notebooks/tutorials/03_segmentation_app.ipynb

{
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    "# Creating a Segmentation App with MONAI Deploy App SDK\n",
    "\n",
    "This tutorial shows how to create an organ segmentation application for a PyTorch model that has been trained with MONAI. Please note that this one does not require the model be a MONAI Bundle.\n",
    "\n",
    "Deploying AI models requires the integration with clinical imaging network, even if just in a for-research-use setting. This means that the AI deploy application will need to support standards-based imaging protocols, and specifically for Radiological imaging, DICOM protocol.\n",
    "\n",
    "Typically, DICOM network communication, either in DICOM TCP/IP network protocol or DICOMWeb, would be handled by DICOM devices or services, e.g. MONAI Deploy Informatics Gateway, so the deploy application itself would only need to use DICOM Part 10 files as input and save the AI result in DICOM Part10 file(s). For segmentation use cases, the DICOM instance file for AI results could be a DICOM Segmentation object or a DICOM RT Structure Set, and for classification, DICOM Structure Report and/or DICOM Encapsulated PDF.\n",
    "\n",
    "During model training, input and label images are typically in non-DICOM volumetric image format, e.g., NIfTI and PNG, converted from a specific DICOM study series. Furthermore, the voxel spacings most likely have been re-sampled to be uniform for all images. When integrated with imaging networks and receiving DICOM instances from modalities and Picture Archiving and Communications System, PACS, an AI deploy application has to deal with a whole DICOM study with multiple series, whose images' spacing may not be the same as expected by the trained model. To address these cases consistently and efficiently, MONAI Deploy Application SDK provides classes, called operators, to parse DICOM studies, select specific series with application-defined rules, and convert the selected DICOM series into domain-specific image format along with meta-data representing the pertinent DICOM attributes. The image is then further processed in the pre-processing stage to normalize spacing, orientation, intensity, etc., before pixel data as Tensors are used for inference.\n",
    "\n",
    "In the following sections, we will demonstrate how to create a MONAI Deploy application package using the MONAI Deploy App SDK.\n",
    "\n",
    ":::{note}\n",
    "For local testing, if there is a lack of DICOM Part 10 files, one can use open source programs, e.g. 3D Slicer, to convert NIfTI to DICOM files.\n",
    "\n",
    ":::\n",
    "\n",
    "## Creating Operators and connecting them in Application class\n",
    "\n",
    "We will implement an application that consists of five Operators:\n",
    "\n",
    "- **DICOMDataLoaderOperator**:\n",
    "    - **Input(dicom_files)**: a folder path (`Path`)\n",
    "    - **Output(dicom_study_list)**: a list of DICOM studies in memory (List[[`DICOMStudy`](/modules/_autosummary/monai.deploy.core.domain.DICOMStudy)])\n",
    "- **DICOMSeriesSelectorOperator**:\n",
    "    - **Input(dicom_study_list)**: a list of DICOM studies in memory (List[[`DICOMStudy`](/modules/_autosummary/monai.deploy.core.domain.DICOMStudy)])\n",
    "    - **Input(selection_rules)**: a selection rule (Dict)\n",
    "    - **Output(study_selected_series_list)**: a DICOM series object in memory ([`StudySelectedSeries`](/modules/_autosummary/monai.deploy.core.domain.StudySelectedSeries))\n",
    "- **DICOMSeriesToVolumeOperator**:\n",
    "    - **Input(study_selected_series_list)**: a DICOM series object in memory ([`StudySelectedSeries`](/modules/_autosummary/monai.deploy.core.domain.StudySelectedSeries))\n",
    "    - **Output(image)**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "- **SpleenSegOperator**:\n",
    "    - **Input(image)**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "    - **Output(seg_image)**: an image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "- **DICOMSegmentationWriterOperator**:\n",
    "    - **Input(seg_image)**: a segmentation image object in memory ([`Image`](/modules/_autosummary/monai.deploy.core.domain.Image))\n",
    "    - **Input(study_selected_series_list)**: a DICOM series object in memory ([`StudySelectedSeries`](/modules/_autosummary/monai.deploy.core.domain.StudySelectedSeries))\n",
    "    - **Output(dicom_seg_instance)**: a file path (`Path`)\n",
    "\n",
    "\n",
    ":::{note}\n",
    "The `DICOMSegmentationWriterOperator` needs both the segmentation image as well as the original DICOM series meta-data in order to use the patient demographics and the DICOM Study level attributes.\n",
    ":::\n",
    "\n",
    "The workflow of the application would look like this.\n",
    "\n",
    "```{mermaid}\n",
    "%%{init: {\"theme\": \"base\", \"themeVariables\": { \"fontSize\": \"16px\"}} }%%\n",
    "\n",
    "classDiagram\n",
    "    direction TB\n",
    "\n",
    "    DICOMDataLoaderOperator --|> DICOMSeriesSelectorOperator : dicom_study_list...dicom_study_list\n",
    "    DICOMSeriesSelectorOperator --|> DICOMSeriesToVolumeOperator : study_selected_series_list...study_selected_series_list\n",
    "    DICOMSeriesToVolumeOperator --|> SpleenSegOperator : image...image\n",
    "    DICOMSeriesSelectorOperator --|> DICOMSegmentationWriterOperator : study_selected_series_list...study_selected_series_list\n",
    "    SpleenSegOperator --|> DICOMSegmentationWriterOperator : seg_image...seg_image\n",
    "\n",
    "\n",
    "    class DICOMDataLoaderOperator {\n",
    "        <in>dicom_files : DISK\n",
    "        dicom_study_list(out) IN_MEMORY\n",
    "    }\n",
    "    class DICOMSeriesSelectorOperator {\n",
    "        <in>dicom_study_list : IN_MEMORY\n",
    "        <in>selection_rules : IN_MEMORY\n",
    "        study_selected_series_list(out) IN_MEMORY\n",
    "    }\n",
    "    class DICOMSeriesToVolumeOperator {\n",
    "        <in>study_selected_series_list : IN_MEMORY\n",
    "        image(out) IN_MEMORY\n",
    "    }\n",
    "    class SpleenSegOperator {\n",
    "        <in>image : IN_MEMORY\n",
    "        seg_image(out) IN_MEMORY\n",
    "    }\n",
    "    class DICOMSegmentationWriterOperator {\n",
    "        <in>seg_image : IN_MEMORY\n",
    "        <in>study_selected_series_list : IN_MEMORY\n",
    "        dicom_seg_instance(out) DISK\n",
    "    }\n",
    "```\n",
    "\n",
    "### Setup environment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install MONAI and other necessary image processing packages for the application\n",
    "!python -c \"import monai\" || pip install --upgrade -q \"monai\"\n",
    "!python -c \"import torch\" || pip install -q \"torch>=1.10.2\"\n",
    "!python -c \"import numpy\" || pip install -q \"numpy>=1.21\"\n",
    "!python -c \"import nibabel\" || pip install -q \"nibabel>=3.2.1\"\n",
    "!python -c \"import pydicom\" || pip install -q \"pydicom>=1.4.2\"\n",
    "!python -c \"import highdicom\" || pip install -q \"highdicom>=0.18.2\"\n",
    "!python -c \"import SimpleITK\" || pip install -q \"SimpleITK>=2.0.0\"\n",
    "\n",
    "# Install MONAI Deploy App SDK package\n",
    "!python -c \"import monai.deploy\" || pip install --upgrade \"monai-deploy-app-sdk\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: you may need to restart the Jupyter kernel to use the updated packages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download/Extract ai_spleen_bundle_data from Google Drive\n",
    "\n",
    "**_Note:_** Data files are now access controlled. Please first request permission to access the [shared folder on Google Drive](https://drive.google.com/drive/folders/1EONJsrwbGsS30td0hs8zl4WKjihew1Z3?usp=sharing). Please download zip file, `ai_spleen_seg_bundle_data.zip` in the `ai_spleen_seg_app` folder, to the same folder as the notebook example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ai_spleen_seg_bundle_data.zip\n",
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      "model.ts\n"
     ]
    }
   ],
   "source": [
    "# Download ai_spleen_bundle_data test data zip file. Please request access and download manually.\n",
    "# !pip install gdown\n",
    "# !gdown \"https://drive.google.com/uc?id=1IwWMpbo2fd38fKIqeIdL8SKTGvkn31tK\"\n",
    "\n",
    "# After downloading ai_spleen_bundle_data zip file from the web browser or using gdown,\n",
    "!unzip -o \"ai_spleen_seg_bundle_data.zip\"\n",
    "\n",
    "# Need to copy the model.ts file to its own clean subfolder for packaging, to work around an issue in the Packager\n",
    "models_folder = \"models\"\n",
    "!rm -rf {models_folder} && mkdir -p {models_folder}/model && cp model.ts {models_folder}/model && ls {models_folder}/model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: HOLOSCAN_INPUT_PATH=dcm\n",
      "env: HOLOSCAN_MODEL_PATH=models\n",
      "env: HOLOSCAN_OUTPUT_PATH=output\n"
     ]
    }
   ],
   "source": [
    "%env HOLOSCAN_INPUT_PATH dcm\n",
    "%env HOLOSCAN_MODEL_PATH {models_folder}\n",
    "%env HOLOSCAN_OUTPUT_PATH output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup imports\n",
    "\n",
    "Let's import necessary classes/decorators to define Application and Operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from numpy import uint8  # Needed if SaveImaged is enabled\n",
    "from pathlib import Path\n",
    "\n",
    "# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package.\n",
    "from pydicom.sr.codedict import codes\n",
    "\n",
    "from monai.deploy.conditions import CountCondition\n",
    "from monai.deploy.core import AppContext, Application, ConditionType, Fragment, Operator, OperatorSpec\n",
    "from monai.deploy.core.domain import Image\n",
    "from monai.deploy.core.io_type import IOType\n",
    "from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator\n",
    "from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription\n",
    "from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator\n",
    "from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator\n",
    "from monai.deploy.operators.monai_seg_inference_operator import InfererType, InMemImageReader, MonaiSegInferenceOperator\n",
    "\n",
    "from monai.transforms import (\n",
    "    Activationsd,\n",
    "    AsDiscreted,\n",
    "    Compose,\n",
    "    EnsureChannelFirstd,\n",
    "    EnsureTyped,\n",
    "    Invertd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    SaveImaged,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Model Specific Inference Operator classes\n",
    "\n",
    "Each Operator class inherits the base `Operator` class. The input/output properties are specified by implementing the `setup()` method, and the business logic implemented in the `compute()` method.\n",
    "\n",
    "The App SDK provides a `MonaiSegInferenceOperator` class to perform segmentation prediction with a Torch Script model. For consistency, this class uses MONAI dictionary-based transforms, as `Compose` object, for pre and post transforms. The model-specific inference operator will then only need to create the pre and post transform `Compose` based on what has been used in the model during training and validation. Note that for deploy application, `ignite` is not needed nor supported.\n",
    "\n",
    "#### SpleenSegOperator\n",
    "\n",
    "The `SpleenSegOperator` gets as input an in-memory [Image](/modules/_autosummary/monai.deploy.core.domain.Image) object that has been converted from a DICOM CT series by the preceding `DICOMSeriesToVolumeOperator`, and as output in-memory segmentation [Image](/modules/_autosummary/monai.deploy.core.domain.Image) object.\n",
    "\n",
    "The `pre_process` function creates the pre-transforms `Compose` object. For `LoadImage`, a specialized `InMemImageReader`, derived from MONAI `ImageReader`, is used to convert the in-memory pixel data and return the `numpy` array as well as the meta-data. Also, the DICOM input pixel spacings are often not the same as expected by the model, so the `Spacingd` transform must be used to re-sample the image with the expected spacing.\n",
    "\n",
    "The `post_process` function creates the post-transform `Compose` object. The `SaveImageD` transform class is used to save the segmentation mask as NIfTI image file, which is optional as the in-memory mask image will be passed down to the DICOM Segmentation writer for creating a DICOM Segmentation instance. The `Invertd` must also be used to revert the segmentation image's orientation and spacing to be the same as the input.\n",
    "\n",
    "When the `MonaiSegInferenceOperator` object is created, the `ROI` size is specified, as well as the transform `Compose` objects. Furthermore, the dataset image key names are set accordingly.\n",
    "\n",
