress.
monai-deploy package examples/apps/simple_imaging_app -c simple_imaging_app/app.yaml -t simple_app:latest --platform x64-workstation -l DEBUG
# Run the app with docker image and an input file locally
## Copy a test input file to 'input' folder
mkdir -p input && rm -rf input/*
cp examples/apps/simple_imaging_app/brain_mr_input.jpg input/
## Launch the app
monai-deploy run simple_app-x64-workstation-dgpu-linux-amd64:latest -i input -o output
```
### [Tutorials](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/index.html)
Tutorials are provided to help getting started with the App SDK, to name but a few below.
#### [1) Creating a simple image processing app](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/simple_app.html)
#### [2) Creating MedNIST Classifier app](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/mednist_app.html)
YouTube Video (to be updated with the new version):
- [MedNIST Classification Example](https://www.youtube.com/watch?v=WwjilJFHuU4)
### [3) Creating a Segmentation app](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/segmentation_app.html)
YouTube Video (to be updated with the new version):
- [Spleen Organ Segmentation - Jupyter Notebook Tutorial](https://www.youtube.com/watch?v=cqDVxzYt9lY)
- [Spleen Organ Segmentation - Deep Dive](https://www.youtube.com/watch?v=nivgfD4pwWE)
### [4) Creating a Segmentation app including visualization with Clara Viz](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/segmentation_clara-viz_app.html)
### [5) Creating a Segmentation app consuming a MONAI Bundle](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/monai_bundle_app.html)
### [Examples](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/examples.html)
has example apps that you can see.
- ai_livertumor_seg_app
- ai_spleen_seg_app
- ai_unetr_seg_app
- dicom_series_to_image_app
- mednist_classifier_monaideploy
- simple_imaging_app
## Contributing
For guidance on making a contribution to MONAI Deploy App SDK, see the [contributing guidelines](https://github.com/Project-MONAI/monai-deploy/blob/main/CONTRIBUTING.md).
## Community
To participate, please join the MONAI Deploy App SDK weekly meetings on the [calendar](https://calendar.google.com/calendar/u/0/[email protected]&ctz=America/New_York) and review the [meeting notes](https://docs.google.com/document/d/1viIh3vyP6_gZBKcnu7gb8fU0tm9aWBOcKCMGezIWNQw/edit#).
Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).
Ask and answer questions over on [MONAI Deploy App SDK's GitHub Discussions tab](https://github.com/Project-MONAI/monai-deploy-app-sdk/discussions).
## Links
- Website:
- API documentation:
- Code:
- Project tracker:
- Issue tracker:
- Wiki:
- Test status:
- PyPI package: