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:
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<=1.5.0"
!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#
Note: Data files are now access controlled. Please first request permission to access the shared folder on Google Drive. Please download zip file, mednist_classifieai_spleen_seg_bundle_data.zip in the ai_spleen_seg_app folder, to the same folder as the notebook example.
# Download ai_spleen_bundle_data test data zip file. Please request access and download manually.
# !pip install gdown
# !gdown "https://drive.google.com/uc?id=1IwWMpbo2fd38fKIqeIdL8SKTGvkn31tK"
# 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 packaging, 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
Archive: ai_spleen_seg_bundle_data.zip
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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__}")
[info] [fragment.cpp:705] Loading extensions from configs...
[2025-04-22 10:18:02,158] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, triton_server_netloc=None, argv=[])
[2025-04-22 10:18:02,166] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=models, workdir=), triton_server_netloc=
[2025-04-22 10:18:02,176] [INFO] (root) - End compose
[info] [gxf_executor.cpp:265] Creating context
[info] [gxf_executor.cpp:2396] Activating Graph...
[info] [gxf_executor.cpp:2426] Running Graph...
[info] [gxf_executor.cpp:2428] Waiting for completion...
[info] [greedy_scheduler.cpp:191] Scheduling 6 entities
[2025-04-22 10:18:02,203] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
[2025-04-22 10:18:02,743] [INFO] (root) - Finding series for Selection named: CT Series
[2025-04-22 10:18:02,744] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
# of series: 1
[2025-04-22 10:18:02,745] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 10:18:02,746] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2025-04-22 10:18:02,746] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2025-04-22 10:18:02,747] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 10:18:02,748] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2025-04-22 10:18:02,748] [INFO] (root) - Series attribute Modality value: CT
[2025-04-22 10:18:02,749] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 10:18:02,750] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2025-04-22 10:18:02,751] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2025-04-22 10:18:02,752] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 10:18:02,753] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 10:18:02,753] [INFO] (root) - Series Selection finalized.
[2025-04-22 10:18:02,754] [INFO] (root) - Series Description of selected DICOM Series for inference: ABD/PANC 3.0 B31f
[2025-04-22 10:18:02,755] [INFO] (root) - Series Instance UID of selected DICOM Series for inference: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 10:18:02,968] [INFO] (root) - Casting to float32
[2025-04-22 10:18:03,025] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts
/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/bundle/reference_resolver.py:216: UserWarning: Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.
warnings.warn(
[2025-04-22 10:18:06,025] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file output/stl/spleen.stl.
[2025-04-22 10:18:07,405] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image
[2025-04-22 10:18:07,406] [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/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.
check_person_name(patient_name)
[2025-04-22 10:18:17,835] [INFO] (highdicom.base) - copy Image-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 10:18:17,836] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2025-04-22 10:18:17,837] [INFO] (highdicom.base) - copy Patient-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 10:18:17,838] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2025-04-22 10:18:17,839] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2025-04-22 10:18:17,839] [INFO] (highdicom.base) - copy Study-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 10:18:17,840] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2025-04-22 10:18:17,841] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2025-04-22 10:18:17,842] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[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.
[info] [greedy_scheduler.cpp:401] Scheduler finished.
[info] [gxf_executor.cpp:2431] Deactivating Graph...
[info] [gxf_executor.cpp:2439] Graph execution finished.
[2025-04-22 10:18:17,958] [INFO] (__main__.AISpleenSegApp) - End run
[2025-04-22 10:18:17,960] [INFO] (root) - End __main__
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
[info] [fragment.cpp:705] Loading extensions from configs...
[2025-04-22 10:18:22,991] [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, triton_server_netloc=None, argv=['my_app', '-i', 'dcm', '-o', 'output', '-m', 'models'])
[2025-04-22 10:18:22,993] [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=), triton_server_netloc=
[2025-04-22 10:18:22,994] [INFO] (root) - End compose
[info] [gxf_executor.cpp:265] Creating context
[info] [gxf_executor.cpp:2396] Activating Graph...
[info] [gxf_executor.cpp:2426] Running Graph...
[info] [gxf_executor.cpp:2428] Waiting for completion...
[info] [greedy_scheduler.cpp:191] Scheduling 6 entities
[2025-04-22 10:18:23,011] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
[2025-04-22 10:18:23,326] [INFO] (root) - Finding series for Selection named: CT Series
[2025-04-22 10:18:23,326] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
# of series: 1
[2025-04-22 10:18:23,326] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 10:18:23,326] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2025-04-22 10:18:23,326] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2025-04-22 10:18:23,326] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 10:18:23,326] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2025-04-22 10:18:23,326] [INFO] (root) - Series attribute Modality value: CT
[2025-04-22 10:18:23,326] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 10:18:23,326] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2025-04-22 10:18:23,326] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2025-04-22 10:18:23,326] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 10:18:23,327] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 10:18:23,327] [INFO] (root) - Series Selection finalized.
[2025-04-22 10:18:23,327] [INFO] (root) - Series Description of selected DICOM Series for inference: ABD/PANC 3.0 B31f
[2025-04-22 10:18:23,327] [INFO] (root) - Series Instance UID of selected DICOM Series for inference: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 10:18:23,535] [INFO] (root) - Casting to float32
[2025-04-22 10:18:23,592] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model/model.ts
/home/mqin/src/monai-deploy-app-sdk/.venv/lib/python3.10/site-packages/monai/bundle/reference_resolver.py:216: UserWarning: Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.
warnings.warn(
[2025-04-22 10:18:26,690] [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.
