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* conditional_compilation * how-to-build-2 * Update local-distribution.md * Update build.md * Update build.md * Update docs/dev/build.md Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * Update docs/dev/build.md Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * Update docs/dev/static_libaries.md Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * Update docs/dev/building_documentation.md Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * Changes after review * Update docs/dev/building_documentation.md * Update docs/dev/static_libaries.md * building articles update --------- Co-authored-by: Ilya Churaev <ilyachur@gmail.com>
266 lines
6.3 KiB
Markdown
266 lines
6.3 KiB
Markdown
# Installing
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Once the project is built you can install OpenVINO™ Runtime into custom location:
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```
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cmake --install <BUILDDIR> --prefix <INSTALLDIR>
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```
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## Installation check
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<details>
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<summary>For versions prior to 2022.1</summary>
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<p>
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1. Obtaining Open Model Zoo tools and models
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To have the ability to run samples and demos, you need to clone the Open Model Zoo repository and copy the folder under `./deployment_tools` to your install directory:
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```
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git clone https://github.com/openvinotoolkit/open_model_zoo.git
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cmake -E copy_directory ./open_model_zoo/ <INSTALLDIR>/deployment_tools/open_model_zoo/
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```
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2. Adding OpenCV to your environment
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Open Model Zoo samples use OpenCV functionality to load images. To use it for demo builds you need to provide the path to your OpenCV custom build by setting `OpenCV_DIR` environment variable and add path OpenCV libraries to the `LD_LIBRARY_PATH (Linux)` or `PATH (Windows)` variable before running demos.
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Linux:
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```sh
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export LD_LIBRARY_PATH=/path/to/opencv_install/lib/:$LD_LIBRARY_PATH
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export OpenCV_DIR=/path/to/opencv_install/cmake
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```
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Windows:
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```sh
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set PATH=\path\to\opencv_install\bin\;%PATH%
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set OpenCV_DIR=\path\to\opencv_install\cmake
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```
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3. Running demo
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To check your installation go to the demo directory and run Classification Demo:
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Linux and macOS:
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```sh
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cd <INSTALLDIR>/deployment_tools/demo
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./demo_squeezenet_download_convert_run.sh
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```
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Windows:
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```sh
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cd <INSTALLDIR>\deployment_tools\demo
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demo_squeezenet_download_convert_run.bat
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```
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Result:
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```
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Top 10 results:
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Image <INSTALLDIR>/deployment_tools/demo/car.png
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classid probability label
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------- ----------- -----
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817 0.6853030 sports car, sport car
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479 0.1835197 car wheel
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511 0.0917197 convertible
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436 0.0200694 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
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751 0.0069604 racer, race car, racing car
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656 0.0044177 minivan
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717 0.0024739 pickup, pickup truck
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581 0.0017788 grille, radiator grille
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468 0.0013083 cab, hack, taxi, taxicab
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661 0.0007443 Model T
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[ INFO ] Execution successful
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```
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</p>
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</details>
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<details open>
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<summary> For 2022.1 and after</summary>
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<p>
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1. Build samples
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To build C++ sample applications, run the following commands:
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Linux and macOS:
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```sh
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cd <INSTALLDIR>/samples/cpp
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./build_samples.sh
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```
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Windows:
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```sh
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cd <INSTALLDIR>\samples\cpp
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build_samples_msvc.bat
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```
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2. Install OpenVINO Development Tools
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> **NOTE**: To build OpenVINO Development Tools (Model Optimizer, Post-Training Optimization Tool, Model Downloader, and Open Model Zoo tools) wheel package locally you are required to use CMake option: `-DENABLE_WHEEL=ON`.