    "Loading of the model and performing the prediction are encapsulated in the `MonaiSegInferenceOperator` and other SDK classes. Once the inference is completed, the segmentation [Image](/modules/_autosummary/monai.deploy.core.domain.Image) object is created and set to the output by the `SpleenSegOperator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SpleenSegOperator(Operator):\n",
    "    \"\"\"Performs Spleen segmentation with a 3D image converted from a DICOM CT series.\n",
    "    \"\"\"\n",
    "\n",
    "    DEFAULT_OUTPUT_FOLDER = Path.cwd() / \"output/saved_images_folder\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        fragment: Fragment,\n",
    "        *args,\n",
    "        app_context: AppContext,\n",
    "        model_path: Path,\n",
    "        output_folder: Path = DEFAULT_OUTPUT_FOLDER,\n",
    "        **kwargs,\n",
    "    ):\n",
    "\n",
    "        self.logger = logging.getLogger(\"{}.{}\".format(__name__, type(self).__name__))\n",
    "        self._input_dataset_key = \"image\"\n",
    "        self._pred_dataset_key = \"pred\"\n",
    "\n",
    "        self.model_path = model_path\n",
    "        self.output_folder = output_folder\n",
    "        self.output_folder.mkdir(parents=True, exist_ok=True)\n",
    "        self.app_context = app_context\n",
    "        self.input_name_image = \"image\"\n",
    "        self.output_name_seg = \"seg_image\"\n",
    "        self.output_name_saved_images_folder = \"saved_images_folder\"\n",
    "\n",
    "        # The base class has an attribute called fragment to hold the reference to the fragment object\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.input(self.input_name_image)\n",
    "        spec.output(self.output_name_seg)\n",
    "        spec.output(self.output_name_saved_images_folder).condition(\n",
    "            ConditionType.NONE\n",
    "        )  # Output not requiring a receiver\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        input_image = op_input.receive(self.input_name_image)\n",
    "        if not input_image:\n",
    "            raise ValueError(\"Input image is not found.\")\n",
    "\n",
    "        # This operator gets an in-memory Image object, so a specialized ImageReader is needed.\n",
    "        _reader = InMemImageReader(input_image)\n",
    "\n",
    "        pre_transforms = self.pre_process(_reader, str(self.output_folder))\n",
    "        post_transforms = self.post_process(pre_transforms, str(self.output_folder))\n",
    "\n",
    "        # Delegates inference and saving output to the built-in operator.\n",
    "        infer_operator = MonaiSegInferenceOperator(\n",
    "            self.fragment,\n",
    "            roi_size=(\n",
    "                96,\n",
    "                96,\n",
    "                96,\n",
    "            ),\n",
    "            pre_transforms=pre_transforms,\n",
    "            post_transforms=post_transforms,\n",
    "            overlap=0.6,\n",
    "            app_context=self.app_context,\n",
    "            model_name=\"\",\n",
    "            inferer=InfererType.SLIDING_WINDOW,\n",
    "            sw_batch_size=4,\n",
    "            model_path=self.model_path,\n",
    "            name=\"monai_seg_inference_op\",\n",
    "        )\n",
    "\n",
    "        # Setting the keys used in the dictionary based transforms may change.\n",
    "        infer_operator.input_dataset_key = self._input_dataset_key\n",
    "        infer_operator.pred_dataset_key = self._pred_dataset_key\n",
    "\n",
    "        # Now emit data to the output ports of this operator\n",
    "        op_output.emit(infer_operator.compute_impl(input_image, context), self.output_name_seg)\n",
    "        op_output.emit(self.output_folder, self.output_name_saved_images_folder)\n",
    "\n",
    "    def pre_process(self, img_reader, out_dir: str = \"./input_images\") -> Compose:\n",
    "        \"\"\"Composes transforms for preprocessing input before predicting on a model.\"\"\"\n",
    "\n",
    "        Path(out_dir).mkdir(parents=True, exist_ok=True)\n",
    "        my_key = self._input_dataset_key\n",
    "\n",
    "        return Compose(\n",
    "            [\n",
    "                LoadImaged(keys=my_key, reader=img_reader),\n",
    "                EnsureChannelFirstd(keys=my_key),\n",
    "                # The SaveImaged transform can be commented out to save 5 seconds.\n",
    "                # Uncompress NIfTI file, nii, is used favoring speed over size, but can be changed to nii.gz\n",
    "                SaveImaged(\n",
    "                    keys=my_key,\n",
    "                    output_dir=out_dir,\n",
    "                    output_postfix=\"\",\n",
    "                    resample=False,\n",
    "                    output_ext=\".nii\",\n",
    "                ),\n",
    "                Orientationd(keys=my_key, axcodes=\"RAS\"),\n",
    "                Spacingd(keys=my_key, pixdim=[1.5, 1.5, 2.9], mode=[\"bilinear\"]),\n",
    "                ScaleIntensityRanged(keys=my_key, a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "                EnsureTyped(keys=my_key),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def post_process(self, pre_transforms: Compose, out_dir: str = \"./prediction_output\") -> Compose:\n",
    "        \"\"\"Composes transforms for postprocessing the prediction results.\"\"\"\n",
    "\n",
    "        Path(out_dir).mkdir(parents=True, exist_ok=True)\n",
    "        pred_key = self._pred_dataset_key\n",
    "\n",
    "        return Compose(\n",
    "            [\n",
    "                Activationsd(keys=pred_key, softmax=True),\n",
    "                Invertd(\n",
    "                    keys=pred_key,\n",
    "                    transform=pre_transforms,\n",
    "                    orig_keys=self._input_dataset_key,\n",
    "                    nearest_interp=False,\n",
    "                    to_tensor=True,\n",
    "                ),\n",
    "                AsDiscreted(keys=pred_key, argmax=True),\n",
    "                # The SaveImaged transform can be commented out to save 5 seconds.\n",
    "                # Uncompress NIfTI file, nii, is used favoring speed over size, but can be changed to nii.gz\n",
    "                SaveImaged(\n",
    "                    keys=pred_key,\n",
    "                    output_dir=out_dir,\n",
    "                    output_postfix=\"seg\",\n",
    "                    output_dtype=uint8,\n",
    "                    resample=False,\n",
    "                    output_ext=\".nii\",\n",
    "                ),\n",
    "            ]\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Application class\n",
    "\n",
    "Our application class would look like below.\n",
    "\n",
    "It defines `App` class, inheriting the base `Application` class.\n",
    "\n",
    "The base class method, `compose`, is overridden. Objects required for DICOM parsing, series selection, pixel data conversion to volume image, and segmentation instance creation are created, so is the model-specific `SpleenSegOperator`. The execution pipeline, as a Directed Acyclic Graph (DAG), is created by connecting these objects through the `add_flow` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AISpleenSegApp(Application):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        \"\"\"Creates an application instance.\"\"\"\n",
    "\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self._logger = logging.getLogger(\"{}.{}\".format(__name__, type(self).__name__))\n",
    "\n",
    "    def run(self, *args, **kwargs):\n",
    "        # This method calls the base class to run. Can be omitted if simply calling through.\n",
    "        self._logger.info(f\"Begin {self.run.__name__}\")\n",
    "        super().run(*args, **kwargs)\n",
    "        self._logger.info(f\"End {self.run.__name__}\")\n",
    "\n",
    "    def compose(self):\n",
    "        \"\"\"Creates the app specific operators and chain them up in the processing DAG.\"\"\"\n",
    "\n",
    "        self._logger.debug(f\"Begin {self.compose.__name__}\")\n",
    "        app_context = Application.init_app_context({})  # Do not pass argv in Jupyter Notebook\n",
    "        app_input_path = Path(app_context.input_path)\n",
    "        app_output_path = Path(app_context.output_path)\n",
    "        model_path = Path(app_context.model_path)\n",
    "\n",
    "        self._logger.info(f\"App input and output path: {app_input_path}, {app_output_path}\")\n",
    "\n",
    "        # instantiates the SDK built-in operator(s).\n",
    "        study_loader_op = DICOMDataLoaderOperator(\n",
    "            self, CountCondition(self, 1), input_folder=app_input_path, name=\"dcm_loader_op\"\n",
    "        )\n",
    "        series_selector_op = DICOMSeriesSelectorOperator(self, rules=Sample_Rules_Text, name=\"series_selector_op\")\n",
    "        series_to_vol_op = DICOMSeriesToVolumeOperator(self, name=\"series_to_vol_op\")\n",
    "\n",
    "        # Model specific inference operator, supporting MONAI transforms.\n",
    "        spleen_seg_op = SpleenSegOperator(self, app_context=app_context, model_path=model_path, name=\"seg_op\")\n",
    "\n",
    "        # Create DICOM Seg writer providing the required segment description for each segment with\n",
    "        # the actual algorithm and the pertinent organ/tissue.\n",
    "        # The segment_label, algorithm_name, and algorithm_version are limited to 64 chars.\n",
    "        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html\n",
    "        # User can Look up SNOMED CT codes at, e.g.\n",
    "        # https://bioportal.bioontology.org/ontologies/SNOMEDCT\n",
    "\n",
    "        _algorithm_name = \"3D segmentation of the Spleen from a CT series\"\n",
    "        _algorithm_family = codes.DCM.ArtificialIntelligence\n",
    "        _algorithm_version = \"0.1.0\"\n",
    "\n",
    "        segment_descriptions = [\n",
    "            SegmentDescription(\n",
    "                segment_label=\"Spleen\",\n",
    "                segmented_property_category=codes.SCT.Organ,\n",
    "                segmented_property_type=codes.SCT.Spleen,\n",
    "                algorithm_name=_algorithm_name,\n",
    "                algorithm_family=_algorithm_family,\n",
    "                algorithm_version=_algorithm_version,\n",
    "            ),\n",
    "        ]\n",
    "\n",
    "        custom_tags = {\"SeriesDescription\": \"AI generated Seg, not for clinical use.\"}\n",
    "\n",
    "        dicom_seg_writer = DICOMSegmentationWriterOperator(\n",
    "            self,\n",
    "            segment_descriptions=segment_descriptions,\n",
    "            custom_tags=custom_tags,\n",
    "            output_folder=app_output_path,\n",
    "            name=\"dcm_seg_writer_op\",\n",
    "        )\n",
    "\n",
    "        # Create the processing pipeline, by specifying the source and destination operators, and\n",
    "        # ensuring the output from the former matches the input of the latter, in both name and type.\n",
    "        self.add_flow(study_loader_op, series_selector_op, {(\"dicom_study_list\", \"dicom_study_list\")})\n",
    "        self.add_flow(\n",
    "            series_selector_op, series_to_vol_op, {(\"study_selected_series_list\", \"study_selected_series_list\")}\n",
    "        )\n",
    "        self.add_flow(series_to_vol_op, spleen_seg_op, {(\"image\", \"image\")})\n",
    "\n",
    "        # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.\n",
    "        self.add_flow(\n",
    "            series_selector_op, dicom_seg_writer, {(\"study_selected_series_list\", \"study_selected_series_list\")}\n",
    "        )\n",
    "        self.add_flow(spleen_seg_op, dicom_seg_writer, {(\"seg_image\", \"seg_image\")})\n",
    "\n",
    "        self._logger.debug(f\"End {self.compose.__name__}\")\n",
    "\n",
    "\n",
    "# This is a sample series selection rule in JSON, simply selecting CT series.\n",
    "# If the study has more than 1 CT series, then all of them will be selected.\n",
    "# Please see more detail in DICOMSeriesSelectorOperator.\n",
    "# For list of string values, e.g. \"ImageType\": [\"PRIMARY\", \"ORIGINAL\"], it is a match if all elements\n",
    "# are all in the multi-value attribute of the DICOM series.\n",
    "\n",
    "Sample_Rules_Text = \"\"\"\n",
    "{\n",
    "    \"selections\": [\n",
    "        {\n",
    "            \"name\": \"CT Series\",\n",
    "            \"conditions\": {\n",
    "                \"StudyDescription\": \"(.*?)\",\n",
    "                \"Modality\": \"(?i)CT\",\n",
    "                \"SeriesDescription\": \"(.*?)\",\n",
    "                \"ImageType\": [\"PRIMARY\", \"ORIGINAL\"]\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing app locally\n",
    "\n",