[2025-04-22 10:18:28,072] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image
[2025-04-22 10:18:28,072] [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/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.
check_person_name(patient_name)
[2025-04-22 10:18:38,458] [INFO] (highdicom.base) - copy Image-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 10:18:38,458] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2025-04-22 10:18:38,458] [INFO] (highdicom.base) - copy Patient-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 10:18:38,458] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2025-04-22 10:18:38,459] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2025-04-22 10:18:38,459] [INFO] (highdicom.base) - copy Study-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 10:18:38,459] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2025-04-22 10:18:38,459] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2025-04-22 10:18:38,459] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[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.
[info] [greedy_scheduler.cpp:401] Scheduler finished.
[info] [gxf_executor.cpp:2431] Deactivating Graph...
[info] [gxf_executor.cpp:2439] Graph execution finished.
[2025-04-22 10:18:38,554] [INFO] (app.AISpleenSegApp) - End run
!ls output
1.2.826.0.1.3680043.10.511.3.141985674848102250562862177103472.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 x86_64 -l DEBUG
[2025-04-22 10:18:40,556] [INFO] (common) - Downloading CLI manifest file...
[2025-04-22 10:18:40,770] [DEBUG] (common) - Validating CLI manifest file...
[2025-04-22 10:18:40,771] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app
[2025-04-22 10:18:40,771] [INFO] (packager.parameters) - Detected application type: Python Module
[2025-04-22 10:18:40,771] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models...
[2025-04-22 10:18:40,772] [DEBUG] (packager) - Model model=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model added.
[2025-04-22 10:18:40,772] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...
[2025-04-22 10:18:40,776] [INFO] (packager) - Generating app.json...
[2025-04-22 10:18:40,776] [INFO] (packager) - Generating pkg.json...
[2025-04-22 10:18:40,780] [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 ================
[2025-04-22 10:18:40,781] [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 ================
[2025-04-22 10:18:40,804] [DEBUG] (packager.builder) -
========== Begin Build Parameters ==========
{'additional_lib_paths': '',
'app_config_file_path': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml'),
'app_dir': PosixPath('/opt/holoscan/app'),
'app_json': '/etc/holoscan/app.json',
'application': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app'),
'application_directory': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app'),
'application_type': 'PythonModule',
'build_cache': PosixPath('/home/mqin/.holoscan_build_cache'),
'cmake_args': '',
'command': '["python3", "/opt/holoscan/app"]',
'command_filename': 'my_app',
'config_file_path': PosixPath('/var/holoscan/app.yaml'),
'docs_dir': PosixPath('/opt/holoscan/docs'),
'full_input_path': PosixPath('/var/holoscan/input'),
'full_output_path': PosixPath('/var/holoscan/output'),
'gid': 1000,
'holoscan_sdk_version': '3.1.0',
'includes': [],
'input_dir': 'input/',
'lib_dir': PosixPath('/opt/holoscan/lib'),
'logs_dir': PosixPath('/var/holoscan/logs'),
'models': {'model': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/models/model')},
'models_dir': PosixPath('/opt/holoscan/models'),
'monai_deploy_app_sdk_version': '0.5.1',
'no_cache': False,
'output_dir': 'output/',
'pip_packages': None,
'pkg_json': '/etc/holoscan/pkg.json',
'requirements_file_path': PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/requirements.txt'),
'sdk': <SdkType.MonaiDeploy: 'monai-deploy'>,
'sdk_type': 'monai-deploy',
'tarball_output': None,
'timeout': 0,
'title': 'MONAI Deploy App Package - MONAI Bundle AI App',
'uid': 1000,
'username': 'holoscan',
'version': 1.0,
'working_dir': PosixPath('/var/holoscan')}
=========== End Build Parameters ===========
[2025-04-22 10:18:40,805] [DEBUG] (packager.builder) -
========== Begin Platform Parameters ==========
{'base_image': 'nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04',
'build_image': None,
'cuda_deb_arch': 'x86_64',
'custom_base_image': False,
'custom_holoscan_sdk': False,
'custom_monai_deploy_sdk': True,
'gpu_type': 'dgpu',
'holoscan_deb_arch': 'amd64',
'holoscan_sdk_file': '3.1.0',
'holoscan_sdk_filename': '3.1.0',
'monai_deploy_sdk_file': PosixPath('/home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl'),
'monai_deploy_sdk_filename': 'monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl',
