openvino/openvino.changes

-------------------------------------------------------------------
Sun Dec 29 03:41:47 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>

- openvino-onnx-ml-defines.patch and 
  openvino-remove-npu-compile-tool.patchhas been removed 
  as it is no longer needed in this version. 
- Update to 2024.4.0
- Summary of major features and improvements  
  * OpenVINO 2024.6 release includes updates for enhanced 
    stability and improved LLM performance.
  * Introduced support for Intel® Arc™ B-Series Graphics 
    (formerly known as Battlemage).
  * Implemented optimizations to improve the inference time and 
    LLM performance on NPUs.
  * Improved LLM performance with GenAI API optimizations and 
    bug fixes.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised. They
    are available to enable a smooth transition to new solutions 
    and will be discontinued in the future. To keep using 
    discontinued features, you will have to revert to the last 
    LTS OpenVINO version supporting them. For more details, refer
    to the OpenVINO Legacy Features and Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA)..
        Consider using the Neural Processing Unit (NPU) for 
        low-powered systems like Intel® Core™ Ultra or 14th 
        generation and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API transition
        guide for reference).
      - All ONNX Frontend legacy API (known as ONNX_IMPORTER_API)
      - 'PerfomanceMode.UNDEFINED' property as part of the 
        OpenVINO Python API
    + Tools:
      - Deployment Manager. See installation and deployment 
        guides for current distribution options.
      - Accuracy Checker.
      - Post-Training Optimization Tool (POT). Neural Network
        Compression Framework (NNCF) should be used instead.
      - A Git patch for NNCF integration with 
        huggingface/transformers. The recommended approach is
        to use huggingface/optimum-intel for applying NNCF
        optimization on top of models from Hugging Face.
      - Support for Apache MXNet, Caffe, and Kaldi model formats.
        Conversion to ONNX may be used as a solution.
  * Deprecated and to be removed in the future:
    + The macOS x86_64 debug bins will no longer be provided 
      with the OpenVINO toolkit, starting with OpenVINO 2024.5.
    + Python 3.8 is no longer supported, starting with 
      OpenVINO 2024.5.
    + As MxNet doesn’t support Python version higher than 3.8,
      according to the MxNet PyPI project, it is no longer 
      supported by OpenVINO, either.
    + Discrete Keem Bay support is no longer supported, starting
      with OpenVINO 2024.5.
    + Support for discrete devices (formerly codenamed Raptor 
      Lake) is no longer available for NPU.

-------------------------------------------------------------------
Tue Dec 10 15:50:41 UTC 2024 - Giacomo Comes <gcomes.obs@gmail.com>

- fix build on tumbleweed
  * currently openvino does not support protobuf v22 or newer

-------------------------------------------------------------------
Tue Oct 15 00:56:54 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>

- Temporarily inserted gcc-13 in Tumbleweed/Factory/Slowroll: 
  Because there is an incompatibility of the source code of the 
  level-zero library and npu module with gcc-14. I am working 
  with Intel on tests to return to native gcc.
- Update to 2024.4.0
- Summary of major features and improvements  
  * More Gen AI coverage and framework integrations to minimize
    code changes
    + Support for GLM-4-9B Chat, MiniCPM-1B, Llama 3 and 3.1,
      Phi-3-Mini, Phi-3-Medium and YOLOX-s models.
    + Noteworthy notebooks added: Florence-2, NuExtract-tiny
      Structure Extraction, Flux.1 Image Generation, PixArt-α:
      Photorealistic Text-to-Image Synthesis, and Phi-3-Vision
      Visual Language Assistant.
  * Broader Large Language Model (LLM) support and more model
    compression techniques.
    + OpenVINO™ runtime optimized for Intel® Xe Matrix Extensions
      (Intel® XMX) systolic arrays on built-in GPUs for efficient
      matrix multiplication resulting in significant LLM
      performance boost with improved 1st and 2nd token
      latency, as well as a smaller memory footprint on
      Intel® Core™ Ultra Processors (Series 2).
    + Memory sharing enabled for NPUs on Intel® Core™ Ultra
      Processors (Series 2) for efficient pipeline integration
      without memory copy overhead.
    + Addition of the PagedAttention feature for discrete GPUs*
      enables a significant boost in throughput for parallel
      inferencing when serving LLMs on Intel® Arc™ Graphics
      or Intel® Data Center GPU Flex Series.
  * More portability and performance to run AI at the edge,
    in the cloud, or locally.
    + OpenVINO™ Model Server now comes with production-quality
      support for OpenAI-compatible API which enables i
      significantly higher throughput for parallel inferencing
      on Intel® Xeon® processors when serving LLMs to many
      concurrent users.
    + Improved performance and memory consumption with prefix
      caching, KV cache compression, and other optimizations
      for serving LLMs using OpenVINO™ Model Server.
    + Support for Python 3.12.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised.
    They are available to enable a smooth transition to new
    solutions and will be discontinued in the future.
    To keep using discontinued features, you will have to
    revert to the last LTS OpenVINO version supporting them.
    For more details, refer to the OpenVINO Legacy Features
    and Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA).
        Consider using the Neural Processing Unit (NPU) for
        low-powered systems like Intel® Core™ Ultra or
        14th generation and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API
        transition guide for reference).
      - All ONNX Frontend legacy API (known as
        ONNX_IMPORTER_API)
      -'PerfomanceMode.UNDEFINED' property as part of the
        OpenVINO Python API
    + Tools:
       - Deployment Manager. See installation and deployment
         guides for current distribution options.
       - Accuracy Checker.
       - Post-Training Optimization Tool (POT). Neural Network
         Compression Framework (NNCF) should be used instead.
       - A Git patch for NNCF integration with huggingface/
         transformers. The recommended approach is to use
         huggingface/optimum-intel for applying NNCF
         optimization on top of models from Hugging Face.
       - Support for Apache MXNet, Caffe, and Kaldi model
         formats. Conversion to ONNX may be used as a
         solution.
  * Deprecated and to be removed in the future:
    + The macOS x86_64 debug bins will no longer be
      provided with the OpenVINO toolkit, starting with
      OpenVINO 2024.5.
    + Python 3.8 is now considered deprecated, and it will not
      be available beyond the 2024.4 OpenVINO version.
    + dKMB support is now considered deprecated and will be
      fully removed with OpenVINO 2024.5
    + Intel® Streaming SIMD Extensions (Intel® SSE) will be
      supported in source code form, but not enabled in the
      binary package by default, starting with OpenVINO 2025.0
    + The openvino-nightly PyPI module will soon be discontinued.
      End-users should proceed with the Simple PyPI nightly repo
      instead. More information in Release Policy.
    + The OpenVINO™ Development Tools package (pip install
      openvino-dev) will be removed from installation options and
      distribution channels beginning with OpenVINO 2025.0.
    + Model Optimizer will be discontinued with OpenVINO 2025.0.
      Consider using the new conversion methods instead. For more
      details, see the model conversion transition guide.
    + OpenVINO property Affinity API will be discontinued with
      OpenVINO 2025.0. It will be replaced with CPU binding
      configurations (ov::hint::enable_cpu_pinning).
    + OpenVINO Model Server components:
      - “auto shape” and “auto batch size” (reshaping a model in
      runtime) will be removed in the future. OpenVINO’s dynamic
      shape models are recommended instead.
    + A number of notebooks have been deprecated. For an
      up-to-date listing of available notebooks, refer to the
      OpenVINO™ Notebook index (openvinotoolkit.github.io).