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To install OpenVINO Development Tools to work with Caffe models, execute the following commands:
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Linux and macOS:
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```sh
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#setup virtual envrinment
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python3 -m venv openvino_env
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source openvino_env/bin/activate
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pip install pip --upgrade
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#install local package from install directory
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pip install openvino_dev-<version>-py3-none-any.whl[caffe] --find-links=<INSTALLDIR>/tools
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```
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Windows:
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```bat
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rem setup virtual envrinment
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python -m venv openvino_env
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openvino_env\Scripts\activate.bat
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pip install pip --upgrade
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rem install local package from install directory
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cd <INSTALLDIR>\tools
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pip install openvino_dev-<version>-py3-none-any.whl[caffe] --find-links=<INSTALLDIR>\tools
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```
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3. Download the Models
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Download the following model to run the Image Classification Sample:
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Linux and macOS:
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```sh
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omz_downloader --name googlenet-v1 --output_dir ~/models
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```
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Windows:
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```bat
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omz_downloader --name googlenet-v1 --output_dir %USERPROFILE%\Documents\models
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```
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4. Convert the Model with Model Optimizer
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Linux and macOS:
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```sh
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mkdir ~/ir
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mo --input_model ~/models/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP16 --output_dir ~/ir
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```
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Windows:
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```bat
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mkdir %USERPROFILE%\Documents\ir
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mo --input_model %USERPROFILE%\Documents\models\public\googlenet-v1\googlenet-v1.caffemodel --data_type FP16 --output_dir %USERPROFILE%\Documents\ir
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```
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5. Run Inference on the Sample
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Set up the OpenVINO environment variables:
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Linux and macOS:
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```sh
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source <INSTALLDIR>/setupvars.sh
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```
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Windows:
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```bat
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<INSTALLDIR>\setupvars.bat
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```
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The following commands run the Image Classification Code Sample using the [`dog.bmp`](https://storage.openvinotoolkit.org/data/test_data/images/224x224/dog.bmp) file as an input image, the model in IR format from the `ir` directory, and on different hardware devices:
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Linux and macOS:
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```sh
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cd ~/inference_engine_cpp_samples_build/intel64/Release
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./classification_sample_async -i ~/Downloads/dog.bmp -m ~/ir/googlenet-v1.xml -d CPU
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```
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Windows:
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```bat
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cd %USERPROFILE%\Documents\Intel\OpenVINO\inference_engine_samples_build\intel64\Release
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.\classification_sample_async.exe -i %USERPROFILE%\Downloads\dog.bmp -m %USERPROFILE%\Documents\ir\googlenet-v1.xml -d CPU
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```
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When the sample application is complete, you see the label and confidence data for the top 10 categories on the display:
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```
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Top 10 results:
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Image dog.bmp
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classid probability
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------- -----------
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156 0.6875963
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215 0.0868125
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218 0.0784114
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212 0.0597296
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217 0.0212105
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219 0.0194193
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247 0.0086272
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157 0.0058511
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216 0.0057589
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154 0.0052615
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```
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</p>
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</details>
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## Adding OpenVINO Runtime (Inference Engine) to Your Project
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<details>
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<summary>For versions prior to 2022.1</summary>
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<p>
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For CMake projects, set the `InferenceEngine_DIR` and when you run CMake tool:
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```sh
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cmake -DInferenceEngine_DIR=/path/to/openvino/build/ .
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```
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Then you can find Inference Engine by [`find_package`]:
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```cmake
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find_package(InferenceEngine REQUIRED)
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target_link_libraries(${PROJECT_NAME} PRIVATE ${InferenceEngine_LIBRARIES})
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```
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</p>
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</details>
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<details open>
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<summary>For 2022.1 and after</summary>
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<p>
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For CMake projects, set the `OpenVINO_DIR` and when you run CMake tool:
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```sh
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cmake -DOpenVINO_DIR=<INSTALLDIR>/runtime/cmake .
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```
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Then you can find OpenVINO Runtime (Inference Engine) by [`find_package`]:
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```cmake
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find_package(OpenVINO REQUIRED)
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add_executable(ov_app main.cpp)
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target_link_libraries(ov_app PRIVATE openvino::runtime)
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add_executable(ov_c_app main.c)
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target_link_libraries(ov_c_app PRIVATE openvino::runtime::c)
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```
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</p>
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</details>
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## See also
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* [OpenVINO README](../../README.md)
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* [OpenVINO Developer Documentation](index.md)
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* [OpenVINO How to Build](build.md)
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