Creating a Deploy App with MONAI Deploy App SDK and MONAI Bundle

This tutorial shows how to create an application for organ segmentation using a PyTorch model that has been trained with MONAI and packaged in the MONAI Bundle format.

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.

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.

DICOM instances received from modalities and Picture Archiving and Communications System (PACS) are often times the whole DICOM study, so 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.

In the following sections, we will demonstrate how to create a MONAI Deploy application package using the MONAI Deploy App SDK, and importantly, using the built-in MONAI Bundle Inference Operator to perform inference with the Spleen CT Segmentation PyTorch model in a MONAI Bundle.

Note

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 a NIfTI file to a DICOM series.

To make running this example simpler, the DICOM files and the Spleen CT Segmentation MONAI Bundle, published in MONAI Model Zoo, have been packaged and shared on Google Drive.

Creating Operators and connecting them in Application class

We will implement an application that consists of five Operators:

• DICOMDataLoaderOperator:

  • Input(dicom_files): a folder path (Path)

  • Output(dicom_study_list): a list of DICOM studies in memory (List[DICOMStudy])

• DICOMSeriesSelectorOperator:

  • Input(dicom_study_list): a list of DICOM studies in memory (List[DICOMStudy])

  • Input(selection_rules): a selection rule (Dict)

  • Output(study_selected_series_list): a DICOM series object in memory (StudySelectedSeries)

• DICOMSeriesToVolumeOperator:

  • Input(study_selected_series_list): a DICOM series object in memory (StudySelectedSeries)

  • Output(image): an image object in memory (Image)

• MonaiBundleInferenceOperator:

  • Input(image): an image object in memory (Image)

  • Output(pred): an image object in memory (Image)

• DICOMSegmentationWriterOperator:

  • Input(seg_image): a segmentation image object in memory (Image)

  • Input(study_selected_series_list): a DICOM series object in memory (StudySelectedSeries)

  • Output(dicom_seg_instance): a file path (Path)

Note

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.

The workflow of the application is illustrated below.

%%{init: {"theme": "base", "themeVariables": { "fontSize": "16px"}} }%% classDiagram direction TB DICOMDataLoaderOperator --|> DICOMSeriesSelectorOperator : dicom_study_list...dicom_study_list DICOMSeriesSelectorOperator --|> DICOMSeriesToVolumeOperator : study_selected_series_list...study_selected_series_list DICOMSeriesToVolumeOperator --|> MonaiBundleInferenceOperator : image...image DICOMSeriesSelectorOperator --|> DICOMSegmentationWriterOperator : study_selected_series_list...study_selected_series_list MonaiBundleInferenceOperator --|> DICOMSegmentationWriterOperator : pred...seg_image class DICOMDataLoaderOperator { <in>dicom_files : DISK dicom_study_list(out) IN_MEMORY } class DICOMSeriesSelectorOperator { <in>dicom_study_list : IN_MEMORY <in>selection_rules : IN_MEMORY study_selected_series_list(out) IN_MEMORY } class DICOMSeriesToVolumeOperator { <in>study_selected_series_list : IN_MEMORY image(out) IN_MEMORY } class MonaiBundleInferenceOperator { <in>image : IN_MEMORY pred(out) IN_MEMORY } class DICOMSegmentationWriterOperator { <in>seg_image : IN_MEMORY <in>study_selected_series_list : IN_MEMORY dicom_seg_instance(out) DISK }

Setup environment

# Install MONAI and other necessary image processing packages for the application
!python -c "import monai" || pip install --upgrade -q "monai"
!python -c "import torch" || pip install -q "torch>=1.12.0"
!python -c "import numpy" || pip install -q "numpy>=1.21.6"
!python -c "import nibabel" || pip install -q "nibabel>=3.2.1"
!python -c "import pydicom" || pip install -q "pydicom>=2.3.0"
!python -c "import highdicom" || pip install -q "highdicom>=0.18.2"
!python -c "import SimpleITK" || pip install -q "SimpleITK>=2.0.0"
!python -c "import skimage" || pip install -q "scikit-image>=0.17.2"
!python -c "import stl" || pip install -q "numpy-stl>=2.12.0"
!python -c "import trimesh" || pip install -q "trimesh>=3.8.11"

# Install MONAI Deploy App SDK package
!python -c "import holoscan" || pip install --upgrade -q "holoscan>=0.6.0"
!python -c "import monai.deploy" || pip install -q "monai-deploy-app-sdk"

Note: you may need to restart the Jupyter kernel to use the updated packages.

Download/Extract input and model/bundle files from Google Drive

# Download the test data and MONAI bundle zip file
!pip install gdown
!gdown "https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ"

# After downloading ai_spleen_bundle_data zip file from the web browser or using gdown,
!unzip -o "ai_spleen_seg_bundle_data.zip"

# Need to copy the model.ts file to its own clean subfolder for pacakging, to workaround an issue in the Packager
models_folder = "models"
!rm -rf {models_folder} && mkdir -p {models_folder}/model && cp model.ts {models_folder}/model && ls {models_folder}/model

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model.ts

Set up environment variables

%env HOLOSCAN_INPUT_PATH dcm
%env HOLOSCAN_MODEL_PATH {models_folder}
%env HOLOSCAN_OUTPUT_PATH output

env: HOLOSCAN_INPUT_PATH=dcm
env: HOLOSCAN_MODEL_PATH=models
env: HOLOSCAN_OUTPUT_PATH=output

Set up imports

Let’s import necessary classes/decorators to define Application and Operator.

import logging
from pathlib import Path

# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package.
from pydicom.sr.codedict import codes

from monai.deploy.conditions import CountCondition
from monai.deploy.core import AppContext, Application
from monai.deploy.core.domain import Image
from monai.deploy.core.io_type import IOType
from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription
from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator
from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
from monai.deploy.operators.monai_bundle_inference_operator import (
    BundleConfigNames,
    IOMapping,
    MonaiBundleInferenceOperator,
)
from monai.deploy.operators.stl_conversion_operator import STLConversionOperator

Determining the Input and Output for the Model Bundle Inference Operator

The App SDK provides a MonaiBundleInferenceOperator class to perform inference with a MONAI Bundle, which is essentially a PyTorch model in TorchScript with additional metadata describing the model network and processing specification. This operator uses the MONAI utilities to parse a MONAI Bundle to automatically instantiate the objects required for input and output processing as well as inference, as such it depends on MONAI transforms, inferers, and in turn their dependencies.

Each Operator class inherits from the base Operator base class, and its input/output properties are specified in the setup function (as opposed to using decorators @inputand @output in Version 0.5 and below).

For the MonaiBundleInferenceOperator class, the input/output need to be defined to match those of the model network, both in name and data type. For the current release, an IOMapping object is used to connect the operator input/output to those of the model network by using the same names. This is likely to change, to be automated, in the future releases once certain limitation in the App SDK is removed.

