Creating a Simple Image Processing App with MONAI Deploy App SDK¶
This tutorial shows how a simple image processing application can be created with MONAI Deploy App SDK.
Creating Operators and connecting them in Application class¶
We will implement an application that consists of three Operators:
SobelOperator: Apply a Sobel edge detector.
Input: a file path (
Path)Output: an image object in memory (
Image)
MedianOperator: Apply a Median filter for noise reduction.
GaussianOperator: Apply a Gaussian filter for smoothening.
Input: an image object in memory (
Image)Output: a file path (
Path)
The workflow of the application would look like this.
%%{init: {"theme": "base", "themeVariables": { "fontSize": "16px"}} }%%
classDiagram
direction LR
SobelOperator --|> MedianOperator : image...image
MedianOperator --|> GaussianOperator : image...image
class SobelOperator {
<in>image : Path
image(out) : IN_MEMORY
}
class MedianOperator {
<in>image : IN_MEMORY
image(out) : IN_MEMORY
}
class GaussianOperator {
<in>image : IN_MEMORY
image(out) : Path
}
Setup environment¶
# Install necessary image loading/processing packages for the application
!python -c "import PIL" || pip install -q "Pillow"
!python -c "import skimage" || pip install -q "scikit-image"
!python -c "import matplotlib" || pip install -q "matplotlib"
%matplotlib inline
# Install MONAI Deploy App SDK package
!python -c "import monai.deploy" || pip install -q "monai-deploy-app-sdk"
Download test input¶
We will use a test input from the following.
Case courtesy of Dr Bruno Di Muzio, Radiopaedia.org. From the case rID: 41113
test_input_folder = "/tmp/simple_app"
test_input_path = test_input_folder + "/normal-brain-mri-4.png"
!python -c "import wget" || pip install -q "wget"
!mkdir -p {test_input_folder}
from skimage import io
import wget
wget.download("https://user-images.githubusercontent.com/1928522/133383228-2357d62d-316c-46ad-af8a-359b56f25c87.png", test_input_path)
print(f"Test input file path: {test_input_path!r}")
test_image = io.imread(test_input_path)
io.imshow(test_image)
Test input file path: '/tmp/simple_app/normal-brain-mri-4.png'
<matplotlib.image.AxesImage at 0x7fd82fbd9810>
Set up environment variables¶
The application uses well-known enviornment variables for the input/output data path, working dir, as well as AI model file path if applicable. Defaults are used if these environment variable are absent.
In this example, only the input data path and output path need to be set.
output_path = "output"
%env HOLOSCAN_INPUT_FOLDER {test_input_folder}
%env HOLOSCAN_INPUT_PATH {test_input_path}
%env HOLOSCAN_OUTPUT_PATH {output_path}
%ls $HOLOSCAN_INPUT_PATH
env: HOLOSCAN_INPUT_FOLDER=/tmp/simple_app
env: HOLOSCAN_INPUT_PATH=/tmp/simple_app/normal-brain-mri-4.png
env: HOLOSCAN_OUTPUT_PATH=output
/tmp/simple_app/normal-brain-mri-4.png
Setup imports¶
Let’s import necessary classes/decorators to define the application and operators.
from pathlib import Path
from monai.deploy.conditions import CountCondition
from monai.deploy.core import AppContext, Application, ConditionType, Fragment, Operator, OperatorSpec
Creating Operator classes¶
Each Operator class inherits from the Operator class, with the input and output ports of the operator specified using the setup method. Business logic would be implemented in the compute method.
Note
the way to specify operator input and output in this version of the App SDK is different from versions, up to and including V0.5, where Python decorators are used. Decorator support will be re-introduced in future releases
the first operator(SobelOperator)’s input and the last operator(GaussianOperator)’s output are data paths, which are not data types supported by operator ports but as object can be used as optional input and output. In the example, these paths are passed in as arguments to the constructor and the operator classes have defined logic on using the paths, e.g. reading from or writing to the path. The application class is responsible for setting the path by parsing the well-known environment variables
SobelOperator¶
SobelOperator is the first operator (the root operator in the workflow graph). It reads from the input file/folder path, which is passed in as an argument on the constructor and assigned to an attribute.
Once loaded and processed, the image data (as a Numpy array) is set to the output (op_output.emit(value, label)).
class SobelOperator(Operator):
"""This Operator implements a Sobel edge detector.
It has the following input and output:
single input:
a image file, first one found in the input folder
single output:
array object in memory
"""
DEFAULT_INPUT_FOLDER = Path.cwd() / "input"
def __init__(self, fragment: Fragment, *args, input_path: Path, **kwargs):
"""Create an instance to be part of the given application (fragment).