    "We can execute the app in Jupyter notebook. Note that the DICOM files of the CT Abdomen series must be present in the `dcm` folder and the TorchScript, `model.ts`, in the folder pointed to by the environment variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[info] [fragment.cpp:599] Loading extensions from configs...\n",
      "[2025-01-29 14:30:17,208] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])\n",
      "[2025-01-29 14:30:17,220] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n",
      "[2025-01-29 14:30:17,222] [INFO] (__main__.AISpleenSegApp) - App input and output path: dcm, output\n",
      "[info] [gxf_executor.cpp:264] Creating context\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'input_folder'\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'dicom_study_list'\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'study_selected_series_list'\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'image'\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'output_folder'\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'study_selected_series_list'\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'seg_image'\n",
      "[info] [gxf_executor.cpp:2208] Activating Graph...\n",
      "[info] [gxf_executor.cpp:2238] Running Graph...\n",
      "[info] [gxf_executor.cpp:2240] Waiting for completion...\n",
      "[info] [greedy_scheduler.cpp:191] Scheduling 5 entities\n",
      "[2025-01-29 14:30:17,274] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n",
      "[2025-01-29 14:30:17,612] [INFO] (root) - Finding series for Selection named: CT Series\n",
      "[2025-01-29 14:30:17,613] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n",
      "  # of series: 1\n",
      "[2025-01-29 14:30:17,614] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n",
      "[2025-01-29 14:30:17,614] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n",
      "[2025-01-29 14:30:17,615] [INFO] (root) -     Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n",
      "[2025-01-29 14:30:17,616] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "[2025-01-29 14:30:17,617] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n",
      "[2025-01-29 14:30:17,618] [INFO] (root) -     Series attribute Modality value: CT\n",
      "[2025-01-29 14:30:17,619] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "[2025-01-29 14:30:17,620] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n",
      "[2025-01-29 14:30:17,622] [INFO] (root) -     Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n",
      "[2025-01-29 14:30:17,623] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "[2025-01-29 14:30:17,624] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n",
      "[2025-01-29 14:30:17,625] [INFO] (root) -     Series attribute ImageType value: None\n",
      "[2025-01-29 14:30:17,626] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n",
      "[2025-01-29 14:30:18,261] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n",
      "[2025-01-29 14:30:18,262] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type <class 'str'>\n",
      "[2025-01-29 14:30:18,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type <class 'str'>\n",
      "[2025-01-29 14:30:18,263] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type <class 'str'>\n",
      "[2025-01-29 14:30:18,265] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type <class 'str'>\n",
      "[2025-01-29 14:30:18,266] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type <class 'str'>\n",
      "[2025-01-29 14:30:18,268] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type <class 'str'>\n",
      "[2025-01-29 14:30:18,269] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type <class 'int'>\n",
      "[2025-01-29 14:30:18,271] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type <class 'float'>\n",
      "[2025-01-29 14:30:18,272] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type <class 'float'>\n",
      "[2025-01-29 14:30:18,273] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type <class 'float'>\n",
      "[2025-01-29 14:30:18,275] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type <class 'list'>\n",
      "[2025-01-29 14:30:18,276] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type <class 'list'>\n",
      "[2025-01-29 14:30:18,277] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type <class 'list'>\n",
      "[2025-01-29 14:30:18,279] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[   0.7890625    0.           0.        -197.60547  ]\n",
      " [   0.           0.7890625    0.        -398.60547  ]\n",
      " [   0.           0.           1.5       -383.       ]\n",
      " [   0.           0.           0.           1.       ]], type <class 'numpy.ndarray'>\n",
      "[2025-01-29 14:30:18,282] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[  -0.7890625   -0.          -0.         197.60547  ]\n",
      " [  -0.          -0.7890625   -0.         398.60547  ]\n",
      " [   0.           0.           1.5       -383.       ]\n",
      " [   0.           0.           0.           1.       ]], type <class 'numpy.ndarray'>\n",
      "[2025-01-29 14:30:18,283] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type <class 'str'>\n",
      "[2025-01-29 14:30:18,284] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type <class 'str'>\n",
      "[2025-01-29 14:30:18,285] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type <class 'str'>\n",
      "[2025-01-29 14:30:18,286] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type <class 'str'>\n",
      "[2025-01-29 14:30:18,290] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type <class 'str'>\n",
      "[2025-01-29 14:30:18,291] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type <class 'str'>\n",
      "[2025-01-29 14:30:18,292] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type <class 'str'>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-29 14:30:19,501 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n",
      "2025-01-29 14:30:23,998 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-01-29 14:30:26,134] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n",
      "[2025-01-29 14:30:26,142] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n",
      "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/base.py:163: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n",
      "  check_person_name(patient_name)\n",
      "[2025-01-29 14:30:28,033] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "[2025-01-29 14:30:28,034] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n",
      "[2025-01-29 14:30:28,035] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "[2025-01-29 14:30:28,036] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n",
      "[2025-01-29 14:30:28,038] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n",
      "[2025-01-29 14:30:28,040] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "[2025-01-29 14:30:28,041] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n",
      "[2025-01-29 14:30:28,042] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n",
      "[2025-01-29 14:30:28,044] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n",
      "[info] [greedy_scheduler.cpp:372] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n",
      "[info] [greedy_scheduler.cpp:401] Scheduler finished.\n",
      "[info] [gxf_executor.cpp:2243] Deactivating Graph...\n",
      "[info] [gxf_executor.cpp:2251] Graph execution finished.\n",
      "[2025-01-29 14:30:28,156] [INFO] (__main__.AISpleenSegApp) - End run\n"
     ]
    }
   ],
   "source": [
    "!rm -rf $HOLOSCAN_OUTPUT_PATH\n",
    "app = AISpleenSegApp()\n",
    "app.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the application is verified inside Jupyter notebook, we can write the above Python code into Python files in an application folder.\n",
    "\n",
    "The application folder structure would look like below:\n",
    "\n",
    "```bash\n",
    "my_app\n",
    "├── __main__.py\n",
    "├── app.py\n",
    "└── spleen_seg_operator.py\n",
    "```\n",
    "\n",
    ":::{note}\n",
    "We can create a single application Python file (such as `spleen_app.py`) that includes the content of the files, instead of creating multiple files.\n",
    "You will see such an example in <a href=\"./02_mednist_app.html#executing-app-locally\">MedNist Classifier Tutorial</a>.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an application folder\n",
    "!mkdir -p my_app && rm -rf my_app/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### spleen_seg_operator.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing my_app/spleen_seg_operator.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile my_app/spleen_seg_operator.py\n",
    "import logging\n",
    "\n",
    "from numpy import uint8\n",
    "from pathlib import Path\n",
    "\n",
    "from monai.deploy.core import AppContext, ConditionType, Fragment, Operator, OperatorSpec\n",
    "from monai.deploy.operators.monai_seg_inference_operator import InfererType, InMemImageReader, MonaiSegInferenceOperator\n",
    "from monai.transforms import (\n",
    "    Activationsd,\n",
    "    AsDiscreted,\n",
    "    Compose,\n",
    "    EnsureChannelFirstd,\n",
    "    EnsureTyped,\n",
    "    Invertd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    SaveImaged,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    ")\n",
    "\n",
    "class SpleenSegOperator(Operator):\n",
    "    \"\"\"Performs Spleen segmentation with a 3D image converted from a DICOM CT series.\n",
    "    \"\"\"\n",
    "\n",
    "    DEFAULT_OUTPUT_FOLDER = Path.cwd() / \"output/saved_images_folder\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        fragment: Fragment,\n",
    "        *args,\n",
    "        app_context: AppContext,\n",
    "        model_path: Path,\n",
    "        output_folder: Path = DEFAULT_OUTPUT_FOLDER,\n",
    "        **kwargs,\n",
    "    ):\n",
    "\n",
    "        self.logger = logging.getLogger(\"{}.{}\".format(__name__, type(self).__name__))\n",
    "        self._input_dataset_key = \"image\"\n",
    "        self._pred_dataset_key = \"pred\"\n",
    "\n",
    "        self.model_path = model_path\n",
    "        self.output_folder = output_folder\n",
    "        self.output_folder.mkdir(parents=True, exist_ok=True)\n",
    "        self.app_context = app_context\n",
    "        self.input_name_image = \"image\"\n",
    "        self.output_name_seg = \"seg_image\"\n",
    "        self.output_name_saved_images_folder = \"saved_images_folder\"\n",
    "\n",
    "        # The base class has an attribute called fragment to hold the reference to the fragment object\n",
    "        super().__init__(fragment, *args, **kwargs)\n",
    "\n",
    "    def setup(self, spec: OperatorSpec):\n",
    "        spec.input(self.input_name_image)\n",
    "        spec.output(self.output_name_seg)\n",
    "        spec.output(self.output_name_saved_images_folder).condition(\n",
    "            ConditionType.NONE\n",
    "        )  # Output not requiring a receiver\n",
    "\n",
    "    def compute(self, op_input, op_output, context):\n",
    "        input_image = op_input.receive(self.input_name_image)\n",
    "        if not input_image:\n",
    "            raise ValueError(\"Input image is not found.\")\n",
    "\n",
    "        # This operator gets an in-memory Image object, so a specialized ImageReader is needed.\n",
    "        _reader = InMemImageReader(input_image)\n",
    "\n",
    "        pre_transforms = self.pre_process(_reader, str(self.output_folder))\n",
    "        post_transforms = self.post_process(pre_transforms, str(self.output_folder))\n",
    "\n",
    "        # Delegates inference and saving output to the built-in operator.\n",
    "        infer_operator = MonaiSegInferenceOperator(\n",
    "            self.fragment,\n",
    "            roi_size=(\n",
    "                96,\n",
    "                96,\n",
    "                96,\n",
    "            ),\n",
    "            pre_transforms=pre_transforms,\n",
    "            post_transforms=post_transforms,\n",
    "            overlap=0.6,\n",
    "            app_context=self.app_context,\n",
    "            model_name=\"\",\n",
    "            inferer=InfererType.SLIDING_WINDOW,\n",
    "            sw_batch_size=4,\n",
    "            model_path=self.model_path,\n",
    "            name=\"monai_seg_inference_op\",\n",
    "        )\n",
    "\n",
    "        # Setting the keys used in the dictionary based transforms may change.\n",
    "        infer_operator.input_dataset_key = self._input_dataset_key\n",
    "        infer_operator.pred_dataset_key = self._pred_dataset_key\n",
    "\n",
    "        # Now emit data to the output ports of this operator\n",
    "        op_output.emit(infer_operator.compute_impl(input_image, context), self.output_name_seg)\n",
    "        op_output.emit(self.output_folder, self.output_name_saved_images_folder)\n",
    "\n",
    "    def pre_process(self, img_reader, out_dir: str = \"./input_images\") -> Compose:\n",
    "        \"\"\"Composes transforms for preprocessing input before predicting on a model.\"\"\"\n",
    "\n",
    "        Path(out_dir).mkdir(parents=True, exist_ok=True)\n",
    "        my_key = self._input_dataset_key\n",
    "\n",
    "        return Compose(\n",
    "            [\n",
    "                LoadImaged(keys=my_key, reader=img_reader),\n",
    "                EnsureChannelFirstd(keys=my_key),\n",
    "                # The SaveImaged transform can be commented out to save 5 seconds.\n",
    "                # Uncompress NIfTI file, nii, is used favoring speed over size, but can be changed to nii.gz\n",
    "                SaveImaged(\n",
    "                    keys=my_key,\n",
    "                    output_dir=out_dir,\n",
    "                    output_postfix=\"\",\n",
    "                    resample=False,\n",
    "                    output_ext=\".nii\",\n",
    "                ),\n",
    "                Orientationd(keys=my_key, axcodes=\"RAS\"),\n",
    "                Spacingd(keys=my_key, pixdim=[1.5, 1.5, 2.9], mode=[\"bilinear\"]),\n",
    "                ScaleIntensityRanged(keys=my_key, a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "                EnsureTyped(keys=my_key),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def post_process(self, pre_transforms: Compose, out_dir: str = \"./prediction_output\") -> Compose:\n",
    "        \"\"\"Composes transforms for postprocessing the prediction results.\"\"\"\n",
    "\n",
    "        Path(out_dir).mkdir(parents=True, exist_ok=True)\n",
    "        pred_key = self._pred_dataset_key\n",
    "\n",
    "        return Compose(\n",
    "            [\n",
    "                Activationsd(keys=pred_key, softmax=True),\n",
    "                Invertd(\n",
    "                    keys=pred_key,\n",
    "                    transform=pre_transforms,\n",
    "                    orig_keys=self._input_dataset_key,\n",
    "                    nearest_interp=False,\n",
    "                    to_tensor=True,\n",
    "                ),\n",
    "                AsDiscreted(keys=pred_key, argmax=True),\n",
    "                # The SaveImaged transform can be commented out to save 5 seconds.\n",
    "                # Uncompress NIfTI file, nii, is used favoring speed over size, but can be changed to nii.gz\n",
    "                SaveImaged(\n",
    "                    keys=pred_key,\n",
    "                    output_dir=out_dir,\n",
    "                    output_postfix=\"seg\",\n",
    "                    output_dtype=uint8,\n",
    "                    resample=False,\n",
    "                    output_ext=\".nii\",\n",
    "                ),\n",
    "            ]\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### app.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing my_app/app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile my_app/app.py\n",
    "import logging\n",
    "from pathlib import Path\n",
    "\n",
    "from spleen_seg_operator import SpleenSegOperator\n",
    "\n",
    "from pydicom.sr.codedict import codes  # Required for setting SegmentDescription attributes.\n",
    "\n",
    "from monai.deploy.conditions import CountCondition\n",
    "from monai.deploy.core import AppContext, Application\n",
    "from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator\n",
    "from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription\n",
    "from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator\n",
    "from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator\n",
    "from monai.deploy.operators.stl_conversion_operator import STLConversionOperator\n",
    "\n",
    "class AISpleenSegApp(Application):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        \"\"\"Creates an application instance.\"\"\"\n",
    "\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self._logger = logging.getLogger(\"{}.{}\".format(__name__, type(self).__name__))\n",
    "\n",
    "    def run(self, *args, **kwargs):\n",
    "        # This method calls the base class to run. Can be omitted if simply calling through.\n",
    "        self._logger.info(f\"Begin {self.run.__name__}\")\n",
    "        super().run(*args, **kwargs)\n",
    "        self._logger.info(f\"End {self.run.__name__}\")\n",
    "\n",
    "    def compose(self):\n",
    "        \"\"\"Creates the app specific operators and chain them up in the processing DAG.\"\"\"\n",
    "\n",
    "        # Use Commandline options over environment variables to init context.\n",
    "        app_context = Application.init_app_context(self.argv)\n",
    "        self._logger.debug(f\"Begin {self.compose.__name__}\")\n",
    "        app_input_path = Path(app_context.input_path)\n",
    "        app_output_path = Path(app_context.output_path)\n",
    "        model_path = Path(app_context.model_path)\n",
    "\n",
    "        self._logger.info(f\"App input and output path: {app_input_path}, {app_output_path}\")\n",
    "\n",
    "        # instantiates the SDK built-in operator(s).\n",
    "        study_loader_op = DICOMDataLoaderOperator(\n",
    "            self, CountCondition(self, 1), input_folder=app_input_path, name=\"dcm_loader_op\"\n",
    "        )\n",
    "        series_selector_op = DICOMSeriesSelectorOperator(self, rules=Sample_Rules_Text, name=\"series_selector_op\")\n",
    "        series_to_vol_op = DICOMSeriesToVolumeOperator(self, name=\"series_to_vol_op\")\n",
    "\n",
    "        # Model specific inference operator, supporting MONAI transforms.\n",
    "        spleen_seg_op = SpleenSegOperator(self, app_context=app_context, model_path=model_path, name=\"seg_op\")\n",
    "\n",
    "        # Create DICOM Seg writer providing the required segment description for each segment with\n",
    "        # the actual algorithm and the pertinent organ/tissue.\n",
    "        # The segment_label, algorithm_name, and algorithm_version are limited to 64 chars.\n",
    "        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html\n",
    "        # User can Look up SNOMED CT codes at, e.g.\n",
    "        # https://bioportal.bioontology.org/ontologies/SNOMEDCT\n",
    "\n",
    "        _algorithm_name = \"3D segmentation of the Spleen from a CT series\"\n",
    "        _algorithm_family = codes.DCM.ArtificialIntelligence\n",
    "        _algorithm_version = \"0.1.0\"\n",
    "\n",
    "        segment_descriptions = [\n",
    "            SegmentDescription(\n",
    "                segment_label=\"Spleen\",\n",
    "                segmented_property_category=codes.SCT.Organ,\n",
    "                segmented_property_type=codes.SCT.Spleen,\n",
    "                algorithm_name=_algorithm_name,\n",
    "                algorithm_family=_algorithm_family,\n",
    "                algorithm_version=_algorithm_version,\n",
    "            ),\n",
    "        ]\n",
    "\n",
    "        custom_tags = {\"SeriesDescription\": \"AI generated Seg, not for clinical use.\"}\n",
    "\n",
    "        dicom_seg_writer = DICOMSegmentationWriterOperator(\n",
    "            self,\n",
    "            segment_descriptions=segment_descriptions,\n",
    "            custom_tags=custom_tags,\n",
    "            output_folder=app_output_path,\n",
    "            name=\"dcm_seg_writer_op\",\n",
    "        )\n",
    "\n",
    "        # Create the processing pipeline, by specifying the source and destination operators, and\n",
    "        # ensuring the output from the former matches the input of the latter, in both name and type.\n",
    "        self.add_flow(study_loader_op, series_selector_op, {(\"dicom_study_list\", \"dicom_study_list\")})\n",
    "        self.add_flow(\n",
    "            series_selector_op, series_to_vol_op, {(\"study_selected_series_list\", \"study_selected_series_list\")}\n",
    "        )\n",
    "        self.add_flow(series_to_vol_op, spleen_seg_op, {(\"image\", \"image\")})\n",
    "\n",
    "        # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.\n",
    "        self.add_flow(\n",
    "            series_selector_op, dicom_seg_writer, {(\"study_selected_series_list\", \"study_selected_series_list\")}\n",
    "        )\n",
    "        self.add_flow(spleen_seg_op, dicom_seg_writer, {(\"seg_image\", \"seg_image\")})\n",
    "\n",
    "        self._logger.debug(f\"End {self.compose.__name__}\")\n",
    "\n",
    "\n",
    "# This is a sample series selection rule in JSON, simply selecting CT series.\n",
    "# If the study has more than 1 CT series, then all of them will be selected.\n",
    "# Please see more detail in DICOMSeriesSelectorOperator.\n",
    "# For list of string values, e.g. \"ImageType\": [\"PRIMARY\", \"ORIGINAL\"], it is a match if all elements\n",
    "# are all in the multi-value attribute of the DICOM series.\n",
    "\n",
    "Sample_Rules_Text = \"\"\"\n",
    "{\n",
    "    \"selections\": [\n",
    "        {\n",
    "            \"name\": \"CT Series\",\n",
    "            \"conditions\": {\n",
    "                \"StudyDescription\": \"(.*?)\",\n",
    "                \"Modality\": \"(?i)CT\",\n",
    "                \"SeriesDescription\": \"(.*?)\",\n",
    "                \"ImageType\": [\"PRIMARY\", \"ORIGINAL\"]\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Creates the app and test it standalone.\n",
    "    AISpleenSegApp().run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "if __name__ == \"__main__\":\n",
    "    AISpleenSegApp().run()\n",
    "```\n",
    "\n",
    "The above lines are needed to execute the application code by using `python` interpreter.\n",
    "\n",
    "### \\_\\_main\\_\\_.py\n",
    "\n",
    "\\_\\_main\\_\\_.py is needed for <a href=\"../../developing_with_sdk/packaging_app.html#required-arguments\">MONAI Application Packager</a> to detect the main application code (`app.py`) when the application is executed with the application folder path (e.g., `python simple_imaging_app`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing my_app/__main__.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile my_app/__main__.py\n",
    "from app import AISpleenSegApp\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    AISpleenSegApp().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "app.py\t__main__.py  spleen_seg_operator.py\n"
     ]
    }
   ],
   "source": [
    "!ls my_app"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this time, let's execute the app in the command line.\n",
    "\n",
    ":::{note}\n",
    "Since the environment variables have been set and contain the correct paths, it is not necessary to provide the command line options on running the application.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[32minfo\u001b[m] [fragment.cpp:599] Loading extensions from configs...\n",
      "[2025-01-29 14:30:34,908] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['my_app'])\n",
      "[2025-01-29 14:30:34,910] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)\n",
      "[2025-01-29 14:30:34,910] [INFO] (app.AISpleenSegApp) - App input and output path: dcm, output\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:264] Creating context\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'input_folder'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'dicom_study_list'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'study_selected_series_list'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'image'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'output_folder'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'study_selected_series_list'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:1797] creating input IOSpec named 'seg_image'\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2208] Activating Graph...\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2238] Running Graph...\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2240] Waiting for completion...\n",
      "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:191] Scheduling 5 entities\n",
      "[2025-01-29 14:30:34,938] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - Finding series for Selection named: CT Series\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n",
      "  # of series: 1\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) -     Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) -     Series attribute Modality value: CT\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) -     Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n",
      "[2025-01-29 14:30:35,857] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "[2025-01-29 14:30:35,858] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n",
      "[2025-01-29 14:30:35,858] [INFO] (root) -     Series attribute ImageType value: None\n",
      "[2025-01-29 14:30:35,858] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type <class 'str'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type <class 'str'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type <class 'str'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type <class 'str'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type <class 'str'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type <class 'str'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type <class 'int'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type <class 'float'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type <class 'float'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type <class 'float'>\n",
      "[2025-01-29 14:30:36,487] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type <class 'list'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type <class 'list'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type <class 'list'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[   0.7890625    0.           0.        -197.60547  ]\n",
      " [   0.           0.7890625    0.        -398.60547  ]\n",
      " [   0.           0.           1.5       -383.       ]\n",
      " [   0.           0.           0.           1.       ]], type <class 'numpy.ndarray'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[  -0.7890625   -0.          -0.         197.60547  ]\n",
      " [  -0.          -0.7890625   -0.         398.60547  ]\n",
      " [   0.           0.           1.5       -383.       ]\n",
      " [   0.           0.           0.           1.       ]], type <class 'numpy.ndarray'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type <class 'str'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type <class 'str'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type <class 'str'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type <class 'str'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type <class 'str'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type <class 'str'>\n",
      "[2025-01-29 14:30:36,488] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type <class 'str'>\n",
      "2025-01-29 14:30:37,548 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n",
      "2025-01-29 14:30:41,063 INFO image_writer.py:197 - writing: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n",
      "[2025-01-29 14:30:42,975] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n",
      "[2025-01-29 14:30:42,981] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n",
      "/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/base.py:163: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n",
      "  check_person_name(patient_name)\n",
      "[2025-01-29 14:30:44,652] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "[2025-01-29 14:30:44,652] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n",
      "[2025-01-29 14:30:44,652] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "[2025-01-29 14:30:44,652] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n",
      "[2025-01-29 14:30:44,652] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n",
      "[2025-01-29 14:30:44,653] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "[2025-01-29 14:30:44,653] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n",
      "[2025-01-29 14:30:44,653] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n",
      "[2025-01-29 14:30:44,653] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n",
      "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:372] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n",
      "[\u001b[32minfo\u001b[m] [greedy_scheduler.cpp:401] Scheduler finished.\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2243] Deactivating Graph...\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:2251] Graph execution finished.\n",
      "[2025-01-29 14:30:44,748] [INFO] (app.AISpleenSegApp) - End run\n",
      "[\u001b[32minfo\u001b[m] [gxf_executor.cpp:294] Destroying context\n"
     ]
    }
   ],
   "source": [
    "!rm -rf $HOLOSCAN_OUTPUT_PATH\n",
    "!python my_app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output:\n",
      "1.2.826.0.1.3680043.10.511.3.51643712983429462828738370758191766.dcm\n",
      "saved_images_folder\n",
      "\n",
      "output/saved_images_folder:\n",
      "1.3.6.1.4.1.14519.5.2.1.7085.2626\n",
      "\n",
      "output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626:\n",
      "1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n",
      "1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n"
     ]
    }
   ],
   "source": [
    "!ls -R $HOLOSCAN_OUTPUT_PATH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packaging app"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's package the app with [MONAI Application Packager](/developing_with_sdk/packaging_app).\n",
    "\n",
    "In this version of the App SDK, we need to write out the configuration yaml file as well as the package requirements file, in the application folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing my_app/app.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile my_app/app.yaml\n",
    "%YAML 1.2\n",
    "---\n",
    "application:\n",
    "  title: MONAI Deploy App Package - MONAI Bundle AI App\n",
    "  version: 1.0\n",
    "  inputFormats: [\"file\"]\n",
    "  outputFormats: [\"file\"]\n",
    "\n",
    "resources:\n",
    "  cpu: 1\n",
    "  gpu: 1\n",
    "  memory: 1Gi\n",
    "  gpuMemory: 6Gi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing my_app/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile my_app/requirements.txt\n",
    "highdicom>=0.18.2\n",
    "monai>=1.0\n",
    "nibabel>=3.2.1\n",
    "numpy>=1.21.6\n",
    "pydicom>=2.3.0\n",
    "setuptools>=59.5.0 # for pkg_resources\n",
    "SimpleITK>=2.0.0\n",
    "torch>=1.12.0\n",
    "holoscan>=2.9.0 # avoid v2.7 and v2.8 for a known issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the CLI package command to build the MONAI Application Package (MAP) container image based on a supported base image.\n",
    "\n",
    ":::{note}\n",
    "Building a MONAI Application Package (Docker image) can take time. Use `-l DEBUG` option to see the progress.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-01-29 14:30:47,335] [INFO] (common) - Downloading CLI manifest file...\n",
      "[2025-01-29 14:30:47,539] [DEBUG] (common) - Validating CLI manifest file...\n",
      "[2025-01-29 14:30:47,539] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app\n",
      "[2025-01-29 14:30:47,540] [INFO] (packager.parameters) - Detected application type: Python Module\n",
      "[2025-01-29 14:30:47,540] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...\n",
      "[2025-01-29 14:30:47,540] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.\n",
      "[2025-01-29 14:30:47,540] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...\n",
      "[2025-01-29 14:30:47,544] [INFO] (packager) - Generating app.json...\n",
      "[2025-01-29 14:30:47,544] [INFO] (packager) - Generating pkg.json...\n",
      "[2025-01-29 14:30:47,549] [DEBUG] (common) - \n",
      "=============== Begin app.json ===============\n",
      "{\n",
      "    \"apiVersion\": \"1.0.0\",\n",
      "    \"command\": \"[\\\"python3\\\", \\\"/opt/holoscan/app\\\"]\",\n",
      "    \"environment\": {\n",
      "        \"HOLOSCAN_APPLICATION\": \"/opt/holoscan/app\",\n",
      "        \"HOLOSCAN_INPUT_PATH\": \"input/\",\n",
      "        \"HOLOSCAN_OUTPUT_PATH\": \"output/\",\n",
      "        \"HOLOSCAN_WORKDIR\": \"/var/holoscan\",\n",
      "        \"HOLOSCAN_MODEL_PATH\": \"/opt/holoscan/models\",\n",
      "        \"HOLOSCAN_CONFIG_PATH\": \"/var/holoscan/app.yaml\",\n",
      "        \"HOLOSCAN_APP_MANIFEST_PATH\": \"/etc/holoscan/app.json\",\n",
      "        \"HOLOSCAN_PKG_MANIFEST_PATH\": \"/etc/holoscan/pkg.json\",\n",
      "        \"HOLOSCAN_DOCS_PATH\": \"/opt/holoscan/docs\",\n",
      "        \"HOLOSCAN_LOGS_PATH\": \"/var/holoscan/logs\"\n",
      "    },\n",
      "    \"input\": {\n",
      "        \"path\": \"input/\",\n",
      "        \"formats\": null\n",
      "    },\n",
      "    \"liveness\": null,\n",
      "    \"output\": {\n",
      "        \"path\": \"output/\",\n",
      "        \"formats\": null\n",
      "    },\n",
      "    \"readiness\": null,\n",
      "    \"sdk\": \"monai-deploy\",\n",
      "    \"sdkVersion\": \"2.0.0\",\n",
      "    \"timeout\": 0,\n",
      "    \"version\": 1.0,\n",
      "    \"workingDirectory\": \"/var/holoscan\"\n",
      "}\n",
      "================ End app.json ================\n",
      "                 \n",
      "[2025-01-29 14:30:47,550] [DEBUG] (common) - \n",
      "=============== Begin pkg.json ===============\n",
      "{\n",
      "    \"apiVersion\": \"1.0.0\",\n",
      "    \"applicationRoot\": \"/opt/holoscan/app\",\n",
      "    \"modelRoot\": \"/opt/holoscan/models\",\n",
      "    \"models\": {\n",
      "        \"model\": \"/opt/holoscan/models/model\"\n",
      "    },\n",
      "    \"resources\": {\n",
      "        \"cpu\": 1,\n",
      "        \"gpu\": 1,\n",
      "        \"memory\": \"1Gi\",\n",
      "        \"gpuMemory\": \"6Gi\"\n",
      "    },\n",
      "    \"version\": 1.0,\n",
      "    \"platformConfig\": \"dgpu\"\n",
      "}\n",
      "================ End pkg.json ================\n",
      "                 \n",
      "[2025-01-29 14:30:47,581] [DEBUG] (packager.builder) - \n",
      "========== Begin Build Parameters ==========\n",
      "{'additional_lib_paths': '',\n",
      " 'app_config_file_path': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml'),\n",
      " 'app_dir': PosixPath('/opt/holoscan/app'),\n",
      " 'app_json': '/etc/holoscan/app.json',\n",
      " 'application': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app'),\n",
      " 'application_directory': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app'),\n",
      " 'application_type': 'PythonModule',\n",
      " 'build_cache': PosixPath('/home/mqin/.holoscan_build_cache'),\n",
      " 'cmake_args': '',\n",
      " 'command': '[\"python3\", \"/opt/holoscan/app\"]',\n",
      " 'command_filename': 'my_app',\n",
      " 'config_file_path': PosixPath('/var/holoscan/app.yaml'),\n",
      " 'docs_dir': PosixPath('/opt/holoscan/docs'),\n",
      " 'full_input_path': PosixPath('/var/holoscan/input'),\n",
      " 'full_output_path': PosixPath('/var/holoscan/output'),\n",
      " 'gid': 1000,\n",
      " 'holoscan_sdk_version': '2.9.0',\n",
      " 'includes': [],\n",
      " 'input_dir': 'input/',\n",
      " 'lib_dir': PosixPath('/opt/holoscan/lib'),\n",
      " 'logs_dir': PosixPath('/var/holoscan/logs'),\n",
      " 'models': {'model': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model')},\n",
      " 'models_dir': PosixPath('/opt/holoscan/models'),\n",
      " 'monai_deploy_app_sdk_version': '2.0.0',\n",
      " 'no_cache': False,\n",
      " 'output_dir': 'output/',\n",
      " 'pip_packages': None,\n",
      " 'pkg_json': '/etc/holoscan/pkg.json',\n",
      " 'requirements_file_path': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/requirements.txt'),\n",
      " 'sdk': <SdkType.MonaiDeploy: 'monai-deploy'>,\n",
      " 'sdk_type': 'monai-deploy',\n",
      " 'tarball_output': None,\n",
      " 'timeout': 0,\n",
      " 'title': 'MONAI Deploy App Package - MONAI Bundle AI App',\n",
      " 'uid': 1000,\n",
      " 'username': 'holoscan',\n",
      " 'version': 1.0,\n",
      " 'working_dir': PosixPath('/var/holoscan')}\n",
      "=========== End Build Parameters ===========\n",
      "\n",
      "[2025-01-29 14:30:47,581] [DEBUG] (packager.builder) - \n",
      "========== Begin Platform Parameters ==========\n",
      "{'base_image': 'nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04',\n",
      " 'build_image': None,\n",
      " 'cuda_deb_arch': 'x86_64',\n",
      " 'custom_base_image': False,\n",
      " 'custom_holoscan_sdk': False,\n",
      " 'custom_monai_deploy_sdk': False,\n",
      " 'gpu_type': 'dgpu',\n",
      " 'holoscan_deb_arch': 'amd64',\n",
      " 'holoscan_sdk_file': '2.9.0',\n",
      " 'holoscan_sdk_filename': '2.9.0',\n",
      " 'monai_deploy_sdk_file': None,\n",
      " 'monai_deploy_sdk_filename': None,\n",
      " 'tag': 'my_app:1.0',\n",
      " 'target_arch': 'x86_64'}\n",
      "=========== End Platform Parameters ===========\n",
      "\n",
      "[2025-01-29 14:30:47,602] [DEBUG] (packager.builder) - \n",
      "========== Begin Dockerfile ==========\n",
      "\n",
      "ARG GPU_TYPE=dgpu\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "FROM nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04 AS base\n",
      "\n",
      "RUN apt-get update \\\n",
      "    && apt-get install -y --no-install-recommends --no-install-suggests \\\n",
      "        curl \\\n",
