2024-05-06 10:14:29 +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/
#
2024-05-07 14:47:00 +00:00
# Note: Will not build on Leap:15.X on account of too old TBB
2024-05-06 10:14:29 +00:00
# Compilation takes ~1 hr on OBS for a single python, don't try all supported flavours
%define pythons python3
%define __builder ninja
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
%define so_ver 2420
2024-05-06 10:14:29 +00:00
%define shlib lib%{name}%{so_ver}
%define shlib_c lib%{name}_c%{so_ver}
%define prj_name OpenVINO
Name : openvino
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
Version : 2024.2.0
2024-05-06 10:14:29 +00:00
Release : 0
Summary : A toolkit for optimizing and deploying AI inference
2024-05-07 14:47:00 +00:00
# Let's be safe and put all third party licenses here, no matter that we use specific thirdparty libs or not
2024-05-07 21:46:43 +00:00
License : Apache-2.0 AND BSD-2-Clause AND BSD-3-Clause AND HPND AND JSON AND MIT AND OFL-1.1 AND Zlib
2024-05-06 10:14:29 +00:00
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
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
# 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
2024-05-06 10:14:29 +00:00
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
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
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
2024-05-06 10:14:29 +00:00
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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-05-06 10:14:29 +00:00
## Main shared libs and devel pkg ##
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%package -n %{shlib_c}
Summary : Shared C library for OpenVINO toolkit
%description -n %{shlib_c}
This package provides the C library for OpenVINO.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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}
2024-06-25 07:09:12 +00:00
%ifarch riscv64
Recommends: %{name} -riscv-cpu-plugin = %{version}
%endif
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-05-06 10:14:29 +00:00
## Plugins ##
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-06-25 07:09:12 +00:00
#
%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.
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
%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.
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-05-06 10:14:29 +00:00
## Frontend shared libs ##
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-05-06 10:14:29 +00:00
## Python module ##
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-05-06 10:14:29 +00:00
## Samples/examples ##
#
2024-06-25 07:09:12 +00:00
2024-05-06 10:14:29 +00:00
%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.
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
2024-06-20 13:47:15 +00:00
2024-06-25 07:09:12 +00:00
#
2024-05-06 10:14:29 +00:00
%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}
# 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
rm %{buildroot} %{_datadir} /%{prj_name} /samples/cpp/thirdparty/nlohmann_json/.cirrus.yml
# 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
Accepting request 1173003 from home:cabelo:branches:science:machinelearning
- Fix sample source path in build script.
- 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.
OBS-URL: https://build.opensuse.org/request/show/1173003
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=5
2024-05-13 17:52:35 +00:00
%files -n %{name}-intel-npu-plugin
%dir %{_libdir} /%{prj_name}
%{_libdir} /%{prj_name} /libopenvino_intel_npu_plugin.so
2024-05-06 10:14:29 +00:00
%endif
%ifarch %{arm64}
%files -n %{name}-arm-cpu-plugin
%dir %{_libdir} /%{prj_name}
%{_libdir} /%{prj_name} /libopenvino_arm_cpu_plugin.so
2024-06-25 07:09:12 +00:00
%endif
%ifarch riscv64
%files -n %{name}-riscv-cpu-plugin
%dir %{_libdir} /%{prj_name}
%{_libdir} /%{prj_name} /libopenvino_riscv_cpu_plugin.so
2024-05-06 10:14:29 +00:00
%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