Args:
fragment (Fragment): An instance of the Application class which is derived from Fragment
input_path (Path): The path of the input image file or folder containing the image file
"""
self.index = 0
# May want to validate the path, but it should really be validated when the compute function is called, also,
# when file path as input is supported in the operator or execution context, input_folder needs not an attribute.
self.input_path = (
input_path if input_path else SobelOperator.DEFAULT_INPUT_FOLDER
)
# Need to call the base class constructor last
super().__init__(fragment, *args, **kwargs)
def setup(self, spec: OperatorSpec):
spec.output("out1")
def compute(self, op_input, op_output, context):
from skimage import filters, io
self.index += 1
print(f"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}")
# Ideally the op_input or execution context should provide the file path
# to read data from, for operators that are file input based.
# For now, use a temporary way to get input path. e.g. value set on init
input_path = self.input_path
print(f"Input from: {input_path}, whose absolute path: {input_path.absolute()}")
if input_path.is_dir():
input_path = next(input_path.glob("*.*")) # take the first file
data_in = io.imread(input_path)[:, :, :3] # discard alpha channel if exists
data_out = filters.sobel(data_in)
op_output.emit(data_out, "out1")
MedianOperator¶
MedianOperator is a middle operator that accepts data from SobelOperator and passes the processed image data to GaussianOperator.
Its input data type is image in Numpy array. Once received at the input (op_input.receive(label)), the image is transformed and set to the output (op_output.emit(value, label)).
class MedianOperator(Operator):
"""This Operator implements a noise reduction.
The algorithm is based on the median operator.
It ingests a single input and provides a single output, both are in-memory image arrays
"""
# Define __init__ method with super().__init__() if you want to override the default behavior.
def __init__(self, fragment: Fragment, *args, **kwargs):
"""Create an instance to be part of the given application (fragment).
Args:
fragment (Fragment): The instance of Application class which is derived from Fragment
"""
self.index = 0
# Need to call the base class constructor last
super().__init__(fragment, *args, **kwargs)
def setup(self, spec: OperatorSpec):
spec.input("in1")
spec.output("out1")
def compute(self, op_input, op_output, context):
from skimage.filters import median
self.index += 1
print(f"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}")
data_in = op_input.receive("in1")
data_out = median(data_in)
op_output.emit(data_out, "out1")
GaussianOperator¶
GaussianOperator is the last operator (a leaf operator in the workflow graph) and saves the processed image to a file, whose path is provided via an argument on the constructor.
This operator can also output the image in Numpy array in memory without requiring a receiver for it. This can be set up by using the optional output condition in the function setup.
class GaussianOperator(Operator):
"""This Operator implements a smoothening based on Gaussian.
It has the following input and output:
single input:
an image array object
single output:
an image arrary object, without enforcing a downsteam receiver
Besides, this operator also saves the image file in the given output folder.
"""
DEFAULT_OUTPUT_FOLDER = Path.cwd() / "output"
def __init__(self, fragment: Fragment, *args, output_folder: Path, **kwargs):
"""Create an instance to be part of the given application (fragment).
Args:
fragment (Fragment): The instance of Application class which is derived from Fragment
output_folder (Path): The folder to save the output file.
"""
self.output_folder = output_folder if output_folder else GaussianOperator.DEFAULT_OUTPUT_FOLDER
self.index = 0
# If `self.sigma_default` is set here (e.g., `self.sigma_default = 0.2`), then
# the default value by `param()` in `setup()` will be ignored.
# (you can just call `spec.param("sigma_default")` in `setup()` to use the
# default value)
self.sigma_default = 0.2
self.channel_axis = 2
# Need to call the base class constructor last
super().__init__(fragment, *args, **kwargs)
def setup(self, spec: OperatorSpec):
spec.input("in1")
spec.output("out1").condition(ConditionType.NONE) # Condition is for no or not-ready receiver ports.
spec.param("sigma_default", 0.2)
spec.param("channel_axis", 2)
def compute(self, op_input, op_output, context):
from skimage.filters import gaussian
from skimage.io import imsave
import numpy as np
self.index += 1
print(f"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}")
data_in = op_input.receive("in1")
data_out = gaussian(data_in, sigma=self.sigma_default, channel_axis=self.channel_axis)
# Make sure the data type is what PIL Image can support, as the imsave function calls PIL Image fromarray()
# Some details can be found at https://stackoverflow.com/questions/55319949/pil-typeerror-cannot-handle-this-data-type
print(f"Data type of output: {type(data_out)!r}, max = {np.max(data_out)!r}")
if np.max(data_out) <= 1:
data_out = (data_out*255).astype(np.uint8)
print(f"Data type of output post conversion: {type(data_out)!r}, max = {np.max(data_out)!r}")
# For now, use attribute of self to find the output path.
self.output_folder.mkdir(parents=True, exist_ok=True)
output_path = self.output_folder / "final_output.png"
imsave(output_path, data_out)
op_output.emit(data_out, "out1")
Creating Application class¶
Our application class would look like below.
It defines App class, inheriting Application class.
In compose() method, objects of SobelOperator, MedianOperator, and GaussianOperator classes are created
and connected through self.add_flow().
add_flow(source_op, destination_op, io_map=None)
io_map is a dictionary of mapping from the source operator’s label to the destination operator’s label(s) and its type is Set[Tuple[str, str]].
We can skip specifying io_map if both the number of source_op’s outputs and the number of destination_op’s inputs are one so self.add_flow(sobel_op, median_op) is same with self.add_flow(sobel_op, median_op, {"image": "image"}) or self.add_flow(sobel_op, median_op, {"image": {"image"}}).
class App(Application):
"""This is a very basic application.
This showcases the MONAI Deploy application framework.
"""
# App's name. <class name>('App') if not specified.
name = "simple_imaging_app"
# App's description. <class docstring> if not specified.
description = "This is a very simple application."
