Creating a Multi-AI Deploy App with Multiple Models¶
This tutorial shows how to create an inference application with multiple models, focusing on model files organization, accessing and inferring with named model network in the application, and finally building an app package.
Typically multiple models will work in tandem, e.g. a lung segmentation model’s output, along with the original image, are then used by a lung nodule detection and classification model. There is, however, a lack of such models in the MONAI Model Zoo as of now. So, for illustration purpose, two independent models will be used in this example, Spleen Segmentation and Pancreas Segmentation, both are trained with DICOM images of CT modality, and both are packaged in the MONAI Bundle format. A single input of a CT Abdomen DICOM Series can be used for both models within the application.
Important Steps¶
Place the model TorchScripts in a defined folder structure, see below for details
Pass the model name to the inference operator instance in the app
Connect the input to and output from the inference operators, as required by the app
Required Model File Organization¶
The model files in TorchScript, be it MONAI Bundle compliant or not, must each be placed in an uniquely named folder. The name of this folder becomes the name of the loaded model network in the application, and is used by the application to retieve the network via the execution context.
The folders containing the individual model file must then be placed under a parent folder. The name of this folder is chosen by the application developer.
The path of the aforementioned parent folder is used to set the well-known environment variable for the model path,
HOLOSCAN_MODEL_PATH, when the application is directly run as a program.When the application is packaged as an MONAI Application Package (MAP), the parent folder is used as the model path, and the Packager copies all of the sub folders to the well-known
modelsfolder in the MAP.
Example Model File Organization¶
In this example, the models are organized as shown below.
multi_models
├── pancreas_ct_dints
│ └── model.ts
└── spleen_ct
└── model.ts
Please note,
The
multi_modelsis the parent folder, whose path is used to set the well-known environment variable for model path. When using App SDK CLI Packager to build the application package, this is the used as the path for models.The sub-folder names become model network names,
pancreas_ct_dintsandspleen_model, respectively.
In the following sections, we will demonstrate how to create and package the application using these two models.
Note
The two models are both MONAI bundles, published in MONAI Model Zoo
The DICOM CT series used as test input is downloaded from TCIA, CT Abdomen Collection ID CPTAC-PDA Subject ID C3N-00198.
Both the DICOM files and the models have been packaged and shared on Google Drive.
Creating Operators and connecting them in Application class¶
We will implement an application that consists of seven 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 x 2:
DICOMSegmentationWriterOperator x2:
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 for reusing the patient demographics and other DICOM Study level attributes, as well as referring to the original SOP instance UID.
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 --|> Spleen_BundleInferenceOperator : image...image
DICOMSeriesSelectorOperator --|> Spleen_DICOMSegmentationWriterOperator : study_selected_series_list...study_selected_series_list
Spleen_BundleInferenceOperator --|> Spleen_DICOMSegmentationWriterOperator : pred...seg_image
DICOMSeriesToVolumeOperator --|> Pancreas_BundleInferenceOperator : image...image
DICOMSeriesSelectorOperator --|> Pancreas_DICOMSegmentationWriterOperator : study_selected_series_list...study_selected_series_list
Pancreas_BundleInferenceOperator --|> Pancreas_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 Spleen_BundleInferenceOperator {
<in>image : IN_MEMORY
pred(out) IN_MEMORY
}
class Pancreas_BundleInferenceOperator {
<in>image : IN_MEMORY
pred(out) IN_MEMORY
}
class Spleen_DICOMSegmentationWriterOperator {
<in>seg_image : IN_MEMORY
<in>study_selected_series_list : IN_MEMORY
dicom_seg_instance(out) DISK
}
class Pancreas_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"
!python -c "import nibabel" || pip install -q "nibabel>=3.2.1"
!python -c "import pydicom" || pip install -q "pydicom>=1.4.2"
!python -c "import highdicom" || pip install -q "highdicom>=0.18.2"
!python -c "import SimpleITK" || pip install -q "SimpleITK>=2.0.0"
# Install MONAI Deploy App SDK package
!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=1llJ4NGNTjY187RLX4MtlmHYhfGxBNWmd"
# After downloading ai_spleen_bundle_data zip file from the web browser or using gdown,
!unzip -o "ai_multi_model_bundle_data.zip"
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inflating: multi_models/pancreas_ct_dints/model.ts
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Set up environment variables¶
models_folder = "multi_models"
%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=multi_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 App(Application):
"""This example demonstrates how to create a multi-model/multi-AI application.