The Spleen CT Segmentation model network has a named input, called “image”, and the named output called “pred”, and both are of image type, which can all be mapped to the App SDK Image. This piece of information is typically acquired by examining the model metadata network_data_format attribute in the bundle, as seen in this [example] (https://github.com/Project-MONAI/model-zoo/blob/dev/models/spleen_ct_segmentation/configs/metadata.json).

Creating Application class

Our application class would look like below.

It defines App class, inheriting the base Application class.

Objects required for DICOM parsing, series selection, pixel data conversion to volume image, model specific inference, and the AI result specific DICOM Segmentation object writers are created. The execution pipeline, as a Directed Acyclic Graph, is then constructed by connecting these objects through self.add_flow().

class AISpleenSegApp(Application):
    """Demonstrates inference with built-in MONAI Bundle inference operator with DICOM files as input/output

    This application loads a set of DICOM instances, select the appropriate series, converts the series to
    3D volume image, performs inference with the built-in MONAI Bundle inference operator, including pre-processing
    and post-processing, save the segmentation image in a DICOM Seg OID in an instance file, and optionally the
    surface mesh in STL format.

    Pertinent MONAI Bundle:
      https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation

    Execution Time Estimate:
      With a Nvidia GV100 32GB GPU, for an input DICOM Series of 515 instances, the execution time is around
      25 seconds with saving both DICOM Seg and surface mesh STL file, and 15 seconds with DICOM Seg only.
    """

    def __init__(self, *args, **kwargs):
        """Creates an application instance."""
        self._logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__))
        super().__init__(*args, **kwargs)

    def run(self, *args, **kwargs):
        # This method calls the base class to run. Can be omitted if simply calling through.
        self._logger.info(f"Begin {self.run.__name__}")
        super().run(*args, **kwargs)
        self._logger.info(f"End {self.run.__name__}")

    def compose(self):
        """Creates the app specific operators and chain them up in the processing DAG."""

        logging.info(f"Begin {self.compose.__name__}")

        app_context = Application.init_app_context({})  # Do not pass argv in Jupyter Notebook
        app_input_path = Path(app_context.input_path)
        app_output_path = Path(app_context.output_path)
        model_path = Path(app_context.model_path)

        # Create the custom operator(s) as well as SDK built-in operator(s).
        study_loader_op = DICOMDataLoaderOperator(
            self, CountCondition(self, 1), input_folder=app_input_path, name="study_loader_op"
        )
        series_selector_op = DICOMSeriesSelectorOperator(self, rules=Sample_Rules_Text, name="series_selector_op")
        series_to_vol_op = DICOMSeriesToVolumeOperator(self, name="series_to_vol_op")

        # Create the inference operator that supports MONAI Bundle and automates the inference.
        # The IOMapping labels match the input and prediction keys in the pre and post processing.
        # The model_name is optional when the app has only one model.
        # The bundle_path argument optionally can be set to an accessible bundle file path in the dev
        # environment, so when the app is packaged into a MAP, the operator can complete the bundle parsing
        # during init.

        config_names = BundleConfigNames(config_names=["inference"])  # Same as the default

        bundle_spleen_seg_op = MonaiBundleInferenceOperator(
            self,
            input_mapping=[IOMapping("image", Image, IOType.IN_MEMORY)],
            output_mapping=[IOMapping("pred", Image, IOType.IN_MEMORY)],
            app_context=app_context,
            bundle_config_names=config_names,
            bundle_path=model_path,
            name="bundle_spleen_seg_op",
        )

        # Create DICOM Seg writer providing the required segment description for each segment with
        # the actual algorithm and the pertinent organ/tissue. The segment_label, algorithm_name,
        # and algorithm_version are of DICOM VR LO type, limited to 64 chars.
        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html
        segment_descriptions = [
            SegmentDescription(
                segment_label="Spleen",
                segmented_property_category=codes.SCT.Organ,
                segmented_property_type=codes.SCT.Spleen,
                algorithm_name="volumetric (3D) segmentation of the spleen from CT image",
                algorithm_family=codes.DCM.ArtificialIntelligence,
                algorithm_version="0.3.2",
            )
        ]

        custom_tags = {"SeriesDescription": "AI generated Seg, not for clinical use."}

        dicom_seg_writer = DICOMSegmentationWriterOperator(
            self,
            segment_descriptions=segment_descriptions,
            custom_tags=custom_tags,
            output_folder=app_output_path,
            name="dicom_seg_writer",
        )

        # Create the processing pipeline, by specifying the source and destination operators, and
        # ensuring the output from the former matches the input of the latter, in both name and type.
        self.add_flow(study_loader_op, series_selector_op, {("dicom_study_list", "dicom_study_list")})
        self.add_flow(
            series_selector_op, series_to_vol_op, {("study_selected_series_list", "study_selected_series_list")}
        )
        self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {("image", "image")})
        # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.
        self.add_flow(
            series_selector_op, dicom_seg_writer, {("study_selected_series_list", "study_selected_series_list")}
        )
        self.add_flow(bundle_spleen_seg_op, dicom_seg_writer, {("pred", "seg_image")})
        # Create the surface mesh STL conversion operator and add it to the app execution flow, if needed, by
        # uncommenting the following couple lines.
        stl_conversion_op = STLConversionOperator(
            self, output_file=app_output_path.joinpath("stl/spleen.stl"), name="stl_conversion_op"
        )
        self.add_flow(bundle_spleen_seg_op, stl_conversion_op, {("pred", "image")})

        logging.info(f"End {self.compose.__name__}")


# This is a sample series selection rule in JSON, simply selecting CT series.
# If the study has more than 1 CT series, then all of them will be selected.
# Please see more detail in DICOMSeriesSelectorOperator.
Sample_Rules_Text = """
{
    "selections": [
        {
            "name": "CT Series",
            "conditions": {
                "StudyDescription": "(.*?)",
                "Modality": "(?i)CT",
                "SeriesDescription": "(.*?)"
            }
        }
    ]
}
"""

Executing app locally

We can execute the app in the Jupyter notebook. Note that the DICOM files of the CT Abdomen series must be present in the dcm folder and the Torch Script model, model.ts, also in the folder as pointed to by the environment variables.

