Configurations for Intel® Neural Compute Stick 2¶

Linux¶

Once you have OpenVINO™ Runtime installed, follow these steps to be able to work on NCS2:

Go to the install_dependencies directory:
```
cd <INSTALL_DIR>/install_dependencies/
```
Run the install_NCS_udev_rules.sh script:
```
./install_NCS_udev_rules.sh
```
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™ workflow and run code samples and demo applications.

Raspbian OS¶

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.
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
```
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
```
Plug in your Intel® Neural Compute Stick 2.
(Optional) If you want to compile and run the Image Classification sample to verify the installation of OpenVINO, follow the steps below.
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
```
2. 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
```
3. 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
```
4. 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 OpenVINO™. You can use the following tutorials to write your applications using Python and C++.

Developing in Python:

Developing in C++:

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