The important steps are:
1. Place the model TorchScripts in a defined folder structure, see below for details
2. Pass the model name to the inference operator instance in the app
3. Connect the input to and output from the inference operators, as required by the app
Required Model Folder Structure:
1. The model TorchScripts, be it MONAI Bundle compliant or not, must be placed in
a parent folder, whose path is used as the path to the model(s) on app execution
2. Each TorchScript file needs to be in a sub-folder, whose name is the model name
An example is shown below, where the `parent_foler` name can be the app's own choosing, and
the sub-folder names become model names, `pancreas_ct_dints` and `spleen_model`, respectively.
<parent_fodler>
├── pancreas_ct_dints
│ └── model.ts
└── spleen_ct
└── model.ts
Note:
1. The TorchScript files of MONAI Bundles can be downloaded from MONAI Model Zoo, at
https://github.com/Project-MONAI/model-zoo/tree/dev/models
https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation, v0.3.2
https://github.com/Project-MONAI/model-zoo/tree/dev/models/pancreas_ct_dints_segmentation, v0.3.8
2. The input DICOM instances are from a DICOM Series of CT Abdomen, similar to the ones
used in the Spleen Segmentation example
3. This example is purely for technical demonstration, not for clinical use
Execution Time Estimate:
With a Nvidia GV100 32GB GPU, the execution time is around 87 seconds for an input DICOM series of 204 instances,
and 167 second for a series of 515 instances.
"""
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)
# 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 needs to be provided as this is a multi-model application and each inference
# operator need to rely on the name to access the named loaded model network.
# create an inference operator for each.
#
# Pertinent MONAI Bundle:
# https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation, v0.3.2
# https://github.com/Project-MONAI/model-zoo/tree/dev/models/pancreas_ct_dints_segmentation, v0.3.8
config_names = BundleConfigNames(config_names=["inference"]) # Same as the default
# This is the inference operator for the spleen_model bundle. Note the model name.
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,
model_name="spleen_ct",
name="bundle_spleen_seg_op",
)
# This is the inference operator for the pancreas_ct_dints bundle. Note the model name.
bundle_pancreas_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,
model_name="pancreas_ct_dints",
name="bundle_pancreas_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
#
# NOTE: Each generated DICOM Seg will be a dcm file with the name based on the SOP instance UID.
# Description for the Spleen seg, and the seg writer obj
seg_descriptions_spleen = [
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_spleen = {"SeriesDescription": "AI Spleen Seg for research use only. Not for clinical use."}
dicom_seg_writer_spleen = DICOMSegmentationWriterOperator(
self,
segment_descriptions=seg_descriptions_spleen,
custom_tags=custom_tags_spleen,
output_folder=app_output_path,
name="dicom_seg_writer_spleen",
)
# Description for the Pancreas seg, and the seg writer obj
_algorithm_name = "Pancreas CT DiNTS segmentation from CT image"
_algorithm_family = codes.DCM.ArtificialIntelligence
_algorithm_version = "0.3.8"
seg_descriptions_pancreas = [
SegmentDescription(
segment_label="Pancreas",
segmented_property_category=codes.SCT.Organ,
segmented_property_type=codes.SCT.Pancreas,
algorithm_name=_algorithm_name,
algorithm_family=_algorithm_family,
algorithm_version=_algorithm_version,
),
SegmentDescription(
segment_label="Tumor",
segmented_property_category=codes.SCT.Tumor,
segmented_property_type=codes.SCT.Tumor,
algorithm_name=_algorithm_name,
algorithm_family=_algorithm_family,
algorithm_version=_algorithm_version,
),
]
custom_tags_pancreas = {"SeriesDescription": "AI Pancreas Seg for research use only. Not for clinical use."}
dicom_seg_writer_pancreas = DICOMSegmentationWriterOperator(
self,
segment_descriptions=seg_descriptions_pancreas,
custom_tags=custom_tags_pancreas,
output_folder=app_output_path,
name="dicom_seg_writer_pancreas",
)
