Skip to main content

A framework and associated tools to design, verify and analyze performance of MONAI apps

Project description

project-monai

💡 If you want to know more about MONAI Deploy WG vision, overall structure, and guidelines, please read https://github.com/Project-MONAI/monai-deploy first.

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.

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

Installation

To install the current release, you can simply run:

pip install monai-deploy-app-sdk  # '--pre' to install a pre-release version.

Getting Started

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 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 -t simple_app:latest -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:latest input output

Examples and notebook tutorials are located at Project-MONAI/monai-deploy-app-sdk.

User guide is available at docs.monai.io.

Contributing

For guidance on making a contribution to MONAI Deploy App SDK, see the contributing guidelines.

Community

To participate in the MONAI Deploy WG, please review https://github.com/Project-MONAI/MONAI/wiki/Deploy-Working-Group.

Join the conversation on Twitter @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI Deploy App SDK's GitHub Discussions tab.

Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

monai_deploy_app_sdk-0.1.0-py3-none-any.whl (114.0 kB view details)

Uploaded Python 3

File details

Details for the file monai_deploy_app_sdk-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: monai_deploy_app_sdk-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 114.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.6.13

File hashes

Hashes for monai_deploy_app_sdk-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 dcabeea94839876b819d0e6219f789fc5defbf243f0f2f650d2d25de09484b1d
MD5 3708787cca8c60a16215e7e6a24717c0
BLAKE2b-256 df082652fb20ce1b1eb7e1c32bc97d3cbd5f39d514b1e71593bf12769eeccc46

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page