Configurations for Intel® Neural Compute Stick 2

This page introduces additional configurations for Intel® Neural Compute Stick 2 with Intel® Distribution of OpenVINO™ toolkit on Linux, Raspbian OS and macOS.

Linux

Once you have your Intel® Distribution of OpenVINO™ toolkit installed, follow the steps to be able to work on NCS2:

  1. Go to the install_dependencies directory:

    cd <INSTALL_DIR>/install_dependencies/
  2. Run the install_NCS_udev_rules.sh script:

    ./install_NCS_udev_rules.sh
  3. You may need to reboot your machine for this to take effect.

You’ve completed all required configuration steps to perform inference on Intel® Neural Compute Stick 2. Proceed to the Get Started Guide section to learn the basic OpenVINO™ toolkit workflow and run code samples and demo applications.

Raspbian OS

  1. Add the current Linux user to the users group:

    sudo usermod -a -G users "$(whoami)"

    Log out and log in for it to take effect.

  2. If you didn’t modify .bashrc to permanently set the environment variables, run setupvars.sh again after logging in:

    source /opt/intel/openvino_2022/setupvars.sh
  3. To perform inference on the Intel® Neural Compute Stick 2, install the USB rules running the install_NCS_udev_rules.sh script:

    sh /opt/intel/openvino_2022/install_dependencies/install_NCS_udev_rules.sh
  4. Plug in your Intel® Neural Compute Stick 2.

  5. (Optional) If you want to compile and run the Image Classification sample to verify the OpenVINO™ toolkit installation follow the next steps.

    1. Navigate to a directory that you have write access to and create a samples build directory. This example uses a directory named build :

    mkdir build && cd build
    1. Build the Hello Classification Sample:

    cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino_2022/samples/cpp
    make -j2 hello_classification
    1. Download the pre-trained squeezenet1.1 image classification model with the Model Downloader or copy it from the host machine:

    git clone --depth 1 https://github.com/openvinotoolkit/open_model_zoo
    cd open_model_zoo/tools/model_tools
    python3 -m pip install --upgrade pip
    python3 -m pip install -r requirements.in
    python3 downloader.py --name squeezenet1.1
    1. Run the sample specifying the model, a path to the input image, and the VPU required to run with the Raspbian OS:

    ./armv7l/Release/hello_classification <path_to_model>/squeezenet1.1.xml <path_to_image> MYRIAD

    The application outputs to console window top 10 classification results.

macOS

These steps are required only if you want to perform inference on Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X VPU.

To perform inference on Intel® Neural Compute Stick 2, the libusb library is required. You can build it from the source code or install using the macOS package manager you prefer: Homebrew, MacPorts or other.

For example, to install the libusb library using Homebrew, use the following command:

brew install libusb

You’ve completed all required configuration steps to perform inference on your Intel® Neural Compute Stick 2.

What’s Next?

Now you are ready to try out the toolkit. You can use the following tutorials to write your applications using Python and C++.

Developing in Python:

Developing in C++: