GXF by Example


This section is legacy (0.2) as we recommend developing extensions and applications using the C++ or Python APIs. Refer to the developer guide for up-to-date recommendations.

Let us look at an example of a GXF entity to try to understand its general anatomy. As an example let’s start with the entity definition for an image format converter entity named format_converter_entity as shown below.

Listing 23 An example GXF Application YAML snippet


%YAML 1.2 --- # other entities declared --- name: format_converter_entity components: - name: in_tensor type: nvidia::gxf::DoubleBufferReceiver - type: nvidia::gxf::MessageAvailableSchedulingTerm parameters: receiver: in_tensor min_size: 1 - name: out_tensor type: nvidia::gxf::DoubleBufferTransmitter - type: nvidia::gxf::DownstreamReceptiveSchedulingTerm parameters: transmitter: out_tensor min_size: 1 - name: pool type: nvidia::gxf::BlockMemoryPool parameters: storage_type: 1 block_size: 4919040 # 854 * 480 * 3 (channel) * 4 (bytes per pixel) num_blocks: 2 - name: format_converter_component type: nvidia::holoscan::formatconverter::FormatConverter parameters: in: in_tensor out: out_tensor out_tensor_name: source_video out_dtype: "float32" scale_min: 0.0 scale_max: 255.0 pool: pool --- # other entities declared --- components: - name: input_connection type: nvidia::gxf::Connection parameters: source: upstream_entity/output target: format_converter/in_tensor --- components: - name: output_connection type: nvidia::gxf::Connection parameters: source: format_converter/out_tensor target: downstream_entity/input --- name: scheduler components: - type: nvidia::gxf::GreedyScheduler


  1. The entity format_converter_entity receives a message in its in_tensor message from an upstream entity upstream_entity as declared in the input_connection.

  2. The received message is passed to the format_converter_component component to convert the tensor element precision from uint8 to float32 and scale any input in the [0, 255] intensity range.

  3. The format_converter_component component finally places the result in the out_tensor message so that its result is made available to a downstream entity (downstream_ent as declared in output_connection).

  4. The Connection components tie the inputs and outputs of various components together, in the above case upstream_entity/output -> format_converter_entity/in_tensor and format_converter_entity/out_tensor -> downstream_entity/input.

  5. The scheduler entity declares a GreedyScheduler “system component” which orchestrates the execution of the entities declared in the graph. In the specific case of GreedyScheduler entities are scheduled to run exclusively, where no more than one entity can run at any given time.

The YAML snippet above can be visually represented as follows.


Fig. 15 Arrangement of components and entities in a Holoscan application

In the image, as in the YAML, you will notice the use of MessageAvailableSchedulingTerm, DownstreamReceptiveSchedulingTerm, and BlockMemoryPool. These are components that play a “supporting” role to in_tensor, out_tensor, and format_converter_component components respectively. Specifically:

  • MessageAvailableSchedulingTerm is a component that takes a Receiver`` (in this caseDoubleBufferReceivernamedin_tensor) and alerts the graphExecutorthat a message is available. This alert triggersformat_converter_component`.

  • DownstreamReceptiveSchedulingTerm is a component that takes a Transmitter (in this case DoubleBufferTransmitter named out_tensor) and alerts the graph Executor that a message has been placed on the output.

  • BlockMemoryPool provides two blocks of almost 5MB allocated on the GPU device and is used by format_converted_ent to allocate the output tensor where the converted data will be placed within the format converted component.

Together these components allow the entity to perform a specific function and coordinate communication with other entities in the graph via the declared scheduler.

More generally, an entity can be thought of as a collection of components where components can be passed to one another to perform specific subtasks (e.g. event triggering or message notification, format conversion, memory allocation), and an application as a graph of entities.

The scheduler is a component of type nvidia::gxf::System which orchestrates the execution components in each entity at application runtime based on triggering rules.

Entities communicate with one another via messages which may contain one or more payloads. Messages are passed and received via a component of type nvidia::gxf::Queue from which both nvidia::gxf::Receiver and nvidia::gxf::Transmitter are derived. Every entity that receives and transmits messages has at least one receiver and one transmitter queue.

Holoscan uses the nvidia::gxf::SchedulingTerm component to coordinate data access and component orchestration for a Scheduler which invokes execution through the tick() function in each Codelet.


A SchedulingTerm defines a specific condition that is used by an entity to let the scheduler know when it’s ready for execution.

In the above example, we used a MessageAvailableSchedulingTerm to trigger the execution of the components waiting for data from in_tensor receiver queue, namely format_converter_component.

Listing 24 MessageAvailableSchedulingTerm


- type: nvidia::gxf::MessageAvailableSchedulingTerm parameters: receiver: in_tensor min_size: 1

Similarly, DownStreamReceptiveSchedulingTerm checks whether the out_tensor transmitter queue has at least one outgoing message in it. If there are one or more outgoing messages, DownStreamReceptiveSchedulingTerm will notify the scheduler which in turn attempts to place the message in the receiver queue of a downstream entity. If, however, the downstream entity has a full receiver queue, the message is held in the out_tensor queue as a means to handle back-pressure.

Listing 25 DownstreamReceptiveSchedulingTerm


- type: nvidia::gxf::DownstreamReceptiveSchedulingTerm parameters: transmitter: out_tensor min_size: 1

If we were to draw the entity in Fig. 15 in greater detail it would look something like the following.


Fig. 16 Receive and transmit Queues and SchedulingTerms in entities.

Up to this point, we have covered the “entity component system” at a high level and showed the functional parts of an entity, namely, the messaging queues and the scheduling terms that support the execution of components in the entity. To complete the picture, the next section covers the anatomy and lifecycle of a component, and how to handle events within it.

GXF components in Holoscan can perform a multitude of sub-tasks ranging from data transformations, to memory management, to entity scheduling. In this section, we will explore an nvidia::gxf::Codelet component which in Holoscan is known as a “GXF extension”. Holoscan (GXF) extensions are typically concerned with application-specific sub-tasks such as data transformations, AI model inference, and the like.

Extension Lifecycle

The lifecycle of a Codelet is composed of the following five stages.

  1. initialize - called only once when the codelet is created for the first time, and use of light-weight initialization.

  2. deinitialize - called only once before the codelet is destroyed, and used for light-weight deinitialization.

  3. start - called multiple times over the lifecycle of the codelet according to the order defined in the lifecycle, and used for heavy initialization tasks such as allocating memory resources.

  4. stop - called multiple times over the lifecycle of the codelet according to the order defined in the lifecycle, and used for heavy deinitialization tasks such as deallocation of all resources previously assigned in start.

  5. tick - called when the codelet is triggered, and is called multiple times over the codelet lifecycle; even multiple times between start and stop.

The flow between these stages is detailed in Fig. 17.


Fig. 17 Sequence of method calls in the lifecycle of a Holoscan extension

Implementing an Extension

In this section, we will implement a simple recorder that will highlight the actions we would perform in the lifecycle methods. The recorder receives data in the input queue and records the data to a configured location on the disk. The output format of the recorder files is the GXF-formatted index/binary replayer files (the format is also used for the data in the sample applications), where the gxf_index file contains timing and sequence metadata that refer to the binary/tensor data held in the gxf_entities file.

Declare the Class That Will Implement the Extension Functionality

The developer can create their Holoscan extension by extending the Codelet class, implementing the extension functionality by overriding the lifecycle methods, and defining the parameters the extension exposes at the application level via the registerInterface method. To define our recorder component we would need to implement some of the methods in the Codelet.

First, clone the Holoscan project from here and create a folder to develop our extension such as under gxf_extensions/my_recorder.


Using Bash we create a Holoscan extension folder as follows.


git clone https://github.com/nvidia-holoscan/holoscan-sdk.git cd clara-holoscan-embedded-sdk mkdir -p gxf_extensions/my_recorder

In our extension folder, we create a header file my_recorder.hpp with a declaration of our Holoscan component.

