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openvino/openvino.spec
Guillaume GARDET 74f2524437 The specification file update was automatically generated by the osc command. 
I am sending this update at the request of the factory team, who asked to mention the .patch in the changelog file.

- Remove NPU Compile Tool
* openvino-remove-npu-compile-tool.patch 
- Update to 2024.3.0 
- Summary of major features and improvements  
  * More Gen AI coverage and framework integrations to minimize 
    code changes
    + OpenVINO pre-optimized models are now available in Hugging 
      Face making it easier for developers to get started with 
      these models.
  * Broader Large Language Model (LLM) support and more model 
    compression techniques.
    + Significant improvement in LLM performance on Intel 
      discrete GPUs with the addition of Multi-Head Attention 
      (MHA) and OneDNN enhancements.
  * More portability and performance to run AI at the edge, in the 
    cloud, or locally.
    + Improved CPU performance when serving LLMs with the 
      inclusion of vLLM and continuous batching in the OpenVINO 
      Model Server (OVMS). vLLM is an easy-to-use open-source 
      library that supports efficient LLM inferencing and model 
      serving.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised. 
    They are available to enable a smooth transition to new 
    solutions and will be discontinued in the future. To keep 
    using discontinued features, you will have to revert to the
    last LTS OpenVINO version supporting them. For more details,
    refer to the OpenVINO Legacy Features and Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA)..Consider
        using the Neural Processing Unit (NPU) for low-powered 
        systems like Intel® Core™ Ultra or 14th generation 
        and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API transition
        guide for reference).
      - All ONNX Frontend legacy API (known as ONNX_IMPORTER_API)
      - 'PerfomanceMode.UNDEFINED' property as part of the OpenVINO
         Python API
    + Tools:
      - Deployment Manager. See installation and deployment guides
        for current distribution options.
      - Accuracy Checker.
      - Post-Training Optimization Tool (POT). Neural Network 
        Compression Framework (NNCF) should be used instead.
      - A Git patch for NNCF integration with huggingface/
        transformers. The recommended approach is to use
        huggingface/optimum-intel for applying NNCF optimization 
        on top of models from Hugging Face.
      - Support for Apache MXNet, Caffe, and Kaldi model formats.
        Conversion to ONNX may be used as a solution.
  * Deprecated and to be removed in the future:
    + The OpenVINO™ Development Tools package (pip install
      openvino-dev) will be removed from installation options
      and distribution channels beginning with OpenVINO 2025.0.
    + Model Optimizer will be discontinued with OpenVINO 2025.0. 
      Consider using the new conversion methods instead. For 
      more details, see the model conversion transition guide.
    + OpenVINO property Affinity API will be discontinued with 
      OpenVINO 2025.0. It will be replaced with CPU binding 
      configurations (ov::hint::enable_cpu_pinning).
    + OpenVINO Model Server components:
      - “auto shape” and “auto batch size” (reshaping a model
        in runtime) will be removed in the future. OpenVINO’s 
        dynamic shape models are recommended instead.
    + A number of notebooks have been deprecated. For an 
      up-to-date listing of available notebooks, refer to
      the OpenVINO™ Notebook index (openvinotoolkit.github.io).
- Add riscv-cpu-plugin subpackage
- Update to 2024.2.0
- More Gen AI coverage and framework integrations to minimize code
  changes
  * Llama 3 optimizations for CPUs, built-in GPUs, and discrete 
    GPUs for improved performance and efficient memory usage.
  * Support for Phi-3-mini, a family of AI models that leverages 
    the power of small language models for faster, more accurate 
    and cost-effective text processing.
  * Python Custom Operation is now enabled in OpenVINO making it
    easier for Python developers to code their custom operations
    instead of using C++ custom operations (also supported). 
    Python Custom Operation empowers users to implement their own
    specialized operations into any model.
  * Notebooks expansion to ensure better coverage for new models.
    Noteworthy notebooks added: DynamiCrafter, YOLOv10, Chatbot
    notebook with Phi-3, and QWEN2.
- Broader Large Language Model (LLM) support and more model
  compression techniques.
  * GPTQ method for 4-bit weight compression added to NNCF for
    more efficient inference and improved performance of 
    compressed LLMs.