      "        jq \\\n",
      "    && rm -rf /var/lib/apt/lists/*\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "# FROM base AS mofed-installer\n",
      "# ARG MOFED_VERSION=23.10-2.1.3.1\n",
      "\n",
      "# # In a container, we only need to install the user space libraries, though the drivers are still\n",
      "# # needed on the host.\n",
      "# # Note: MOFED's installation is not easily portable, so we can't copy the output of this stage\n",
      "# # to our final stage, but must inherit from it. For that reason, we keep track of the build/install\n",
      "# # only dependencies in the `MOFED_DEPS` variable (parsing the output of `--check-deps-only`) to\n",
      "# # remove them in that same layer, to ensure they are not propagated in the final image.\n",
      "# WORKDIR /opt/nvidia/mofed\n",
      "# ARG MOFED_INSTALL_FLAGS=\"--dpdk --with-mft --user-space-only --force --without-fw-update\"\n",
      "# RUN UBUNTU_VERSION=$(cat /etc/lsb-release | grep DISTRIB_RELEASE | cut -d= -f2) \\\n",
      "#     && OFED_PACKAGE=\"MLNX_OFED_LINUX-${MOFED_VERSION}-ubuntu${UBUNTU_VERSION}-$(uname -m)\" \\\n",
      "#     && curl -S -# -o ${OFED_PACKAGE}.tgz -L \\\n",
      "#         https://www.mellanox.com/downloads/ofed/MLNX_OFED-${MOFED_VERSION}/${OFED_PACKAGE}.tgz \\\n",
      "#     && tar xf ${OFED_PACKAGE}.tgz \\\n",
      "#     && MOFED_INSTALLER=$(find . -name mlnxofedinstall -type f -executable -print) \\\n",
      "#     && MOFED_DEPS=$(${MOFED_INSTALLER} ${MOFED_INSTALL_FLAGS} --check-deps-only 2>/dev/null | tail -n1 |  cut -d' ' -f3-) \\\n",
      "#     && apt-get update \\\n",
      "#     && apt-get install --no-install-recommends -y ${MOFED_DEPS} \\\n",
      "#     && ${MOFED_INSTALLER} ${MOFED_INSTALL_FLAGS} \\\n",
      "#     && rm -r * \\\n",
      "#     && apt-get remove -y ${MOFED_DEPS} && apt-get autoremove -y \\\n",
      "#     && rm -rf /var/lib/apt/lists/*\n",
      "\n",
      "FROM base AS release\n",
      "ENV DEBIAN_FRONTEND=noninteractive\n",
      "ENV TERM=xterm-256color\n",
      "\n",
      "ARG GPU_TYPE\n",
      "ARG UNAME\n",
      "ARG UID\n",
      "ARG GID\n",
      "\n",
      "RUN mkdir -p /etc/holoscan/ \\\n",
      "        && mkdir -p /opt/holoscan/ \\\n",
      "        && mkdir -p /var/holoscan \\\n",
      "        && mkdir -p /opt/holoscan/app \\\n",
      "        && mkdir -p /var/holoscan/input \\\n",
      "        && mkdir -p /var/holoscan/output\n",
      "\n",
      "LABEL base=\"nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\"\n",
      "LABEL tag=\"my_app:1.0\"\n",
      "LABEL org.opencontainers.image.title=\"MONAI Deploy App Package - MONAI Bundle AI App\"\n",
      "LABEL org.opencontainers.image.version=\"1.0\"\n",
      "LABEL org.nvidia.holoscan=\"2.9.0\"\n",
      "\n",
      "LABEL org.monai.deploy.app-sdk=\"2.0.0\"\n",
      "\n",
      "ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input\n",
      "ENV HOLOSCAN_OUTPUT_PATH=/var/holoscan/output\n",
      "ENV HOLOSCAN_WORKDIR=/var/holoscan\n",
      "ENV HOLOSCAN_APPLICATION=/opt/holoscan/app\n",
      "ENV HOLOSCAN_TIMEOUT=0\n",
      "ENV HOLOSCAN_MODEL_PATH=/opt/holoscan/models\n",
      "ENV HOLOSCAN_DOCS_PATH=/opt/holoscan/docs\n",
      "ENV HOLOSCAN_CONFIG_PATH=/var/holoscan/app.yaml\n",
      "ENV HOLOSCAN_APP_MANIFEST_PATH=/etc/holoscan/app.json\n",
      "ENV HOLOSCAN_PKG_MANIFEST_PATH=/etc/holoscan/pkg.json\n",
      "ENV HOLOSCAN_LOGS_PATH=/var/holoscan/logs\n",
      "ENV HOLOSCAN_VERSION=2.9.0\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "# If torch is installed, we can skip installing Python\n",
      "ENV PYTHON_VERSION=3.10.6-1~22.04\n",
      "ENV PYTHON_PIP_VERSION=22.0.2+dfsg-*\n",
      "\n",
      "RUN apt update \\\n",
      "    && apt-get install -y --no-install-recommends --no-install-suggests \\\n",
      "        python3-minimal=${PYTHON_VERSION} \\\n",
      "        libpython3-stdlib=${PYTHON_VERSION} \\\n",
      "        python3=${PYTHON_VERSION} \\\n",
      "        python3-venv=${PYTHON_VERSION} \\\n",
      "        python3-pip=${PYTHON_PIP_VERSION} \\\n",
      "    && rm -rf /var/lib/apt/lists/*\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "RUN groupadd -f -g $GID $UNAME\n",
      "RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME\n",
      "RUN chown -R holoscan /var/holoscan && \\\n",
      "    chown -R holoscan /var/holoscan/input && \\\n",
      "    chown -R holoscan /var/holoscan/output\n",
      "\n",
      "# Set the working directory\n",
      "WORKDIR /var/holoscan\n",
      "\n",
      "# Copy HAP/MAP tool script\n",
      "COPY ./tools /var/holoscan/tools\n",
      "RUN chmod +x /var/holoscan/tools\n",
      "\n",
      "# Set the working directory\n",
      "WORKDIR /var/holoscan\n",
      "\n",
      "USER $UNAME\n",
      "\n",
      "ENV PATH=/home/${UNAME}/.local/bin:/opt/nvidia/holoscan/bin:$PATH\n",
      "ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/${UNAME}/.local/lib/python3.10/site-packages/holoscan/lib\n",
      "\n",
      "COPY ./pip/requirements.txt /tmp/requirements.txt\n",
      "\n",
      "RUN pip install --upgrade pip\n",
      "RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n",
      "\n",
      "\n",
      "# Install MONAI Deploy App SDK\n",
      "\n",
      "# Install MONAI Deploy from PyPI org\n",
      "RUN pip install monai-deploy-app-sdk==2.0.0\n",
      "\n",
      "\n",
      "COPY ./models  /opt/holoscan/models\n",
      "\n",
      "\n",
      "COPY ./map/app.json /etc/holoscan/app.json\n",
      "COPY ./app.config /var/holoscan/app.yaml\n",
      "COPY ./map/pkg.json /etc/holoscan/pkg.json\n",
      "\n",
      "COPY ./app /opt/holoscan/app\n",
      "\n",
      "\n",
      "ENTRYPOINT [\"/var/holoscan/tools\"]\n",
      "=========== End Dockerfile ===========\n",
      "\n",
      "[2025-01-29 14:30:47,602] [INFO] (packager.builder) - \n",
      "===============================================================================\n",
      "Building image for:                 x64-workstation\n",
      "    Architecture:                   linux/amd64\n",
      "    Base Image:                     nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "    Build Image:                    N/A\n",
      "    Cache:                          Enabled\n",
      "    Configuration:                  dgpu\n",
      "    Holoscan SDK Package:           2.9.0\n",
      "    MONAI Deploy App SDK Package:   N/A\n",
      "    gRPC Health Probe:              N/A\n",
      "    SDK Version:                    2.9.0\n",
      "    SDK:                            monai-deploy\n",
      "    Tag:                            my_app-x64-workstation-dgpu-linux-amd64:1.0\n",
      "    Included features/dependencies: N/A\n",
      "    \n",
      "[2025-01-29 14:30:48,162] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`\n",
      "[2025-01-29 14:30:48,163] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0\n",
      "#0 building with \"holoscan_app_builder\" instance using docker-container driver\n",
      "\n",
      "#1 [internal] load build definition from Dockerfile\n",
      "#1 transferring dockerfile: 4.56kB done\n",
      "#1 DONE 0.1s\n",
      "\n",
      "#2 [auth] nvidia/cuda:pull token for nvcr.io\n",
      "#2 DONE 0.0s\n",
      "\n",
      "#3 [internal] load metadata for nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "#3 DONE 0.5s\n",
      "\n",
      "#4 [internal] load .dockerignore\n",
      "#4 transferring context: 1.80kB done\n",
      "#4 DONE 0.1s\n",
      "\n",
      "#5 importing cache manifest from nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "#5 ...\n",
      "\n",
      "#6 [internal] load build context\n",
      "#6 DONE 0.0s\n",
      "\n",
      "#7 importing cache manifest from local:9689312761338090214\n",
      "#7 inferred cache manifest type: application/vnd.oci.image.index.v1+json done\n",
      "#7 DONE 0.0s\n",
      "\n",
      "#8 [base 1/2] FROM nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04@sha256:22fc009e5cea0b8b91d94c99fdd419d2366810b5ea835e47b8343bc15800c186\n",
      "#8 resolve nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04@sha256:22fc009e5cea0b8b91d94c99fdd419d2366810b5ea835e47b8343bc15800c186 0.0s done\n",
      "#8 DONE 0.0s\n",
      "\n",
      "#5 importing cache manifest from nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04\n",
      "#5 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done\n",
      "#5 DONE 0.4s\n",
      "\n",
      "#6 [internal] load build context\n",
      "#6 transferring context: 19.43MB 0.2s done\n",
      "#6 DONE 0.3s\n",
      "\n",
      "#9 [release  5/18] RUN chown -R holoscan /var/holoscan &&     chown -R holoscan /var/holoscan/input &&     chown -R holoscan /var/holoscan/output\n",
      "#9 CACHED\n",
      "\n",
      "#10 [release  1/18] RUN mkdir -p /etc/holoscan/         && mkdir -p /opt/holoscan/         && mkdir -p /var/holoscan         && mkdir -p /opt/holoscan/app         && mkdir -p /var/holoscan/input         && mkdir -p /var/holoscan/output\n",
      "#10 CACHED\n",
      "\n",
      "#11 [base 2/2] RUN apt-get update     && apt-get install -y --no-install-recommends --no-install-suggests         curl         jq     && rm -rf /var/lib/apt/lists/*\n",
      "#11 CACHED\n",
      "\n",
      "#12 [release  3/18] RUN groupadd -f -g 1000 holoscan\n",
      "#12 CACHED\n",
      "\n",
      "#13 [release  6/18] WORKDIR /var/holoscan\n",
      "#13 CACHED\n",
      "\n",
      "#14 [release  4/18] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan\n",
      "#14 CACHED\n",
      "\n",
      "#15 [release  7/18] COPY ./tools /var/holoscan/tools\n",
      "#15 CACHED\n",
      "\n",
      "#16 [release  8/18] RUN chmod +x /var/holoscan/tools\n",
      "#16 CACHED\n",
      "\n",
      "#17 [release  2/18] RUN apt update     && apt-get install -y --no-install-recommends --no-install-suggests         python3-minimal=3.10.6-1~22.04         libpython3-stdlib=3.10.6-1~22.04         python3=3.10.6-1~22.04         python3-venv=3.10.6-1~22.04         python3-pip=22.0.2+dfsg-*     && rm -rf /var/lib/apt/lists/*\n",
      "#17 CACHED\n",
      "\n",
      "#18 [release  9/18] WORKDIR /var/holoscan\n",
      "#18 CACHED\n",
      "\n",
      "#19 [release 10/18] COPY ./pip/requirements.txt /tmp/requirements.txt\n",
      "#19 DONE 0.8s\n",
      "\n",
      "#20 [release 11/18] RUN pip install --upgrade pip\n",
      "#20 0.933 Defaulting to user installation because normal site-packages is not writeable\n",
      "#20 0.985 Requirement already satisfied: pip in /usr/lib/python3/dist-packages (22.0.2)\n",
      "#20 1.141 Collecting pip\n",
      "#20 1.201   Downloading pip-25.0-py3-none-any.whl (1.8 MB)\n",
      "#20 1.272      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 27.4 MB/s eta 0:00:00\n",
      "#20 1.298 Installing collected packages: pip\n",
      "#20 2.043 Successfully installed pip-25.0\n",
      "#20 DONE 2.2s\n",
      "\n",
      "#21 [release 12/18] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt\n",
      "#21 0.735 Collecting highdicom>=0.18.2 (from -r /tmp/requirements.txt (line 1))\n",
      "#21 0.749   Downloading highdicom-0.24.0-py3-none-any.whl.metadata (4.7 kB)\n",
      "#21 0.838 Collecting monai>=1.0 (from -r /tmp/requirements.txt (line 2))\n",
      "#21 0.844   Downloading monai-1.4.0-py3-none-any.whl.metadata (11 kB)\n",
      "#21 0.927 Collecting nibabel>=3.2.1 (from -r /tmp/requirements.txt (line 3))\n",
      "#21 0.932   Downloading nibabel-5.3.2-py3-none-any.whl.metadata (9.1 kB)\n",
      "#21 1.065 Collecting numpy>=1.21.6 (from -r /tmp/requirements.txt (line 4))\n",
      "#21 1.069   Downloading numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n",
      "#21 1.101 Collecting pydicom>=2.3.0 (from -r /tmp/requirements.txt (line 5))\n",
      "#21 1.106   Downloading pydicom-3.0.1-py3-none-any.whl.metadata (9.4 kB)\n",
      "#21 1.114 Requirement already satisfied: setuptools>=59.5.0 in /usr/lib/python3/dist-packages (from -r /tmp/requirements.txt (line 6)) (59.6.0)\n",
      "#21 1.144 Collecting SimpleITK>=2.0.0 (from -r /tmp/requirements.txt (line 7))\n",
      "#21 1.148   Downloading SimpleITK-2.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.9 kB)\n",
      "#21 1.180 Collecting torch>=1.12.0 (from -r /tmp/requirements.txt (line 8))\n",
      "#21 1.184   Downloading torch-2.6.0-cp310-cp310-manylinux1_x86_64.whl.metadata (28 kB)\n",
      "#21 1.204 Collecting holoscan>=2.9.0 (from -r /tmp/requirements.txt (line 9))\n",
      "#21 1.209   Downloading holoscan-2.9.0-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (7.3 kB)\n",
      "#21 1.369 Collecting pillow>=8.3 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))\n",
      "#21 1.374   Downloading pillow-11.1.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.1 kB)\n",