Listing 26 gxf_extensions/my_recorder/my_recorder.hpp


#include <string> #include "gxf/core/handle.hpp" #include "gxf/std/codelet.hpp" #include "gxf/std/receiver.hpp" #include "gxf/std/transmitter.hpp" #include "gxf/serialization/file_stream.hpp" #include "gxf/serialization/entity_serializer.hpp" class MyRecorder : public nvidia::gxf::Codelet { public: gxf_result_t registerInterface(nvidia::gxf::Registrar* registrar) override; gxf_result_t initialize() override; gxf_result_t deinitialize() override; gxf_result_t start() override; gxf_result_t tick() override; gxf_result_t stop() override; private: nvidia::gxf::Parameter<nvidia::gxf::Handle<nvidia::gxf::Receiver>> receiver_; nvidia::gxf::Parameter<nvidia::gxf::Handle<nvidia::gxf::EntitySerializer>> my_serializer_; nvidia::gxf::Parameter<std::string> directory_; nvidia::gxf::Parameter<std::string> basename_; nvidia::gxf::Parameter<bool> flush_on_tick_; // File stream for data index nvidia::gxf::FileStream index_file_stream_; // File stream for binary data nvidia::gxf::FileStream binary_file_stream_; // Offset into binary file size_t binary_file_offset_; };

Declare the Parameters to Expose at the Application Level

Next, we can start implementing our lifecycle methods in the my_recorder.cpp file, which we also create in gxf_extensions/my_recorder path.

Our recorder will need to expose the nvidia::gxf::Parameter variables to the application so the parameters can be modified by configuration.

Listing 27 registerInterface in gxf_extensions/my_recorder/my_recorder.cpp


#include "my_recorder.hpp" gxf_result_t MyRecorder::registerInterface(nvidia::gxf::Registrar* registrar) { nvidia::gxf::Expected<void> result; result &= registrar->parameter( receiver_, "receiver", "Entity receiver", "Receiver channel to log"); result &= registrar->parameter( my_serializer_, "serializer", "Entity serializer", "Serializer for serializing input data"); result &= registrar->parameter( directory_, "out_directory", "Output directory path", "Directory path to store received output"); result &= registrar->parameter( basename_, "basename", "File base name", "User specified file name without extension", nvidia::gxf::Registrar::NoDefaultParameter(), GXF_PARAMETER_FLAGS_OPTIONAL); result &= registrar->parameter( flush_on_tick_, "flush_on_tick", "Boolean to flush on tick", "Flushes output buffer on every `tick` when true", false); // default value `false` return nvidia::gxf::ToResultCode(result); }

For pure GXF applications, our component’s parameters can be specified in the following format in the YAML file:

Listing 28 Example parameters for MyRecorder component


name: my_recorder_entity components: - name: my_recorder_component type: MyRecorder parameters: receiver: receiver serializer: my_serializer out_directory: /home/user/out_path basename: my_output_file # optional # flush_on_tick: false # optional

Note that all the parameters exposed at the application level are mandatory except for flush_on_tick, which defaults to false, and basename, whose default is handled at initialize() below.

Implement the Lifecycle Methods

This extension does not need to perform any heavy-weight initialization tasks, so we will concentrate on initialize(), tick(), and deinitialize() methods which define the core functionality of our component. At initialization, we will create a file stream and keep track of the bytes we write on tick() via binary_file_offset.

Listing 29 initialize in gxf_extensions/my_recorder/my_recorder.cpp


gxf_result_t MyRecorder::initialize() { // Create path by appending receiver name to directory path if basename is not provided std::string path = directory_.get() + '/'; if (const auto& basename = basename_.try_get()) { path += basename.value(); } else { path += receiver_->name(); } // Initialize index file stream as write-only index_file_stream_ = nvidia::gxf::FileStream("", path + nvidia::gxf::FileStream::kIndexFileExtension); // Initialize binary file stream as write-only binary_file_stream_ = nvidia::gxf::FileStream("", path + nvidia::gxf::FileStream::kBinaryFileExtension); // Open index file stream nvidia::gxf::Expected<void> result = index_file_stream_.open(); if (!result) { return nvidia::gxf::ToResultCode(result); } // Open binary file stream result = binary_file_stream_.open(); if (!result) { return nvidia::gxf::ToResultCode(result); } binary_file_offset_ = 0; return GXF_SUCCESS; }

When de-initializing, our component will take care of closing the file streams that were created at initialization.

Listing 30 deinitialize in gxf_extensions/my_recorder/my_recorder.cpp


gxf_result_t MyRecorder::deinitialize() { // Close binary file stream nvidia::gxf::Expected<void> result = binary_file_stream_.close(); if (!result) { return nvidia::gxf::ToResultCode(result); } // Close index file stream result = index_file_stream_.close(); if (!result) { return nvidia::gxf::ToResultCode(result); } return GXF_SUCCESS; }

In our recorder, no heavy-weight initialization tasks are required so we implement the following, however, we would use start() and stop() methods for heavy-weight tasks such as memory allocation and deallocation.