  * Significant LLM performance improvements and reduced latency
    for both built-in GPUs and discrete GPUs.
  * Significant improvement in 2nd token latency and memory 
    footprint of FP16 weight LLMs on AVX2 (13th Gen Intel® Core™
    processors) and AVX512 (3rd Gen Intel® Xeon® Scalable 
    Processors) based CPU platforms, particularly for small 
    batch sizes.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Model Serving Enhancements:
  * Preview: OpenVINO Model Server (OVMS) now supports 
    OpenAI-compatible API along with Continuous Batching and 
    PagedAttention, enabling significantly higher throughput 
    for parallel inferencing, especially on Intel® Xeon® 
    processors, when serving LLMs to many concurrent users.
  * OpenVINO backend for Triton Server now supports built-in 
    GPUs and discrete GPUs, in addition to dynamic 
    shapes support.
  * Integration of TorchServe through torch.compile OpenVINO
    backend for easy model deployment, provisioning to 
    multiple instances, model versioning, and maintenance.
  * Preview: addition of the Generate API, a simplified API 
    for text generation using large language models with only
    a few lines of code. The API is available through the newly
    launched OpenVINO GenAI package.
  * Support for Intel Atom® Processor X Series. For more details,
    see System Requirements.
  * Preview: Support for Intel® Xeon® 6 processor.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised. 
    They are available to enable a smooth transition to new 
    solutions and will be discontinued in the future. 
    To keep using discontinued features, you will have to revert
    to the last LTS OpenVINO version supporting them. For more 
    details, refer to the OpenVINO Legacy Features and 
    Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA).
        Consider using the Neural Processing Unit (NPU) for 
        low-powered systems like Intel® Core™ Ultra or 14th
        generation and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API 
        transition guide for reference).
      - All ONNX Frontend legacy API (known as ONNX_IMPORTER_API)
      - 'PerfomanceMode.UNDEFINED' property as part of the 
        OpenVINO Python API
    + Tools:
      - Deployment Manager. See installation and deployment 
        guides for current distribution options.
      - Accuracy Checker.
      - Post-Training Optimization Tool (POT). Neural Network 
        Compression Framework (NNCF) should be used instead.
      - A Git patch for NNCF integration with 
        huggingface/transformers. The recommended approach 
        is to use huggingface/optimum-intel for applying NNCF
        optimization on top of models from Hugging Face.
      - Support for Apache MXNet, Caffe, and Kaldi model formats.
        Conversion to ONNX may be used as a solution.
  * Deprecated and to be removed in the future:
    + The OpenVINO™ Development Tools package (pip install
      openvino-dev) will be removed from installation options
      and distribution channels beginning with OpenVINO 2025.0.
    + Model Optimizer will be discontinued with OpenVINO 2025.0. 
      Consider using the new conversion methods instead. For
      more details, see the model conversion transition guide.
    + OpenVINO property Affinity API will be discontinued with
      OpenVINO 2025.0. It will be replaced with CPU binding
      configurations (ov::hint::enable_cpu_pinning).
    + OpenVINO Model Server components:
    + “auto shape” and “auto batch size” (reshaping a model in 
      runtime) will be removed in the future. OpenVINO’s dynamic
      shape models are recommended instead.
    + A number of notebooks have been deprecated. For an 
      up-to-date listing of available notebooks, refer to the 
      OpenVINO™ Notebook index (openvinotoolkit.github.io).
- Fix sample source path in build script:
  * openvino-fix-build-sample-path.patch
- Update to 2024.1.0
- More Generative AI coverage and framework integrations to
  minimize code changes.
  * Mixtral and URLNet models optimized for performance 
    improvements on Intel® Xeon® processors.
  * Stable Diffusion 1.5, ChatGLM3-6B, and Qwen-7B models 
    optimized for improved inference speed on Intel® Core™
    Ultra processors with integrated GPU.
  * Support for Falcon-7B-Instruct, a GenAI Large Language Model
    (LLM) ready-to-use chat/instruct model with superior
    performance metrics.
  * New Jupyter Notebooks added: YOLO V9, YOLO V8
    Oriented Bounding Boxes Detection (OOB), Stable Diffusion 
    in Keras, MobileCLIP, RMBG-v1.4 Background Removal, Magika, 
    TripoSR, AnimateAnyone, LLaVA-Next, and RAG system with 
    OpenVINO and LangChain.
