Prepared changes for releasing v3

.github/workflows/pr.yml

@@ -14,10 +14,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
-      - name: Set up Python 3.8
+      - name: Set up Python 3.9
         uses: actions/setup-python@v2
         with:
-          python-version: "3.8"
+          python-version: "3.9"
       - name: Setup Dev Environment
         run: |
           pip install virtualenv

.readthedocs.yml

@@ -8,7 +8,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.8"
+    python: "3.9"
     # You can also specify other tool versions:
     # nodejs: "20"
     # rust: "1.70"

CONTRIBUTING.md

@@ -138,7 +138,7 @@ MONAI Deploy App SDK's code coverage report is available at [CodeCov](https://co
 #### Building the documentation
 
 :::{note}
-Please note that the documentation builds successfully in Python 3.8 environment, but fails with Python 3.10.
+Please note that the documentation builds successfully in Python 3.9 environment, but fails with Python 3.10.
 :::
 
 MONAI's documentation is located at `docs/`.

README.md

@@ -15,7 +15,7 @@ MONAI Deploy App SDK offers a framework and associated tools to design, develop
 - 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
+- Out-of-the-box support for in-proc PyTorch based inference, as well as remote inference via Triton Inference Server
 - 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
@@ -33,7 +33,7 @@ If you have used MONAI in your research, please cite us! The citation can be exp
 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.
+pip install monai-deploy-app-sdk
 ```
 
 ### Prerequisites
@@ -48,7 +48,7 @@ pip install monai-deploy-app-sdk  # '--pre' to install a pre-release version.
 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.
+pip install monai-deploy-app-sdk
 
 # Clone monai-deploy-app-sdk repository for accessing examples.
 git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git
@@ -62,7 +62,7 @@ python examples/apps/simple_imaging_app/app.py -i examples/apps/simple_imaging_a
 
 # Package app (creating MAP Docker image), using `-l DEBUG` option to see progress.
 # Also please note that postfix will be added to user supplied tag for identifying CPU architecture and GPU type etc.
-monai-deploy package examples/apps/simple_imaging_app -c examples/apps/simple_imaging_app/app.yaml -t simple_app:latest --platform x64-workstation -l DEBUG
+monai-deploy package examples/apps/simple_imaging_app -c examples/apps/simple_imaging_app/app.yaml -t simple_app:latest --platform x86_64 -l DEBUG
 
 # Run the app with docker image and an input file locally
 ## Copy a test input file to 'input' folder
@@ -86,7 +86,7 @@ YouTube Video (to be updated with the new version):
 
 ### [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):
+YouTube Video (demonstrating the previous version of the App SDK):
 
 - [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)
@@ -97,14 +97,16 @@ YouTube Video (to be updated with the new version):
 
 ### [Examples](https://docs.monai.io/projects/monai-deploy-app-sdk/en/stable/getting_started/examples.html)
 
-<https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps> has example apps that you can see.
+<https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps> has example apps that you can see, to name but a few
 
+- simple_imaging_app
 - ai_livertumor_seg_app
 - ai_spleen_seg_app
 - ai_unetr_seg_app
 - dicom_series_to_image_app
 - mednist_classifier_monaideploy
-- simple_imaging_app
+- ai_remote_infer_app
+
 
 ## Contributing
 

docs/requirements.txt

@@ -1,6 +1,6 @@
 Sphinx==4.1.2
 sphinx-autobuild==2021.3.14
-myst-nb==0.17.2  # this version is fine in python 3.8 and avoids pulling in multiple nbformat packages
+myst-nb==0.17.2  # this version is fine in python 3.9 and avoids pulling in multiple nbformat packages
 myst-parser==0.18.0
 lxml_html_clean  # needed by myst-nb
 linkify-it-py==1.0.1  # https://myst-parser.readthedocs.io/en/latest/syntax/optional.html?highlight=linkify#linkify

docs/source/developing_with_sdk/packaging_app.md

@@ -13,15 +13,15 @@ It is required that the application configuration yaml file as well as the depen
 ### Basic Usage of MONAI Application Packager
 