'tag': 'my_app:1.0',
'target_arch': 'x86_64'}
=========== End Platform Parameters ===========
[2025-04-22 10:18:40,822] [DEBUG] (packager.builder) -
========== Begin Dockerfile ==========
ARG GPU_TYPE=dgpu
FROM nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04 AS base
RUN apt-get update \
&& apt-get install -y --no-install-recommends --no-install-suggests \
curl \
jq \
&& rm -rf /var/lib/apt/lists/*
# FROM base AS mofed-installer
# ARG MOFED_VERSION=23.10-2.1.3.1
# # In a container, we only need to install the user space libraries, though the drivers are still
# # needed on the host.
# # Note: MOFED's installation is not easily portable, so we can't copy the output of this stage
# # to our final stage, but must inherit from it. For that reason, we keep track of the build/install
# # only dependencies in the `MOFED_DEPS` variable (parsing the output of `--check-deps-only`) to
# # remove them in that same layer, to ensure they are not propagated in the final image.
# WORKDIR /opt/nvidia/mofed
# ARG MOFED_INSTALL_FLAGS="--dpdk --with-mft --user-space-only --force --without-fw-update"
# RUN UBUNTU_VERSION=$(cat /etc/lsb-release | grep DISTRIB_RELEASE | cut -d= -f2) \
# && OFED_PACKAGE="MLNX_OFED_LINUX-${MOFED_VERSION}-ubuntu${UBUNTU_VERSION}-$(uname -m)" \
# && curl -S -# -o ${OFED_PACKAGE}.tgz -L \
# https://www.mellanox.com/downloads/ofed/MLNX_OFED-${MOFED_VERSION}/${OFED_PACKAGE}.tgz \
# && tar xf ${OFED_PACKAGE}.tgz \
# && MOFED_INSTALLER=$(find . -name mlnxofedinstall -type f -executable -print) \
# && MOFED_DEPS=$(${MOFED_INSTALLER} ${MOFED_INSTALL_FLAGS} --check-deps-only 2>/dev/null | tail -n1 | cut -d' ' -f3-) \
# && apt-get update \
# && apt-get install --no-install-recommends -y ${MOFED_DEPS} \
# && ${MOFED_INSTALLER} ${MOFED_INSTALL_FLAGS} \
# && rm -r * \
# && apt-get remove -y ${MOFED_DEPS} && apt-get autoremove -y \
# && rm -rf /var/lib/apt/lists/*
FROM base AS release
ENV DEBIAN_FRONTEND=noninteractive
ENV TERM=xterm-256color
ARG GPU_TYPE
ARG UNAME
ARG UID
ARG GID
RUN mkdir -p /etc/holoscan/ \
&& mkdir -p /opt/holoscan/ \
&& mkdir -p /var/holoscan \
&& mkdir -p /opt/holoscan/app \
&& mkdir -p /var/holoscan/input \
&& mkdir -p /var/holoscan/output
LABEL base="nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04"
LABEL tag="my_app:1.0"
LABEL org.opencontainers.image.title="MONAI Deploy App Package - MONAI Bundle AI App"
LABEL org.opencontainers.image.version="1.0"
LABEL org.nvidia.holoscan="3.1.0"
LABEL org.monai.deploy.app-sdk="0.5.1"
ENV HOLOSCAN_INPUT_PATH=/var/holoscan/input
ENV HOLOSCAN_OUTPUT_PATH=/var/holoscan/output
ENV HOLOSCAN_WORKDIR=/var/holoscan
ENV HOLOSCAN_APPLICATION=/opt/holoscan/app
ENV HOLOSCAN_TIMEOUT=0
ENV HOLOSCAN_MODEL_PATH=/opt/holoscan/models
ENV HOLOSCAN_DOCS_PATH=/opt/holoscan/docs
ENV HOLOSCAN_CONFIG_PATH=/var/holoscan/app.yaml
ENV HOLOSCAN_APP_MANIFEST_PATH=/etc/holoscan/app.json
ENV HOLOSCAN_PKG_MANIFEST_PATH=/etc/holoscan/pkg.json
ENV HOLOSCAN_LOGS_PATH=/var/holoscan/logs
ENV HOLOSCAN_VERSION=3.1.0
# If torch is installed, we can skip installing Python
ENV PYTHON_VERSION=3.10.6-1~22.04
ENV PYTHON_PIP_VERSION=22.0.2+dfsg-*
RUN apt update \
&& apt-get install -y --no-install-recommends --no-install-suggests \
python3-minimal=${PYTHON_VERSION} \
libpython3-stdlib=${PYTHON_VERSION} \
python3=${PYTHON_VERSION} \
python3-venv=${PYTHON_VERSION} \
python3-pip=${PYTHON_PIP_VERSION} \
&& rm -rf /var/lib/apt/lists/*
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 && \
chown -R holoscan /var/holoscan/input && \
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
# Set the working directory
WORKDIR /var/holoscan
USER $UNAME
ENV PATH=/home/${UNAME}/.local/bin:/opt/nvidia/holoscan/bin:$PATH
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/${UNAME}/.local/lib/python3.10/site-packages/holoscan/lib
COPY ./pip/requirements.txt /tmp/requirements.txt
RUN pip install --upgrade pip
RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
# Install MONAI Deploy App SDK
# Copy user-specified MONAI Deploy SDK file
COPY ./monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl
RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+37.g96f7e31.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 ===========
[2025-04-22 10:18:40,822] [INFO] (packager.builder) -
===============================================================================
Building image for: x64-workstation
Architecture: linux/amd64
Base Image: nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04
Build Image: N/A
Cache: Enabled
Configuration: dgpu
Holoscan SDK Package: 3.1.0
MONAI Deploy App SDK Package: /home/mqin/src/monai-deploy-app-sdk/dist/monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl
gRPC Health Probe: N/A
SDK Version: 3.1.0
SDK: monai-deploy
Tag: my_app-x64-workstation-dgpu-linux-amd64:1.0
Included features/dependencies: N/A
[2025-04-22 10:18:41,210] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`
[2025-04-22 10:18:41,210] [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: 4.74kB done
#1 DONE 0.1s
#2 [auth] nvidia/cuda:pull token for nvcr.io
#2 DONE 0.0s
#3 [internal] load metadata for nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04
#3 DONE 0.5s
#4 [internal] load .dockerignore
#4 transferring context: 1.80kB done
#4 DONE 0.1s
#5 importing cache manifest from local:3932312145486245041
#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done
#5 DONE 0.0s
#6 [internal] load build context
#6 DONE 0.0s
#7 [base 1/2] FROM nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04@sha256:22fc009e5cea0b8b91d94c99fdd419d2366810b5ea835e47b8343bc15800c186
#7 resolve nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04@sha256:22fc009e5cea0b8b91d94c99fdd419d2366810b5ea835e47b8343bc15800c186 0.0s done
#7 DONE 0.0s
#8 importing cache manifest from nvcr.io/nvidia/cuda:12.6.0-runtime-ubuntu22.04
#8 inferred cache manifest type: application/vnd.docker.distribution.manifest.list.v2+json done
#8 DONE 0.3s
#6 [internal] load build context
#6 transferring context: 19.58MB 0.1s done
#6 DONE 0.5s
#9 [release 4/19] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan
#9 CACHED
#10 [release 5/19] RUN chown -R holoscan /var/holoscan && chown -R holoscan /var/holoscan/input && chown -R holoscan /var/holoscan/output
#10 CACHED
#11 [release 3/19] RUN groupadd -f -g 1000 holoscan
#11 CACHED
#12 [release 6/19] WORKDIR /var/holoscan
#12 CACHED
#13 [release 7/19] COPY ./tools /var/holoscan/tools
#13 CACHED
#14 [release 8/19] RUN chmod +x /var/holoscan/tools
#14 CACHED
#15 [release 1/19] 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
#15 CACHED
#16 [release 2/19] 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/*
#16 CACHED
#17 [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/*
#17 CACHED
#18 [release 9/19] WORKDIR /var/holoscan
#18 CACHED
#19 [release 10/19] COPY ./pip/requirements.txt /tmp/requirements.txt
#19 DONE 0.2s
#20 [release 11/19] RUN pip install --upgrade pip
#20 0.826 Defaulting to user installation because normal site-packages is not writeable
#20 0.854 Requirement already satisfied: pip in /usr/lib/python3/dist-packages (22.0.2)
#20 1.009 Collecting pip
#20 1.083 Downloading pip-25.0.1-py3-none-any.whl (1.8 MB)
#20 1.257 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 10.9 MB/s eta 0:00:00
#20 1.288 Installing collected packages: pip
#20 2.028 Successfully installed pip-25.0.1
#20 DONE 2.2s
#21 [release 12/19] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
#21 0.646 Collecting highdicom>=0.18.2 (from -r /tmp/requirements.txt (line 1))
#21 0.682 Downloading highdicom-0.25.1-py3-none-any.whl.metadata (5.0 kB)
#21 0.718 Collecting monai>=1.0 (from -r /tmp/requirements.txt (line 2))
#21 0.731 Downloading monai-1.4.0-py3-none-any.whl.metadata (11 kB)
#21 0.833 Collecting nibabel>=3.2.1 (from -r /tmp/requirements.txt (line 3))
#21 0.845 Downloading nibabel-5.3.2-py3-none-any.whl.metadata (9.1 kB)
#21 1.074 Collecting numpy>=1.21.6 (from -r /tmp/requirements.txt (line 4))
#21 1.085 Downloading numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)
#21 1.122 Collecting pydicom>=2.3.0 (from -r /tmp/requirements.txt (line 5))
#21 1.136 Downloading pydicom-3.0.1-py3-none-any.whl.metadata (9.4 kB)
#21 1.144 Requirement already satisfied: setuptools>=59.5.0 in /usr/lib/python3/dist-packages (from -r /tmp/requirements.txt (line 6)) (59.6.0)
#21 1.183 Collecting SimpleITK>=2.0.0 (from -r /tmp/requirements.txt (line 7))
#21 1.193 Downloading SimpleITK-2.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.9 kB)
#21 1.259 Collecting scikit-image>=0.17.2 (from -r /tmp/requirements.txt (line 8))
#21 1.270 Downloading scikit_image-0.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)
#21 1.303 Collecting numpy-stl>=2.12.0 (from -r /tmp/requirements.txt (line 9))
#21 1.315 Downloading numpy_stl-3.2.0-py3-none-any.whl.metadata (18 kB)
#21 1.414 Collecting trimesh>=3.8.11 (from -r /tmp/requirements.txt (line 10))
#21 1.425 Downloading trimesh-4.6.8-py3-none-any.whl.metadata (18 kB)
#21 1.477 Collecting torch>=1.12.0 (from -r /tmp/requirements.txt (line 11))
#21 1.488 Downloading torch-2.6.0-cp310-cp310-manylinux1_x86_64.whl.metadata (28 kB)
#21 1.650 Collecting pillow>=8.3 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))
#21 1.662 Downloading pillow-11.2.1-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (8.9 kB)
#21 1.711 Collecting pyjpegls>=1.0.0 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))
#21 1.724 Downloading pyjpegls-1.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
#21 1.757 Collecting typing-extensions>=4.0.0 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))
#21 1.771 Downloading typing_extensions-4.13.2-py3-none-any.whl.metadata (3.0 kB)
#21 1.797 Collecting numpy>=1.21.6 (from -r /tmp/requirements.txt (line 4))
#21 1.819 Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
#21 1.859 Collecting importlib-resources>=5.12 (from nibabel>=3.2.1->-r /tmp/requirements.txt (line 3))
#21 1.871 Downloading importlib_resources-6.5.2-py3-none-any.whl.metadata (3.9 kB)
#21 1.923 Collecting packaging>=20 (from nibabel>=3.2.1->-r /tmp/requirements.txt (line 3))
#21 1.934 Downloading packaging-25.0-py3-none-any.whl.metadata (3.3 kB)
#21 2.041 Collecting scipy>=1.11.4 (from scikit-image>=0.17.2->-r /tmp/requirements.txt (line 8))
#21 2.052 Downloading scipy-1.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
#21 2.086 Collecting networkx>=3.0 (from scikit-image>=0.17.2->-r /tmp/requirements.txt (line 8))
#21 2.097 Downloading networkx-3.4.2-py3-none-any.whl.metadata (6.3 kB)
#21 2.139 Collecting imageio!=2.35.0,>=2.33 (from scikit-image>=0.17.2->-r /tmp/requirements.txt (line 8))
#21 2.151 Downloading imageio-2.37.0-py3-none-any.whl.metadata (5.2 kB)
#21 2.212 Collecting tifffile>=2022.8.12 (from scikit-image>=0.17.2->-r /tmp/requirements.txt (line 8))
#21 2.224 Downloading tifffile-2025.3.30-py3-none-any.whl.metadata (32 kB)
#21 2.248 Collecting lazy-loader>=0.4 (from scikit-image>=0.17.2->-r /tmp/requirements.txt (line 8))
#21 2.259 Downloading lazy_loader-0.4-py3-none-any.whl.metadata (7.6 kB)
#21 2.293 Collecting python-utils>=3.4.5 (from numpy-stl>=2.12.0->-r /tmp/requirements.txt (line 9))
#21 2.305 Downloading python_utils-3.9.1-py2.py3-none-any.whl.metadata (9.8 kB)
#21 2.367 Collecting filelock (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.378 Downloading filelock-3.18.0-py3-none-any.whl.metadata (2.9 kB)
#21 2.412 Collecting jinja2 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.423 Downloading jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB)
#21 2.457 Collecting fsspec (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.468 Downloading fsspec-2025.3.2-py3-none-any.whl.metadata (11 kB)
#21 2.550 Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.561 Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