- Broader Large Language Model (LLM) support and more model
  compression techniques.
  * LLM compilation time reduced through additional optimizations
    with compressed embedding. Improved 1st token performance of
    LLMs on 4th and 5th generations of Intel® Xeon® processors 
    with Intel® Advanced Matrix Extensions (Intel® AMX).
  * Better LLM compression and improved performance with oneDNN,
    INT4, and INT8 support for Intel® Arc™ GPUs.
  * Significant memory reduction for select smaller GenAI
    models on Intel® Core™ Ultra processors with integrated GPU.
- More portability and performance to run AI at the edge, 
  in the cloud, or locally.
  * The preview NPU plugin for Intel® Core™ Ultra processors
    is now available in the OpenVINO open-source GitHub 
    repository, in addition to the main OpenVINO package on PyPI.
  * The JavaScript API is now more easily accessible through
    the npm repository, enabling JavaScript developers’ seamless 
    access to the OpenVINO API.
  * FP16 inference on ARM processors now enabled for the 
    Convolutional Neural Network (CNN) by default.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised. They
    are available to enable a smooth transition to new solutions 
    and will be discontinued in the future. To keep using 
    Discontinued features, you will have to revert to the last 
    LTS OpenVINO version supporting them.
  * For more details, refer to the OpenVINO Legacy Features 
    and Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA).
        Consider using the Neural Processing Unit (NPU) 
        for low-powered systems like Intel® Core™ Ultra or
        14th generation and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API 
        transition guide for reference).
      - All ONNX Frontend legacy API (known as 
        ONNX_IMPORTER_API)
      - 'PerfomanceMode.UNDEFINED' property as part of
         the OpenVINO Python API
    + Tools:
      - Deployment Manager. See installation and deployment
        guides for current distribution options.
      - Accuracy Checker.
      - Post-Training Optimization Tool (POT). Neural Network
        Compression Framework (NNCF) should be used instead.
      - A Git patch for NNCF integration with 
        huggingface/transformers. The recommended approach
        is to use huggingface/optimum-intel for applying 
        NNCF optimization on top of models from Hugging 
        Face.
      - Support for Apache MXNet, Caffe, and Kaldi model 
        formats. Conversion to ONNX may be used as 
        a solution.
  * Deprecated and to be removed in the future:
    + The OpenVINO™ Development Tools package (pip install
      openvino-dev) will be removed from installation options
      and distribution channels beginning with OpenVINO 2025.0.
    + Model Optimizer will be discontinued with OpenVINO 2025.0.
      Consider using the new conversion methods instead. For 
      more details, see the model conversion transition guide.
    + OpenVINO property Affinity API will be discontinued with 
      OpenVINO 2025.0. It will be replaced with CPU binding 
      configurations (ov::hint::enable_cpu_pinning).
    + OpenVINO Model Server components:
      - “auto shape” and “auto batch size” (reshaping a model
        in runtime) will be removed in the future. OpenVINO’s
        dynamic shape models are recommended instead.
- License update: play safe and list all third party licenses as
  part of the License tag.
- Switch to _service file as tagged Source tarball does not
  include `./thirdparty` submodules.
- Update openvino-fix-install-paths.patch to fix python module
  install path.
- Enable python module and split it out into a python subpackage
  (for now default python3 only).
- Explicitly build python metadata (dist-info) and install it
  (needs simple sed hackery to support "officially" unsupported
  platform ppc64le).
- Specify ENABLE_JS=OFF to turn off javascript bindings as
  building these requires downloading npm stuff from the network.
- Build with system pybind11.
- Bump _constraints for updated disk space requirements.
- Drop empty %check section, rpmlint was misleading when it
  recommended adding this.
- Numerous specfile cleanups:
  * Drop redundant `mv` commands and use `install` where
    appropriate.
  * Build with system protobuf.
  * Fix Summary tags.
  * Trim package descriptions.
  * Drop forcing CMAKE_BUILD_TYPE=Release, let macro default
    RelWithDebInfo be used instead.
  * Correct naming of shared library packages.
  * Separate out libopenvino_c.so.* into own shared lib package.