 ```bash
-monai-deploy package <APP_PATH> --config <CONFIG> --tag <TAG> --platform <x64-workstation> [--models <MODEL_PATH>] [--log-level <LEVEL>] [-h]
+monai-deploy package <APP_PATH> --config <CONFIG> --tag <TAG> --platform <x86_64> [--models <MODEL_PATH>] [--log-level <LEVEL>] [-h]
 ```
 
 #### Required Arguments
 
 * `<APP_PATH>`: A path to MONAI Deploy Application folder or main code.
 * `--config, -c <CONFIG>`: Path to the application configuration file.
 * `--tag, -t <TAG>`: A MAP name and optionally a tag in the 'name:tag' format.
-* `--platform <PLATFORM>`: Platform type of the container image, must be `x64-workstation` for x86-64 system.
+* `--platform <PLATFORM>`: Platform type of the container image, must be `x86_64` for x86-64 system.
 
 :::{note}
 If `<APP_PATH>` refers to a python code (such as `./my_app.py`), the whole parent folder of the file would be packaged into the MAP container image, effectively the same as specifying the application folder path which includes `__main__.py` file. In both cases, the image's environment variable, `HOLOSCAN_APPLICATION` will be set with the path of the application folder in the image, i.e. `HOLOSCAN_APPLICATION=/opt/holoscan/app`. So, it is essential to provide the `__main__.py` file in the application folder, which usually would look like below:
@@ -54,7 +54,7 @@ The following lists a few most likely used [optional arguments](https://docs.nvi
 Given an example MONAI Deploy App SDK application with its code residing in a directory `./my_app`, packaging this application with the Packager to create a Docker image tagged `my_app:latest` would look like this:
 
 ```bash
-$ monai-deploy package ./my_app -c --config ./my_app/app.yaml -t my_app:latest --models ./model.ts --platform x64-workstation
+$ monai-deploy package ./my_app -c --config ./my_app/app.yaml -t my_app:latest --models ./model.ts --platform x86_64
 
 Building MONAI Application Package...
 Successfully built my_app:latest
@@ -65,7 +65,7 @@ The MAP image name will be postfixed with the platform info to become `my_app-x6
 :::{note}
 * The current implementation of the Packager **ONLY** supports a set of [platform](https://docs.nvidia.com/holoscan/sdk-user-guide/cli/package.html#platform-platform) specific base images from `nvcr.io` as base images for the MAP.
 
-* To package a MAP to run on ARMv8 AArch64 on Linux with discrete GPU, replace the commandline option `--platform x64-workstation` with `--platform igx-orin-devkit --platform-config dgpu`. It has been tested on [NVIDIA IGX Orin](https://www.nvidia.com/en-us/edge-computing/products/igx/).
+* To package a MAP to run on ARMv8 AArch64 on Linux with discrete GPU, replace the commandline option `--platform x86_64` with `--platform igx-dgpu`. It has been tested on [NVIDIA IGX Orin](https://www.nvidia.com/en-us/edge-computing/products/igx/).
 :::
 
 ## Next Step

docs/source/getting_started/tutorials/mednist_app.md

@@ -5,9 +5,9 @@ This tutorial demos the process of packaging up a trained model using MONAI Depl
 ## Setup
 
 ```bash
-# Create a virtual environment with Python 3.8.
+# Create a virtual environment with Python 3.9.
 # Skip if you are already in a virtual environment.
-conda create -n mednist python=3.8 pytorch jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
+conda create -n mednist python=3.9 pytorch jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
 conda activate mednist
 
 # Launch JupyterLab if you want to work on Jupyter Notebook
@@ -98,7 +98,7 @@ monai-deploy package examples/apps/mednist_classifier_monaideploy/mednist_classi
     --config examples/apps/mednist_classifier_monaideploy/app.yaml \
     --tag mednist_app:latest \
     --models mednist_model/classifier.zip \
-    --platform x64-workstation \
+    --platform x86_64 \
     -l DEBUG
 
 # Note: for AMD GPUs, nvidia-docker is not required, but the dependency of the App SDK, namely Holoscan SDK

docs/source/getting_started/tutorials/monai_bundle_app.md

@@ -5,9 +5,9 @@ This tutorial shows how to create an organ segmentation application for a PyTorc
 ## Setup
 
