Creating an Application

In this section, we’ll address:

Note

This section covers basics of applications running as a single fragment. For multi-fragment applications, refer to the distributed application documentation.

The following code snippet shows an example Application code skeleton:

  • We define the App class that inherits from the Application base class.

  • We create an instance of the App class in main() using the make_application() function.

  • The run() method starts the application which will execute its compose() method where the custom workflow will be defined.

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#include <holoscan/holoscan.hpp> class App : public holoscan::Application { public: void compose() override { // Define Operators and workflow // ... } }; int main() { auto app = holoscan::make_application<App>(); app->run(); return 0; }

  • We define the App class that inherits from the Application base class.

  • We create an instance of the App class in __main__.

  • The run() method starts the application which will execute its compose() method where the custom workflow will be defined.

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from holoscan.core import Application class App(Application): def compose(self): # Define Operators and workflow # ... if __name__ == "__main__": app = App() app.run()

Tip

This is also illustrated in the hello_world example.


It is also possible to instead launch the application asynchronously (i.e. non-blocking for the thread launching the application), as shown below:

This can be done simply by replacing the call to run() with run_async() which returns a std::future. Calling future.wait() will block until the application has finished running.

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int main() { auto app = holoscan::make_application<App>(); future = app->run_async(); future.wait(); return 0; }

This can be done simply by replacing the call to run() with run_async() which returns a Python concurrent.futures.Future. Calling future.result() will block until the application has finished running.

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if __name__ == "__main__": app = App() future = app.run_async() future.result()

Tip

This is also illustrated in the ping_simple_run_async example.

An application can be configured at different levels:

  1. providing the GXF extensions that need to be loaded (when using GXF operators)

  2. configuring parameters for your application, including for:

    1. the operators in the workflow

    2. the scheduler of your application

  3. configuring some runtime properties when deploying for production

The sections below will describe how to configure each of them, starting with a native support for YAML-based configuration for convenience.

YAML Configuration support

Holoscan supports loading arbitrary parameters from a YAML configuration file at runtime, making it convenient to configure each item listed above, or other custom parameters you wish to add on top of the existing API. For C++ applications, it also provides the ability to change the behavior of your application without needing to recompile it.

Note

Usage of the YAML utility is optional. Configurations can be hardcoded in your program, or done using any parser of your choosing.

Here is an example YAML configuration:

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string_param: "test" float_param: 0.50 bool_param: true dict_param: key_1: value_1 key_2: value_2

Ingesting these parameters can be done using the two methods below:

  • The config() method takes the path to the YAML configuration file. If the input path is relative, it will be relative to the current working directory.

  • The from_config() method returns an ArgList object for a given key in the YAML file. It holds a list of Arg objects, each of which holds a name (key) and a value.

    • If the ArgList object has only one Arg (when the key is pointing to a scalar item), it can be converted to the desired type using the as() method by passing the type as an argument.

    • The key can be a dot-separated string to access nested fields.

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// Pass configuration file auto app = holoscan::make_application<App>(); app->config("path/to/app_config.yaml"); // Scalars auto string_param = app->from_config("string_param").as<std::string>(); auto float_param = app->from_config("float_param").as<float>(); auto bool_param = app->from_config("bool_param").as<bool>(); // Dict auto dict_param = app->from_config("dict_param"); auto dict_nested_param = app->from_config("dict_param.key_1").as<std::string>(); // Print std::cout << "string_param: " << string_param << std::endl; std::cout << "float_param: " << float_param << std::endl; std::cout << "bool_param: " << bool_param << std::endl; std::cout << "dict_param:\n" << dict_param.description() << std::endl; std::cout << "dict_param['key1']: " << dict_nested_param << std::endl; // // Output // string_param: test // float_param: 0.5 // bool_param: 1 // dict_param: // name: arglist // args: // - name: key_1 // type: YAML::Node // value: value_1 // - name: key_2 // type: YAML::Node // value: value_2 // dict_param['key1']: value_1

  • The config() method takes the path to the YAML configuration file. If the input path is relative, it will be relative to the current working directory.

  • The kwargs() method return a regular python dict for a given key in the YAML file.

    • Advanced: this method wraps the from_config() method similar to the C++ equivalent, which returns an ArgList object if the key is pointing to a map item, or an Arg object if the key is pointing to a scalar item. An Arg object can be cast to the desired type (e.g., str(app.from_config("string_param"))).