!rm -rf $HOLOSCAN_OUTPUT_PATH
logging.info(f"Begin {__name__}")
AISpleenSegApp().run()
logging.info(f"End {__name__}")

[2024-04-23 16:51:59,898] [INFO] (root) - Begin __main__
[2024-04-23 16:51:59,901] [INFO] (__main__.AISpleenSegApp) - Begin run
[2024-04-23 16:51:59,902] [INFO] (root) - Begin compose
[2024-04-23 16:51:59,909] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])
[2024-04-23 16:51:59,915] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=)
[2024-04-23 16:51:59,920] [INFO] (root) - End compose
[info] [gxf_executor.cpp:247] Creating context
[info] [gxf_executor.cpp:1672] Loading extensions from configs...
[info] [gxf_executor.cpp:1842] Activating Graph...
[info] [gxf_executor.cpp:1874] Running Graph...
[info] [gxf_executor.cpp:1876] Waiting for completion...
[2024-04-23 16:51:59,943] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None

2024-04-23 16:51:59.940 INFO  gxf/std/greedy_scheduler.cpp@191: Scheduling 8 entities

[2024-04-23 16:52:00,393] [INFO] (root) - Finding series for Selection named: CT Series
[2024-04-23 16:52:00,394] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
  # of series: 1
[2024-04-23 16:52:00,395] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:52:00,396] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2024-04-23 16:52:00,397] [INFO] (root) -     Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2024-04-23 16:52:00,397] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:52:00,398] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2024-04-23 16:52:00,399] [INFO] (root) -     Series attribute Modality value: CT
[2024-04-23 16:52:00,399] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:52:00,400] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2024-04-23 16:52:00,400] [INFO] (root) -     Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2024-04-23 16:52:00,401] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:52:00,402] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:52:00,641] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts
[2024-04-23 16:52:03,610] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.
[2024-04-23 16:52:05,137] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image
[2024-04-23 16:52:05,138] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)
[2024-04-23 16:52:14,942] [INFO] (highdicom.base) - copy Image-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2024-04-23 16:52:14,943] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 16:52:14,944] [INFO] (highdicom.base) - copy Patient-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2024-04-23 16:52:14,945] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 16:52:14,946] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 16:52:14,947] [INFO] (highdicom.base) - copy Study-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2024-04-23 16:52:14,948] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 16:52:14,949] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 16:52:14,951] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[info] [gxf_executor.cpp:1879] Deactivating Graph...
[info] [gxf_executor.cpp:1887] Graph execution finished.
[2024-04-23 16:52:15,056] [INFO] (__main__.AISpleenSegApp) - End run
[2024-04-23 16:52:15,057] [INFO] (root) - End __main__

2024-04-23 16:52:15.054 INFO  gxf/std/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.
2024-04-23 16:52:15.055 INFO  gxf/std/greedy_scheduler.cpp@401: Scheduler finished.

Once the application is verified inside Jupyter notebook, we can write the above Python code into Python files in an application folder.

The application folder structure would look like below:

my_app
├── __main__.py
└── app.py

# Create an application folder
!mkdir -p my_app && rm -rf my_app/*

app.py

%%writefile my_app/app.py

# Copyright 2021-2023 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
from pathlib import Path

# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package.
from pydicom.sr.codedict import codes

from monai.deploy.conditions import CountCondition
from monai.deploy.core import AppContext, Application
from monai.deploy.core.domain import Image
from monai.deploy.core.io_type import IOType
from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription
from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator
from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
from monai.deploy.operators.monai_bundle_inference_operator import (
    BundleConfigNames,
    IOMapping,
    MonaiBundleInferenceOperator,
)
from monai.deploy.operators.stl_conversion_operator import STLConversionOperator


class AISpleenSegApp(Application):
    """Demonstrates inference with built-in MONAI Bundle inference operator with DICOM files as input/output

    This application loads a set of DICOM instances, select the appropriate series, converts the series to
    3D volume image, performs inference with the built-in MONAI Bundle inference operator, including pre-processing
    and post-processing, save the segmentation image in a DICOM Seg OID in an instance file, and optionally the
    surface mesh in STL format.

    Pertinent MONAI Bundle:
      https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation

    Execution Time Estimate:
      With a Nvidia GV100 32GB GPU, for an input DICOM Series of 515 instances, the execution time is around
      25 seconds with saving both DICOM Seg and surface mesh STL file, and 15 seconds with DICOM Seg only.
    """

    def __init__(self, *args, **kwargs):
        """Creates an application instance."""
        self._logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__))
        super().__init__(*args, **kwargs)

    def run(self, *args, **kwargs):
        # This method calls the base class to run. Can be omitted if simply calling through.
        self._logger.info(f"Begin {self.run.__name__}")
        super().run(*args, **kwargs)
        self._logger.info(f"End {self.run.__name__}")

    def compose(self):
        """Creates the app specific operators and chain them up in the processing DAG."""

        logging.info(f"Begin {self.compose.__name__}")

        # Use Commandline options over environment variables to init context.
        app_context = Application.init_app_context(self.argv)
        app_input_path = Path(app_context.input_path)
        app_output_path = Path(app_context.output_path)
        model_path = Path(app_context.model_path)

        # Create the custom operator(s) as well as SDK built-in operator(s).
        study_loader_op = DICOMDataLoaderOperator(
            self, CountCondition(self, 1), input_folder=app_input_path, name="study_loader_op"
        )
        series_selector_op = DICOMSeriesSelectorOperator(self, rules=Sample_Rules_Text, name="series_selector_op")
        series_to_vol_op = DICOMSeriesToVolumeOperator(self, name="series_to_vol_op")

        # Create the inference operator that supports MONAI Bundle and automates the inference.
        # The IOMapping labels match the input and prediction keys in the pre and post processing.
        # The model_name is optional when the app has only one model.
        # The bundle_path argument optionally can be set to an accessible bundle file path in the dev
        # environment, so when the app is packaged into a MAP, the operator can complete the bundle parsing
        # during init.

        config_names = BundleConfigNames(config_names=["inference"])  # Same as the default

        bundle_spleen_seg_op = MonaiBundleInferenceOperator(
            self,
            input_mapping=[IOMapping("image", Image, IOType.IN_MEMORY)],
            output_mapping=[IOMapping("pred", Image, IOType.IN_MEMORY)],
            app_context=app_context,
            bundle_config_names=config_names,
            bundle_path=model_path,
            name="bundle_spleen_seg_op",
        )

        # Create DICOM Seg writer providing the required segment description for each segment with
        # the actual algorithm and the pertinent organ/tissue. The segment_label, algorithm_name,
        # and algorithm_version are of DICOM VR LO type, limited to 64 chars.
        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html
        segment_descriptions = [
            SegmentDescription(
                segment_label="Spleen",
                segmented_property_category=codes.SCT.Organ,
                segmented_property_type=codes.SCT.Spleen,
                algorithm_name="volumetric (3D) segmentation of the spleen from CT image",
                algorithm_family=codes.DCM.ArtificialIntelligence,
                algorithm_version="0.3.2",
            )
        ]

        custom_tags = {"SeriesDescription": "AI generated Seg, not for clinical use."}

        dicom_seg_writer = DICOMSegmentationWriterOperator(
            self,
            segment_descriptions=segment_descriptions,
            custom_tags=custom_tags,
            output_folder=app_output_path,
            name="dicom_seg_writer",
        )