#21 2.581 Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.592 Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
#21 2.612 Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.623 Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
#21 2.642 Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.653 Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
#21 2.672 Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.683 Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
#21 2.702 Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
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#21 2.733 Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
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#21 2.762 Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.773 Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
#21 2.804 Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.815 Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
#21 2.831 Collecting nvidia-cusparselt-cu12==0.6.2 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.842 Downloading nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl.metadata (6.8 kB)
#21 2.861 Collecting nvidia-nccl-cu12==2.21.5 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.872 Downloading nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)
#21 2.895 Collecting nvidia-nvtx-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
#21 2.907 Downloading nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.7 kB)
#21 2.930 Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
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#21 2.960 Collecting triton==3.2.0 (from torch>=1.12.0->-r /tmp/requirements.txt (line 11))
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#21 3.144 INFO: pip is looking at multiple versions of pyjpegls to determine which version is compatible with other requirements. This could take a while.
#21 3.145 Collecting pyjpegls>=1.0.0 (from highdicom>=0.18.2->-r /tmp/requirements.txt (line 1))
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#21 3.252 Collecting MarkupSafe>=2.0 (from jinja2->torch>=1.12.0->-r /tmp/requirements.txt (line 11))
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#21 3.751 Downloading SimpleITK-2.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.4 MB)
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#21 4.377 Downloading scikit_image-0.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.8 MB)
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#21 4.598 Downloading torch-2.6.0-cp310-cp310-manylinux1_x86_64.whl (766.7 MB)
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#21 11.69 Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)
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#21 15.06 Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)
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#21 15.35 Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)
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#21 21.34 Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)
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#21 23.19 Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)
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#21 23.80 Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)
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#21 25.05 Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)
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#21 26.92 Downloading nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl (150.1 MB)
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#21 28.27 Downloading nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl (188.7 MB)
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#21 30.01 Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)
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#21 30.21 Downloading nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (99 kB)
#21 30.23 Downloading sympy-1.13.1-py3-none-any.whl (6.2 MB)
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#21 30.30 Downloading triton-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (253.1 MB)
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#21 32.99 Downloading networkx-3.4.2-py3-none-any.whl (1.7 MB)
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#21 33.02 Downloading packaging-25.0-py3-none-any.whl (66 kB)
#21 33.04 Downloading pillow-11.2.1-cp310-cp310-manylinux_2_28_x86_64.whl (4.6 MB)
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#21 33.10 Downloading pyjpegls-1.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB)
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#21 33.14 Downloading python_utils-3.9.1-py2.py3-none-any.whl (32 kB)
#21 33.15 Downloading scipy-1.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.6 MB)
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#21 33.51 Downloading tifffile-2025.3.30-py3-none-any.whl (226 kB)
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#21 33.54 Downloading filelock-3.18.0-py3-none-any.whl (16 kB)
#21 33.56 Downloading fsspec-2025.3.2-py3-none-any.whl (194 kB)
#21 33.57 Downloading jinja2-3.1.6-py3-none-any.whl (134 kB)
#21 33.59 Downloading MarkupSafe-3.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20 kB)
#21 33.60 Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)
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#21 41.09 Installing collected packages: triton, SimpleITK, nvidia-cusparselt-cu12, mpmath, typing-extensions, sympy, pydicom, 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, fsspec, filelock, trimesh, tifffile, scipy, python-utils, pyjpegls, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nibabel, lazy-loader, jinja2, imageio, scikit-image, nvidia-cusolver-cu12, numpy-stl, highdicom, torch, monai