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# Pass configuration file app = App() app.config("path/to/app_config.yaml") # Scalars string_param = app.kwargs("string_param")["string_param"] float_param = app.kwargs("float_param")["float_param"] bool_param = app.kwargs("bool_param")["bool_param"] # Dict dict_param = app.kwargs("dict_param") dict_nested_param = dict_param["key_1"] # Print print(f"string_param:{string_param}") print(f"float_param:{float_param}") print(f"bool_param:{bool_param}") print(f"dict_param:{dict_param}") print(f"dict_param['key_1']:{dict_nested_param}") # # Output: # string_param: test # float_param: 0.5 # bool_param: True # dict_param: {'key_1': 'value_1', 'key_2': 'value_2'} # dict_param['key_1']: 'value_1'

Warning

from_config() cannot be used as inputs to the built-in operators at this time, it’s therefore recommended to use kwargs() in Python.

Tip

This is also illustrated in the video_replayer example.

Attention

With both from_config and kwargs, the returned ArgList/dictionary will include both the key and its associated item if that item value is a scalar. If the item is a map/dictionary itself, the input key is dropped, and the output will only hold the key/values from that item.

Loading GXF extensions

If you use operators that depend on GXF extensions for their implementations (known as GXF operators), the shared libraries (.so) of these extensions need to be dynamically loaded as plugins at runtime.

The SDK already automatically handles loading the required extensions for the built-in operators in both C++ and Python, as well as common extensions (listed here). To load additional extensions for your own operators, you can use one of the following approach:

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extensions: - libgxf_myextension1.so - /path/to/libgxf_myextension2.so

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auto app = holoscan::make_application<App>(); auto exts = {"libgxf_myextension1.so", "/path/to/libgxf_myextension2.so"}; for (auto& ext : exts) { app->executor().extension_manager()->load_extension(ext); }

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from holoscan.gxf import load_extensions from holoscan.core import Application app = Application() context = app.executor.context_uint64 exts = ["libgxf_myextension1.so", "/path/to/libgxf_myextension2.so"] load_extensions(context, exts)

Note

To be discoverable, paths to these shared libraries need to either be absolute, relative to your working directory, installed in the lib/gxf_extensions folder of the holoscan package, or listed under the HOLOSCAN_LIB_PATH or LD_LIBRARY_PATH environment variables.

Configuring operators

Operators are defined in the compose() method of your application. They are not instantiated (with the initialize method) until an application’s run() method is called.

Operators have three type of fields which can be configured: parameters, conditions, and resources.

Configuring operator parameters

Operators could have parameters defined in their setup method to better control their behavior (see details when creating your own operators). The snippet below would be the implementation of this method for a minimal operator named MyOp, that takes a string and a boolean as parameters; we’ll ignore any extra details for the sake of this example:

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void setup(OperatorSpec& spec) override { spec.param(string_param_, "string_param"); spec.param(bool_param_, "bool_param"); }

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def setup(self, spec: OperatorSpec): spec.param("string_param") spec.param("bool_param") # Optional in python. Could define `self.<param_name>` instead in `def __init__`

Tip

Given an instance of an operator class, you can print a human-readable description of its specification to inspect the parameter fields that can be configured on that operator class:

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std::cout << operator_object->spec()->description() << std::endl;

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print(operator_object.spec)

Given this YAML configuration:

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myop_param: string_param: "test" bool_param: true bool_param: false # we'll use this later

We can configure an instance of the MyOp operator in the application’s compose method like this:

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void compose() override { // Using YAML auto my_op1 = make_operator<MyOp>("my_op1", from_config("myop_param")); // Same as above auto my_op2 = make_operator<MyOp>("my_op2", Arg("string_param", std::string("test")), // can use Arg(key, value)... Arg("bool_param") = true // ... or Arg(key) = value ); }

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def compose(self): # Using YAML my_op1 = MyOp(self, name="my_op1", **self.kwargs("myop_param")) # Same as above my_op2 = MyOp(self, name="my_op2", string_param="test", bool_param=True, )

Tip

This is also illustrated in the ping_custom_op example.