 ```bash
-# Create a virtual environment with Python 3.8.
+# Create a virtual environment with Python 3.9.
 # Skip if you are already in a virtual environment.
-conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
+conda create -n monai python=3.9 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook
@@ -38,7 +38,7 @@ jupyter-lab
 
 ```{raw} html
 <p style="text-align: center;">
-    <a class="sphinx-bs btn text-wrap btn-outline-primary col-md-6 reference external" href="../../_static/notebooks/tutorials/05_monai_bundle_app.ipynb">
+    <a class="sphinx-bs btn text-wrap btn-outline-primary col-md-6 reference external" href="../../_static/notebooks/tutorials/04_monai_bundle_app.ipynb">
         <span>Download 04_monai_bundle_app.ipynb</span>
     </a>
 </p>
@@ -86,7 +86,7 @@ monai-deploy package examples/apps/ai_spleen_seg_app \
     --config examples/apps/ai_spleen_seg_app/app.yaml \
     --tag seg_app:latest \
     --models spleen_model/model.ts \
-    --platform x64-workstation \
+    --platform x86_64 \
     -l DEBUG
 
 # Note: for AMD GPUs, nvidia-docker is not required, but the dependency of the App SDK, namely Holoscan SDK

docs/source/getting_started/tutorials/multi_model_app.md

@@ -7,9 +7,9 @@ The models used in this example are trained with MONAI, and are packaged in the
 ## Setup
 
 ```bash
-# Create a virtual environment with Python 3.8.
+# Create a virtual environment with Python 3.9.
 # Skip if you are already in a virtual environment.
-conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
+conda create -n monai python=3.9 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook
@@ -70,7 +70,7 @@ monai-deploy package examples/apps/ai_multi_ai_app \
     --tag multi_model_app:latest \
     --config examples/apps/ai_multi_ai_app/app.yaml \
     --models multi_models \
-    --platform x64-workstation \
+    --platform x86_64 \
     -l DEBUG
 
 # Note: for AMD GPUs, nvidia-docker is not required, but the dependency of the App SDK, namely Holoscan SDK

docs/source/getting_started/tutorials/segmentation_app.md

@@ -7,9 +7,9 @@ Please note that the following steps are for demonstration purpose. The code pul
 ## Setup
 
 ```bash
-# Create a virtual environment with Python 3.8.
+# Create a virtual environment with Python 3.9.
 # Skip if you are already in a virtual environment.
-conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
+conda create -n monai python=3.9 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook
@@ -72,7 +72,7 @@ monai-deploy package examples/apps/ai_spleen_seg_app \
     --config examples/apps/ai_spleen_seg_app/app.yaml \
     --tag seg_app:latest \
     --models spleen_model/model.ts \
-    --platform x64-workstation \
+    --platform x86_64 \
     -l DEBUG
 
 # Note: for AMD GPUs, nvidia-docker is not required, but the dependency of the App SDK, namely Holoscan SDK

docs/source/getting_started/tutorials/segmentation_clara-viz_app.md

@@ -5,9 +5,9 @@ This tutorial shows how to create an organ segmentation application for a PyTorc
 ## Setup
 