Listing 31 start/stop in gxf_extensions/my_recorder/my_recorder.cpp


gxf_result_t MyRecorder::start() { return GXF_SUCCESS; } gxf_result_t MyRecorder::stop() { return GXF_SUCCESS; }


For a detailed implementation of start() and stop(), and how memory management can be handled therein, please refer to the implementation of the AJA Video source extension.

Finally, we write the component-specific functionality of our extension by implementing tick().

Listing 32 tick in gxf_extensions/my_recorder/my_recorder.cpp


gxf_result_t MyRecorder::tick() { // Receive entity nvidia::gxf::Expected<nvidia::gxf::Entity> entity = receiver_->receive(); if (!entity) { return nvidia::gxf::ToResultCode(entity); } // Write entity to binary file nvidia::gxf::Expected<size_t> size = my_serializer_->serializeEntity(entity.value(), &binary_file_stream_); if (!size) { return nvidia::gxf::ToResultCode(size); } // Create entity index nvidia::gxf::EntityIndex index; index.log_time = std::chrono::system_clock::now().time_since_epoch().count(); index.data_size = size.value(); index.data_offset = binary_file_offset_; // Write entity index to index file nvidia::gxf::Expected<size_t> result = index_file_stream_.writeTrivialType(&index); if (!result) { return nvidia::gxf::ToResultCode(result); } binary_file_offset_ += size.value(); if (flush_on_tick_) { // Flush binary file output stream nvidia::gxf::Expected<void> result = binary_file_stream_.flush(); if (!result) { return nvidia::gxf::ToResultCode(result); } // Flush index file output stream result = index_file_stream_.flush(); if (!result) { return nvidia::gxf::ToResultCode(result); } } return GXF_SUCCESS; }

Register the Extension as a Holoscan Component

As a final step, we must register our extension so it is recognized as a component and loaded by the application executor. For this we create a simple declaration in my_recorder_ext.cpp as follows.

Listing 33 gxf_extensions/my_recorder/my_recorder_ext.cpp


#include "gxf/std/extension_factory_helper.hpp" #include "my_recorder.hpp" GXF_EXT_FACTORY_BEGIN() GXF_EXT_FACTORY_SET_INFO(0xb891cef3ce754825, 0x9dd3dcac9bbd8483, "MyRecorderExtension", "My example recorder extension", "NVIDIA", "0.1.0", "LICENSE"); GXF_EXT_FACTORY_ADD(0x2464fabf91b34ccf, 0xb554977fa22096bd, MyRecorder, nvidia::gxf::Codelet, "My example recorder codelet."); GXF_EXT_FACTORY_END()

GXF_EXT_FACTORY_SET_INFO configures the extension with the following information in order:

  • UUID which can be generated using scripts/generate_extension_uuids.py which defines the extension id

  • extension name

  • extension description

  • author

  • extension version

  • license text

GXF_EXT_FACTORY_ADD registers the newly built extension as a valid Codelet component with the following information in order:

  • UUID which can be generated using scripts/generate_extension_uuids.py which defines the component id (this must be different from the extension id),

  • fully qualified extension class,

  • fully qualifies base class,

  • component description

To build a shared library for our new extension which can be loaded by a Holoscan application at runtime we use a CMake file under gxf_extensions/my_recorder/CMakeLists.txt with the following content.

Listing 34 gxf_extensions/my_recorder/CMakeLists.txt


# Create library add_library(my_recorder_lib SHARED my_recorder.cpp my_recorder.hpp ) target_link_libraries(my_recorder_lib PUBLIC GXF::std GXF::serialization yaml-cpp ) # Create extension add_library(my_recorder SHARED my_recorder_ext.cpp ) target_link_libraries(my_recorder PUBLIC my_recorder_lib ) # Install GXF extension as a component 'holoscan-gxf_extensions' install_gxf_extension(my_recorder) # this will also install my_recorder_lib # install_gxf_extension(my_recorder_lib) # this statement is not necessary because this library follows `<extension library name>_lib` convention.

Here, we create a library my_recorder_lib with the implementation of the lifecycle methods, and the extension my_recorder which exposes the C API necessary for the application runtime to interact with our component.

To make our extension discoverable from the project root we add the line



to the CMake file gxf_extensions/CMakeLists.txt.


To build our extension, we can follow the steps in the README.

At this point, we have a complete extension that records data coming into its receiver queue to the specified location on the disk using the GXF-formatted binary/index files.

For our application, we create the directory apps/my_recorder_app_gxf with the application definition file my_recorder_gxf.yaml. The my_recorder_gxf.yaml application is as follows:

Listing 35 apps/my_recorder_app_gxf/my_recorder_gxf.yaml


%YAML 1.2 --- name: replayer components: - name: output type: nvidia::gxf::DoubleBufferTransmitter - name: allocator type: nvidia::gxf::UnboundedAllocator - name: component_serializer type: nvidia::gxf::StdComponentSerializer parameters: allocator: allocator - name: entity_serializer type: nvidia::holoscan::stream_playback::VideoStreamSerializer # inheriting from nvidia::gxf::EntitySerializer parameters: component_serializers: [component_serializer] - type: nvidia::holoscan::stream_playback::VideoStreamReplayer parameters: transmitter: output entity_serializer: entity_serializer boolean_scheduling_term: boolean_scheduling directory: "/workspace/data/endoscopy/video" basename: "surgical_video" frame_rate: 0 # as specified in timestamps repeat: false # default: false realtime: true # default: true count: 0 # default: 0 (no frame count restriction) - name: boolean_scheduling type: nvidia::gxf::BooleanSchedulingTerm - type: nvidia::gxf::DownstreamReceptiveSchedulingTerm parameters: transmitter: output min_size: 1 --- name: recorder components: - name: input type: nvidia::gxf::DoubleBufferReceiver - name: allocator type: nvidia::gxf::UnboundedAllocator - name: component_serializer type: nvidia::gxf::StdComponentSerializer parameters: allocator: allocator - name: entity_serializer type: nvidia::holoscan::stream_playback::VideoStreamSerializer # inheriting from nvidia::gxf::EntitySerializer parameters: component_serializers: [component_serializer] - type: MyRecorder parameters: receiver: input serializer: entity_serializer out_directory: "/tmp" basename: "tensor_out" - type: nvidia::gxf::MessageAvailableSchedulingTerm parameters: receiver: input min_size: 1 --- components: - name: input_connection type: nvidia::gxf::Connection parameters: source: replayer/output target: recorder/input --- name: scheduler components: - name: clock type: nvidia::gxf::RealtimeClock - name: greedy_scheduler type: nvidia::gxf::GreedyScheduler parameters: clock: clock


  • The replayer reads data from /workspace/data/endoscopy/video/surgical_video.gxf_[index|entities] files, deserializes the binary data to a nvidia::gxf::Tensor using VideoStreamSerializer, and puts the data on an output message in the replayer/output transmitter queue.

  • The input_connection component connects the replayer/output transmitter queue to the recorder/input receiver queue.

  • The recorder reads the data in the input receiver queue, uses StdEntitySerializer to convert the received nvidia::gxf::Tensor to a binary stream, and outputs to the /tmp/tensor_out.gxf_[index|entities] location specified in the parameters.

  • The scheduler component, while not explicitly connected to the application-specific entities, performs the orchestration of the components discussed in the Data Flow and Triggering Rules.

Note the use of the component_serializer in our newly built recorder. This component is declared separately in the entity


- name: entity_serializer type: nvidia::holoscan::stream_playback::VideoStreamSerializer # inheriting from nvidia::gxf::EntitySerializer parameters: component_serializers: [component_serializer]

and passed into MyRecorder via the serializer parameter which we exposed in the extension development section (Declare the Parameters to Expose at the Application Level).


- type: MyRecorder parameters: receiver: input serializer: entity_serializer directory: "/tmp" basename: "tensor_out"

For our app to be able to load (and also compile where necessary) the extensions required at runtime, we need to declare a CMake file apps/my_recorder_app_gxf/CMakeLists.txt as follows.