        # Create the processing pipeline, by specifying the source and destination operators, and
        # ensuring the output from the former matches the input of the latter, in both name and type.
        self.add_flow(study_loader_op, series_selector_op, {("dicom_study_list", "dicom_study_list")})
        self.add_flow(
            series_selector_op, series_to_vol_op, {("study_selected_series_list", "study_selected_series_list")}
        )
        self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {("image", "image")})
        # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.
        self.add_flow(
            series_selector_op, dicom_seg_writer, {("study_selected_series_list", "study_selected_series_list")}
        )
        self.add_flow(bundle_spleen_seg_op, dicom_seg_writer, {("pred", "seg_image")})
        # Create the surface mesh STL conversion operator and add it to the app execution flow, if needed, by
        # uncommenting the following couple lines.
        stl_conversion_op = STLConversionOperator(
            self, output_file=app_output_path.joinpath("stl/spleen.stl"), name="stl_conversion_op"
        )
        self.add_flow(bundle_spleen_seg_op, stl_conversion_op, {("pred", "image")})

        logging.info(f"End {self.compose.__name__}")


# This is a sample series selection rule in JSON, simply selecting CT series.
# If the study has more than 1 CT series, then all of them will be selected.
# Please see more detail in DICOMSeriesSelectorOperator.
Sample_Rules_Text = """
{
    "selections": [
        {
            "name": "CT Series",
            "conditions": {
                "StudyDescription": "(.*?)",
                "Modality": "(?i)CT",
                "SeriesDescription": "(.*?)"
            }
        }
    ]
}
"""

if __name__ == "__main__":
    AISpleenSegApp().run()

Writing my_app/app.py

if __name__ == "__main__":
    AISpleenSegApp().run()

The above lines are needed to execute the application code by using python interpreter.

__main__.py

__main__.py is needed for MONAI Application Packager to detect the main application code (app.py) when the application is executed with the application folder path (e.g., python simple_imaging_app).

%%writefile my_app/__main__.py
from app import AISpleenSegApp

if __name__ == "__main__":
    AISpleenSegApp().run()

Writing my_app/__main__.py

!ls my_app

app.py	__main__.py

This time, let’s execute the app in the command line.

Note

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. The following command demonstrates the use of the options.

!rm -rf $HOLOSCAN_OUTPUT_PATH
!python my_app -i dcm -o output -m models

[2024-04-23 16:52:19,843] [INFO] (root) - Parsed args: Namespace(log_level=None, input=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/dcm'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), model=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models'), workdir=None, argv=['my_app', '-i', 'dcm', '-o', 'output', '-m', 'models'])
[2024-04-23 16:52:19,845] [INFO] (root) - AppContext object: AppContext(input_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/dcm, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models, workdir=)
[2024-04-23 16:52:19,847] [INFO] (root) - End compose
[info] [gxf_executor.cpp:247] Creating context
[info] [gxf_executor.cpp:1672] Loading extensions from configs...
[info] [gxf_executor.cpp:1842] Activating Graph...
[info] [gxf_executor.cpp:1874] Running Graph...
[info] [gxf_executor.cpp:1876] Waiting for completion...
2024-04-23 16:52:19.876 INFO  gxf/std/greedy_scheduler.cpp@191: Scheduling 8 entities
[2024-04-23 16:52:19,877] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
[2024-04-23 16:52:20,224] [INFO] (root) - Finding series for Selection named: CT Series
[2024-04-23 16:52:20,224] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
  # of series: 1
[2024-04-23 16:52:20,224] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:52:20,224] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2024-04-23 16:52:20,224] [INFO] (root) -     Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2024-04-23 16:52:20,224] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:52:20,225] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2024-04-23 16:52:20,225] [INFO] (root) -     Series attribute Modality value: CT
[2024-04-23 16:52:20,225] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:52:20,225] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2024-04-23 16:52:20,225] [INFO] (root) -     Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2024-04-23 16:52:20,225] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:52:20,225] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:52:20,449] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts
[2024-04-23 16:52:26,711] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output/stl/spleen.stl.
[2024-04-23 16:52:28,387] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image
[2024-04-23 16:52:28,388] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)
/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/highdicom/valuerep.py:54: 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.
  warnings.warn(
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy Image-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy Patient-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy Study-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2024-04-23 16:52:39,441] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 16:52:39,442] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 16:52:39,442] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
2024-04-23 16:52:39.552 INFO  gxf/std/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.
2024-04-23 16:52:39.552 INFO  gxf/std/greedy_scheduler.cpp@401: Scheduler finished.
[info] [gxf_executor.cpp:1879] Deactivating Graph...
[info] [gxf_executor.cpp:1887] Graph execution finished.
[2024-04-23 16:52:39,552] [INFO] (app.AISpleenSegApp) - End run

!ls output

1.2.826.0.1.3680043.10.511.3.31677856801140848641305221346725457.dcm  stl

Packaging app

Let’s package the app with MONAI Application Packager.

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.

%%writefile my_app/app.yaml
%YAML 1.2
---
application:
  title: MONAI Deploy App Package - MONAI Bundle AI App
  version: 1.0
  inputFormats: ["file"]
  outputFormats: ["file"]

resources:
  cpu: 1
  gpu: 1
  memory: 1Gi
  gpuMemory: 6Gi

Writing my_app/app.yaml

%%writefile my_app/requirements.txt
highdicom>=0.18.2
monai>=1.0
nibabel>=3.2.1
numpy>=1.21.6
pydicom>=2.3.0
setuptools>=59.5.0 # for pkg_resources
SimpleITK>=2.0.0
scikit-image>=0.17.2
numpy-stl>=2.12.0
trimesh>=3.8.11
torch>=1.12.0

Writing my_app/requirements.txt

Now we can use the CLI package command to build the MONAI Application Package (MAP) container image based on a supported base image.

Note

Building a MONAI Application Package (Docker image) can take time. Use -l DEBUG option to see the progress.

tag_prefix = "my_app"

!monai-deploy package my_app -m {models_folder} -c my_app/app.yaml -t {tag_prefix}:1.0 --platform x64-workstation -l DEBUG

[2024-04-23 16:52:41,994] [INFO] (common) - Downloading CLI manifest file...
[2024-04-23 16:52:42,236] [DEBUG] (common) - Validating CLI manifest file...
[2024-04-23 16:52:42,237] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app
[2024-04-23 16:52:42,238] [INFO] (packager.parameters) - Detected application type: Python Module
[2024-04-23 16:52:42,238] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...
[2024-04-23 16:52:42,238] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.
[2024-04-23 16:52:42,238] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...
[2024-04-23 16:52:42,240] [INFO] (packager) - Generating app.json...
[2024-04-23 16:52:42,240] [INFO] (packager) - Generating pkg.json...
[2024-04-23 16:52:42,246] [DEBUG] (common) - 
=============== Begin app.json ===============
{
    "apiVersion": "1.0.0",
    "command": "[\"python3\", \"/opt/holoscan/app\"]",
    "environment": {
        "HOLOSCAN_APPLICATION": "/opt/holoscan/app",
        "HOLOSCAN_INPUT_PATH": "input/",
        "HOLOSCAN_OUTPUT_PATH": "output/",
        "HOLOSCAN_WORKDIR": "/var/holoscan",
        "HOLOSCAN_MODEL_PATH": "/opt/holoscan/models",
        "HOLOSCAN_CONFIG_PATH": "/var/holoscan/app.yaml",
        "HOLOSCAN_APP_MANIFEST_PATH": "/etc/holoscan/app.json",
        "HOLOSCAN_PKG_MANIFEST_PATH": "/etc/holoscan/pkg.json",
        "HOLOSCAN_DOCS_PATH": "/opt/holoscan/docs",
        "HOLOSCAN_LOGS_PATH": "/var/holoscan/logs"
    },
    "input": {
        "path": "input/",
        "formats": null
    },
    "liveness": null,
    "output": {
        "path": "output/",
        "formats": null
    },
    "readiness": null,
    "sdk": "monai-deploy",
    "sdkVersion": "0.5.1",
    "timeout": 0,
    "version": 1.0,
    "workingDirectory": "/var/holoscan"
}
================ End app.json ================
                 