 ```bash
-# Create a virtual environment with Python 3.8.
+# Create a virtual environment with Python 3.9.
 # Skip if you are already in a virtual environment.
-conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
+conda create -n monai python=3.9 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook

docs/source/getting_started/tutorials/simple_app.md

@@ -5,9 +5,9 @@ This tutorial shows how a simple image processing application can be created wit
 ## Setup
 
 ```bash
-# Create a virtual environment with Python 3.8.
+# Create a virtual environment with Python 3.9.
 # Skip if you are already in a virtual environment.
-conda create -n monai python=3.8 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
+conda create -n monai python=3.9 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
 conda activate monai
 
 # Launch JupyterLab if you want to work on Jupyter Notebook
@@ -69,7 +69,7 @@ ls output
 # 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 examples/apps/simple_imaging_app -c examples/apps/simple_imaging_app/app.yaml -t simple_app:latest --platform x64-workstation -l DEBUG
+monai-deploy package examples/apps/simple_imaging_app -c examples/apps/simple_imaging_app/app.yaml -t simple_app:latest --platform x86_64 -l DEBUG
 
 # Show the application and package manifest files of the MONAI Application Package
 

docs/source/release_notes/index.md

@@ -4,6 +4,13 @@
 :hidden:
 :maxdepth: 2
 
+```
+## Version 3.0
+
+```{toctree}
+:maxdepth: 1
+
+v3.0.0
 ```
 ## Version 2.0
 

docs/source/release_notes/v3.0.0.md

@@ -0,0 +1,29 @@
+# Version 2.0.0 (April 24th, 2024)
+
+## What's new in 3.0.0
+
+- This version of the App SDK is based on the newly released [Holoscan SDK v3](https://pypi.org/project/holoscan/), and is expected to be so with future minor releases of Holoscan SDK v3.
+
+- Starting with version 3.0.0, [Holoscan SDK](https://pypi.org/project/holoscan/) and [Holoscan CLI](https://pypi.org/project/holoscan-cli/) are released in separate packages, and as such, this version of the MONAI Deploy App SDK has both as dependencies. As of now, version 3 of both packages are compatible.
+
+- Remote inference on [Triton Inference Server](https://github.com/triton-inference-server) is now supported. Effort was made to make it user-friendly so existing example applications can be easily converted to use this feature by simply providing the network location of the server as well as the [Triton model configuration file](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md) sans the actual model files. [An example application](https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps/ai_remote_infer_app) has been provided to demonstrate such use case.
+
+### Key changes
+
+- [Cincinnati Children's Hospital Medical Cente](https://www.cincinnatichildrens.org/) researchers, @[Bryan](https://github.com/bluna301) @[Will](https://github.com/WillButAgain) and Elan, contributed numerous enhancements from experience developing and deploying MONAI based AI applications in clinical environments, to name but a few
+
+  - Enhanced the DICOM data loader to handle multi-phase DICOM series where multiple acquisitions exist and some DICOM SOP instances have the same image pospositiontion patient.
+
+  - Enahnced the DICOM series to volume operator to better handle the data types of the converted volume image for improved efficiency and memory usage.
+
+  - Enhanced the DICOM Segmentation operator to populate DICOM tags with AI model information which are consistent with other DICOM writers in the SDK.
+
+
+Please also see the <a href="https://github.com/Project-MONAI/monai-deploy-app-sdk/issues?q=is%3Aissue+is%3Aclosed">closed issues on Github</a> and the <a href="https://github.com/Project-MONAI/monai-deploy-app-sdk/pulls?q=is%3Apr+is%3Aclosed">closed pull requests on Github</a>.
+
+## Additional information
+Please visit [GETTING STARTED](/getting_started/index) guide and follow the tutorials.
+
+You can learn more about SDK usage through [DEVELOPING WITH SDK](/developing_with_sdk/index).
+
+Please let us know how you like it and what could be improved by [submitting an issue](https://github.com/Project-MONAI/monai-deploy-app-sdk/issues/new/choose) or [asking questions](https://github.com/Project-MONAI/monai-deploy-app-sdk/discussions)