#21 113.8 Successfully installed MarkupSafe-3.0.2 SimpleITK-2.4.1 filelock-3.18.0 fsspec-2025.3.2 highdicom-0.25.1 imageio-2.37.0 importlib-resources-6.5.2 jinja2-3.1.6 lazy-loader-0.4 monai-1.4.0 mpmath-1.3.0 networkx-3.4.2 nibabel-5.3.2 numpy-1.26.4 numpy-stl-3.2.0 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-25.0 pillow-11.2.1 pydicom-3.0.1 pyjpegls-1.4.0 python-utils-3.9.1 scikit-image-0.25.2 scipy-1.15.2 sympy-1.13.1 tifffile-2025.3.30 torch-2.6.0 trimesh-4.6.8 triton-3.2.0 typing-extensions-4.13.2
#21 DONE 117.7s
#22 [release 13/19] COPY ./monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl
#22 DONE 0.5s
#23 [release 14/19] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl
#23 0.662 Defaulting to user installation because normal site-packages is not writeable
#23 0.785 Processing /tmp/monai_deploy_app_sdk-0.5.1+37.g96f7e31.dirty-py3-none-any.whl
#23 0.794 Requirement already satisfied: numpy>=1.21.6 in /home/holoscan/.local/lib/python3.10/site-packages (from monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty) (1.26.4)
#23 0.876 Collecting holoscan~=3.0 (from monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 0.901 Downloading holoscan-3.1.0-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (7.0 kB)
#23 0.960 Collecting holoscan-cli~=3.0 (from monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 0.967 Downloading holoscan_cli-3.1.0-py3-none-any.whl.metadata (4.0 kB)
#23 1.037 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.043 Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
#23 1.119 Collecting tritonclient>=2.53.0 (from tritonclient[all]>=2.53.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.124 Downloading tritonclient-2.56.0-py3-none-manylinux1_x86_64.whl.metadata (2.8 kB)
#23 1.217 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.222 Downloading typeguard-4.4.2-py3-none-any.whl.metadata (3.8 kB)
#23 1.252 Requirement already satisfied: pip>22.0.2 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty) (25.0.1)
#23 1.303 Collecting cupy-cuda12x<14.0,>=12.2 (from holoscan~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.308 Downloading cupy_cuda12x-13.4.1-cp310-cp310-manylinux2014_x86_64.whl.metadata (2.6 kB)
#23 1.380 Collecting cloudpickle<4.0,>=3.0 (from holoscan~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.385 Downloading cloudpickle-3.1.1-py3-none-any.whl.metadata (7.1 kB)
#23 1.439 Collecting wheel-axle-runtime<1.0 (from holoscan~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.445 Downloading wheel_axle_runtime-0.0.6-py3-none-any.whl.metadata (8.1 kB)
#23 1.467 Requirement already satisfied: Jinja2<4.0.0,>=3.1.5 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan-cli~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty) (3.1.6)
#23 1.515 Collecting packaging<24.0,>=23.1 (from holoscan-cli~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.520 Downloading packaging-23.2-py3-none-any.whl.metadata (3.2 kB)
#23 1.646 Collecting psutil<7.0.0,>=6.0.0 (from holoscan-cli~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.650 Downloading psutil-6.1.1-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (22 kB)
#23 1.724 Collecting python-on-whales<0.61.0,>=0.60.1 (from holoscan-cli~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.731 Downloading python_on_whales-0.60.1-py3-none-any.whl.metadata (16 kB)
#23 1.816 Collecting pyyaml<7.0,>=6.0 (from holoscan-cli~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.820 Downloading PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)
#23 1.904 Collecting requests<3.0.0,>=2.31.0 (from holoscan-cli~=3.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 1.908 Downloading requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)
#23 2.039 Collecting python-rapidjson>=0.9.1 (from tritonclient>=2.53.0->tritonclient[all]>=2.53.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 2.044 Downloading python_rapidjson-1.20-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (22 kB)
#23 2.122 Collecting urllib3>=2.0.7 (from tritonclient>=2.53.0->tritonclient[all]>=2.53.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
#23 2.126 Downloading urllib3-2.4.0-py3-none-any.whl.metadata (6.5 kB)
#23 2.575 Collecting aiohttp<4.0.0,>=3.8.1 (from tritonclient[all]>=2.53.0->monai-deploy-app-sdk==0.5.1+37.g96f7e31.dirty)
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#23 14.38 Uninstalling packaging-25.0:
#23 14.40 Successfully uninstalled packaging-25.0
#23 19.15 Successfully installed aiohappyeyeballs-2.6.1 aiohttp-3.11.18 aiosignal-1.3.2 async-timeout-5.0.1 attrs-25.3.0 brotli-1.1.0 certifi-2025.1.31 charset-normalizer-3.4.1 click-8.1.8 cloudpickle-3.1.1 colorama-0.4.6 cuda-bindings-12.8.0 cuda-python-12.8.0 cupy-cuda12x-13.4.1 fastrlock-0.8.3 frozenlist-1.6.0 gevent-25.4.1 geventhttpclient-2.3.3 greenlet-3.2.1 grpcio-1.67.1 holoscan-3.1.0 holoscan-cli-3.1.0 idna-3.10 markdown-it-py-3.0.0 mdurl-0.1.2 monai-deploy-app-sdk-0.5.1+37.g96f7e31.dirty multidict-6.4.3 packaging-23.2 propcache-0.3.1 protobuf-5.29.4 psutil-6.1.1 pydantic-1.10.21 pygments-2.19.1 python-on-whales-0.60.1 python-rapidjson-1.20 pyyaml-6.0.2 requests-2.32.3 rich-14.0.0 shellingham-1.5.4 tqdm-4.67.1 tritonclient-2.56.0 typeguard-4.4.2 typer-0.15.2 urllib3-2.4.0 wheel-axle-runtime-0.0.6 yarl-1.20.0 zope.event-5.0 zope.interface-7.2
#23 DONE 22.0s
#24 [release 15/19] COPY ./models /opt/holoscan/models
#24 DONE 0.3s
#25 [release 16/19] COPY ./map/app.json /etc/holoscan/app.json
#25 DONE 0.1s
#26 [release 17/19] COPY ./app.config /var/holoscan/app.yaml
#26 DONE 0.1s
#27 [release 18/19] COPY ./map/pkg.json /etc/holoscan/pkg.json
#27 DONE 0.1s
#28 [release 19/19] COPY ./app /opt/holoscan/app
#28 DONE 0.1s
#29 exporting to docker image format
#29 exporting layers
#29 exporting layers 187.0s done
#29 exporting manifest sha256:cac1ac4d69726995d3c9e061377448492061d466def990799cc72e811e162e90 0.0s done
#29 exporting config sha256:aacceda07071b8e9c4e0b360fd0b819d987eef71230c11c95f76c3053bfbd861 0.0s done
#29 sending tarball
#29 ...