      "#21 1.475 Collecting pyjpegls>=1.0.0 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))\n",
      "#21 1.481   Downloading pyjpegls-1.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)\n",
      "#21 1.498 Collecting typing-extensions>=4.0.0 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))\n",
      "#21 1.502   Downloading typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)\n",
      "#21 1.513 Collecting numpy>=1.21.6 (from -r /tmp/requirements.txt (line 4))\n",
      "#21 1.518   Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n",
      "#21 1.543 Collecting importlib-resources>=5.12 (from nibabel>=3.2.1->-r /tmp/requirements.txt (line 3))\n",
      "#21 1.547   Downloading importlib_resources-6.5.2-py3-none-any.whl.metadata (3.9 kB)\n",
      "#21 1.671 Collecting packaging>=20 (from nibabel>=3.2.1->-r /tmp/requirements.txt (line 3))\n",
      "#21 1.674   Downloading packaging-24.2-py3-none-any.whl.metadata (3.2 kB)\n",
      "#21 1.707 Collecting filelock (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.712   Downloading filelock-3.17.0-py3-none-any.whl.metadata (2.9 kB)\n",
      "#21 1.758 Collecting networkx (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.763   Downloading networkx-3.4.2-py3-none-any.whl.metadata (6.3 kB)\n",
      "#21 1.792 Collecting jinja2 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.797   Downloading jinja2-3.1.5-py3-none-any.whl.metadata (2.6 kB)\n",
      "#21 1.824 Collecting fsspec (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.828   Downloading fsspec-2024.12.0-py3-none-any.whl.metadata (11 kB)\n",
      "#21 1.905 Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.910   Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "#21 1.923 Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.927   Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "#21 1.943 Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.948   Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "#21 1.963 Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.968   Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "#21 1.981 Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 1.985   Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "#21 1.998 Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 2.003   Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "#21 2.015 Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 2.020   Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
      "#21 2.032 Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 2.036   Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "#21 2.050 Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 2.055   Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
      "#21 2.068 Collecting nvidia-cusparselt-cu12==0.6.2 (from torch>=1.12.0->-r /tmp/requirements.txt (line 8))\n",
      "#21 2.074   Downloading nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl.metadata (6.8 kB)\n",
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      "#21 41.78    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.0/2.0 MB 116.5 MB/s eta 0:00:00\n",
      "#21 41.78 Downloading urllib3-2.3.0-py3-none-any.whl (128 kB)\n",
      "#21 41.79 Downloading annotated_types-0.7.0-py3-none-any.whl (13 kB)\n",
      "#21 54.16 Installing collected packages: triton, SimpleITK, nvidia-cusparselt-cu12, mpmath, fastrlock, urllib3, typing-extensions, sympy, pyyaml, pydicom, psutil, pillow, packaging, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, importlib-resources, idna, fsspec, filelock, cloudpickle, charset-normalizer, certifi, annotated-types, wheel-axle-runtime, requests, pyjpegls, pydantic-core, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nibabel, jinja2, cupy-cuda12x, pydantic, nvidia-cusolver-cu12, highdicom, torch, python-on-whales, monai, holoscan\n",
      "#21 123.1 Successfully installed MarkupSafe-3.0.2 SimpleITK-2.4.1 annotated-types-0.7.0 certifi-2024.12.14 charset-normalizer-3.4.1 cloudpickle-3.1.1 cupy-cuda12x-13.3.0 fastrlock-0.8.3 filelock-3.17.0 fsspec-2024.12.0 highdicom-0.24.0 holoscan-2.9.0 idna-3.10 importlib-resources-6.5.2 jinja2-3.1.5 monai-1.4.0 mpmath-1.3.0 networkx-3.4.2 nibabel-5.3.2 numpy-1.26.4 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-cusparselt-cu12-0.6.2 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 packaging-24.2 pillow-11.1.0 psutil-6.1.1 pydantic-2.10.6 pydantic-core-2.27.2 pydicom-3.0.1 pyjpegls-1.4.0 python-on-whales-0.75.1 pyyaml-6.0.2 requests-2.32.3 sympy-1.13.1 torch-2.6.0 triton-3.2.0 typing-extensions-4.12.2 urllib3-2.3.0 wheel-axle-runtime-0.0.6\n",
      "#21 DONE 124.4s\n",
      "\n",
      "#22 [release 13/18] RUN pip install monai-deploy-app-sdk==2.0.0\n",
      "#22 1.361 Defaulting to user installation because normal site-packages is not writeable\n",
      "#22 1.564 Collecting monai-deploy-app-sdk==2.0.0\n",
      "#22 1.579   Downloading monai_deploy_app_sdk-2.0.0-py3-none-any.whl.metadata (7.6 kB)\n",
      "#22 1.602 Requirement already satisfied: numpy>=1.21.6 in /home/holoscan/.local/lib/python3.10/site-packages (from monai-deploy-app-sdk==2.0.0) (1.26.4)\n",
      "#22 1.604 Requirement already satisfied: holoscan~=2.0 in /home/holoscan/.local/lib/python3.10/site-packages (from monai-deploy-app-sdk==2.0.0) (2.9.0)\n",
      "#22 1.647 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==2.0.0)\n",
      "#22 1.652   Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n",
      "#22 1.725 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==2.0.0)\n",
      "#22 1.731   Downloading typeguard-4.4.1-py3-none-any.whl.metadata (3.7 kB)\n",
      "#22 1.768 Requirement already satisfied: pip>22.0.2 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (25.0)\n",
      "#22 1.769 Requirement already satisfied: cupy-cuda12x<14.0,>=12.2 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (13.3.0)\n",
      "#22 1.770 Requirement already satisfied: cloudpickle<4.0,>=3.0 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (3.1.1)\n",
      "#22 1.771 Requirement already satisfied: python-on-whales<1.0,>=0.60.1 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (0.75.1)\n",
      "#22 1.772 Requirement already satisfied: Jinja2<4.0,>=3.1.3 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (3.1.5)\n",
      "#22 1.773 Requirement already satisfied: packaging>=23.1 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (24.2)\n",
      "#22 1.774 Requirement already satisfied: pyyaml<7.0,>=6.0 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (6.0.2)\n",
      "#22 1.776 Requirement already satisfied: requests<3.0,>=2.31.0 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (2.32.3)\n",
      "#22 1.776 Requirement already satisfied: psutil<7.0,>=6.0.0 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (6.1.1)\n",
      "#22 1.777 Requirement already satisfied: wheel-axle-runtime<1.0 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (0.0.6)\n",
      "#22 1.781 Requirement already satisfied: typing-extensions>=4.10.0 in /home/holoscan/.local/lib/python3.10/site-packages (from typeguard>=3.0.0->monai-deploy-app-sdk==2.0.0) (4.12.2)\n",
      "#22 1.788 Requirement already satisfied: fastrlock>=0.5 in /home/holoscan/.local/lib/python3.10/site-packages (from cupy-cuda12x<14.0,>=12.2->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (0.8.3)\n",
      "#22 1.789 Requirement already satisfied: MarkupSafe>=2.0 in /home/holoscan/.local/lib/python3.10/site-packages (from Jinja2<4.0,>=3.1.3->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (3.0.2)\n",
      "#22 1.799 Requirement already satisfied: pydantic!=2.0.*,<3,>=2 in /home/holoscan/.local/lib/python3.10/site-packages (from python-on-whales<1.0,>=0.60.1->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (2.10.6)\n",
      "#22 1.804 Requirement already satisfied: charset-normalizer<4,>=2 in /home/holoscan/.local/lib/python3.10/site-packages (from requests<3.0,>=2.31.0->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (3.4.1)\n",
      "#22 1.805 Requirement already satisfied: idna<4,>=2.5 in /home/holoscan/.local/lib/python3.10/site-packages (from requests<3.0,>=2.31.0->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (3.10)\n",
      "#22 1.805 Requirement already satisfied: urllib3<3,>=1.21.1 in /home/holoscan/.local/lib/python3.10/site-packages (from requests<3.0,>=2.31.0->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (2.3.0)\n",
      "#22 1.806 Requirement already satisfied: certifi>=2017.4.17 in /home/holoscan/.local/lib/python3.10/site-packages (from requests<3.0,>=2.31.0->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (2024.12.14)\n",
      "#22 1.811 Requirement already satisfied: filelock in /home/holoscan/.local/lib/python3.10/site-packages (from wheel-axle-runtime<1.0->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (3.17.0)\n",
      "#22 1.830 Requirement already satisfied: annotated-types>=0.6.0 in /home/holoscan/.local/lib/python3.10/site-packages (from pydantic!=2.0.*,<3,>=2->python-on-whales<1.0,>=0.60.1->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (0.7.0)\n",
      "#22 1.831 Requirement already satisfied: pydantic-core==2.27.2 in /home/holoscan/.local/lib/python3.10/site-packages (from pydantic!=2.0.*,<3,>=2->python-on-whales<1.0,>=0.60.1->holoscan~=2.0->monai-deploy-app-sdk==2.0.0) (2.27.2)\n",
      "#22 1.849 Downloading monai_deploy_app_sdk-2.0.0-py3-none-any.whl (132 kB)\n",
      "#22 1.872 Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
      "#22 1.894 Downloading typeguard-4.4.1-py3-none-any.whl (35 kB)\n",
      "#22 2.064 Installing collected packages: typeguard, colorama, monai-deploy-app-sdk\n",
      "#22 2.201 Successfully installed colorama-0.4.6 monai-deploy-app-sdk-2.0.0 typeguard-4.4.1\n",
      "#22 DONE 2.6s\n",
      "\n",
      "#23 [release 14/18] COPY ./models  /opt/holoscan/models\n",
      "#23 DONE 0.2s\n",
      "\n",
      "#24 [release 15/18] COPY ./map/app.json /etc/holoscan/app.json\n",
      "#24 DONE 0.1s\n",
      "\n",
      "#25 [release 16/18] COPY ./app.config /var/holoscan/app.yaml\n",
      "#25 DONE 0.1s\n",
      "\n",
      "#26 [release 17/18] COPY ./map/pkg.json /etc/holoscan/pkg.json\n",
      "#26 DONE 0.1s\n",
      "\n",
      "#27 [release 18/18] COPY ./app /opt/holoscan/app\n",
      "#27 DONE 0.1s\n",
      "\n",
      "#28 exporting to docker image format\n",
      "#28 exporting layers\n",
      "#28 exporting layers 203.4s done\n",
      "#28 exporting manifest sha256:089e3612567f5b5a28edd5e2ffd6ddf01264ddbcdc641694b12620fdbfa40828 0.0s done\n",
      "#28 exporting config sha256:a863c524e0de40b25e7e8b519c4009196f543dc45dde5399124c1f9317a67b4e 0.0s done\n",
      "#28 sending tarball\n",
      "#28 ...\n",
      "\n",
      "#29 importing to docker\n",
      "#29 loading layer 4dfc251f5c56 289B / 289B\n",
      "#29 loading layer 74089dc02aa9 65.54kB / 5.10MB\n",
      "#29 loading layer efdd29e523f7 557.06kB / 3.34GB\n",
      "#29 loading layer efdd29e523f7 159.32MB / 3.34GB 6.4s\n",
      "#29 loading layer efdd29e523f7 330.33MB / 3.34GB 10.4s\n",
      "#29 loading layer efdd29e523f7 557.06MB / 3.34GB 16.6s\n",
      "#29 loading layer efdd29e523f7 797.70MB / 3.34GB 20.7s\n",
      "#29 loading layer efdd29e523f7 1.01GB / 3.34GB 24.7s\n",
      "#29 loading layer efdd29e523f7 1.25GB / 3.34GB 28.9s\n",
      "#29 loading layer efdd29e523f7 1.45GB / 3.34GB 33.0s\n",
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      "#29 loading layer efdd29e523f7 1.95GB / 3.34GB 41.1s\n",
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      "#29 loading layer efdd29e523f7 2.31GB / 3.34GB 51.9s\n",
      "#29 loading layer efdd29e523f7 2.53GB / 3.34GB 58.0s\n",
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      "#29 loading layer b97e5e112e3f 3.94kB / 3.94kB\n",
      "#29 loading layer 04571d3d4439 315B / 315B 0.5s done\n",
      "#29 loading layer 4dfc251f5c56 289B / 289B 78.7s done\n",
      "#29 loading layer 74089dc02aa9 65.54kB / 5.10MB 78.6s done\n",
      "#29 loading layer efdd29e523f7 3.25GB / 3.34GB 78.1s done\n",
      "#29 loading layer 47536db86449 32.77kB / 578.01kB 1.2s done\n",
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      "#29 loading layer b97e5e112e3f 3.94kB / 3.94kB 0.4s done\n",
      "#29 DONE 78.7s\n",
      "\n",
      "#28 exporting to docker image format\n",
      "#28 sending tarball 123.2s done\n",
      "#28 DONE 326.7s\n",
      "\n",
      "#30 exporting cache to client directory\n",
      "#30 preparing build cache for export\n",
      "#30 writing layer sha256:05b02574544ba690caa3ab6304a23e2eb00610e44ca586c3f6045abe235d4886\n",
      "#30 writing layer sha256:05b02574544ba690caa3ab6304a23e2eb00610e44ca586c3f6045abe235d4886 0.0s done\n",
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      "#30 writing layer sha256:fa35667ed3d919e87dc31994d8dfd51eab483c48ae72fb0ad2ab83e2b544c7c3\n",