  * Drop rpmlintrc rule used to hide shlib naming mistakes.
  * Rename Source tarball to %{name}-%{version}.EXT pattern.
  * Use ldconfig_scriptlet macro for post(un).
- Add openvino-onnx-ml-defines.patch -- Define ONNX_ML at compile
  time when using system onnx to allow using 'onnx-ml.pb.h'
  instead of 'onnx.pb.h', the latter not being shipped with
  openSUSE's onnx-devel package (gh#onnx/onnx#3074).
- Add openvino-fix-install-paths.patch: Change hard-coded install
  paths in upstream cmake macro to standard Linux dirs.
- Add openvino-ComputeLibrary-include-string.patch: Include header
  for std::string.
- Add external devel packages as Requires for openvino-devel.
- Pass -Wl,-z,noexecstack to %build_ldflags to avoid an exec stack
  issue with intel CPU plugin.
- Use ninja for build.
- Adapt _constraits file for correct disk space and memory
  requirements.
- Add empty %check section.
- Initial package
- Version 2024.0.0
- Add openvino-rpmlintrc.

OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=14
2024-08-28 06:11:03 +00:00

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#
# spec file for package openvino
#
# Copyright (c) 2024 SUSE LLC
# Copyright (c) 2024 Alessandro de Oliveira Faria (A.K.A. CABELO) <cabelo@opensuse.org> or <alessandro.faria@owasp.org>
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
# upon. The license for this file, and modifications and additions to the
# file, is the same license as for the pristine package itself (unless the
# license for the pristine package is not an Open Source License, in which
# case the license is the MIT License). An "Open Source License" is a
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.
# Please submit bugfixes or comments via https://bugs.opensuse.org/
#
# Note: Will not build on Leap:15.X on account of too old TBB
# Compilation takes ~1 hr on OBS for a single python, don't try all supported flavours
%define pythons python3
%define __builder ninja
%define so_ver 2430
%define shlib lib%{name}%{so_ver}
%define shlib_c lib%{name}_c%{so_ver}
%define prj_name OpenVINO
Name: openvino
Version: 2024.3.0
Release: 0
Summary: A toolkit for optimizing and deploying AI inference
# Let's be safe and put all third party licenses here, no matter that we use specific thirdparty libs or not
License: Apache-2.0 AND BSD-2-Clause AND BSD-3-Clause AND HPND AND JSON AND MIT AND OFL-1.1 AND Zlib
URL: https://github.com/openvinotoolkit/openvino
Source0: %{name}-%{version}.tar.zst
Source1: %{name}-rpmlintrc
# PATCH-FEATURE-OPENSUSE openvino-onnx-ml-defines.patch badshah400@gmail.com -- Define ONNX_ML at compile time when using system onnx to allow using 'onnx-ml.pb.h' instead of 'onnx.pb.h', the latter not being shipped with openSUSE's onnx-devel package
Patch0: openvino-onnx-ml-defines.patch
# PATCH-FEATURE-OPENSUSE openvino-fix-install-paths.patch badshah400@gmail.com -- Fix installation paths hardcoded into upstream defined cmake macros