[2024-04-23 16:52:42,246] [DEBUG] (common) - 
=============== Begin pkg.json ===============
{
    "apiVersion": "1.0.0",
    "applicationRoot": "/opt/holoscan/app",
    "modelRoot": "/opt/holoscan/models",
    "models": {
        "model": "/opt/holoscan/models/model"
    },
    "resources": {
        "cpu": 1,
        "gpu": 1,
        "memory": "1Gi",
        "gpuMemory": "6Gi"
    },
    "version": 1.0,
    "platformConfig": "dgpu"
}
================ End pkg.json ================
                 
[2024-04-23 16:52:42,273] [DEBUG] (packager.builder) - 
========== Begin Dockerfile ==========


FROM nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu

ENV DEBIAN_FRONTEND=noninteractive
ENV TERM=xterm-256color

ARG UNAME
ARG UID
ARG GID

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

LABEL base="nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu"
LABEL tag="my_app:1.0"
LABEL org.opencontainers.image.title="MONAI Deploy App Package - MONAI Bundle AI App"
LABEL org.opencontainers.image.version="1.0"
LABEL org.nvidia.holoscan="2.0.0"
LABEL org.monai.deploy.app-sdk="0.5.1"


ENV HOLOSCAN_ENABLE_HEALTH_CHECK=true
ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input
ENV HOLOSCAN_OUTPUT_PATH=/var/holoscan/output
ENV HOLOSCAN_WORKDIR=/var/holoscan
ENV HOLOSCAN_APPLICATION=/opt/holoscan/app
ENV HOLOSCAN_TIMEOUT=0
ENV HOLOSCAN_MODEL_PATH=/opt/holoscan/models
ENV HOLOSCAN_DOCS_PATH=/opt/holoscan/docs
ENV HOLOSCAN_CONFIG_PATH=/var/holoscan/app.yaml
ENV HOLOSCAN_APP_MANIFEST_PATH=/etc/holoscan/app.json
ENV HOLOSCAN_PKG_MANIFEST_PATH=/etc/holoscan/pkg.json
ENV HOLOSCAN_LOGS_PATH=/var/holoscan/logs
ENV PATH=/root/.local/bin:/opt/nvidia/holoscan:$PATH
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/libtorch/1.13.1/lib/:/opt/nvidia/holoscan/lib

RUN apt-get update \
    && apt-get install -y curl jq \
    && rm -rf /var/lib/apt/lists/*

ENV PYTHONPATH="/opt/holoscan/app:$PYTHONPATH"


RUN groupadd -f -g $GID $UNAME
RUN useradd -rm -d /home/$UNAME -s /bin/bash -g $GID -G sudo -u $UID $UNAME
RUN chown -R holoscan /var/holoscan 
RUN chown -R holoscan /var/holoscan/input 
RUN chown -R holoscan /var/holoscan/output 

# Set the working directory
WORKDIR /var/holoscan

# Copy HAP/MAP tool script
COPY ./tools /var/holoscan/tools
RUN chmod +x /var/holoscan/tools


# Copy gRPC health probe

USER $UNAME

ENV PATH=/root/.local/bin:/home/holoscan/.local/bin:/opt/nvidia/holoscan:$PATH

COPY ./pip/requirements.txt /tmp/requirements.txt

RUN pip install --upgrade pip
RUN pip install --no-cache-dir --user -r /tmp/requirements.txt

 
# MONAI Deploy

# Copy user-specified MONAI Deploy SDK file
COPY ./monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl


COPY ./models  /opt/holoscan/models

COPY ./map/app.json /etc/holoscan/app.json
COPY ./app.config /var/holoscan/app.yaml
COPY ./map/pkg.json /etc/holoscan/pkg.json

COPY ./app /opt/holoscan/app

ENTRYPOINT ["/var/holoscan/tools"]
=========== End Dockerfile ===========

[2024-04-23 16:52:42,273] [INFO] (packager.builder) - 
===============================================================================
Building image for:                 x64-workstation
    Architecture:                   linux/amd64
    Base Image:                     nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu
    Build Image:                    N/A
    Cache:                          Enabled
    Configuration:                  dgpu
    Holoscan SDK Package:           pypi.org
    MONAI Deploy App SDK Package:   /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
    gRPC Health Probe:              N/A
    SDK Version:                    2.0.0
    SDK:                            monai-deploy
    Tag:                            my_app-x64-workstation-dgpu-linux-amd64:1.0
    
[2024-04-23 16:52:42,564] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`
[2024-04-23 16:52:42,564] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=my_app-x64-workstation-dgpu-linux-amd64:1.0
#0 building with "holoscan_app_builder" instance using docker-container driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.66kB done
#1 DONE 0.1s

#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu
#2 DONE 0.5s

#3 [internal] load .dockerignore
#3 transferring context: 1.79kB done
#3 DONE 0.1s

#4 [internal] load build context
#4 DONE 0.0s

#5 importing cache manifest from local:13600691502778489948
#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done
#5 DONE 0.0s

#6 [ 1/21] FROM nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu@sha256:20adbccd2c7b12dfb1798f6953f071631c3b85cd337858a7506f8e420add6d4a
#6 resolve nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu@sha256:20adbccd2c7b12dfb1798f6953f071631c3b85cd337858a7506f8e420add6d4a 0.0s done
#6 DONE 0.1s

#7 importing cache manifest from nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu
#7 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done
#7 DONE 0.7s

#4 [internal] load build context
#4 transferring context: 19.56MB 0.1s done
#4 DONE 0.2s

#8 [ 3/21] RUN apt-get update     && apt-get install -y curl jq     && rm -rf /var/lib/apt/lists/*
#8 CACHED