#30 importing to docker
#30 loading layer 49b545b4149c 283B / 283B
#30 loading layer c44b5ca75fdc 65.54kB / 5.09MB
#30 loading layer 3370fbb67e83 557.06kB / 3.26GB
#30 loading layer 3370fbb67e83 210.01MB / 3.26GB 6.3s
#30 loading layer 3370fbb67e83 337.02MB / 3.26GB 12.5s
#30 loading layer 3370fbb67e83 550.37MB / 3.26GB 18.7s
#30 loading layer 3370fbb67e83 735.31MB / 3.26GB 22.8s
#30 loading layer 3370fbb67e83 963.15MB / 3.26GB 27.0s
#30 loading layer 3370fbb67e83 1.18GB / 3.26GB 31.0s
#30 loading layer 3370fbb67e83 1.33GB / 3.26GB 37.2s
#30 loading layer 3370fbb67e83 1.56GB / 3.26GB 41.3s
#30 loading layer 3370fbb67e83 1.75GB / 3.26GB 45.4s
#30 loading layer 3370fbb67e83 2.02GB / 3.26GB 49.4s
#30 loading layer 3370fbb67e83 2.19GB / 3.26GB 55.6s
#30 loading layer 3370fbb67e83 2.24GB / 3.26GB 62.9s
#30 loading layer 3370fbb67e83 2.36GB / 3.26GB 69.0s
#30 loading layer 3370fbb67e83 2.56GB / 3.26GB 73.1s
#30 loading layer 3370fbb67e83 2.75GB / 3.26GB 77.1s
#30 loading layer 3370fbb67e83 2.93GB / 3.26GB 83.3s
#30 loading layer 3370fbb67e83 3.10GB / 3.26GB 89.5s
#30 loading layer af8c5ba7bee3 32.77kB / 144.30kB
#30 loading layer 8f8123670c0a 557.06kB / 398.53MB
#30 loading layer 8f8123670c0a 197.20MB / 398.53MB 2.1s
#30 loading layer 8f8123670c0a 228.95MB / 398.53MB 4.1s
#30 loading layer 8f8123670c0a 270.73MB / 398.53MB 6.2s
#30 loading layer 8f8123670c0a 334.23MB / 398.53MB 8.2s
#30 loading layer 8f8123670c0a 368.21MB / 398.53MB 10.3s
#30 loading layer c277737c154f 196.61kB / 17.81MB
#30 loading layer 01f35eabeadd 493B / 493B
#30 loading layer 21a6f7132dd0 316B / 316B
#30 loading layer 22aecd36f9de 302B / 302B
#30 loading layer ab9d985fcc93 3.33kB / 3.33kB
#30 loading layer 8f8123670c0a 398.53MB / 398.53MB 13.6s done
#30 loading layer 49b545b4149c 283B / 283B 109.9s done
#30 loading layer c44b5ca75fdc 5.09MB / 5.09MB 109.9s done
#30 loading layer 3370fbb67e83 3.26GB / 3.26GB 109.2s done
#30 loading layer af8c5ba7bee3 144.30kB / 144.30kB 13.7s done
#30 loading layer c277737c154f 17.81MB / 17.81MB 1.1s done
#30 loading layer 01f35eabeadd 493B / 493B 0.7s done
#30 loading layer 21a6f7132dd0 316B / 316B 0.6s done
#30 loading layer 22aecd36f9de 302B / 302B 0.5s done
#30 loading layer ab9d985fcc93 3.33kB / 3.33kB 0.5s done
#30 DONE 109.9s
#29 exporting to docker image format
#29 sending tarball 147.3s done
#29 DONE 334.4s
#31 exporting cache to client directory
#31 preparing build cache for export
#31 writing layer sha256:000344a04deee760c0681e29294ee3f527b8299026aef2cfc3fa93e327c63df7
#31 writing layer sha256:000344a04deee760c0681e29294ee3f527b8299026aef2cfc3fa93e327c63df7 0.1s done
#31 writing layer sha256:048e6a80d9e1847dcc9526191c8d16c8bdda32e8440cb0e287ba48983787b2ea 0.0s done
#31 writing layer sha256:1a0d52c93099897b518eb6cc6cd0fa3d52ff733e8606b4d8c92675ba9e7101ff done
#31 writing layer sha256:21ef12df128643f4e171d286035dc9c1a1e744f0ff52681473844fa3ebf148f9
#31 writing layer sha256:21ef12df128643f4e171d286035dc9c1a1e744f0ff52681473844fa3ebf148f9 51.1s done
#31 writing layer sha256:234b866f57e0c5d555af2d87a1857a17ec4ac7e70d2dc6c31ff0a072a4607f24
#31 writing layer sha256:234b866f57e0c5d555af2d87a1857a17ec4ac7e70d2dc6c31ff0a072a4607f24 done
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#31 writing layer sha256:8a18a20487f64bf7fe1ac7ead38280d387b11f3d714179e19b4ecfba68a09f93
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#31 writing layer sha256:9897f28322ebd7b633908503d8877b342e03a47293fcacdc8206b2e5d9f20923
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#31 writing layer sha256:ac52600be001236a2c291a4c5902c915bf5ec9d2441c06d2a54c587b76345847
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#31 writing layer sha256:ff7fc9bdba2b206dc4eb678f49b36a99daf566bf71753dc2cec30a0195f7a41a
#31 writing layer sha256:ff7fc9bdba2b206dc4eb678f49b36a99daf566bf71753dc2cec30a0195f7a41a 6.8s done
#31 preparing build cache for export 58.8s done
#31 writing config sha256:1401476261a3e0a96b4b64a3270960c3fa51a70d62e0cd7a30991c3da24af97e 0.0s done
#31 writing cache manifest sha256:b4e7496beec087df8709332743a34f2f69a9993f4fdf0ac0d1871c03d8664448 0.0s done
#31 DONE 58.8s
[2025-04-22 10:27:40,090] [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 aacceda07071 6 minutes ago 9.25GB
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"
}
2025-04-22 17:27:43 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app
2025-04-22 17:27:43 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json
2025-04-22 17:27:43 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json
2025-04-22 17:27:43 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml
2025-04-22 17:27:43 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models
2025-04-22 17:27:43 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs
2025-04-22 17:27:43 [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
[2025-04-22 10:27:44,899] [INFO] (runner) - Checking dependencies...
