2024-10-03 06:36:01 +00:00
#
# 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/
#
%if 0%{?suse_version} < 1600
%define isLeap15 %nil
%else
%undefine isLeap15
%endif
# Compilation takes ~1 hr on OBS for a single python, don't try all supported flavours
%if %{defined isLeap15}
2024-10-03 06:50:49 +00:00
%define x86_64 x86_64
2024-10-03 06:36:01 +00:00
%define pythons python311
%else
%define pythons python3
%endif
%define __builder ninja
- Temporarily inserted gcc-13 in Tumbleweed/Factory/Slowroll:
Because there is an incompatibility of the source code of the
level-zero library and npu module with gcc-14. I am working
with Intel on tests to return to native gcc.
- Update to 2024.4.0
- Summary of major features and improvements
* More Gen AI coverage and framework integrations to minimize
code changes
+ Support for GLM-4-9B Chat, MiniCPM-1B, Llama 3 and 3.1,
Phi-3-Mini, Phi-3-Medium and YOLOX-s models.
+ Noteworthy notebooks added: Florence-2, NuExtract-tiny
Structure Extraction, Flux.1 Image Generation, PixArt-α:
Photorealistic Text-to-Image Synthesis, and Phi-3-Vision
Visual Language Assistant.
* Broader Large Language Model (LLM) support and more model
compression techniques.
+ OpenVINO™ runtime optimized for Intel® Xe Matrix Extensions
(Intel® XMX) systolic arrays on built-in GPUs for efficient
matrix multiplication resulting in significant LLM
performance boost with improved 1st and 2nd token
latency, as well as a smaller memory footprint on
Intel® Core™ Ultra Processors (Series 2).
+ Memory sharing enabled for NPUs on Intel® Core™ Ultra
Processors (Series 2) for efficient pipeline integration
without memory copy overhead.
+ Addition of the PagedAttention feature for discrete GPUs*
enables a significant boost in throughput for parallel
inferencing when serving LLMs on Intel® Arc™ Graphics
or Intel® Data Center GPU Flex Series.
* More portability and performance to run AI at the edge,
in the cloud, or locally.
+ OpenVINO™ Model Server now comes with production-quality
support for OpenAI-compatible API which enables i
significantly higher throughput for parallel inferencing
on Intel® Xeon® processors when serving LLMs to many
concurrent users.
+ Improved performance and memory consumption with prefix
caching, KV cache compression, and other optimizations
for serving LLMs using OpenVINO™ Model Server.
+ Support for Python 3.12.
- 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 macOS x86_64 debug bins will no longer be
provided with the OpenVINO toolkit, starting with
OpenVINO 2024.5.
+ Python 3.8 is now considered deprecated, and it will not
be available beyond the 2024.4 OpenVINO version.
+ dKMB support is now considered deprecated and will be
fully removed with OpenVINO 2024.5
+ Intel® Streaming SIMD Extensions (Intel® SSE) will be
supported in source code form, but not enabled in the
binary package by default, starting with OpenVINO 2025.0
+ The openvino-nightly PyPI module will soon be discontinued.
End-users should proceed with the Simple PyPI nightly repo
instead. More information in Release Policy.
+ 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=19
2024-10-17 06:21:51 +00:00
%define so_ver 2440
2024-10-03 06:36:01 +00:00
%define shlib lib%{name}%{so_ver}
%define shlib_c lib%{name}_c%{so_ver}
%define prj_name OpenVINO
Name : openvino
- Temporarily inserted gcc-13 in Tumbleweed/Factory/Slowroll:
Because there is an incompatibility of the source code of the
level-zero library and npu module with gcc-14. I am working
with Intel on tests to return to native gcc.
- Update to 2024.4.0
- Summary of major features and improvements
* More Gen AI coverage and framework integrations to minimize
code changes
+ Support for GLM-4-9B Chat, MiniCPM-1B, Llama 3 and 3.1,
Phi-3-Mini, Phi-3-Medium and YOLOX-s models.