# NOTE: Sharp eyed readers can already see that the above instantiation of object can be simply parameterized.
# Very true, but leaving them as if for easy reading. In fact the whole app can be parameterized for general use.
# Create the processing pipeline, by specifying the upstream and downstream 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")}
)
# Feed the input image to all inference operators
self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {("image", "image")})
# The Pancreas CT Seg bundle requires PyTorch 1.12.0 to avoid failure to load.
self.add_flow(series_to_vol_op, bundle_pancreas_seg_op, {("image", "image")})
# Create DICOM Seg for one of the inference output
# Note below the dicom_seg_writer requires two inputs, each coming from a upstream operator.
self.add_flow(
series_selector_op, dicom_seg_writer_spleen, {("study_selected_series_list", "study_selected_series_list")}
)
self.add_flow(bundle_spleen_seg_op, dicom_seg_writer_spleen, {("pred", "seg_image")})
# Create DICOM Seg for one of the inference output
# Note below the dicom_seg_writer requires two inputs, each coming from a upstream operator.
self.add_flow(
series_selector_op,
dicom_seg_writer_pancreas,
{("study_selected_series_list", "study_selected_series_list")},
)
self.add_flow(bundle_pancreas_seg_op, dicom_seg_writer_pancreas, {("pred", "seg_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 input folder, the models are already staged, and environment variables are set.
!rm -rf $HOLOSCAN_OUTPUT_PATH
app = App().run()
[2024-04-23 16:11:04,518] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])
[2024-04-23 16:11:04,535] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=multi_models, workdir=)
[2024-04-23 16:11:04,542] [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 16:11:04,579] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
2024-04-23 16:11:04.577 INFO gxf/std/greedy_scheduler.cpp@191: Scheduling 9 entities
[2024-04-23 16:11:05,124] [INFO] (root) - Finding series for Selection named: CT Series
[2024-04-23 16:11:05,125] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
# of series: 1
[2024-04-23 16:11:05,126] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:11:05,128] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2024-04-23 16:11:05,129] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2024-04-23 16:11:05,130] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:11:05,131] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2024-04-23 16:11:05,132] [INFO] (root) - Series attribute Modality value: CT
[2024-04-23 16:11:05,134] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:11:05,136] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2024-04-23 16:11:05,136] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2024-04-23 16:11:05,137] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:11:05,137] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:11:05,373] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints/model.ts
[2024-04-23 16:12:44,188] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct/model.ts
/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 16:12:49,664] [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 16:12:49,665] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 16:12:49,666] [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 16:12:49,667] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 16:12:49,669] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 16:12:49,670] [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 16:12:49,672] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 16:12:49,673] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 16:12:49,675] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[2024-04-23 16:12:50,843] [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 16:12:50,844] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 16:12:50,845] [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 16:12:50,846] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 16:12:50,847] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 16:12:50,848] [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 16:12:50,849] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 16:12:50,850] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 16:12:50,852] [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 16:12:50,954] [INFO] (__main__.App) - End run
2024-04-23 16:12:50.952 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 16:12:50.952 INFO gxf/std/greedy_scheduler.cpp@401: Scheduler finished.
Once the application is verified inside Jupyter notebook, we can write the whole application as a file, adding the following lines:
if __name__ == "__main__":
App().run()
The above lines are needed to execute the application code by using python interpreter.
A __main__.py file should also be added, so 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,
)
class App(Application):
"""This example demonstrates how to create a multi-model/multi-AI application.