#9 [ 7/21] RUN chown -R holoscan /var/holoscan/input
#9 CACHED

#10 [ 8/21] RUN chown -R holoscan /var/holoscan/output
#10 CACHED

#11 [11/21] RUN chmod +x /var/holoscan/tools
#11 CACHED

#12 [ 4/21] RUN groupadd -f -g 1000 holoscan
#12 CACHED

#13 [ 9/21] WORKDIR /var/holoscan
#13 CACHED

#14 [ 2/21] 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
#14 CACHED

#15 [ 6/21] RUN chown -R holoscan /var/holoscan
#15 CACHED

#16 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan
#16 CACHED

#17 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt
#17 CACHED

#18 [10/21] COPY ./tools /var/holoscan/tools
#18 CACHED

#19 [13/21] RUN pip install --upgrade pip
#19 CACHED

#20 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
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#20 11.36 Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)
#20 16.03    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 410.6/410.6 MB 117.5 MB/s eta 0:00:00
#20 16.04 Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)
#20 16.17    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.1/14.1 MB 118.4 MB/s eta 0:00:00
#20 16.18 Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)
#20 16.45    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.7/23.7 MB 117.6 MB/s eta 0:00:00
#20 16.46 Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)
#20 16.47    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 823.6/823.6 kB 153.8 MB/s eta 0:00:00
#20 16.49 Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)
#20 24.48    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 731.7/731.7 MB 117.1 MB/s eta 0:00:00
#20 24.49 Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)
#20 25.76    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 121.6/121.6 MB 117.6 MB/s eta 0:00:00
#20 25.77 Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)
#20 26.35    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.5/56.5 MB 63.6 MB/s eta 0:00:00
#20 26.36 Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)
#20 27.50    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.2/124.2 MB 104.0 MB/s eta 0:00:00
#20 27.51 Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)
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#20 29.36 Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)
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#20 30.98 Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)
#20 30.98    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 99.1/99.1 kB 245.1 MB/s eta 0:00:00
#20 30.99 Downloading triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)
#20 32.54    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 167.9/167.9 MB 123.3 MB/s eta 0:00:00
#20 32.54 Downloading imageio-2.34.1-py3-none-any.whl (313 kB)
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#20 32.55 Downloading lazy_loader-0.4-py3-none-any.whl (12 kB)
#20 32.56 Downloading networkx-3.3-py3-none-any.whl (1.7 MB)
#20 32.57    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 135.6 MB/s eta 0:00:00
#20 32.58 Downloading pillow-10.3.0-cp310-cp310-manylinux_2_28_x86_64.whl (4.5 MB)
#20 32.62    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.5/4.5 MB 121.7 MB/s eta 0:00:00
#20 32.63 Downloading pillow_jpls-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (305 kB)
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#20 32.64 Downloading python_utils-3.8.2-py2.py3-none-any.whl (27 kB)
#20 32.64 Downloading scipy-1.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)
#20 33.02    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 38.6/38.6 MB 108.0 MB/s eta 0:00:00
#20 33.02 Downloading tifffile-2024.4.18-py3-none-any.whl (224 kB)
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#20 33.04 Downloading typing_extensions-4.11.0-py3-none-any.whl (34 kB)
#20 33.04 Downloading filelock-3.13.4-py3-none-any.whl (11 kB)
#20 33.05 Downloading fsspec-2024.3.1-py3-none-any.whl (171 kB)
#20 33.06    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 172.0/172.0 kB 77.4 MB/s eta 0:00:00
#20 33.07 Downloading sympy-1.12-py3-none-any.whl (5.7 MB)
#20 33.12    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.7/5.7 MB 117.1 MB/s eta 0:00:00
#20 33.13 Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)
#20 33.14    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 146.2 MB/s eta 0:00:00
#20 33.14 Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)
#20 33.34    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.1/21.1 MB 117.6 MB/s eta 0:00:00
#20 40.94 Installing collected packages: SimpleITK, mpmath, typing-extensions, trimesh, tifffile, sympy, scipy, pydicom, pillow, 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, nibabel, networkx, lazy-loader, fsspec, filelock, triton, python-utils, pillow-jpls, nvidia-cusparse-cu12, nvidia-cudnn-cu12, imageio, scikit-image, nvidia-cusolver-cu12, numpy-stl, highdicom, torch, monai
#20 94.76 Successfully installed SimpleITK-2.3.1 filelock-3.13.4 fsspec-2024.3.1 highdicom-0.22.0 imageio-2.34.1 lazy-loader-0.4 monai-1.3.0 mpmath-1.3.0 networkx-3.3 nibabel-5.2.1 numpy-stl-3.1.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 pillow-10.3.0 pillow-jpls-1.3.2 pydicom-2.4.4 python-utils-3.8.2 scikit-image-0.23.2 scipy-1.13.0 sympy-1.12 tifffile-2024.4.18 torch-2.2.2 trimesh-4.3.1 triton-2.2.0 typing-extensions-4.11.0
#20 DONE 98.0s

#21 [15/21] COPY ./monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#21 DONE 0.5s

#22 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#22 0.651 Defaulting to user installation because normal site-packages is not writeable
#22 0.747 Processing /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#22 0.758 Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (1.23.5)
#22 0.873 Collecting holoscan~=2.0 (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 0.964   Downloading holoscan-2.0.0-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (6.7 kB)
#22 1.036 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.040   Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
#22 1.125 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.129   Downloading typeguard-4.2.1-py3-none-any.whl.metadata (3.7 kB)
#22 1.167 Requirement already satisfied: pip>=20.3 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (24.0)
#22 1.168 Requirement already satisfied: cupy-cuda12x==12.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (12.2.0)
#22 1.169 Requirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.2.1)
#22 1.170 Requirement already satisfied: python-on-whales==0.60.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.60.1)
#22 1.171 Requirement already satisfied: Jinja2==3.1.3 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.1.3)
#22 1.172 Requirement already satisfied: packaging==23.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (23.1)
#22 1.172 Requirement already satisfied: pyyaml==6.0 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (6.0)
#22 1.173 Requirement already satisfied: requests==2.31.0 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.31.0)
#22 1.174 Requirement already satisfied: psutil==5.9.6 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (5.9.6)
#22 1.219 Collecting wheel-axle-runtime<1.0 (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.224   Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl.metadata (7.7 kB)
#22 1.261 Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.10/dist-packages (from cupy-cuda12x==12.2->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.8.2)
#22 1.266 Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2==3.1.3->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.1.3)
#22 1.286 Requirement already satisfied: pydantic<2,>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (1.10.15)
#22 1.287 Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (4.66.2)
#22 1.288 Requirement already satisfied: typer>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.12.3)
#22 1.288 Requirement already satisfied: typing-extensions in /home/holoscan/.local/lib/python3.10/site-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (4.11.0)
#22 1.297 Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.3.2)
#22 1.298 Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.7)
#22 1.298 Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.2.1)
#22 1.299 Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2024.2.2)
#22 1.319 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==0.5.1+20.gb869749.dirty) (3.13.4)
#22 1.341 Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (8.1.7)
#22 1.342 Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (1.5.4)
#22 1.343 Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (13.7.1)
#22 1.380 Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.0.0)
#22 1.380 Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.17.2)
#22 1.400 Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.1.2)
#22 1.415 Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)
#22 1.437 Downloading holoscan-2.0.0-cp310-cp310-manylinux_2_35_x86_64.whl (33.2 MB)
#22 2.034    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 33.2/33.2 MB 28.4 MB/s eta 0:00:00
#22 2.039 Downloading typeguard-4.2.1-py3-none-any.whl (34 kB)
#22 2.064 Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl (12 kB)
#22 2.573 Installing collected packages: wheel-axle-runtime, typeguard, colorama, holoscan, monai-deploy-app-sdk
#22 3.306 Successfully installed colorama-0.4.6 holoscan-2.0.0 monai-deploy-app-sdk-0.5.1+20.gb869749.dirty typeguard-4.2.1 wheel-axle-runtime-0.0.5
#22 DONE 4.1s