# App's version. <git version tag> or '0.0.0' if not specified.
version = "0.1.0"
def compose(self):
"""This application has three operators.
Each operator has a single input and a single output port.
Each operator performs some kind of image processing function.
"""
app_context = Application.init_app_context({}) # Do not pass argv in Jupyter notebook
sample_data_path = Path(app_context.input_path)
output_data_path = Path(app_context.output_path)
print(f"sample_data_path: {sample_data_path}")
# Please note that the Application object, self, is passed as the first positonal argument
# and the others as kwargs.
# Also note the CountCondition of 1 on the first operator, indicating to the application executor
# to invoke this operator, hence the pipleline, only once.
sobel_op = SobelOperator(self, CountCondition(self, 1), input_path=sample_data_path, name="sobel_op")
median_op = MedianOperator(self, name="median_op")
gaussian_op = GaussianOperator(self, output_folder=output_data_path, name="gaussian_op")
self.add_flow(
sobel_op,
median_op,
{
("out1", "in1"),
},
)
self.add_flow(
median_op,
gaussian_op,
{
(
"out1",
"in1",
)
},
) # Using port name is optional for single port cases
if __name__ == "__main__":
print("The statement, App().run(), is needed when this is run directly by the interpreter.")
# App().run()
The statement, App().run(), is needed when this is run directly by the interpreter.
Executing app locally¶
We can execute the app in the Jupyter notebook.
!rm -rf {output_path}
App().run()
[2024-04-23 15:26:29,737] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=[])
[2024-04-23 15:26:29,745] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app/normal-brain-mri-4.png, output_path=output, model_path=models, workdir=)
sample_data_path: /tmp/simple_app/normal-brain-mri-4.png
2024-04-23 15:26:29.768 INFO gxf/std/greedy_scheduler.cpp@191: Scheduling 3 entities
Number of times operator sobel_op whose class is defined in __main__ called: 1
Input from: /tmp/simple_app/normal-brain-mri-4.png, whose absolute path: /tmp/simple_app/normal-brain-mri-4.png
[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...
Number of times operator median_op whose class is defined in __main__ called: 1
Number of times operator gaussian_op whose class is defined in __main__ called: 1
Data type of output: <class 'numpy.ndarray'>, max = 0.35821119421406195
Data type of output post conversion: <class 'numpy.ndarray'>, max = 91
2024-04-23 15:26:30.023 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 15:26:30.023 INFO gxf/std/greedy_scheduler.cpp@401: Scheduler finished.
[info] [gxf_executor.cpp:1879] Deactivating Graph...
[info] [gxf_executor.cpp:1887] Graph execution finished.
[info] [gxf_executor.cpp:275] Destroying context
!ls {output_path}
final_output.png
output_image_path = output_path + "/final_output.png"
output_image = io.imread(output_image_path)
io.imshow(output_image)
<matplotlib.image.AxesImage at 0x7fd82f735f90>
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:
simple_imaging_app
├── __main__.py
├── app.py
├── gaussian_operator.py
├── median_operator.py
└── sobel_operator.py
Note
We can create a single application Python file (such as simple_imaging_app.py) that includes the content of the files, instead of creating multiple files.
You will see such example in MedNist Classifier Tutorial.
# Create an application folder
!mkdir -p simple_imaging_app
sobel_operator.py¶
%%writefile simple_imaging_app/sobel_operator.py
from pathlib import Path
from monai.deploy.core import Fragment, Operator, OperatorSpec
class SobelOperator(Operator):
"""This Operator implements a Sobel edge detector.
It has the following input and output:
single input:
a image file, first one found in the input folder
single output:
array object in memory
"""
DEFAULT_INPUT_FOLDER = Path.cwd() / "input"
def __init__(self, fragment: Fragment, *args, input_path: Path, **kwargs):
"""Create an instance to be part of the given application (fragment).
Args:
fragment (Fragment): An instance of the Application class which is derived from Fragment
input_path (Path): The path of the input image file or folder containing the image file
"""
self.index = 0
# May want to validate the path, but it should really be validated when the compute function is called, also,
# when file path as input is supported in the operator or execution context, input_folder needs not an attribute.
self.input_path = (
input_path if input_path else SobelOperator.DEFAULT_INPUT_FOLDER
)
# Need to call the base class constructor last
super().__init__(fragment, *args, **kwargs)
def setup(self, spec: OperatorSpec):
spec.output("out1")
def compute(self, op_input, op_output, context):
from skimage import filters, io
self.index += 1
print(f"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}")
# Ideally the op_input or execution context should provide the file path
# to read data from, for operators that are file input based.
# For now, use a temporary way to get input path. e.g. value set on init
input_path = self.input_path
print(f"Input from: {input_path}, whose absolute path: {input_path.absolute()}")
if input_path.is_dir():
input_path = next(input_path.glob("*.*")) # take the first file
data_in = io.imread(input_path)[:, :, :3] # discard alpha channel if exists
data_out = filters.sobel(data_in)
op_output.emit(data_out, "out1")
Overwriting simple_imaging_app/sobel_operator.py
median_operator.py¶
%%writefile simple_imaging_app/median_operator.py
from monai.deploy.core import Fragment, Operator, OperatorSpec
# Decorator support is not available in this version of the SDK, to be re-introduced later
# @md.env(pip_packages=["scikit-image >= 0.17.2"])
class MedianOperator(Operator):
"""This Operator implements a noise reduction.