The important steps are:
1. Place the model TorchScripts in a defined folder structure, see below for details
2. Pass the model name to the inference operator instance in the app
3. Connect the input to and output from the inference operators, as required by the app
Required Model Folder Structure:
1. The model TorchScripts, be it MONAI Bundle compliant or not, must be placed in
a parent folder, whose path is used as the path to the model(s) on app execution
2. Each TorchScript file needs to be in a sub-folder, whose name is the model name
An example is shown below, where the `parent_foler` name can be the app's own choosing, and
the sub-folder names become model names, `pancreas_ct_dints` and `spleen_model`, respectively.
<parent_fodler>
├── pancreas_ct_dints
│ └── model.ts
└── spleen_ct
└── model.ts
Note:
1. The TorchScript files of MONAI Bundles can be downloaded from MONAI Model Zoo, at
https://github.com/Project-MONAI/model-zoo/tree/dev/models
https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation, v0.3.2
https://github.com/Project-MONAI/model-zoo/tree/dev/models/pancreas_ct_dints_segmentation, v0.3.8
2. The input DICOM instances are from a DICOM Series of CT Abdomen, similar to the ones
used in the Spleen Segmentation example
3. This example is purely for technical demonstration, not for clinical use
Execution Time Estimate:
With a Nvidia GV100 32GB GPU, the execution time is around 87 seconds for an input DICOM series of 204 instances,
and 167 second for a series of 515 instances.
"""
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)
# 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 needs to be provided as this is a multi-model application and each inference
# operator need to rely on the name to access the named loaded model network.
# create an inference operator for each.
#
# Pertinent MONAI Bundle:
# https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation, v0.3.2
# https://github.com/Project-MONAI/model-zoo/tree/dev/models/pancreas_ct_dints_segmentation, v0.3.8
config_names = BundleConfigNames(config_names=["inference"]) # Same as the default
# This is the inference operator for the spleen_model bundle. Note the model name.
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,
model_name="spleen_ct",
name="bundle_spleen_seg_op",
)
# This is the inference operator for the pancreas_ct_dints bundle. Note the model name.
bundle_pancreas_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,
model_name="pancreas_ct_dints",
name="bundle_pancreas_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
#
# NOTE: Each generated DICOM Seg will be a dcm file with the name based on the SOP instance UID.
# Description for the Spleen seg, and the seg writer obj
seg_descriptions_spleen = [
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_spleen = {"SeriesDescription": "AI Spleen Seg for research use only. Not for clinical use."}
dicom_seg_writer_spleen = DICOMSegmentationWriterOperator(
self,
segment_descriptions=seg_descriptions_spleen,
custom_tags=custom_tags_spleen,
output_folder=app_output_path,
name="dicom_seg_writer_spleen",
)
# Description for the Pancreas seg, and the seg writer obj
_algorithm_name = "Pancreas CT DiNTS segmentation from CT image"
_algorithm_family = codes.DCM.ArtificialIntelligence
_algorithm_version = "0.3.8"
seg_descriptions_pancreas = [
SegmentDescription(
segment_label="Pancreas",
segmented_property_category=codes.SCT.Organ,
segmented_property_type=codes.SCT.Pancreas,
algorithm_name=_algorithm_name,
algorithm_family=_algorithm_family,
algorithm_version=_algorithm_version,
),
SegmentDescription(
segment_label="Tumor",
segmented_property_category=codes.SCT.Tumor,
segmented_property_type=codes.SCT.Tumor,
algorithm_name=_algorithm_name,
algorithm_family=_algorithm_family,
algorithm_version=_algorithm_version,
),
]
custom_tags_pancreas = {"SeriesDescription": "AI Pancreas Seg for research use only. Not for clinical use."}
dicom_seg_writer_pancreas = DICOMSegmentationWriterOperator(
self,
segment_descriptions=seg_descriptions_pancreas,
custom_tags=custom_tags_pancreas,
output_folder=app_output_path,
name="dicom_seg_writer_pancreas",
)
