[DOCS] fix npu and install 23.3 (#22125)
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Karol Blaszczak
GitHub
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.. {#openvino_docs_install_guides_overview}
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Install OpenVINO™ 2023.2
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Install OpenVINO™ 2023.3
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==========================
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.. meta::
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:description: install OpenVINO Runtime package, using the distribution channel
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:description: install OpenVINO Runtime package, using the distribution channel
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of your choice.
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@ -15,7 +15,7 @@ Install OpenVINO™ 2023.2
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OpenVINO Runtime on Linux <openvino_docs_install_guides_installing_openvino_linux_header>
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OpenVINO Runtime on Windows <openvino_docs_install_guides_installing_openvino_windows_header>
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OpenVINO Runtime on macOS <openvino_docs_install_guides_installing_openvino_macos_header>
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OpenVINO Runtime on macOS <openvino_docs_install_guides_installing_openvino_macos_header>
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Create a Yocto Image <openvino_docs_install_guides_installing_openvino_yocto>
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@ -26,8 +26,8 @@ Install OpenVINO™ 2023.2
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<iframe id="selector" src="_static/selector-tool/selector-5cca680.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
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.. warning::
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The OpenVINO Development Tools package has been deprecated and removed from the default
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The OpenVINO™ Development Tools package has been deprecated and removed from the default
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installation options. For new projects, the OpenVINO runtime package now includes
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all necessary components.
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@ -36,30 +36,30 @@ Install OpenVINO™ 2023.2
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.. tip::
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OpenVINO 2023.2, described here, is not a Long-Term-Support version!
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OpenVINO 2023.3, described here, is a Long-Term-Support version!
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All currently supported versions are:
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* 2023.2 (development)
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* 2023.3 (LTS)
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* 2022.3 (LTS)
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* 2021.4 (LTS)
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Moreover, different OpenVINO distributions may support slightly different sets of features.
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Read installation guides for particular distributions for more details.
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Read installation guides for particular distributions for more details.
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.. dropdown:: Distribution Comparison for OpenVINO 2023.2
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=============== ========== ====== ========= ======== ============ ========== ==========
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Device Archives PyPI APT/YUM Conda Homebrew vcpkg Conan
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=============== ========== ====== ========= ======== ============ ========== ==========
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CPU V V V V V V V
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GPU V V V V V V V
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GNA V n/a n/a n/a n/a n/a n/a
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NPU V n/a n/a n/a n/a n/a n/a
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=============== ========== ====== ========= ======== ============ ========== ==========
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.. dropdown:: Distribution Comparison for OpenVINO 2023.3
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=============== ========== ====== ========= ======== ============ ========== ==========
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Device Archives PyPI APT/YUM Conda Homebrew vcpkg Conan
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=============== ========== ====== ========= ======== ============ ========== ==========
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CPU V V V V V V V
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GPU V V V V V V V
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GNA V n/a n/a n/a n/a n/a n/a
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NPU V n/a n/a n/a n/a n/a n/a
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=============== ========== ====== ========= ======== ============ ========== ==========
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| **Build OpenVINO from source**
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| OpenVINO Toolkit source files are available on GitHub as open source. If you want to build your own version of OpenVINO for your platform,
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| OpenVINO Toolkit source files are available on GitHub as open source. If you want to build your own version of OpenVINO for your platform,
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follow the `OpenVINO Build Instructions <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md>`__.
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@ -4,7 +4,7 @@ NPU Device
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==========
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.. meta::
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:description: OpenVINO™ supports the Neural Processing Unit,
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:description: OpenVINO™ supports the Neural Processing Unit,
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a low-power processing device dedicated to running AI inference.
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@ -13,17 +13,21 @@ Intel® Core™ Ultra generation of CPUs (formerly known as Meteor Lake). It ena
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you to offload certain neural network computation tasks from other devices,
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for more streamlined resource management.