The algorithm is based on the median operator.
It ingests a single input and provides a single output, both are in-memory image arrays
"""
# Define __init__ method with super().__init__() if you want to override the default behavior.
def __init__(self, fragment: Fragment, *args, **kwargs):
"""Create an instance to be part of the given application (fragment).
Args:
fragment (Fragment): The instance of Application class which is derived from Fragment
"""
self.index = 0
# Need to call the base class constructor last
super().__init__(fragment, *args, **kwargs)
def setup(self, spec: OperatorSpec):
spec.input("in1")
spec.output("out1")
def compute(self, op_input, op_output, context):
from skimage.filters import median
self.index += 1
print(f"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}")
data_in = op_input.receive("in1")
data_out = median(data_in)
op_output.emit(data_out, "out1")
Overwriting simple_imaging_app/median_operator.py
gaussian_operator.py¶
%%writefile simple_imaging_app/gaussian_operator.py
from pathlib import Path
from monai.deploy.core import ConditionType, Fragment, Operator, OperatorSpec
# Decorator support is not available in this version of the SDK, to be re-introduced later
# @md.env(pip_packages=["scikit-image >= 0.17.2"])
class GaussianOperator(Operator):
"""This Operator implements a smoothening based on Gaussian.
It has the following input and output:
single input:
an image array object
single output:
an image arrary object, without enforcing a downsteam receiver
Besides, this operator also saves the image file in the given output folder.
"""
DEFAULT_OUTPUT_FOLDER = Path.cwd() / "output"
def __init__(self, fragment: Fragment, *args, output_folder: Path, **kwargs):
"""Create an instance to be part of the given application (fragment).
Args:
fragment (Fragment): The instance of Application class which is derived from Fragment
output_folder (Path): The folder to save the output file.
"""
self.output_folder = output_folder if output_folder else GaussianOperator.DEFAULT_OUTPUT_FOLDER
self.index = 0
# If `self.sigma_default` is set here (e.g., `self.sigma_default = 0.2`), then
# the default value by `param()` in `setup()` will be ignored.
# (you can just call `spec.param("sigma_default")` in `setup()` to use the
# default value)
self.sigma_default = 0.2
self.channel_axis = 2
# Need to call the base class constructor last
super().__init__(fragment, *args, **kwargs)
def setup(self, spec: OperatorSpec):
spec.input("in1")
spec.output("out1").condition(ConditionType.NONE) # Condition is for no or not-ready receiver ports.
spec.param("sigma_default", 0.2)
spec.param("channel_axis", 2)
def compute(self, op_input, op_output, context):
from skimage.filters import gaussian
from skimage.io import imsave
import numpy as np
self.index += 1
print(f"Number of times operator {self.name} whose class is defined in {__name__} called: {self.index}")
data_in = op_input.receive("in1")
data_out = gaussian(data_in, sigma=self.sigma_default, channel_axis=self.channel_axis)
# Make sure the data type is what PIL Image can support, as the imsave function calls PIL Image fromarray()
# Some details can be found at https://stackoverflow.com/questions/55319949/pil-typeerror-cannot-handle-this-data-type
print(f"Data type of output: {type(data_out)!r}, max = {np.max(data_out)!r}")
if np.max(data_out) <= 1:
data_out = (data_out*255).astype(np.uint8)
print(f"Data type of output post conversion: {type(data_out)!r}, max = {np.max(data_out)!r}")
# For now, use attribute of self to find the output path.
self.output_folder.mkdir(parents=True, exist_ok=True)
output_path = self.output_folder / "final_output.png"
imsave(output_path, data_out)
op_output.emit(data_out, "out1")
Overwriting simple_imaging_app/gaussian_operator.py
app.py¶
%%writefile simple_imaging_app/app.py
import logging
from pathlib import Path
from gaussian_operator import GaussianOperator
from median_operator import MedianOperator
from sobel_operator import SobelOperator
from monai.deploy.conditions import CountCondition
from monai.deploy.core import AppContext, Application
# Decorator support is not available in this version of the SDK, to be re-introduced later
# @resource(cpu=1)
class App(Application):
"""This is a very basic application.
This showcases the MONAI Deploy application framework.
"""
# App's name. <class name>('App') if not specified.
name = "simple_imaging_app"
# App's description. <class docstring> if not specified.
description = "This is a very simple application."
# App's version. <git version tag> or '0.0.0' if not specified.
version = "0.1.0"
def compose(self):
"""This application has three operators.
Each operator has a single input and a single output port.
Each operator performs some kind of image processing function.