# NOTE: Sharp eyed readers can already see that the above instantiation of object can be simply parameterized.
# Very true, but leaving them as if for easy reading. In fact the whole app can be parameterized for general use.
# Create the processing pipeline, by specifying the upstream and downstream 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")}
)
# Feed the input image to all inference operators
self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {("image", "image")})
# The Pancreas CT Seg bundle requires PyTorch 1.12.0 to avoid failure to load.
self.add_flow(series_to_vol_op, bundle_pancreas_seg_op, {("image", "image")})
# Create DICOM Seg for one of the inference output
# Note below the dicom_seg_writer requires two inputs, each coming from a upstream operator.
self.add_flow(
series_selector_op, dicom_seg_writer_spleen, {("study_selected_series_list", "study_selected_series_list")}
)
self.add_flow(bundle_spleen_seg_op, dicom_seg_writer_spleen, {("pred", "seg_image")})
# Create DICOM Seg for one of the inference output
# Note below the dicom_seg_writer requires two inputs, each coming from a upstream operator.
self.add_flow(
series_selector_op,
dicom_seg_writer_pancreas,
{("study_selected_series_list", "study_selected_series_list")},
)
self.add_flow(bundle_pancreas_seg_op, dicom_seg_writer_pancreas, {("pred", "seg_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__":
logging.info(f"Begin {__name__}")
App().run()
logging.info(f"End {__name__}")
Writing my_app/app.py
%%writefile my_app/__main__.py
from app import App
if __name__ == "__main__":
App().run()
Writing my_app/__main__.py
!ls my_app
app.py __main__.py
At this time, let’s execute the app on the command line. Note the required e.
Note
Since the environment variables have been set with the specific input data and model paths from earlier steps, it is not necessary to provide the command line options on running the application.
!rm -rf $HOLOSCAN_OUTPUT_PATH
!python my_app
[2024-04-23 16:12:55,648] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['my_app'])
[2024-04-23 16:12:55,653] [INFO] (root) - AppContext object: AppContext(input_path=dcm, output_path=output, model_path=multi_models, workdir=)
[2024-04-23 16:12:55,655] [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 16:12:55.684 INFO gxf/std/greedy_scheduler.cpp@191: Scheduling 9 entities
[2024-04-23 16:12:55,685] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
[2024-04-23 16:12:56,030] [INFO] (root) - Finding series for Selection named: CT Series
[2024-04-23 16:12:56,030] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
# of series: 1
[2024-04-23 16:12:56,030] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:12:56,030] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2024-04-23 16:12:56,030] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2024-04-23 16:12:56,030] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:12:56,031] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2024-04-23 16:12:56,031] [INFO] (root) - Series attribute Modality value: CT
[2024-04-23 16:12:56,031] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:12:56,031] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2024-04-23 16:12:56,031] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2024-04-23 16:12:56,031] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 16:12:56,031] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 16:12:56,243] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints/model.ts
[2024-04-23 16:14:36,645] [INFO] (root) - Parsing from bundle_path: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct/model.ts
/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 16:14:42,011] [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 16:14:42,011] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 16:14:42,011] [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 16:14:42,011] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 16:14:42,012] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 16:14:42,012] [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 16:14:42,012] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 16:14:42,012] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 16:14:42,012] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[2024-04-23 16:14:43,063] [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 16:14:43,063] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 16:14:43,064] [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 16:14:43,064] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 16:14:43,064] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 16:14:43,064] [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 16:14:43,064] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 16:14:43,064] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 16:14:43,065] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
2024-04-23 16:14:43.154 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 16:14:43.155 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 16:14:43,155] [INFO] (app.App) - End run
!ls $HOLOSCAN_OUTPUT_PATH
1.2.826.0.1.3680043.10.511.3.10288134553125230635901121822410318.dcm
1.2.826.0.1.3680043.10.511.3.96971936458025058184826371322949123.dcm
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 - Multi Model App
version: 1.0
inputFormats: ["file"]
outputFormats: ["file"]
resources:
cpu: 1
gpu: 1
memory: 1Gi
gpuMemory: 10Gi
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
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 16:14:45,501] [INFO] (common) - Downloading CLI manifest file...
[2024-04-23 16:14:45,792] [DEBUG] (common) - Validating CLI manifest file...