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Note that the NPU plugin is currently available only with the Archive distribution of OpenVINO™
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and you need to :doc:`install a proper NPU driver <openvino_docs_install_guides_configurations_for_intel_npu>`
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to use it successfully. For an in-depth description of the NPU plugin, see:
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For an in-depth description of the NPU plugin, see:
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• `NPU plugin developer documentation <https://github.com/openvinotoolkit/npu_plugin/blob/develop/docs/VPUX_DG/index.md>`__
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• `OpenVINO Runtime NPU plugin source files <https://github.com/openvinotoolkit/npu_plugin>`__
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* `NPU plugin developer documentation <https://github.com/openvinotoolkit/npu_plugin/blob/develop/docs/VPUX_DG/index.md>`__
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* `OpenVINO Runtime NPU plugin source files <https://github.com/openvinotoolkit/npu_plugin>`__
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| **Supported Platforms:**
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| Host: Intel® Core™ Ultra (former Meteor Lake)
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| NPU device: NPU 3720
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| OS: Ubuntu* 20, MS Windows* 11 (both 64-bit)
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| **Supported Inference Data Types**
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| The NPU plugin supports the following data types as inference precision of internal primitives:
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@ -32,7 +36,7 @@ For an in-depth description of the NPU plugin, see:
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| Computation precision for the HW is fp16.
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| For more details on how to get a quantized model, refer to the
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:doc:`Model Optimization guide <openvino_docs_model_optimization_guide>`, and
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:doc:`Model Optimization guide <openvino_docs_model_optimization_guide>`, and
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:doc:`NNCF tool quantization guide <basic_quantization_flow>`.
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@ -56,13 +60,13 @@ UMD Dynamic Model Caching
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+++++++++++++++++++++++++++++
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UMD model caching is a solution enabled by default in the current NPU driver.
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It improves time to first inference (FIL) by storing the model in the cache
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It improves time to first inference (FIL) by storing the model in the cache
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after the compilation (included in FEIL), based on a hash key. The process
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may be summarized in three stages:
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1. UMD generates the key from the input IR model and build arguments
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2. UMD requests the DirectX Shader cache session to store the model
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with the computed key.
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with the computed key.
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3. All subsequent requests to compile the same IR model with the same arguments
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use the pre-compiled model, reading it from the cache instead of recompiling.
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@ -73,7 +77,7 @@ OpenVINO Model Caching
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OpenVINO Model Caching is a common mechanism for all OpenVINO device plugins and
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can be enabled by setting the ``ov::cache_dir`` property. This way, the UMD model
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caching is automatically bypassed by the NPU plugin, which means the model
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will only be stored in the OpenVINO cache after compilation. When a cache hit
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will only be stored in the OpenVINO cache after compilation. When a cache hit
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occurs for subsequent compilation requests, the plugin will import the model
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instead of recompiling it.
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@ -86,8 +90,8 @@ Supported Features and properties
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The NPU device is currently supported by AUTO and MULTI inference modes.
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The NPU support in OpenVINO is still under active development and may
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offer a limited set of supported OpenVINO features.
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The NPU support in OpenVINO is still under active development and may
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offer a limited set of supported OpenVINO features.
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**Supported Properties:**
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@ -110,11 +114,11 @@ offer a limited set of supported OpenVINO features.
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ov::intel_vpux::dpu_groups
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ov::intel_vpux::dma_engines
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ov::intel_vpux::compilation_mode
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ov::intel_vpux::compilation_mode_params
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ov::intel_vpux::print_profiling
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ov::intel_vpux::profiling_output_file
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ov::intel_vpux::vpux_platform
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ov::intel_vpux::use_elf_compiler_backend
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ov::intel_vpux::compilation_mode_params
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ov::intel_vpux::print_profiling
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ov::intel_vpux::profiling_output_file
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ov::intel_vpux::vpux_platform
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ov::intel_vpux::use_elf_compiler_backend
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.. tab-item:: Read-only properties
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@ -132,7 +136,7 @@ offer a limited set of supported OpenVINO features.
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ov::intel_vpux::device_total_mem_size
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ov::intel_vpux::driver_version
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.. note::
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.. note::
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The optimum number of inference requests returned by the plugin
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based on the performance mode is **4 for THROUGHPUT** and **1 for LATENCY**.
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@ -142,10 +146,10 @@ offer a limited set of supported OpenVINO features.
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Limitations
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#############################
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* Currently, only the models with static shapes are supported on NPU.
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* Currently, only the models with static shapes are supported on NPU.
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* If the path to the model file includes non-Unicode symbols, such as in Chinese,
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the model cannot be used for inference on NPU. It will return an error.
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* Running the Alexnet model with NPU may result in a drop in accuracy.
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the model cannot be used for inference on NPU. It will return an error.
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* Running the Alexnet model with NPU may result in a drop in accuracy.
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At this moment, the googlenet-v4 model is recommended for classification tasks.
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