"""
# Use Commandline options over environment variables to init context.
app_context = Application.init_app_context(self.argv)
sample_data_path = Path(app_context.input_path)
output_data_path = Path(app_context.output_path)
logging.info(f"sample_data_path: {sample_data_path}")
# Please note that the Application object, self, is passed as the first positonal argument
# and the others as kwargs.
# Also note the CountCondition of 1 on the first operator, indicating to the application executor
# to invoke this operator, hence the pipleline, only once.
sobel_op = SobelOperator(self, CountCondition(self, 1), input_path=sample_data_path, name="sobel_op")
median_op = MedianOperator(self, name="median_op")
gaussian_op = GaussianOperator(self, output_folder=output_data_path, name="gaussian_op")
self.add_flow(
sobel_op,
median_op,
{
("out1", "in1"),
},
)
self.add_flow(
median_op,
gaussian_op,
{
(
"out1",
"in1",
)
},
)
if __name__ == "__main__":
logging.info(f"Begin {__name__}")
App().run()
logging.info(f"End {__name__}")
Overwriting simple_imaging_app/app.py
if __name__ == "__main__":
App().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 simple_imaging_app/__main__.py
from app import App
if __name__ == "__main__":
App().run()
Overwriting simple_imaging_app/__main__.py
!ls simple_imaging_app
app.py gaussian_operator.py median_operator.py requirements.txt
app.yaml __main__.py __pycache__ sobel_operator.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, though the following demonstrates the use of the options.
!rm -rf {output_path}
!python simple_imaging_app -i {test_input_folder} -o {output_path} -l DEBUG
[2024-04-23 15:26:34,943] [INFO] (root) - Parsed args: Namespace(log_level='DEBUG', input=PosixPath('/tmp/simple_app'), output=PosixPath('/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output'), model=None, workdir=None, argv=['simple_imaging_app', '-i', '/tmp/simple_app', '-o', 'output', '-l', 'DEBUG'])
[2024-04-23 15:26:34,945] [INFO] (root) - AppContext object: AppContext(input_path=/tmp/simple_app, output_path=/home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/output, model_path=models, workdir=)
[2024-04-23 15:26:34,945] [INFO] (root) - sample_data_path: /tmp/simple_app
[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 15:26:34.968 INFO gxf/std/greedy_scheduler.cpp@191: Scheduling 3 entities
Number of times operator sobel_op whose class is defined in sobel_operator called: 1
Input from: /tmp/simple_app, whose absolute path: /tmp/simple_app
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IHDR' 16 13
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'sRGB' 41 1
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'gAMA' 54 4
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'pHYs' 70 9
[2024-04-23 15:26:34,996] [DEBUG] (PIL.PngImagePlugin) - STREAM b'IDAT' 91 65445
[2024-04-23 15:26:35,002] [DEBUG] (PIL.Image) - Error closing: Operation on closed image
Number of times operator median_op whose class is defined in median_operator called: 1
Number of times operator gaussian_op whose class is defined in gaussian_operator called: 1
Data type of output: <class 'numpy.ndarray'>, max = 0.35821119421406195
Data type of output post conversion: <class 'numpy.ndarray'>, max = 91
[2024-04-23 15:26:35,225] [DEBUG] (PIL.Image) - Importing BlpImagePlugin
[2024-04-23 15:26:35,226] [DEBUG] (PIL.Image) - Importing BmpImagePlugin
[2024-04-23 15:26:35,226] [DEBUG] (PIL.Image) - Importing BufrStubImagePlugin
[2024-04-23 15:26:35,227] [DEBUG] (PIL.Image) - Importing CurImagePlugin
[2024-04-23 15:26:35,227] [DEBUG] (PIL.Image) - Importing DcxImagePlugin
[2024-04-23 15:26:35,227] [DEBUG] (PIL.Image) - Importing DdsImagePlugin
[2024-04-23 15:26:35,230] [DEBUG] (PIL.Image) - Importing EpsImagePlugin
[2024-04-23 15:26:35,231] [DEBUG] (PIL.Image) - Importing FitsImagePlugin
[2024-04-23 15:26:35,231] [DEBUG] (PIL.Image) - Importing FliImagePlugin
[2024-04-23 15:26:35,231] [DEBUG] (PIL.Image) - Importing FpxImagePlugin
[2024-04-23 15:26:35,231] [DEBUG] (PIL.Image) - Image: failed to import FpxImagePlugin: No module named 'olefile'
[2024-04-23 15:26:35,231] [DEBUG] (PIL.Image) - Importing FtexImagePlugin
[2024-04-23 15:26:35,232] [DEBUG] (PIL.Image) - Importing GbrImagePlugin
[2024-04-23 15:26:35,232] [DEBUG] (PIL.Image) - Importing GifImagePlugin
[2024-04-23 15:26:35,232] [DEBUG] (PIL.Image) - Importing GribStubImagePlugin
[2024-04-23 15:26:35,232] [DEBUG] (PIL.Image) - Importing Hdf5StubImagePlugin
[2024-04-23 15:26:35,232] [DEBUG] (PIL.Image) - Importing IcnsImagePlugin
[2024-04-23 15:26:35,233] [DEBUG] (PIL.Image) - Importing IcoImagePlugin
[2024-04-23 15:26:35,233] [DEBUG] (PIL.Image) - Importing ImImagePlugin
[2024-04-23 15:26:35,234] [DEBUG] (PIL.Image) - Importing ImtImagePlugin
[2024-04-23 15:26:35,234] [DEBUG] (PIL.Image) - Importing IptcImagePlugin
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Importing JpegImagePlugin
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Importing Jpeg2KImagePlugin
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Importing McIdasImagePlugin
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Importing MicImagePlugin
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Image: failed to import MicImagePlugin: No module named 'olefile'
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Importing MpegImagePlugin
[2024-04-23 15:26:35,235] [DEBUG] (PIL.Image) - Importing MpoImagePlugin
[2024-04-23 15:26:35,236] [DEBUG] (PIL.Image) - Importing MspImagePlugin
[2024-04-23 15:26:35,237] [DEBUG] (PIL.Image) - Importing PalmImagePlugin
[2024-04-23 15:26:35,237] [DEBUG] (PIL.Image) - Importing PcdImagePlugin
[2024-04-23 15:26:35,237] [DEBUG] (PIL.Image) - Importing PcxImagePlugin
[2024-04-23 15:26:35,237] [DEBUG] (PIL.Image) - Importing PdfImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing PixarImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing PngImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing PpmImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing PsdImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing QoiImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing SgiImagePlugin
[2024-04-23 15:26:35,242] [DEBUG] (PIL.Image) - Importing SpiderImagePlugin
[2024-04-23 15:26:35,243] [DEBUG] (PIL.Image) - Importing SunImagePlugin
[2024-04-23 15:26:35,243] [DEBUG] (PIL.Image) - Importing TgaImagePlugin
[2024-04-23 15:26:35,243] [DEBUG] (PIL.Image) - Importing TiffImagePlugin
[2024-04-23 15:26:35,243] [DEBUG] (PIL.Image) - Importing WebPImagePlugin
[2024-04-23 15:26:35,244] [DEBUG] (PIL.Image) - Importing WmfImagePlugin
[2024-04-23 15:26:35,245] [DEBUG] (PIL.Image) - Importing XbmImagePlugin
[2024-04-23 15:26:35,245] [DEBUG] (PIL.Image) - Importing XpmImagePlugin
[2024-04-23 15:26:35,245] [DEBUG] (PIL.Image) - Importing XVThumbImagePlugin
2024-04-23 15:26:35.273 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 15:26:35.273 INFO gxf/std/greedy_scheduler.cpp@401: Scheduler finished.
[info] [gxf_executor.cpp:1879] Deactivating Graph...
[info] [gxf_executor.cpp:1887] Graph execution finished.
[info] [gxf_executor.cpp:275] Destroying context
#output_image_path was set as before, output_image_path = output_path + "/final_output.png"
output_image = io.imread(output_image_path)
io.imshow(output_image)
<matplotlib.image.AxesImage at 0x7fd82f7c2800>
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 simple_imaging_app/app.yaml
%YAML 1.2
---
application:
title: MONAI Deploy App Package - Simple Imaging App
version: 1.0
inputFormats: ["file"]
outputFormats: ["file"]
resources:
cpu: 1
gpu: 1
memory: 1Gi
gpuMemory: 1Gi
Overwriting simple_imaging_app/app.yaml
%%writefile simple_imaging_app/requirements.txt
scikit-image
setuptools>=59.5.0 # for pkg_resources
Overwriting simple_imaging_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.
tag_prefix = "simple_imaging_app"
!monai-deploy package simple_imaging_app -c simple_imaging_app/app.yaml -t {tag_prefix}:1.0 --platform x64-workstation -l DEBUG
[2024-04-23 15:26:37,078] [INFO] (common) - Downloading CLI manifest file...
[2024-04-23 15:26:37,629] [DEBUG] (common) - Validating CLI manifest file...
[2024-04-23 15:26:37,631] [INFO] (packager.parameters) - Application: /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app
[2024-04-23 15:26:37,632] [INFO] (packager.parameters) - Detected application type: Python Module
[2024-04-23 15:26:37,632] [INFO] (packager) - Reading application configuration from /home/mqin/src/monai-deploy-app-sdk/notebooks/tutorials/simple_imaging_app/app.yaml...
[2024-04-23 15:26:37,636] [INFO] (packager) - Generating app.json...
[2024-04-23 15:26:37,636] [INFO] (packager) - Generating pkg.json...
