Build Your Application with Deployment Package

When you find an optimal configuration for your model, the next step is to use this model with optimal parameters in your own application on a target device. OpenVINO™ toolkit includes all you need to run the application on the target. However, the target might have a limited drive space to store all OpenVINO™ components. OpenVINO™ Deployment Manager available inside the DL Workbench extracts the minimum set of libraries required for a target device. Refer to the section below to learn how to download a deployment package for your project.

IMPORTANT: Deployment Manager available inside the DL Workbench provides libraries compatible only with Ubuntu 18.04.

Once you download the package, see how to create a binary with your application on your developer machine and deploy it on a target device.

NOTE: The machine where you use the DL Workbench to download the package and where you prepare your own application is a developer machine. The machine where you deploy the application is a target machine.

Download Deployment Package

NOTE: Perform these steps on your developer machine.

On the Configurations page, find the Selected Configuration form and go to the Pack Tab:


In this tab, select all the targets you want to apply your model to. You can also opt whether to include the model, Python API, and installation scripts. Python API enables the Inference Engine (IE) to work in Python scripts. You can then import the Python API into your own scripts and use IE via it. Installation scripts install OpenVINO™ dependencies and drivers for selected targets if needed.

The package size displayed at the bottom of the form changes depending on your selection. If you do not include the model in the package, the archive contains only libraries for selected plugins.

NOTE: Ubuntu* 18.04 is the default target operating system and cannot be changed.

Once you click Pack, the packaging process starts on the server followed by an automatic archive download:


Now you have an archive that contains the required libraries and your model.

IMPORTANT: The archive does not contain your application, and copying the archive to the target device does not mean deployment.

IMPORTANT: The archive contains C++* libraries, so your application can be written in C++ only. A Python* application cannot use these libraries directly and Python bindings are not included in the deployment package. This document does not contain instructions on how to prepare a Python application for deployment.

Your application should be compiled into a binary file. If you do not have an application, see Create Binary Sample. The next step is moving a binary to the target device and deploying it there.

Create Binary Sample

NOTE: Perform these steps on your developer machine.


Install the Intel® Distribution of OpenVINO™ toolkit for Linux* on your development machine. OpenVINO™ toolkit and DL Workbench should be of the same release version.

Step 1. Create main.cpp

Create a file named main.cpp with the source code of your application:

View main.cpp

#include <vector>
using namespace InferenceEngine;
int main(int argc, char *argv[]) {
if (argc < 3) {
std::cerr << "Usage: " << argv[0] << " PATH_TO_MODEL_XML DEVICE" << std::endl;
return 1;
int batchSize = 1;
int numInferReq = 1;
if (argc == 5) {
batchSize = std::stoi(argv[3]);
numInferReq = std::stoi(argv[4]);
const std::string modelXml = argv[1];
std::string device = argv[2];
std::transform(device.begin(), device.end(), device.begin(), ::toupper);
Core ie;
// Start setting number of streams
int numStreams = numInferReq;
if (device == "CPU") {
ie.SetConfig({{CONFIG_KEY(CPU_THROUGHPUT_STREAMS), std::to_string(numStreams)}}, device);
if (device == "GPU") {
numStreams = numInferReq / 2;
if (numStreams % 2) {
ie.SetConfig({{CONFIG_KEY(GPU_THROUGHPUT_STREAMS), std::to_string(numStreams)}}, device);
// Finish setting number of streams
CNNNetwork network = ie.ReadNetwork(modelXml);
// Set batch
ExecutableNetwork executableNetwork = ie.LoadNetwork(network, device);
std::vector<InferRequest> requests(numInferReq);
for (std::size_t i = 0; i < numInferReq; i++) {
// Create an InferRequest
requests[i] = executableNetwork.CreateInferRequest();
// run the InferRequest
for (std::size_t i = 0; i < numInferReq; i++){
StatusCode status = requests[i].Wait(IInferRequest::WaitMode::RESULT_READY);
if (status != StatusCode::OK){
std::cout<< "inferRequest " << i << "failed" << std::endl;
std::cout << "Inference completed successfully"<<std::endl;
return 0;

Step 2. Create CMakeLists.txt

In the same folder as main.cpp, create a file named CMakeLists.txt with the following commands to compile main.cpp into an executable file:

View CMakeLists.txt

cmake_minimum_required(VERSION 3.10)
set(IE_SAMPLE_NAME ie_sample)
find_package(InferenceEngine 2.1 REQUIRED)
target_link_libraries(${IE_SAMPLE_NAME} PUBLIC ${InferenceEngine_LIBRARIES})

Step 3. Compile Application

Open a terminal in the directory with main.cpp and CMakeLists.txt, and run the following commands to build the sample:

NOTE: Replace <INSTALL_OPENVINO_DIR> with the directory you installed the OpenVINO™ package in. By default, the package is installed to /opt/intel/openvino or ~/intel/openvino.

mkdir build
cd build
cmake ../

Once the commands are executed, find the ie_sample binary in the build folder in the directory with the source files.

Deploy Your Application on Target Machine

Make sure you have the following components on your developer machine:

Unarchive the deployment package. Place the binary and model inside the deployment_package folder as follows:

|-- deployment_package
|-- bin
|-- deployment_tools
|-- install_dependencies
|-- model
|-- model.xml
|-- model.bin
|-- ie_sample

Then archive the deployment_package folder and copy it to the target machine.

NOTE: Perform the steps below on your target machine.

  1. Open a terminal in the deployment_package folder on the target machine.
  2. (Optional: for inference on Intel® GPU, Intel® Movidius™ VPU, or Intel® Vision Accelerator Design with Intel® Movidius™ VPUs targets) Install dependencies by running the script:
    sudo -E ./install_dependencies/
  3. Set up the environment variables by running bin/
    source ./bin/
  4. Run your application:

    NOTE: Replace <path> and <model> with the path to your model and its name respectively.

    ./ie_sample <path>/<model>.xml CPU

NOTE: In the command above, the application is run on a CPU device. If you run it on other devices, set the following flags instead of CPU:

  • Intel® Processor Graphics: GPU
  • Intel® Movidius™ Neural Compute Stick 2 (NCS 2): MYRIAD
  • Intel® Vision Accelerator Design with Intel® Movidius™ VPUs: HDDL

If you run the application created in the Create Binary Sample, you get the following output:

Inference completed successfully

See Also