      "#30 writing layer sha256:fa35667ed3d919e87dc31994d8dfd51eab483c48ae72fb0ad2ab83e2b544c7c3 0.3s done\n",
      "#30 preparing build cache for export 50.1s done\n",
      "#30 writing config sha256:dff8a5cdb36509e072452e50bce7b5a26df3d17fd6570f7f4ab9dc79ea47e02e 0.0s done\n",
      "#30 writing cache manifest sha256:bf6b0dabb704fd0021d37ae93daa4ad6ff38ef9d7b58d131e90451dbbcdcd709 0.0s done\n",
      "#30 DONE 50.1s\n",
      "[2025-01-29 14:39:18,007] [INFO] (packager) - Build Summary:\n",
      "\n",
      "Platform: x64-workstation/dgpu\n",
      "    Status:     Succeeded\n",
      "    Docker Tag: my_app-x64-workstation-dgpu-linux-amd64:1.0\n",
      "    Tarball:    None\n"
     ]
    }
   ],
   "source": [
    "tag_prefix = \"my_app\"\n",
    "\n",
    "!monai-deploy package my_app -m {models_folder} -c my_app/app.yaml -t {tag_prefix}:1.0 --platform x64-workstation -l DEBUG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the MAP Docker image is created.\n",
    "\n",
    "We can choose to display and inspect the MAP manifests by running the container with the `show` command, as well as extracting the manifests and other contents in the MAP by using the `extract` command, but not demonstrated in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_app-x64-workstation-dgpu-linux-amd64                                       1.0                            a863c524e0de   6 minutes ago    8.63GB\n"
     ]
    }
   ],
   "source": [
    "!docker image ls | grep {tag_prefix}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing packaged app locally\n",
    "\n",
    "The packaged app can be run locally through [MONAI Application Runner](/developing_with_sdk/executing_packaged_app_locally)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output\n",
      "dcm\n",
      "[2025-01-29 14:39:20,640] [INFO] (runner) - Checking dependencies...\n",
      "[2025-01-29 14:39:20,641] [INFO] (runner) - --> Verifying if \"docker\" is installed...\n",
      "\n",
      "[2025-01-29 14:39:20,641] [INFO] (runner) - --> Verifying if \"docker-buildx\" is installed...\n",
      "\n",
      "[2025-01-29 14:39:20,641] [INFO] (runner) - --> Verifying if \"my_app-x64-workstation-dgpu-linux-amd64:1.0\" is available...\n",
      "\n",
      "[2025-01-29 14:39:20,713] [INFO] (runner) - Reading HAP/MAP manifest...\n",
      "Successfully copied 2.56kB to /tmp/tmpguo6yiqf/app.json\n",
      "Successfully copied 2.05kB to /tmp/tmpguo6yiqf/pkg.json\n",
      "2f1c2c8df19c8ecc58cc0d8136bdd27b4143e88c67da72b5340f86b49eec22bb\n",
      "[2025-01-29 14:39:21,151] [INFO] (runner) - --> Verifying if \"nvidia-ctk\" is installed...\n",
      "\n",
      "[2025-01-29 14:39:21,152] [INFO] (runner) - --> Verifying \"nvidia-ctk\" version...\n",
      "\n",
      "[2025-01-29 14:39:21,443] [INFO] (common) - Launching container (d6429624e9ee) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...\n",
      "    container name:      relaxed_zhukovsky\n",
      "    host name:           mingq-dt\n",
      "    network:             host\n",
      "    user:                1000:1000\n",
      "    ulimits:             memlock=-1:-1, stack=67108864:67108864\n",
      "    cap_add:             CAP_SYS_PTRACE\n",
      "    ipc mode:            host\n",
      "    shared memory size:  67108864\n",
      "    devices:             \n",
      "    group_add:           44\n",
      "2025-01-29 22:39:22 [INFO] Launching application python3 /opt/holoscan/app ...\n",
      "\n",
      "[info] [fragment.cpp:599] Loading extensions from configs...\n",
      "\n",
      "[info] [gxf_executor.cpp:264] Creating context\n",
      "\n",
      "[2025-01-29 22:39:28,954] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])\n",
      "\n",
      "[2025-01-29 22:39:28,960] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)\n",
      "\n",
      "[2025-01-29 22:39:28,961] [INFO] (app.AISpleenSegApp) - App input and output path: /var/holoscan/input, /var/holoscan/output\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'input_folder'\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'dicom_study_list'\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'study_selected_series_list'\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'image'\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'output_folder'\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'study_selected_series_list'\n",
      "\n",
      "[info] [gxf_executor.cpp:1797] creating input IOSpec named 'seg_image'\n",
      "\n",
      "[info] [gxf_executor.cpp:2208] Activating Graph...\n",
      "\n",
      "[info] [gxf_executor.cpp:2238] Running Graph...\n",
      "\n",
      "[info] [gxf_executor.cpp:2240] Waiting for completion...\n",
      "\n",
      "[info] [greedy_scheduler.cpp:191] Scheduling 5 entities\n",
      "\n",
      "[2025-01-29 22:39:29,005] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None\n",
      "\n",
      "[2025-01-29 22:39:29,927] [INFO] (root) - Finding series for Selection named: CT Series\n",
      "\n",
      "[2025-01-29 22:39:29,927] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291\n",
      "\n",
      "  # of series: 1\n",
      "\n",
      "[2025-01-29 22:39:29,927] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n",
      "\n",
      "[2025-01-29 22:39:29,927] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n",
      "\n",
      "[2025-01-29 22:39:29,927] [INFO] (root) -     Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST\n",
      "\n",
      "[2025-01-29 22:39:29,927] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) -     Series attribute Modality value: CT\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) -     Series attribute SeriesDescription value: ABD/PANC 3.0 B31f\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) - Series attribute string value did not match. Try regEx.\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) - On attribute: 'ImageType' to match value: ['PRIMARY', 'ORIGINAL']\n",
      "\n",
      "[2025-01-29 22:39:29,928] [INFO] (root) -     Series attribute ImageType value: None\n",
      "\n",
      "[2025-01-29 22:39:29,929] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239\n",
      "\n",
      "[2025-01-29 22:39:30,619] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Converted Image object metadata:\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDate: 20090831, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesTime: 101721.452, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Modality: CT, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesDescription: ABD/PANC 3.0 B31f, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - PatientPosition: HFS, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - SeriesNumber: 8, type <class 'int'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_pixel_spacing: 0.7890625, type <class 'float'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_pixel_spacing: 0.7890625, type <class 'float'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_pixel_spacing: 1.5, type <class 'float'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - row_direction_cosine: [1.0, 0.0, 0.0], type <class 'list'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - col_direction_cosine: [0.0, 1.0, 0.0], type <class 'list'>\n",
      "\n",
      "[2025-01-29 22:39:30,620] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - depth_direction_cosine: [0.0, 0.0, 1.0], type <class 'list'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - dicom_affine_transform: [[   0.7890625    0.           0.        -197.60547  ]\n",
      "\n",
      " [   0.           0.7890625    0.        -398.60547  ]\n",
      "\n",
      " [   0.           0.           1.5       -383.       ]\n",
      "\n",
      " [   0.           0.           0.           1.       ]], type <class 'numpy.ndarray'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - nifti_affine_transform: [[  -0.7890625   -0.          -0.         197.60547  ]\n",
      "\n",
      " [  -0.          -0.7890625   -0.         398.60547  ]\n",
      "\n",
      " [   0.           0.           1.5       -383.       ]\n",
      "\n",
      " [   0.           0.           0.           1.       ]], type <class 'numpy.ndarray'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyID: , type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDate: 20090831, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyTime: 095948.599, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - StudyDescription: CT ABDOMEN W IV CONTRAST, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - AccessionNumber: 5471978513296937, type <class 'str'>\n",
      "\n",
      "[2025-01-29 22:39:30,621] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - selection_name: CT Series, type <class 'str'>\n",
      "\n",
      "2025-01-29 22:39:31,772 INFO image_writer.py:197 - writing: /var/holoscan/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n",
      "\n",
      "2025-01-29 22:39:36,704 INFO image_writer.py:197 - writing: /var/holoscan/output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626/1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n",
      "\n",
      "[2025-01-29 22:39:38,595] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image numpy array shaped: (204, 512, 512)\n",
      "\n",
      "[2025-01-29 22:39:38,601] [INFO] (monai.deploy.operators.monai_seg_inference_operator.MonaiSegInferenceOperator) - Output Seg image pixel max value: 1\n",
      "\n",
      "/home/holoscan/.local/lib/python3.10/site-packages/highdicom/base.py:163: UserWarning: The string \"C3N-00198\" is unlikely to represent the intended person name since it contains only a single component. Construct a person name according to the format in described in https://dicom.nema.org/dicom/2013/output/chtml/part05/sect_6.2.html#sect_6.2.1.2, or, in pydicom 2.2.0 or later, use the pydicom.valuerep.PersonName.from_named_components() method to construct the person name correctly. If a single-component name is really intended, add a trailing caret character to disambiguate the name.\n",
      "\n",
      "  check_person_name(patient_name)\n",
      "\n",
      "[2025-01-29 22:39:40,097] [INFO] (highdicom.base) - copy Image-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "\n",
      "[2025-01-29 22:39:40,097] [INFO] (highdicom.base) - copy attributes of module \"Specimen\"\n",
      "\n",
      "[2025-01-29 22:39:40,097] [INFO] (highdicom.base) - copy Patient-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "\n",
      "[2025-01-29 22:39:40,097] [INFO] (highdicom.base) - copy attributes of module \"Patient\"\n",
      "\n",
      "[2025-01-29 22:39:40,098] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Subject\"\n",
      "\n",
      "[2025-01-29 22:39:40,098] [INFO] (highdicom.base) - copy Study-related attributes from dataset \"1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191\"\n",
      "\n",
      "[2025-01-29 22:39:40,098] [INFO] (highdicom.base) - copy attributes of module \"General Study\"\n",
      "\n",
      "[2025-01-29 22:39:40,098] [INFO] (highdicom.base) - copy attributes of module \"Patient Study\"\n",
      "\n",
      "[2025-01-29 22:39:40,099] [INFO] (highdicom.base) - copy attributes of module \"Clinical Trial Study\"\n",
      "\n",
      "[info] [greedy_scheduler.cpp:372] Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock.\n",
      "\n",
      "[info] [greedy_scheduler.cpp:401] Scheduler finished.\n",
      "\n",
      "[info] [gxf_executor.cpp:2243] Deactivating Graph...\n",
      "\n",
      "[info] [gxf_executor.cpp:2251] Graph execution finished.\n",
      "\n",
      "[2025-01-29 22:39:40,203] [INFO] (app.AISpleenSegApp) - End run\n",
      "\n",
      "[info] [gxf_executor.cpp:294] Destroying context\n",
      "\n",
      "[2025-01-29 14:39:42,029] [INFO] (common) - Container 'relaxed_zhukovsky'(d6429624e9ee) exited.\n"
     ]
    }
   ],
   "source": [
    "# Clear the output folder and run the MAP. The input is expected to be a folder.\n",
    "!echo $HOLOSCAN_OUTPUT_PATH\n",
    "!echo $HOLOSCAN_INPUT_PATH\n",
    "!rm -rf $HOLOSCAN_OUTPUT_PATH\n",
    "!monai-deploy run -i $HOLOSCAN_INPUT_PATH -o $HOLOSCAN_OUTPUT_PATH my_app-x64-workstation-dgpu-linux-amd64:1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output:\n",
      "1.2.826.0.1.3680043.10.511.3.9998962080747738621710125447135664.dcm\n",
      "saved_images_folder\n",
      "\n",
      "output/saved_images_folder:\n",
      "1.3.6.1.4.1.14519.5.2.1.7085.2626\n",
      "\n",
      "output/saved_images_folder/1.3.6.1.4.1.14519.5.2.1.7085.2626:\n",
      "1.3.6.1.4.1.14519.5.2.1.7085.2626.nii\n",
      "1.3.6.1.4.1.14519.5.2.1.7085.2626_seg.nii\n"
     ]
    }
   ],
   "source": [
    "!ls -R $HOLOSCAN_OUTPUT_PATH"
   ]
  }
 ],
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