[2024-04-23 15:26:37,649] [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 15:26:37,650] [DEBUG] (common) -
=============== Begin pkg.json ===============
{
"apiVersion": "1.0.0",
"applicationRoot": "/opt/holoscan/app",
"modelRoot": "/opt/holoscan/models",
"models": {},
"resources": {
"cpu": 1,
"gpu": 1,
"memory": "1Gi",
"gpuMemory": "1Gi"
},
"version": 1.0,
"platformConfig": "dgpu"
}
================ End pkg.json ================
[2024-04-23 15:26:37,673] [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="simple_imaging_app:1.0"
LABEL org.opencontainers.image.title="MONAI Deploy App Package - Simple Imaging 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 ./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 15:26:37,673] [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: simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0
[2024-04-23 15:26:38,231] [INFO] (common) - Using existing Docker BuildKit builder `holoscan_app_builder`
[2024-04-23 15:26:38,231] [DEBUG] (packager.builder) - Building Holoscan Application Package: tag=simple_imaging_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: 2.63kB done
#1 DONE 0.0s
#2 [internal] load metadata for nvcr.io/nvidia/clara-holoscan/holoscan:v2.0.0-dgpu
#2 DONE 0.5s
#3 [internal] load .dockerignore
#3 transferring context: 1.79kB done
#3 DONE 0.0s
#4 [internal] load build context
#4 DONE 0.0s
#5 importing cache manifest from local:10678196058931023490
#5 inferred cache manifest type: application/vnd.oci.image.index.v1+json done
#5 DONE 0.0s
#6 [ 1/20] 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 0.9s
#4 [internal] load build context
#4 transferring context: 157.77kB 0.0s done
#4 DONE 0.0s
#8 [ 5/20] RUN useradd -rm -d /home/holoscan -s /bin/bash -g 1000 -G sudo -u 1000 holoscan
#8 CACHED
#9 [ 3/20] RUN apt-get update && apt-get install -y curl jq && rm -rf /var/lib/apt/lists/*
#9 CACHED
#10 [ 6/20] RUN chown -R holoscan /var/holoscan
#10 CACHED
#11 [13/20] RUN pip install --upgrade pip
#11 CACHED
#12 [ 8/20] RUN chown -R holoscan /var/holoscan/output
#12 CACHED
#13 [ 7/20] RUN chown -R holoscan /var/holoscan/input
#13 CACHED
#14 [ 2/20] RUN mkdir -p /etc/holoscan/ && mkdir -p /opt/holoscan/ && mkdir -p /var/holoscan && mkdir -p /opt/holoscan/app && mkdir -p /var/holoscan/input && mkdir -p /var/holoscan/output
#14 CACHED
#15 [12/20] COPY ./pip/requirements.txt /tmp/requirements.txt
#15 CACHED
#16 [11/20] RUN chmod +x /var/holoscan/tools
#16 CACHED
#17 [ 9/20] WORKDIR /var/holoscan
#17 CACHED
#18 [ 4/20] RUN groupadd -f -g 1000 holoscan
#18 CACHED
#19 [10/20] COPY ./tools /var/holoscan/tools
#19 CACHED
#20 [14/20] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
#20 CACHED
#21 [15/20] COPY ./monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#21 DONE 0.1s
#22 [16/20] RUN pip install /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#22 0.725 Defaulting to user installation because normal site-packages is not writeable
#22 0.802 Processing /tmp/monai_deploy_app_sdk-0.5.1+20.gb869749.dirty-py3-none-any.whl
#22 0.815 Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (1.23.5)
#22 1.004 Collecting holoscan~=2.0 (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.074 Downloading holoscan-2.0.0-cp310-cp310-manylinux_2_35_x86_64.whl.metadata (6.7 kB)
#22 1.118 Collecting colorama>=0.4.1 (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.122 Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
#22 1.172 Collecting typeguard>=3.0.0 (from monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.176 Downloading typeguard-4.2.1-py3-none-any.whl.metadata (3.7 kB)
#22 1.192 Requirement already satisfied: pip>=20.3 in /home/holoscan/.local/lib/python3.10/site-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (24.0)
#22 1.193 Requirement already satisfied: cupy-cuda12x==12.2 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (12.2.0)
#22 1.193 Requirement already satisfied: cloudpickle==2.2.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.2.1)
#22 1.194 Requirement already satisfied: python-on-whales==0.60.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.60.1)
#22 1.195 Requirement already satisfied: Jinja2==3.1.3 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.1.3)
#22 1.195 Requirement already satisfied: packaging==23.1 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (23.1)
#22 1.196 Requirement already satisfied: pyyaml==6.0 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (6.0)
#22 1.197 Requirement already satisfied: requests==2.31.0 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.31.0)
#22 1.197 Requirement already satisfied: psutil==5.9.6 in /usr/local/lib/python3.10/dist-packages (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (5.9.6)
#22 1.296 Collecting wheel-axle-runtime<1.0 (from holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.302 Downloading wheel_axle_runtime-0.0.5-py3-none-any.whl.metadata (7.7 kB)
#22 1.326 Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.10/dist-packages (from cupy-cuda12x==12.2->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.8.2)
#22 1.329 Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2==3.1.3->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.1.3)
#22 1.342 Requirement already satisfied: pydantic<2,>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (1.10.15)
#22 1.342 Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (4.66.2)
#22 1.343 Requirement already satisfied: typer>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.12.3)
#22 1.343 Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (4.7.1)
#22 1.352 Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.3.2)
#22 1.352 Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (3.7)
#22 1.353 Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2.2.1)
#22 1.353 Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests==2.31.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (2024.2.2)
#22 1.389 Collecting typing-extensions (from python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
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#22 1.451 Collecting filelock (from wheel-axle-runtime<1.0->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty)
#22 1.455 Downloading filelock-3.13.4-py3-none-any.whl.metadata (2.8 kB)
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#22 1.556 Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer>=0.4.1->python-on-whales==0.60.1->holoscan~=2.0->monai-deploy-app-sdk==0.5.1+20.gb869749.dirty) (0.1.2)
#22 1.570 Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)
#22 1.585 Downloading holoscan-2.0.0-cp310-cp310-manylinux_2_35_x86_64.whl (33.2 MB)
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#22 3.799 Installing collected packages: typing-extensions, filelock, colorama, wheel-axle-runtime, typeguard, holoscan, monai-deploy-app-sdk
#22 4.520 Successfully installed colorama-0.4.6 filelock-3.13.4 holoscan-2.0.0 monai-deploy-app-sdk-0.5.1+20.gb869749.dirty typeguard-4.2.1 typing-extensions-4.11.0 wheel-axle-runtime-0.0.5
#22 DONE 5.2s
#23 [17/20] COPY ./map/app.json /etc/holoscan/app.json
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#25 DONE 0.0s
#26 [20/20] COPY ./app /opt/holoscan/app
#26 DONE 0.0s
#27 exporting to docker image format
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#27 sending tarball
#27 ...
#28 importing to docker
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#27 exporting to docker image format
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#29 DONE 1.7s
[2024-04-23 15:27:32,621] [INFO] (packager) - Build Summary:
Platform: x64-workstation/dgpu
Status: Succeeded
Docker Tag: simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0
Tarball: None
Note
Building a MONAI Application Package (Docker image) can take time. Use -l DEBUG option if you want to see the progress.
We can see that the MAP Docker image is created.
!docker image ls | grep {tag_prefix}
simple_imaging_app-x64-workstation-dgpu-linux-amd64 1.0 6485f181da93 46 seconds ago 12.5GB
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": {},
"resources": {
"cpu": 1,
"gpu": 1,
"memory": "1Gi",
"gpuMemory": "1Gi"
},
"version": 1,
"platformConfig": "dgpu"
}
2024-04-23 22:27:35 [INFO] Copying application from /opt/holoscan/app to /var/run/holoscan/export/app
2024-04-23 22:27:35 [INFO] Copying application manifest file from /etc/holoscan/app.json to /var/run/holoscan/export/config/app.json
2024-04-23 22:27:35 [INFO] Copying pkg manifest file from /etc/holoscan/pkg.json to /var/run/holoscan/export/config/pkg.json
2024-04-23 22:27:35 [INFO] Copying application configuration from /var/holoscan/app.yaml to /var/run/holoscan/export/config/app.yaml
2024-04-23 22:27:35 [INFO] Copying models from /opt/holoscan/models to /var/run/holoscan/export/models
2024-04-23 22:27:35 [INFO] '/opt/holoscan/models' cannot be found.
2024-04-23 22:27:35 [INFO] Copying documentation from /opt/holoscan/docs/ to /var/run/holoscan/export/docs
2024-04-23 22:27:35 [INFO] '/opt/holoscan/docs/' cannot be found.
app config
Executing packaged app locally¶
The packaged app can be run locally through MONAI Application Runner.
# Clear the output folder and run the MAP container. The input is expected to be a folder
!rm -rf {output_path}
!monai-deploy run -i {test_input_folder} -o {output_path} {tag_prefix}-x64-workstation-dgpu-linux-amd64:1.0
[2024-04-23 15:27:36,245] [INFO] (runner) - Checking dependencies...
[2024-04-23 15:27:36,245] [INFO] (runner) - --> Verifying if "docker" is installed...
[2024-04-23 15:27:36,245] [INFO] (runner) - --> Verifying if "docker-buildx" is installed...
[2024-04-23 15:27:36,245] [INFO] (runner) - --> Verifying if "simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0" is available...
[2024-04-23 15:27:36,326] [INFO] (runner) - Reading HAP/MAP manifest...
Preparing to copy...?25lCopying from container - 0B?25hSuccessfully copied 2.56kB to /tmp/tmpv5e8cb_i/app.json
Preparing to copy...?25lCopying from container - 0B?25hSuccessfully copied 2.05kB to /tmp/tmpv5e8cb_i/pkg.json
[2024-04-23 15:27:36,492] [INFO] (runner) - --> Verifying if "nvidia-ctk" is installed...
[2024-04-23 15:27:36,492] [INFO] (runner) - --> Verifying "nvidia-ctk" version...
[2024-04-23 15:27:36,761] [INFO] (common) - Launching container (94febd6eabce) using image 'simple_imaging_app-x64-workstation-dgpu-linux-amd64:1.0'...
container name: dazzling_kowalevski
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 22:27:37 [INFO] Launching application python3 /opt/holoscan/app ...
[2024-04-23 22:27:37,772] [INFO] (root) - Parsed args: Namespace(log_level=None, input=None, output=None, model=None, workdir=None, argv=['/opt/holoscan/app'])
[2024-04-23 22:27:37,772] [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 22:27:37,772] [INFO] (root) - sample_data_path: /var/holoscan/input
[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 22:27:37.797 INFO gxf/std/greedy_scheduler.cpp@191: Scheduling 3 entities
2024-04-23 22:27:38.257 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 22:27:38.257 INFO gxf/std/greedy_scheduler.cpp@401: Scheduler finished.
[info] [gxf_executor.cpp:1879] Deactivating Graph...
[info] [gxf_executor.cpp:1887] Graph execution finished.
[info] [gxf_executor.cpp:275] Destroying context
Number of times operator sobel_op whose class is defined in sobel_operator called: 1
Input from: /var/holoscan/input, whose absolute path: /var/holoscan/input
Number of times operator median_op whose class is defined in median_operator called: 1
Number of times operator gaussian_op whose class is defined in gaussian_operator called: 1
Data type of output: <class 'numpy.ndarray'>, max = 0.35821119421406195
Data type of output post conversion: <class 'numpy.ndarray'>, max = 91
[2024-04-23 15:27:38,526] [INFO] (common) - Container 'dazzling_kowalevski'(94febd6eabce) exited.
#output_image_path was set as before, output_image_path = output_path + "/final_output.png"
output_image = io.imread(output_image_path)
io.imshow(output_image)
<matplotlib.image.AxesImage at 0x7fd82f333b20>