[2024-04-23 16:14:45,794] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app
[2024-04-23 16:14:45,795] [INFO] (packager.parameters) - Detected application type: Python Module
[2024-04-23 16:14:45,795] [INFO] (packager) - Scanning for models in /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models...
[2024-04-23 16:14:45,795] [DEBUG] (packager) - Model spleen_ct=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/spleen_ct added.
[2024-04-23 16:14:45,795] [DEBUG] (packager) - Model pancreas_ct_dints=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/multi_models/pancreas_ct_dints added.
[2024-04-23 16:14:45,796] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/my_app/app.yaml...
[2024-04-23 16:14:45,799] [INFO] (packager) - Generating app.json...
[2024-04-23 16:14:45,799] [INFO] (packager) - Generating pkg.json...
[2024-04-23 16:14:45,809] [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 16:14:45,810] [DEBUG] (common) -
=============== Begin pkg.json ===============
{
"apiVersion": "1.0.0",
"applicationRoot": "/opt/holoscan/app",
"modelRoot": "/opt/holoscan/models",
"models": {
"spleen_ct": "/opt/holoscan/models/spleen_ct",
"pancreas_ct_dints": "/opt/holoscan/models/pancreas_ct_dints"
},
"resources": {
"cpu": 1,
"gpu": 1,
"memory": "1Gi",
"gpuMemory": "10Gi"
},
"version": 1.0,
"platformConfig": "dgpu"
}
================ End pkg.json ================
[2024-04-23 16:14:46,274] [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 - Multi Model 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 16:14:46,274] [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 16:14:46,562] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`
[2024-04-23 16:14:46,562] [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: 2.65kB done
#1 DONE 0.0s
#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu
#2 DONE 0.4s
#3 [internal] load .dockerignore
#3 transferring context: 1.79kB done
#3 DONE 0.0s
#4 [internal] load build context
#4 DONE 0.0s
#5 importing cache manifest from local:13557986215550987099
#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 0.0s
#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 0.4s
#4 [internal] load build context
#4 transferring context: 636.05MB 3.2s done
#4 DONE 3.2s
#8 [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
#8 CACHED
#9 [ 6/21] RUN chown -R holoscan /var/holoscan
#9 CACHED
#10 [ 5/21] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan
#10 CACHED
#11 [ 4/21] RUN groupadd -f -g 1000 holoscan
#11 CACHED
#12 [ 3/21] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*
#12 CACHED
#13 [11/21] RUN chmod +x /var/holoscan/tools
#13 CACHED
#14 [12/21] COPY ./pip/requirements.txt /tmp/requirements.txt
#14 CACHED
#15 [ 7/21] RUN chown -R holoscan /var/holoscan/input
#15 CACHED
#16 [14/21] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
#16 CACHED
#17 [ 8/21] RUN chown -R holoscan /var/holoscan/output
#17 CACHED
#18 [ 9/21] WORKDIR /var/holoscan
#18 CACHED
#19 [ 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
#19 CACHED
#20 [10/21] COPY ./tools /var/holoscan/tools
#20 CACHED
#21 [13/21] RUN pip install --upgrade pip
#21 CACHED
#22 [16/21] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#22 CACHED
#23 [17/21] COPY ./models /opt/holoscan/models
#23 DONE 3.9s
#24 [18/21] COPY ./map/app.json /etc/holoscan/app.json
#24 DONE 0.0s
#25 [19/21] COPY ./app.config /var/holoscan/app.yaml
#25 DONE 0.0s
#26 [20/21] COPY ./map/pkg.json /etc/holoscan/pkg.json
#26 DONE 0.0s
#27 [21/21] COPY ./app /opt/holoscan/app
#27 DONE 0.0s
#28 exporting to docker image format
#28 exporting layers
#28 exporting layers 17.8s done
#28 exporting manifest sha256:26808437a116257ae2799583b42bbf04923e1157f8f98341c3403ee35eb234bf 0.0s done
#28 exporting config sha256:95614919d60e7f30ab5d64a25a5fc25ad64d0b166046c60549b5abae2745be7c 0.0s done
#28 sending tarball
#28 ...