Listing 36 apps/my_recorder_app_gxf/CMakeLists.txt


create_gxe_application( NAME my_recorder_gxf YAML my_recorder_gxf.yaml EXTENSIONS GXF::std GXF::cuda GXF::multimedia GXF::serialization my_recorder stream_playback ) # Create a CMake target for the gxf-based application add_custom_target(my_recorder_gxf ALL) # Download the associated dataset if needed if(HOLOSCAN_DOWNLOAD_DATASETS) add_dependencies(my_recorder_gxf endoscopy_data) endif()

In the declaration of create_gxe_application we list:

  • my_recorder component declared in the CMake file of the extension development section under the EXTENSIONS argument

  • the existing stream_playback Holoscan extension which reads data from disk

To make our newly built application discoverable by the build, in the root of the repository, we add the following line to apps/CMakeLists.txt:



We now have a minimal working application to test the integration of our newly built MyRecorder extension.

To run our application in a local development container:

  1. Follow the instructions under the Using a Development Container section steps 1-5 (try clearing the CMake cache by removing the build folder before compiling).

    You can execute the following commands to build


    ./run build # ./run clear_cache # if you want to clear build/install/cache folders

  2. Our test application can now be run in the development container using the command



    from inside the development container.

    (You can execute ./run launch to run the development container.)


    @LINUX:/workspace/holoscan-sdk/build$ ./apps/my_recorder_app_gxf/my_recorder_gxf 2022-08-24 04:46:47.333 INFO gxf/gxe/gxe.cpp@230: Creating context 2022-08-24 04:46:47.339 INFO gxf/gxe/gxe.cpp@107: Loading app: 'apps/my_recorder_app_gxf/my_recorder_gxf.yaml' 2022-08-24 04:46:47.339 INFO gxf/std/yaml_file_loader.cpp@117: Loading GXF entities from YAML file 'apps/my_recorder_app_gxf/my_recorder_gxf.yaml'... 2022-08-24 04:46:47.340 INFO gxf/gxe/gxe.cpp@291: Initializing... 2022-08-24 04:46:47.437 INFO gxf/gxe/gxe.cpp@298: Running... 2022-08-24 04:46:47.437 INFO gxf/std/greedy_scheduler.cpp@170: Scheduling 2 entities 2022-08-24 04:47:14.829 INFO /workspace/holoscan-sdk/gxf_extensions/stream_playback/video_stream_replayer.cpp@144: Reach end of file or playback count reaches to the limit. Stop ticking. 2022-08-24 04:47:14.829 INFO gxf/std/greedy_scheduler.cpp@329: Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock. 2022-08-24 04:47:14.829 INFO gxf/std/greedy_scheduler.cpp@353: Scheduler finished. 2022-08-24 04:47:14.829 INFO gxf/gxe/gxe.cpp@320: Deinitializing... 2022-08-24 04:47:14.863 INFO gxf/gxe/gxe.cpp@327: Destroying context 2022-08-24 04:47:14.863 INFO gxf/gxe/gxe.cpp@333: Context destroyed.

A successful run (it takes about 30 secs) will result in output files (tensor_out.gxf_index and tensor_out.gxf_entities in /tmp) that match the original input files (surgical_video.gxf_index and surgical_video.gxf_entities under data/endoscopy/video) exactly.


@LINUX:/workspace/holoscan-sdk/build$ ls -al /tmp/ total 821384 drwxrwxrwt 1 root root 4096 Aug 24 04:37 . drwxr-xr-x 1 root root 4096 Aug 24 04:36 .. drwxrwxrwt 2 root root 4096 Aug 11 21:42 .X11-unix -rw-r--r-- 1 1000 1000 729309 Aug 24 04:47 gxf_log -rw-r--r-- 1 1000 1000 840054484 Aug 24 04:47 tensor_out.gxf_entities -rw-r--r-- 1 1000 1000 16392 Aug 24 04:47 tensor_out.gxf_index @LINUX:/workspace/holoscan-sdk/build$ ls -al ../data/endoscopy/video/ total 839116 drwxr-xr-x 2 1000 1000 4096 Aug 24 02:08 . drwxr-xr-x 4 1000 1000 4096 Aug 24 02:07 .. -rw-r--r-- 1 1000 1000 19164125 Jun 17 16:31 raw.mp4 -rw-r--r-- 1 1000 1000 840054484 Jun 17 16:31 surgical_video.gxf_entities -rw-r--r-- 1 1000 1000 16392 Jun 17 16:31 surgical_video.gxf_index

© Copyright 2022, NVIDIA. Last updated on Mar 20, 2023.