#23 [17/21] COPY ./models  /opt/holoscan/models
#23 DONE 0.3s

#24 [18/21] COPY ./map/app.json /etc/holoscan/app.json
#24 DONE 0.1s

#25 [19/21] COPY ./app.config /var/holoscan/app.yaml
#25 DONE 0.1s

#26 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json
#26 DONE 0.1s

#27 [21/21] COPY ./app /opt/holoscan/app
#27 DONE 0.1s

#28 exporting to docker image format
#28 exporting layers
#28 exporting layers 161.2s done
#28 exporting manifest sha256:db72052410ed3875bd2689b115d7ea706f74caa44be88c9b455c9761c991f225 0.0s done
#28 exporting config sha256:4fe495cf55e1086e3b466f4799218faa2b377e58348ae9b51253b56b560295b3 0.0s done
#28 sending tarball
#28 ...

#29 importing to docker
#29 loading layer 414c50de1c1c 557.06kB / 2.97GB
#29 loading layer 414c50de1c1c 103.06MB / 2.97GB 6.1s
#29 loading layer 414c50de1c1c 325.88MB / 2.97GB 10.2s
#29 loading layer 414c50de1c1c 520.85MB / 2.97GB 14.3s
#29 loading layer 414c50de1c1c 719.72MB / 2.97GB 18.4s
#29 loading layer 414c50de1c1c 903.54MB / 2.97GB 22.5s
#29 loading layer 414c50de1c1c 1.11GB / 2.97GB 26.6s
#29 loading layer 414c50de1c1c 1.36GB / 2.97GB 30.7s
#29 loading layer 414c50de1c1c 1.56GB / 2.97GB 34.9s
#29 loading layer 414c50de1c1c 1.81GB / 2.97GB 38.9s
#29 loading layer 414c50de1c1c 1.97GB / 2.97GB 43.1s
#29 loading layer 414c50de1c1c 2.04GB / 2.97GB 50.1s
#29 loading layer 414c50de1c1c 2.17GB / 2.97GB 56.4s
#29 loading layer 414c50de1c1c 2.42GB / 2.97GB 62.5s
#29 loading layer 414c50de1c1c 2.63GB / 2.97GB 66.6s
#29 loading layer 414c50de1c1c 2.79GB / 2.97GB 70.7s
#29 loading layer 2d2337d002b1 32.77kB / 125.83kB
#29 loading layer 05a9e332a339 557.06kB / 67.35MB
#29 loading layer 05a9e332a339 59.60MB / 67.35MB 2.1s
#29 loading layer b466f5b35107 196.61kB / 17.81MB
#29 loading layer 66a2f54a1b71 493B / 493B
#29 loading layer 9b5da571c07c 316B / 316B
#29 loading layer 69c6b6426338 301B / 301B
#29 loading layer bfe49db5ef06 3.33kB / 3.33kB
#29 loading layer b466f5b35107 196.61kB / 17.81MB 5.1s done
#29 loading layer 414c50de1c1c 2.90GB / 2.97GB 85.2s done
#29 loading layer 2d2337d002b1 32.77kB / 125.83kB 8.8s done
#29 loading layer 05a9e332a339 59.60MB / 67.35MB 8.7s done
#29 loading layer 66a2f54a1b71 493B / 493B 3.4s done
#29 loading layer 9b5da571c07c 316B / 316B 2.7s done
#29 loading layer 69c6b6426338 301B / 301B 2.0s done
#29 loading layer bfe49db5ef06 3.33kB / 3.33kB 1.3s done
#29 DONE 85.2s

#28 exporting to docker image format
#28 sending tarball 146.6s done
#28 DONE 307.9s

#30 exporting cache to client directory
#30 preparing build cache for export
#30 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015
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#30 writing layer sha256:fcb10e9f191b92679f1cac7623b152400f374a3e3d90f3d2248bfced02b6bdca
#30 preparing build cache for export 50.0s done
#30 writing layer sha256:fcb10e9f191b92679f1cac7623b152400f374a3e3d90f3d2248bfced02b6bdca 0.0s done
#30 writing config sha256:1af23d9bb67b68807f26cc256ea0ba50bba21a62a52cdfab0bc82567fef2a35e 0.0s done
#30 writing cache manifest sha256:3e6252fe3fb73d49377342ccff9ce009489fdd34a8709b3c2e0e2c2cd1a54372 0.0s done
#30 DONE 50.0s
[2024-04-23 17:00:27,074] [INFO] (packager) - Build Summary:

Platform: x64-workstation/dgpu
    Status:     Succeeded
    Docker Tag: my_app-x64-workstation-dgpu-linux-amd64:1.0
    Tarball:    None

We can see that the MAP Docker image is created

!docker image ls | grep {tag_prefix}

my_app-x64-workstation-dgpu-linux-amd64                                                   1.0                 4fe495cf55e1   5 minutes ago       17.9GB

We can choose to display and inspect the MAP manifests by running the container with the show command. Furthermore, we can also extract the manifests and other contents in the MAP by using the extract command while mapping specific folder to the host’s (we know that our MAP is compliant and supports these commands).

Note

The host folder for storing the extracted content must first be created by the user, and if it has been created by Docker on running the container, the folder needs to be deleted and re-created.

!echo "Display manifests and extract MAP contents to the host folder, ./export"
!docker run --rm {tag_prefix}-x64-workstation-dgpu-linux-amd64:1.0 show
!rm -rf `pwd`/export && mkdir -p `pwd`/export
!docker run --rm -v `pwd`/export/:/var/run/holoscan/export/ {tag_prefix}-x64-workstation-dgpu-linux-amd64:1.0 extract
!ls `pwd`/export

Display manifests and extract MAP contents to the host folder, ./export

============================== app.json ==============================
{
  "apiVersion": "1.0.0",
  "command": "[\"python3\", \"/opt/holoscan/app\"]",
  "environment": {
    "HOLOSCAN_APPLICATION": "/opt/holoscan/app",
    "HOLOSCAN_INPUT_PATH": "input/",
    "HOLOSCAN_OUTPUT_PATH": "output/",
    "HOLOSCAN_WORKDIR": "/var/holoscan",
    "HOLOSCAN_MODEL_PATH": "/opt/holoscan/models",
    "HOLOSCAN_CONFIG_PATH": "/var/holoscan/app.yaml",
    "HOLOSCAN_APP_MANIFEST_PATH": "/etc/holoscan/app.json",
    "HOLOSCAN_PKG_MANIFEST_PATH": "/etc/holoscan/pkg.json",
    "HOLOSCAN_DOCS_PATH": "/opt/holoscan/docs",
    "HOLOSCAN_LOGS_PATH": "/var/holoscan/logs"
  },
  "input": {
    "path": "input/",
    "formats": null
  },
  "liveness": null,
  "output": {
    "path": "output/",
    "formats": null
  },
  "readiness": null,
  "sdk": "monai-deploy",
  "sdkVersion": "0.5.1",
  "timeout": 0,
  "version": 1,
  "workingDirectory": "/var/holoscan"
}