#29 importing to docker
#29 loading layer ecf27683cffd 557.06kB / 584.49MB
#29 loading layer ecf27683cffd 154.86MB / 584.49MB 2.1s
#29 loading layer ecf27683cffd 309.72MB / 584.49MB 4.1s
#29 loading layer ecf27683cffd 464.03MB / 584.49MB 6.2s
#29 loading layer 3202d6efcdaa 493B / 493B
#29 loading layer 84f909517c68 311B / 311B
#29 loading layer 1d1958b729ff 323B / 323B
#29 loading layer 8f69ecd226c9 4.00kB / 4.00kB
#29 loading layer 3202d6efcdaa 493B / 493B 2.6s done
#29 loading layer ecf27683cffd 464.03MB / 584.49MB 10.6s done
#29 loading layer 84f909517c68 311B / 311B 2.2s done
#29 loading layer 1d1958b729ff 323B / 323B 1.8s done
#29 loading layer 8f69ecd226c9 4.00kB / 4.00kB 1.5s done
#29 DONE 10.6s
#28 exporting to docker image format
#28 sending tarball 72.8s done
#28 DONE 90.7s
#30 exporting cache to client directory
#30 preparing build cache for export
#30 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015
#30 writing layer sha256:014cff740c9ec6e9a30d0b859219a700ae880eb385d62095d348f5ea136d6015 done
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#30 writing layer sha256:0cbe3b20b9b7d01bcb9770de54ba9a54febb401dc371bbb1d8debf1f9850b356 0.0s done
#30 writing layer sha256:0ec682bf99715a9f88631226f3749e2271b8b9f254528ef61f65ed829984821c done
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#30 preparing build cache for export 11.6s done
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#30 writing cache manifest sha256:62a85925ced740e444a8f19b3c59ade29144854a44168d8786e00e93dbad2f5f 0.0s done
#30 DONE 11.6s
[2024-04-23 16:16:38,598] [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 Docker image is created.
!docker image ls | grep {tag_prefix}
my_app-x64-workstation-dgpu-linux-amd64 1.0 95614919d60e About a minute ago 18.3GB
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": {
"spleen_ct": "/opt/holoscan/models/spleen_ct",
"pancreas_ct_dints": "/opt/holoscan/models/pancreas_ct_dints"
},
"resources": {
"cpu": 1,
"gpu": 1,
"memory": "1Gi",
"gpuMemory": "10Gi"
},
"version": 1,
"platformConfig": "dgpu"
}
2024-04-23 23:16:41 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app
2024-04-23 23:16:41 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json
2024-04-23 23:16:41 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json
2024-04-23 23:16:41 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml
2024-04-23 23:16:41 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models
2024-04-23 23:16:42 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs
2024-04-23 23:16:42 [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 16:16:45,664] [INFO] (runner) - Checking dependencies...
[2024-04-23 16:16:45,664] [INFO] (runner) - --> Verifying if "docker" is installed...
[2024-04-23 16:16:45,664] [INFO] (runner) - --> Verifying if "docker-buildx" is installed...
[2024-04-23 16:16:45,664] [INFO] (runner) - --> Verifying if "my_app-x64-workstation-dgpu-linux-amd64:1.0" is available...
[2024-04-23 16:16:45,739] [INFO] (runner) - Reading HAP/MAP manifest...
Preparing to copy...?25lCopying from container - 0B?25hSuccessfully copied 2.56kB to /tmp/tmpymoiz9gd/app.json
Preparing to copy...?25lCopying from container - 0B?25hSuccessfully copied 2.05kB to /tmp/tmpymoiz9gd/pkg.json
[2024-04-23 16:16:46,001] [INFO] (runner) - --> Verifying if "nvidia-ctk" is installed...
[2024-04-23 16:16:46,002] [INFO] (runner) - --> Verifying "nvidia-ctk" version...
[2024-04-23 16:16:46,316] [INFO] (common) - Launching container (42b411558fd7) using image 'my_app-x64-workstation-dgpu-linux-amd64:1.0'...
container name: silly_davinci
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
2024-04-23 23:16:46 [INFO] Launching application python3 /opt/holoscan/app ...