If multiple ArgList are provided with duplicate keys, the latest one overrides them:

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void compose() override { // Using YAML auto my_op1 = make_operator<MyOp>("my_op1", from_config("myop_param"), from_config("bool_param") ); // Same as above auto my_op2 = make_operator<MyOp>("my_op2", Arg("string_param", "test"), Arg("bool_param") = true, Arg("bool_param") = false ); // -> my_op `bool_param_` will be set to `false` }

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def compose(self): # Using YAML my_op1 = MyOp(self, name="my_op1", from_config("myop_param"), from_config("bool_param"), ) # Note: We're using from_config above since we can't merge automatically with kwargs # as this would create duplicated keys. However, we recommend using kwargs in python # to avoid limitations with wrapped operators, so the code below is preferred. # Same as above params = self.kwargs("myop_param").update(self.kwargs("bool_param")) my_op2 = MyOp(self, name="my_op2", params) # -> my_op `bool_param` will be set to `False`

Configuring operator conditions

By default, operators with no input ports will continuously run, while operators with input ports will run as long as they receive inputs (as they’re configured with the MessageAvailableCondition).

To change that behavior, one or more other conditions classes can be passed to the constructor of an operator to define when it should execute.

For example, we set three conditions on this operator my_op:

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void compose() override { // Limit to 10 iterations auto c1 = make_condition<CountCondition>("my_count_condition", 10); // Wait at least 200 milliseconds between each execution auto c2 = make_condition<PeriodicCondition>("my_periodic_condition", "200ms"); // Stop when the condition calls `disable_tick()` auto c3 = make_condition<BooleanCondition>("my_bool_condition"); // Pass directly to the operator constructor auto my_op = make_operator<MyOp>("my_op", c1, c2, c3); }

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def compose(self): # Limit to 10 iterations c1 = CountCondition(self, 10, name="my_count_condition") # Wait at least 200 milliseconds between each execution c2 = PeriodicCondition(self, timedelta(milliseconds=200), name="my_periodic_condition") # Stop when the condition calls `disable_tick()` c3 = BooleanCondition(self, name="my_bool_condition") # Pass directly to the operator constructor my_op = MyOp(self, c1, c2, c3, name="my_op")

Tip

This is also illustrated in the conditions examples.

Note

You’ll need to specify a unique name for the conditions if there are multiple conditions applied to an operator.

Configuring operator resources

Some resources can be passed to the operator’s constructor, typically an allocator passed as a regular parameter.

For example:

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void compose() override { // Allocating memory pool of specific size on the GPU // ex: width * height * channels * channel size in bytes auto block_size = 640 * 480 * 4 * 2; auto p1 = make_resource<BlockMemoryPool>("my_pool1", 1, size, 1); // Provide unbounded memory pool auto p2 = make_condition<UnboundedAllocator>("my_pool2"); // Pass to operator as parameters (name defined in operator setup) auto my_op = make_operator<MyOp>("my_op", Arg("pool1", p1), Arg("pool2", p2)); }

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def compose(self): # Allocating memory pool of specific size on the GPU # ex: width * height * channels * channel size in bytes block_size = 640 * 480 * 4 * 2; p1 = BlockMemoryPool(self, name="my_pool1", storage_type=1, block_size=block_size, num_blocks=1) # Provide unbounded memory pool p2 = UnboundedAllocator(self, name="my_pool2") # Pass to operator as parameters (name defined in operator setup) auto my_op = MyOp(self, name="my_op", pool1=p1, pool2=p2)

Configuring the scheduler

The scheduler controls how the application schedules the execution of the operators that make up its workflow.

The default scheduler is a single-threaded GreedyScheduler. An application can be configured to use a different scheduler Scheduler (C++/Python) or change the parameters from the default scheduler, using the scheduler() function (C++/Python).

For example, if an application needs to run multiple operators in parallel, a MultiThreadScheduler can instead be used.

The code snippet belows shows how to set and configure a non-default scheduler:

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auto app = holoscan::make_application<App>(); auto scheduler = app->make_scheduler<holoscan::MultiThreadScheduler>( "myscheduler", Arg("worker_thread_number", 4), Arg("stop_on_deadlock", true) ); app->scheduler(scheduler); app->run();

  • We create an instance of a Scheduler class in the schedulers module. Like operators, parameters can come from an explicit Arg or ArgList, or from a YAML configuration.

  • The scheduler() method assigns the scheduler to be used by the application.

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app = App() scheduler = holoscan.schedulers.MultiThreadScheduler( app, name="myscheduler", worker_thread_number=4, stop_on_deadlock=True, ) app.scheduler(scheduler) app.run()

Tip

This is also illustrated in the multithread example.