+ Noteworthy notebooks added: Florence-2, NuExtract-tiny
Structure Extraction, Flux.1 Image Generation, PixArt-α:
Photorealistic Text-to-Image Synthesis, and Phi-3-Vision
Visual Language Assistant.
* Broader Large Language Model (LLM) support and more model
compression techniques.
+ OpenVINO™ runtime optimized for Intel® Xe Matrix Extensions
(Intel® XMX) systolic arrays on built-in GPUs for efficient
matrix multiplication resulting in significant LLM
performance boost with improved 1st and 2nd token
latency, as well as a smaller memory footprint on
Intel® Core™ Ultra Processors (Series 2).
+ Memory sharing enabled for NPUs on Intel® Core™ Ultra
Processors (Series 2) for efficient pipeline integration
without memory copy overhead.
+ Addition of the PagedAttention feature for discrete GPUs*
enables a significant boost in throughput for parallel
inferencing when serving LLMs on Intel® Arc™ Graphics
or Intel® Data Center GPU Flex Series.
* More portability and performance to run AI at the edge,
in the cloud, or locally.
+ OpenVINO™ Model Server now comes with production-quality
support for OpenAI-compatible API which enables i
significantly higher throughput for parallel inferencing
on Intel® Xeon® processors when serving LLMs to many
concurrent users.
+ Improved performance and memory consumption with prefix
caching, KV cache compression, and other optimizations
for serving LLMs using OpenVINO™ Model Server.
+ Support for Python 3.12.
- 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 macOS x86_64 debug bins will no longer be
provided with the OpenVINO toolkit, starting with
OpenVINO 2024.5.
+ Python 3.8 is now considered deprecated, and it will not
be available beyond the 2024.4 OpenVINO version.
+ dKMB support is now considered deprecated and will be
fully removed with OpenVINO 2024.5
+ Intel® Streaming SIMD Extensions (Intel® SSE) will be
supported in source code form, but not enabled in the
binary package by default, starting with OpenVINO 2025.0
+ The openvino-nightly PyPI module will soon be discontinued.
End-users should proceed with the Simple PyPI nightly repo
instead. More information in Release Policy.
+ 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=19
2024-10-17 06:21:51 +00:00
Version : 2024.4.0
2024-10-03 06:36:01 +00:00
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 : gcc13-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(flatbuffers)
BuildRequires : pkgconfig(libva)
BuildRequires : pkgconfig(nlohmann_json)
BuildRequires : pkgconfig(ocl-icd)
BuildRequires : pkgconfig(protobuf)
BuildRequires : pkgconfig(pugixml)
%if %{defined isLeap15}
BuildRequires : opencl-headers
BuildRequires : snappy-devel
BuildRequires : tbb-devel
%else
BuildRequires : pkgconfig(OpenCL-Headers)
BuildRequires : pkgconfig(snappy)
BuildRequires : pkgconfig(tbb)
%endif
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.
%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(flatbuffers)
Requires : pkgconfig(libva)
Requires : pkgconfig(nlohmann_json)
Requires : pkgconfig(ocl-icd)
Requires : pkgconfig(protobuf)
Requires : pkgconfig(pugixml)
%if %{defined isLeap15}
Requires : opencl-headers
Requires : snappy-devel
Requires : tbb-devel
%else
Requires : pkgconfig(OpenCL-Headers)
Requires : pkgconfig(snappy)
Requires : pkgconfig(tbb)
%endif
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.
%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.
%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.
%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.
%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
export CC=gcc-13 CXX=g++-13
# 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 \
%if %{defined isLeap15}
-DENABLE_TBBBIND_2_5=OFF \
%endif
-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 / { A R C H } / % { _ a r c h } / " ../src/bindings/python/wheel/setup.py
%endif
%python_exec ../src/bindings/python/wheel/setup.py dist_info -o ../
%install
%cmake_install
# Hash-bangs in non-exec python sample scripts
sed -Ei " 1 { \ @ / u s r / b i n / e n v @ 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