Patch2: openvino-fix-install-paths.patch
# PATCH-FIX-UPSTREAM openvino-ComputeLibrary-include-string.patch badshah400@gmail.com -- Include header for std::string
Patch3: openvino-ComputeLibrary-include-string.patch
# PATCH-FIX-UPSTREAM openvino-fix-build-sample-path.patch cabelo@opensuse.org -- Fix sample source path in build script
Patch4: openvino-fix-build-sample-path.patch
# PATCH-FIX-UPSTREAM openvino-remove-npu-compile-tool.patch cabelo@opensuse.org -- Remove NPU Compile Tool
Patch5: openvino-remove-npu-compile-tool.patch
BuildRequires: ade-devel
BuildRequires: cmake
BuildRequires: fdupes
BuildRequires: gcc-c++
BuildRequires: ninja
BuildRequires: opencl-cpp-headers
# FIXME: /usr/include/onnx/onnx-ml.pb.h:17:2: error: This file was generated by
# an older version of protoc which is incompatible with your Protocol Buffer
# headers. Please regenerate this file with a newer version of protoc.
#BuildRequires: cmake(ONNX)
BuildRequires: pkgconfig
BuildRequires: %{python_module devel}
BuildRequires: %{python_module pip}
BuildRequires: %{python_module pybind11-devel}
BuildRequires: %{python_module setuptools}
BuildRequires: %{python_module wheel}
BuildRequires: python-rpm-macros
BuildRequires: zstd
BuildRequires: pkgconfig(OpenCL-Headers)
BuildRequires: pkgconfig(flatbuffers)
BuildRequires: pkgconfig(libva)
BuildRequires: pkgconfig(nlohmann_json)
BuildRequires: pkgconfig(ocl-icd)
BuildRequires: pkgconfig(protobuf)
BuildRequires: pkgconfig(pugixml)
BuildRequires: pkgconfig(snappy)
BuildRequires: pkgconfig(tbb)
BuildRequires: pkgconfig(zlib)
%ifarch %{arm64}
BuildRequires: scons
%endif
# No 32-bit support
ExcludeArch: %{ix86} %{arm32} ppc
%define python_subpackage_only 1
%python_subpackages
%description
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
## Main shared libs and devel pkg ##
#
%package -n %{shlib}
Summary: Shared library for OpenVINO toolkit
%description -n %{shlib}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the shared library for OpenVINO.
#
%package -n %{shlib_c}
Summary: Shared C library for OpenVINO toolkit
%description -n %{shlib_c}
This package provides the C library for OpenVINO.
#
%package -n %{name}-devel
Summary: Headers and sources for OpenVINO toolkit
Requires: %{shlib_c} = %{version}
Requires: %{shlib} = %{version}
Requires: lib%{name}_ir_frontend%{so_ver} = %{version}
Requires: lib%{name}_onnx_frontend%{so_ver} = %{version}
Requires: lib%{name}_paddle_frontend%{so_ver} = %{version}
Requires: lib%{name}_pytorch_frontend%{so_ver} = %{version}
Requires: lib%{name}_tensorflow_frontend%{so_ver} = %{version}
Requires: lib%{name}_tensorflow_lite_frontend%{so_ver} = %{version}
Requires: pkgconfig(OpenCL-Headers)
Requires: pkgconfig(flatbuffers)
Requires: pkgconfig(libva)
Requires: pkgconfig(nlohmann_json)
Requires: pkgconfig(ocl-icd)
Requires: pkgconfig(protobuf)
Requires: pkgconfig(pugixml)
Requires: pkgconfig(snappy)
Requires: pkgconfig(tbb)
Recommends: %{name}-auto-batch-plugin = %{version}
Recommends: %{name}-auto-plugin = %{version}
Recommends: %{name}-hetero-plugin = %{version}
Recommends: %{name}-intel-cpu-plugin = %{version}
%ifarch riscv64
Recommends: %{name}-riscv-cpu-plugin = %{version}
%endif
%description -n %{name}-devel
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the headers and sources for developing applications with
OpenVINO.
## Plugins ##
#
%package -n %{name}-arm-cpu-plugin
Summary: Intel CPU plugin for OpenVINO toolkit
%description -n %{name}-arm-cpu-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the ARM CPU plugin for OpenVINO on %{arm64} archs.
#
%package -n %{name}-riscv-cpu-plugin
Summary: RISC-V CPU plugin for OpenVINO toolkit
%description -n %{name}-riscv-cpu-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the RISC-V CPU plugin for OpenVINO on riscv64 archs.
#
%package -n %{name}-auto-plugin
Summary: Auto / Multi software plugin for OpenVINO toolkit
%description -n %{name}-auto-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the Auto / Multi software plugin for OpenVINO.
#
%package -n %{name}-auto-batch-plugin
Summary: Automatic batch software plugin for OpenVINO toolkit
%description -n %{name}-auto-batch-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the automatic batch software plugin for OpenVINO.
#
%package -n %{name}-hetero-plugin
Summary: Hetero frontend for Intel OpenVINO toolkit
%description -n %{name}-hetero-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the hetero frontend for OpenVINO.
#
%package -n %{name}-intel-cpu-plugin
Summary: Intel CPU plugin for OpenVINO toolkit
%description -n %{name}-intel-cpu-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the intel CPU plugin for OpenVINO for %{x86_64} archs.