Configuring runtime properties

As described below, applications can run simply by executing the C++ or Python application manually on a given node, or by packaging it in a HAP container. With the latter, runtime properties need to be configured: refer to the App Runner Configuration for details.

Note

Operators are initialized according to the topological order of its fragment-graph. When an application runs, the operators are executed in the same topological order. Topological ordering of the graph ensures that all the data dependencies of an operator are satisfied before its instantiation and execution. If there is a cycle in the graph, the initialization and execution order of the operators are undefined. Currently, we do not support specifying a different and explicit instantiation and execution order of the operators.

One-operator Workflow

The simplest form of a workflow would be a single operator.

%%{init: {"theme": "base", "themeVariables": { "fontSize": "16px"}} }%% classDiagram direction LR class MyOp { }

Fig. 12 A one-operator workflow

The graph above shows an Operator (C++/Python) (named MyOp) that has neither inputs nor output ports.

  • Such an operator may accept input data from the outside (e.g., from a file) and produce output data (e.g., to a file) so that it acts as both the source and the sink operator.

  • Arguments to the operator (e.g., input/output file paths) can be passed as parameters as described in the section above.

We can add an operator to the workflow by calling add_operator (C++/Python) method in the compose() method.

The following code shows how to define a one-operator workflow in compose() method of the App class (assuming that the operator class MyOp is declared/defined in the same file).

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class App : public holoscan::Application { public: void compose() override { // Define Operators auto my_op = make_operator<MyOp>("my_op"); // Define the workflow add_operator(my_op); } };

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class App(Application): def compose(self): # Define Operators my_op = MyOp(self, name="my_op") # Define the workflow self.add_operator(my_op)

Linear Workflow

Here is an example workflow where the operators are connected linearly:

%%{init: {"theme": "base", "themeVariables": { "fontSize": "16px"}} }%% classDiagram direction LR SourceOp --|> ProcessOp : output...input ProcessOp --|> SinkOp : output...input class SourceOp { output(out) Tensor } class ProcessOp { [in]input : Tensor output(out) Tensor } class SinkOp { [in]input : Tensor }

Fig. 13 A linear workflow

In this example, SourceOp produces a message and passes it to ProcessOp. ProcessOp produces another message and passes it to SinkOp.

We can connect two operators by calling the add_flow() method (C++/Python) in the compose() method.

The add_flow() method (C++/Python) takes the source operator, the destination operator, and the optional port name pairs. The port name pair is used to connect the output port of the source operator to the input port of the destination operator. The first element of the pair is the output port name of the upstream operator and the second element is the input port name of the downstream operator. An empty port name (“”) can be used for specifying a port name if the operator has only one input/output port. If there is only one output port in the upstream operator and only one input port in the downstream operator, the port pairs can be omitted.

The following code shows how to define a linear workflow in the compose() method of the App class (assuming that the operator classes SourceOp, ProcessOp, and SinkOp are declared/defined in the same file).

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class App : public holoscan::Application { public: void compose() override { // Define Operators auto source = make_operator<SourceOp>("source"); auto process = make_operator<ProcessOp>("process"); auto sink = make_operator<SinkOp>("sink"); // Define the workflow add_flow(source, process); // same as `add_flow(source, process, {{"output", "input"}});` add_flow(process, sink); // same as `add_flow(process, sink, {{"", ""}});` } };

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class App(Application): def compose(self): # Define Operators source = SourceOp(self, name="source") process = ProcessOp(self, name="process") sink = SinkOp(self, name="sink") # Define the workflow self.add_flow(source, process) # same as `self.add_flow(source, process, {("output", "input")})` self.add_flow(process, sink) # same as `self.add_flow(process, sink, {("", "")})`

Complex Workflow (Multiple Inputs and Outputs)

You can design a complex workflow like below where some operators have multi-inputs and/or multi-outputs:

%%{init: {"theme": "base", "themeVariables": { "fontSize": "16px"}} }%% classDiagram direction TB Reader1 --|> Processor1 : image...{image1,image2}\nmetadata...metadata Reader2 --|> Processor2 : roi...roi Processor1 --|> Processor2 : image...image Processor2 --|> Processor3 : image...image Processor2 --|> Notifier : image...image Processor1 --|> Writer : image...image Processor3 --|> Writer : seg_image...seg_image class Reader1 { image(out) metadata(out) } class Reader2 { roi(out) } class Processor1 { [in]image1 [in]image2 [in]metadata image(out) } class Processor2 { [in]image [in]roi image(out) } class Processor3 { [in]image seg_image(out) } class Writer { [in]image [in]seg_image } class Notifier { [in]image }