[2024-04-23 23:16:50,090] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])
[2024-04-23 23:16:50,095] [INFO] (root) - AppContext object: AppContext(input_path=/var/holoscan/input, output_path=/var/holoscan/output, model_path=/opt/holoscan/models, workdir=/var/holoscan)
[2024-04-23 23:16:50,097] [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...
[info] [gxf_executor.cpp:1874] Running Graph...
[info] [gxf_executor.cpp:1876] Waiting for completion...
2024-04-23 23:16:50.129 INFO gxf/std/greedy_scheduler.cpp@191: Scheduling 9 entities
[2024-04-23 23:16:50,131] [INFO] (monai.deploy.operators.dicom_data_loader_operator.DICOMDataLoaderOperator) - No or invalid input path from the optional input port: None
[2024-04-23 23:16:50,512] [INFO] (root) - Finding series for Selection named: CT Series
[2024-04-23 23:16:50,512] [INFO] (root) - Searching study, : 1.3.6.1.4.1.14519.5.2.1.7085.2626.822645453932810382886582736291
# of series: 1
[2024-04-23 23:16:50,512] [INFO] (root) - Working on series, instance UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 23:16:50,512] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'
[2024-04-23 23:16:50,512] [INFO] (root) - Series attribute StudyDescription value: CT ABDOMEN W IV CONTRAST
[2024-04-23 23:16:50,512] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 23:16:50,512] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'
[2024-04-23 23:16:50,512] [INFO] (root) - Series attribute Modality value: CT
[2024-04-23 23:16:50,512] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 23:16:50,513] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'
[2024-04-23 23:16:50,513] [INFO] (root) - Series attribute SeriesDescription value: ABD/PANC 3.0 B31f
[2024-04-23 23:16:50,513] [INFO] (root) - Series attribute string value did not match. Try regEx.
[2024-04-23 23:16:50,513] [INFO] (root) - Selected Series, UID: 1.3.6.1.4.1.14519.5.2.1.7085.2626.119403521930927333027265674239
[2024-04-23 23:16:50,745] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/pancreas_ct_dints/model.ts
[2024-04-23 23:18:36,681] [INFO] (root) - Parsing from bundle_path: /opt/holoscan/models/spleen_ct/model.ts
/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(
[2024-04-23 23:18:40,078] [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 23:18:40,078] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 23:18:40,078] [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 23:18:40,078] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 23:18:40,078] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 23:18:40,079] [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 23:18:40,079] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 23:18:40,079] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 23:18:40,079] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
[2024-04-23 23:18:41,286] [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 23:18:41,286] [INFO] (highdicom.base) - copy attributes of module "Specimen"
[2024-04-23 23:18:41,286] [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 23:18:41,286] [INFO] (highdicom.base) - copy attributes of module "Patient"
[2024-04-23 23:18:41,286] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Subject"
[2024-04-23 23:18:41,286] [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 23:18:41,286] [INFO] (highdicom.base) - copy attributes of module "General Study"
[2024-04-23 23:18:41,286] [INFO] (highdicom.base) - copy attributes of module "Patient Study"
[2024-04-23 23:18:41,287] [INFO] (highdicom.base) - copy attributes of module "Clinical Trial Study"
2024-04-23 23:18:41.373 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...
2024-04-23 23:18:41.377 INFO gxf/std/greedy_scheduler.cpp@401: Scheduler finished.
[info] [gxf_executor.cpp:1887] Graph execution finished.
[2024-04-23 23:18:41,385] [INFO] (app.App) - End run
[2024-04-23 16:18:42,406] [INFO] (common) - Container 'silly_davinci'(42b411558fd7) exited.
In the output folder are the DICOM segementation files.
!ls $HOLOSCAN_OUTPUT_PATH
1.2.826.0.1.3680043.10.511.3.11468162564679998832192298844993783.dcm
1.2.826.0.1.3680043.10.511.3.22886221740567104559887431846790837.dcm