#
%package -n %{name}-intel-npu-plugin
Summary: Intel NPU plugin for OpenVINO toolkit
%description -n %{name}-intel-npu-plugin
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the intel NPU plugin for OpenVINO for %{x86_64} archs.
## Frontend shared libs ##
#
%package -n lib%{name}_ir_frontend%{so_ver}
Summary: Paddle frontend for Intel OpenVINO toolkit
%description -n lib%{name}_ir_frontend%{so_ver}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the ir frontend for OpenVINO.
#
%package -n lib%{name}_onnx_frontend%{so_ver}
Summary: Onnx frontend for OpenVINO toolkit
%description -n lib%{name}_onnx_frontend%{so_ver}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the onnx frontend for OpenVINO.
#
%package -n lib%{name}_paddle_frontend%{so_ver}
Summary: Paddle frontend for Intel OpenVINO toolkit
%description -n lib%{name}_paddle_frontend%{so_ver}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the paddle frontend for OpenVINO.
#
%package -n lib%{name}_pytorch_frontend%{so_ver}
Summary: PyTorch frontend for OpenVINO toolkit
%description -n lib%{name}_pytorch_frontend%{so_ver}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the pytorch frontend for OpenVINO.
#
%package -n lib%{name}_tensorflow_frontend%{so_ver}
Summary: TensorFlow frontend for OpenVINO toolkit
%description -n lib%{name}_tensorflow_frontend%{so_ver}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the tensorflow frontend for OpenVINO.
#
%package -n lib%{name}_tensorflow_lite_frontend%{so_ver}
Summary: TensorFlow Lite frontend for OpenVINO toolkit
%description -n lib%{name}_tensorflow_lite_frontend%{so_ver}
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides the tensorflow-lite frontend for OpenVINO.
## Python module ##
#
%package -n python-openvino
Summary: Python module for openVINO toolkit
Requires: python-numpy < 2
Requires: python-openvino-telemetry
%description -n python-openvino
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides a Python module for interfacing with openVINO toolkit.
## Samples/examples ##
#
%package -n %{name}-sample
Summary: Samples for use with OpenVINO toolkit
BuildArch: noarch
%description -n %{name}-sample
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
This package provides some samples for use with openVINO.
#
%prep
%autosetup -p1
%build
# Otherwise intel_cpu plugin declares an executable stack
%ifarch %{x86_64}
%define build_ldflags -Wl,-z,noexecstack
%endif
%cmake \
-DCMAKE_CXX_STANDARD=17 \
-DBUILD_SHARED_LIBS=ON \
-DENABLE_OV_ONNX_FRONTEND=ON \
-DENABLE_OV_PADDLE_FRONTEND=ON \
-DENABLE_OV_PYTORCH_FRONTEND=ON \
-DENABLE_OV_IR_FRONTEND=ON \
-DENABLE_OV_TF_FRONTEND=ON \
-DENABLE_OV_TF_LITE_FRONTEND=ON \
-DENABLE_INTEL_GPU=OFF \
-DENABLE_JS=OFF \
-DENABLE_PYTHON=ON \
-DENABLE_WHEEL=OFF \
-DENABLE_SYSTEM_OPENCL=ON \
-DENABLE_SYSTEM_PROTOBUF=ON \
-DENABLE_SYSTEM_PUGIXML=ON \
-DENABLE_SYSTEM_SNAPPY=ON \
-DENABLE_SYSTEM_TBB=ON \
-DONNX_USE_PROTOBUF_SHARED_LIBS=ON \
-DProtobuf_USE_STATIC_LIBS=OFF \
%{nil}
%cmake_build
# Manually generate dist-info dir
export WHEEL_VERSION=%{version} \
BUILD_TYPE=RelWithDebInfo
%ifarch %{power64}
# RelWithDebInfo
# Manual hackery for power64 because it not "officially" supported
sed -i "s/{ARCH}/%{_arch}/" ../src/bindings/python/wheel/setup.py
%endif
%python_exec ../src/bindings/python/wheel/setup.py dist_info -o ../
%install
%cmake_install
rm %{buildroot}%{_datadir}/%{prj_name}/samples/cpp/thirdparty/nlohmann_json/.cirrus.yml
# Hash-bangs in non-exec python sample scripts
sed -Ei "1{\@/usr/bin/env@d}" \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/benchmark/bert_benchmark/bert_benchmark.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/benchmark/sync_benchmark/sync_benchmark.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/benchmark/throughput_benchmark/throughput_benchmark.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/classification_sample_async/classification_sample_async.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/hello_classification/hello_classification.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/hello_query_device/hello_query_device.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/hello_reshape_ssd/hello_reshape_ssd.py \
%{buildroot}%{_datadir}/%{prj_name}/samples/python/model_creation_sample/model_creation_sample.py