Fig. 14 A complex workflow (multiple inputs and outputs)

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class App : public holoscan::Application { public: void compose() override { // Define Operators auto reader1 = make_operator<Reader1>("reader1"); auto reader2 = make_operator<Reader2>("reader2"); auto processor1 = make_operator<Processor1>("processor1"); auto processor2 = make_operator<Processor2>("processor2"); auto processor3 = make_operator<Processor3>("processor3"); auto writer = make_operator<Writer>("writer"); auto notifier = make_operator<Notifier>("notifier"); // Define the workflow add_flow(reader1, processor1, {{"image", "image1"}, {"image", "image2"}, {"metadata", "metadata"}}); add_flow(reader1, processor1, {{"image", "image2"}}); add_flow(reader2, processor2, {{"roi", "roi"}}); add_flow(processor1, processor2, {{"image", "image"}}); add_flow(processor1, writer, {{"image", "image"}}); add_flow(processor2, notifier); add_flow(processor2, processor3); add_flow(processor3, writer, {{"seg_image", "seg_image"}}); } };

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class App(Application): def compose(self): # Define Operators reader1 = Reader1Op(self, name="reader1") reader2 = Reader2Op(self, name="reader2") processor1 = Processor1Op(self, name="processor1") processor2 = Processor2Op(self, name="processor2") processor3 = Processor3Op(self, name="processor3") notifier = NotifierOp(self, name="notifier") writer = WriterOp(self, name="writer") # Define the workflow self.add_flow(reader1, processor1, {("image", "image1"), ("image", "image2"), ("metadata", "metadata")}) self.add_flow(reader2, processor2, {("roi", "roi")}) self.add_flow(processor1, processor2, {("image", "image")}) self.add_flow(processor1, writer, {("image", "image")}) self.add_flow(processor2, notifier) self.add_flow(processor2, processor3) self.add_flow(processor3, writer, {("seg_image", "seg_image")})

You can build your C++ application using CMake, by calling find_package(holoscan) in your CMakeLists.txt to load the SDK libraries. Your executable will need to link against:

  • holoscan::core

  • any operator defined outside your main.cpp which you wish to use in your app workflow, such as:

    • SDK built-in operators under the holoscan::ops namespace

    • operators created separately in your project with add_library

    • operators imported externally using with find_library or find_package

Listing 1 /CMakeLists.txt

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# Your CMake project cmake_minimum_required(VERSION 3.20) project(my_project CXX) # Finds the holoscan SDK find_package(holoscan REQUIRED CONFIG PATHS "/opt/nvidia/holoscan") # Create an executable for your application add_executable(my_app main.cpp) # Link your application against holoscan::core and any existing operators you'd like to use target_link_libraries(my_app PRIVATE holoscan::core holoscan::ops::<some_built_in_operator_target> <some_other_operator_target> <...> )


Tip

This is also illustrated in all the examples:

  • in CMakeLists.txt for the SDK installation directory - /opt/nvidia/holoscan/examples

  • in CMakeLists.min.txt for the SDK source directory

Once your CMakeLists.txt is ready in <src_dir>, you can build in <build_dir> with the command line below. You can optionally pass Holoscan_ROOT if the SDK installation you’d like to use differs from the PATHS given to find_package(holoscan) above.

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# Configure cmake -S <src_dir> -B <build_dir> -D Holoscan_ROOT="/opt/nvidia/holoscan" # Build cmake --build <build_dir> -j

You can then run your application by running <build_dir>/my_app.

Python applications do not require building. Simply ensure that:

  • The holoscan python module is installed in your dist-packages or is listed under the PYTHONPATH env variable so you can import holoscan.core and any built-in operator you might need in holoscan.operators.

  • Any external operators are available in modules in your dist-packages or contained in PYTHONPATH.

Note

While python applications do not need to be built, they might depend on operators that wrap C++ operators. All python operators built-in in the SDK already ship with the python bindings pre-built. Follow this section if you are wrapping C++ operators yourself to use in your python application.

You can then run your application by running python3 my_app.py.

Note

Given a CMake project, a pre-built executable, or a python application, you can also use the Holoscan CLI to package and run your Holoscan application in a OCI-compliant container image.

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