[2025-04-22 10:27:44,899] [INFO] (runner) - --> Verifying if "docker" is installed...
[2025-04-22 10:27:44,899] [INFO] (runner) - --> Verifying if "docker-buildx" is installed...
[2025-04-22 10:27:44,900] [INFO] (runner) - --> Verifying if "my_app-x64-workstation-dgpu-linux-amd64:1.0" is available...
[2025-04-22 10:27:44,977] [INFO] (runner) - Reading HAP/MAP manifest...
Successfully copied 2.56kB to /tmp/tmpmnebv7ra/app.json
Successfully copied 2.05kB to /tmp/tmpmnebv7ra/pkg.json
bb0cf20f8662e86bcda22ed7a5faae90e0b66cdd38d6f64a8e2ceb4e95a0ebca
[2025-04-22 10:27:45,406] [INFO] (runner) - --> Verifying if "nvidia-ctk" is installed...
[2025-04-22 10:27:45,407] [INFO] (runner) - --> Verifying "nvidia-ctk" version...
[2025-04-22 10:27:45,714] [INFO] (common) - Launching container (21c6001bf0ef) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...
container name: youthful_jepsen
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
2025-04-22 17:27:46 [INFO] Launching application python3 /opt/holoscan/app ...
[info] [fragment.cpp:705] Loading extensions from configs...
[info] [gxf_executor.cpp:265] Creating context
[2025-04-22 17:27:53,854] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, triton_server_netloc=None, argv=['/opt/holoscan/app'])
[2025-04-22 17:27:53,858] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan), triton_server_netloc=
[2025-04-22 17:27:53,860] [INFO] (root) - End compose
[info] [gxf_executor.cpp:2396] Activating Graph...
[info] [gxf_executor.cpp:2426] Running Graph...
[info] [gxf_executor.cpp:2428] Waiting for completion...
[info] [greedy_scheduler.cpp:191] Scheduling 6 entities
[2025-04-22 17:27:53,886] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
[2025-04-22 17:27:54,799] [INFO] (root) - Finding series for Selection named: CT Series
[2025-04-22 17:27:54,799] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
# of series: 1
[2025-04-22 17:27:54,799] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 17:27:54,799] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2025-04-22 17:27:54,800] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2025-04-22 17:27:54,800] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 17:27:54,800] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2025-04-22 17:27:54,800] [INFO] (root) - Series attribute Modality value: CT
[2025-04-22 17:27:54,800] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 17:27:54,800] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2025-04-22 17:27:54,800] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2025-04-22 17:27:54,800] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2025-04-22 17:27:54,800] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 17:27:54,800] [INFO] (root) - Series Selection finalized.
[2025-04-22 17:27:54,800] [INFO] (root) - Series Description of selected DICOM Series for inference: ABD/PANC 3.0 B31f
[2025-04-22 17:27:54,800] [INFO] (root) - Series Instance UID of selected DICOM Series for inference: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2025-04-22 17:27:55,081] [INFO] (root) - Casting to float32
[2025-04-22 17:27:55,383] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/model/model.ts
/home/holoscan/.local/lib/python3.10/site-packages/monai/bundle/reference_resolver.py:216: UserWarning: Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.
warnings.warn(
[2025-04-22 17:27:59,716] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConversionOperator) - Output will be saved in file /var/holoscan/output/stl/spleen.stl.
[2025-04-22 17:28:01,195] [INFO] (monai.deploy.operators.stl_conversion_operator.SpatialImage) - 3D image
[2025-04-22 17:28:01,196] [INFO] (monai.deploy.operators.stl_conversion_operator.STLConverter) - Image ndarray shape:(204, 512, 512)
/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.
check_person_name(patient_name)
[2025-04-22 17:28:12,576] [INFO] (highdicom.base) - copy Image-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 17:28:12,576] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2025-04-22 17:28:12,576] [INFO] (highdicom.base) - copy Patient-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 17:28:12,576] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2025-04-22 17:28:12,577] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2025-04-22 17:28:12,577] [INFO] (highdicom.base) - copy Study-related attributes from dataset "1.3.6.1.4.1.14519.5.2.1.7085.2626.936983343951485811186213470191"
[2025-04-22 17:28:12,577] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2025-04-22 17:28:12,577] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2025-04-22 17:28:12,578] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[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.
[info] [greedy_scheduler.cpp:401] Scheduler finished.
[info] [gxf_executor.cpp:2431] Deactivating Graph...
[info] [gxf_executor.cpp:2439] Graph execution finished.
[2025-04-22 17:28:12,698] [INFO] (app.AISpleenSegApp) - End run
[2025-04-22 10:28:14,349] [INFO] (common) - Container 'youthful_jepsen'(21c6001bf0ef) exited.
!ls $HOLOSCAN_OUTPUT_PATH
1.2.826.0.1.3680043.10.511.3.36310308785029269065941040056862019.dcm stl