# Unnecessary if we get our package dependencies and lib paths right!
rm -fr %{buildroot}%{_prefix}/install_dependencies \
%{buildroot}%{_prefix}/setupvars.sh
%{python_expand rm %{buildroot}%{$python_sitearch}/requirements.txt
chmod -x %{buildroot}%{$python_sitearch}/%{name}/tools/ovc/ovc.py
cp -r %{name}-%{version}.dist-info %{buildroot}%{$python_sitearch}/
%fdupes %{buildroot}%{$python_sitearch}/%{name}/
}
%fdupes %{buildroot}%{_datadir}/
# We do not use bundled thirdparty libs
rm -fr %{buildroot}%{_datadir}/licenses/*
%ldconfig_scriptlets -n %{shlib}
%ldconfig_scriptlets -n %{shlib_c}
%ldconfig_scriptlets -n lib%{name}_ir_frontend%{so_ver}
%ldconfig_scriptlets -n lib%{name}_onnx_frontend%{so_ver}
%ldconfig_scriptlets -n lib%{name}_paddle_frontend%{so_ver}
%ldconfig_scriptlets -n lib%{name}_pytorch_frontend%{so_ver}
%ldconfig_scriptlets -n lib%{name}_tensorflow_lite_frontend%{so_ver}
%ldconfig_scriptlets -n lib%{name}_tensorflow_frontend%{so_ver}
%files -n %{shlib}
%license LICENSE
%{_libdir}/libopenvino.so.*
%files -n %{shlib_c}
%license LICENSE
%{_libdir}/libopenvino_c.so.*
%files -n %{name}-auto-batch-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_auto_batch_plugin.so
%files -n %{name}-auto-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_auto_plugin.so
%ifarch %{x86_64}
%files -n %{name}-intel-cpu-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_intel_cpu_plugin.so
%files -n %{name}-intel-npu-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_intel_npu_plugin.so
%endif
%ifarch %{arm64}
%files -n %{name}-arm-cpu-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_arm_cpu_plugin.so
%endif
%ifarch riscv64
%files -n %{name}-riscv-cpu-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_riscv_cpu_plugin.so
%endif
%files -n %{name}-hetero-plugin
%dir %{_libdir}/%{prj_name}
%{_libdir}/%{prj_name}/libopenvino_hetero_plugin.so
%files -n lib%{name}_onnx_frontend%{so_ver}
%{_libdir}/libopenvino_onnx_frontend.so.*
%files -n lib%{name}_ir_frontend%{so_ver}
%{_libdir}/libopenvino_ir_frontend.so.*
%files -n lib%{name}_paddle_frontend%{so_ver}
%{_libdir}/libopenvino_paddle_frontend.so.*
%files -n lib%{name}_pytorch_frontend%{so_ver}
%{_libdir}/libopenvino_pytorch_frontend.so.*
%files -n lib%{name}_tensorflow_frontend%{so_ver}
%{_libdir}/libopenvino_tensorflow_frontend.so.*
%files -n lib%{name}_tensorflow_lite_frontend%{so_ver}
%{_libdir}/libopenvino_tensorflow_lite_frontend.so.*
%files -n %{name}-sample
%license LICENSE
%{_datadir}/%{prj_name}/
%files -n %{name}-devel
%license LICENSE
%{_includedir}/%{name}/
%{_libdir}/cmake/%{prj_name}/
%{_libdir}/*.so
%{_libdir}/pkgconfig/openvino.pc
%files %{python_files openvino}
%license LICENSE
%{python_sitearch}/openvino/
%{python_sitearch}/openvino*-info/
%changelog
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