============================== pkg.json ==============================
{
  "apiVersion": "1.0.0",
  "applicationRoot": "/opt/holoscan/app",
  "modelRoot": "/opt/holoscan/models",
  "models": {
    "model": "/opt/holoscan/models/model"
  },
  "resources": {
    "cpu": 1,
    "gpu": 1,
    "memory": "1Gi",
    "gpuMemory": "6Gi"
  },
  "version": 1,
  "platformConfig": "dgpu"
}

2024-04-24 00:00:30 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app

2024-04-24 00:00:30 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json
2024-04-24 00:00:30 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json
2024-04-24 00:00:30 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml

2024-04-24 00:00:30 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models

2024-04-24 00:00:30 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs
2024-04-24 00:00:30 [INFO] '/opt/holoscan/docs/' cannot be found.

app  config  models

Executing packaged app locally

The packaged app can be run locally through MONAI Application Runner.

# Clear the output folder and run the MAP. The input is expected to be a folder.
!rm -rf $HOLOSCAN_OUTPUT_PATH
!monai-deploy run -i $HOLOSCAN_INPUT_PATH -o $HOLOSCAN_OUTPUT_PATH my_app-x64-workstation-dgpu-linux-amd64:1.0

[2024-04-23 17:00:31,900] [INFO] (runner) - Checking dependencies...
[2024-04-23 17:00:31,900] [INFO] (runner) - --> Verifying if "docker" is installed...

[2024-04-23 17:00:31,901] [INFO] (runner) - --> Verifying if "docker-buildx" is installed...

[2024-04-23 17:00:31,901] [INFO] (runner) - --> Verifying if "my_app-x64-workstation-dgpu-linux-amd64:1.0" is available...

[2024-04-23 17:00:31,971] [INFO] (runner) - Reading HAP/MAP manifest...
Preparing to copy...?25lCopying from container - 0B?25hSuccessfully copied 2.56kB to /tmp/tmpxev2hpxc/app.json
Preparing to copy...?25lCopying from container - 0B?25hSuccessfully copied 2.05kB to /tmp/tmpxev2hpxc/pkg.json
[2024-04-23 17:00:32,215] [INFO] (runner) - --> Verifying if "nvidia-ctk" is installed...

[2024-04-23 17:00:32,216] [INFO] (runner) - --> Verifying "nvidia-ctk" version...

[2024-04-23 17:00:32,528] [INFO] (common) - Launching container (6ffaea9917bd) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...
    container name:      fervent_bell
    host name:           mingq-dt
    network:             host
    user:                1000:1000
    ulimits:             memlock=-1:-1, stack=67108864:67108864
    cap_add:             CAP_SYS_PTRACE
    ipc mode:            host
    shared memory size:  67108864
    devices:             
    group_add:           44
2024-04-24 00:00:33 [INFO] Launching application python3 /opt/holoscan/app ...

[2024-04-24 00:00:36,780] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])

[2024-04-24 00:00:36,783] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)

[2024-04-24 00:00:36,784] [INFO] (root) - End compose

[info] [app_driver.cpp:1161] Launching the driver/health checking service

[info] [gxf_executor.cpp:247] Creating context

[info] [server.cpp:87] Health checking server listening on 0.0.0.0:8777

[info] [gxf_executor.cpp:1672] Loading extensions from configs...

[info] [gxf_executor.cpp:1842] Activating Graph...

2024-04-24 00:00:36.818 INFO  gxf/std/greedy_scheduler.cpp@191: Scheduling 8 entities

[info] [gxf_executor.cpp:1874] Running Graph...

[info] [gxf_executor.cpp:1876] Waiting for completion...

[2024-04-24 00:00:36,819] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None

[2024-04-24 00:00:37,735] [INFO] (root) - Finding series for Selection named: CT Series

[2024-04-24 00:00:37,735] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291

  # of series: 1

[2024-04-24 00:00:37,735] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239

[2024-04-24 00:00:37,735] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'

[2024-04-24 00:00:37,735] [INFO] (root) -     Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST

[2024-04-24 00:00:37,735] [INFO] (root) - Series attribute string value did not match. Try regEx.

[2024-04-24 00:00:37,735] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'

[2024-04-24 00:00:37,735] [INFO] (root) -     Series attribute Modality value: CT

[2024-04-24 00:00:37,735] [INFO] (root) - Series attribute string value did not match. Try regEx.

[2024-04-24 00:00:37,735] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'

[2024-04-24 00:00:37,735] [INFO] (root) -     Series attribute SeriesDescription value: ABD/PANC 3.0 B31f

[2024-04-24 00:00:37,735] [INFO] (root) - Series attribute string value did not match. Try regEx.

[2024-04-24 00:00:37,735] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239

[2024-04-24 00:00:38,147] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/model/model.ts

[2024-04-24 00:00:41,393] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /var/holoscan/output/stl/spleen.stl.

[2024-04-24 00:00:43,045] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image

[2024-04-24 00:00:43,045] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)

/home/holoscan/.local/lib/python3.10/site-packages/highdicom/valuerep.py:54: 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.

  warnings.warn(

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy Image-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy attributes of module "Specimen"

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy Patient-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy attributes of module "Patient"

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy Study-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"

[2024-04-24 00:00:53,817] [INFO] (highdicom.base) - copy attributes of module "General Study"

[2024-04-24 00:00:53,818] [INFO] (highdicom.base) - copy attributes of module "Patient Study"

[2024-04-24 00:00:53,818] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"

2024-04-24 00:00:53.906 INFO  gxf/std/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.

[info] [gxf_executor.cpp:1879] Deactivating Graph...

2024-04-24 00:00:53.908 INFO  gxf/std/greedy_scheduler.cpp@401: Scheduler finished.

[info] [gxf_executor.cpp:1887] Graph execution finished.

[2024-04-24 00:00:53,915] [INFO] (app.AISpleenSegApp) - End run

[2024-04-23 17:00:55,176] [INFO] (common) - Container 'fervent_bell'(6ffaea9917bd) exited.

!ls $HOLOSCAN_OUTPUT_PATH

1.2.826.0.1.3680043.10.511.3.91573472497917482579554686696126426.dcm  stl

Creating a Segmentation App Consuming a MONAI Bundle Creating a Segmentation App Including Including Visualization with Clara-Viz