-------------------------------------------------------------------
Wed Oct  2 20:56:59 UTC 2024 - Giacomo Comes <gcomes.obs@gmail.com>

- Add Leap15 build
- Remove comment lines in the spec file that cause the insertion
  of extra lines during a commit

-------------------------------------------------------------------
Sat Aug 10 01:41:06 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>

- Remove NPU Compile Tool
* openvino-remove-npu-compile-tool.patch 
- Update to 2024.3.0 
- Summary of major features and improvements  
  * More Gen AI coverage and framework integrations to minimize 
    code changes
    + OpenVINO pre-optimized models are now available in Hugging 
      Face making it easier for developers to get started with 
      these models.
  * Broader Large Language Model (LLM) support and more model 
    compression techniques.
    + Significant improvement in LLM performance on Intel 
      discrete GPUs with the addition of Multi-Head Attention 
      (MHA) and OneDNN enhancements.
  * More portability and performance to run AI at the edge, in the 
    cloud, or locally.
    + Improved CPU performance when serving LLMs with the 
      inclusion of vLLM and continuous batching in the OpenVINO 
      Model Server (OVMS). vLLM is an easy-to-use open-source 
      library that supports efficient LLM inferencing and model 
      serving.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised. 
    They are available to enable a smooth transition to new 
    solutions and will be discontinued in the future. To keep 
    using discontinued features, you will have to revert to the
    last LTS OpenVINO version supporting them. For more details,
    refer to the OpenVINO Legacy Features and Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA)..Consider
        using the Neural Processing Unit (NPU) for low-powered 
        systems like Intel® Core™ Ultra or 14th generation 
        and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API transition
        guide for reference).
      - All ONNX Frontend legacy API (known as ONNX_IMPORTER_API)
      - 'PerfomanceMode.UNDEFINED' property as part of the OpenVINO
         Python API
    + Tools:
      - Deployment Manager. See installation and deployment guides
        for current distribution options.
      - Accuracy Checker.
      - Post-Training Optimization Tool (POT). Neural Network 
        Compression Framework (NNCF) should be used instead.
      - A Git patch for NNCF integration with huggingface/
        transformers. The recommended approach is to use
        huggingface/optimum-intel for applying NNCF optimization 
        on top of models from Hugging Face.
      - Support for Apache MXNet, Caffe, and Kaldi model formats.
        Conversion to ONNX may be used as a solution.
  * Deprecated and to be removed in the future:
    + The OpenVINO™ Development Tools package (pip install
      openvino-dev) will be removed from installation options
      and distribution channels beginning with OpenVINO 2025.0.
    + Model Optimizer will be discontinued with OpenVINO 2025.0. 
      Consider using the new conversion methods instead. For 
      more details, see the model conversion transition guide.
    + OpenVINO property Affinity API will be discontinued with 
      OpenVINO 2025.0. It will be replaced with CPU binding 
      configurations (ov::hint::enable_cpu_pinning).
    + OpenVINO Model Server components:
      - “auto shape” and “auto batch size” (reshaping a model
        in runtime) will be removed in the future. OpenVINO’s 
        dynamic shape models are recommended instead.
    + A number of notebooks have been deprecated. For an 
      up-to-date listing of available notebooks, refer to
      the OpenVINO™ Notebook index (openvinotoolkit.github.io).

-------------------------------------------------------------------
Sat Jun 22 12:01:23 UTC 2024 - Andreas Schwab <schwab@suse.de>

- Add riscv-cpu-plugin subpackage

-------------------------------------------------------------------
Wed Jun 19 21:36:01 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>

- 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).

-------------------------------------------------------------------
Thu May  9 22:56:53 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>

- Fix sample source path in build script:
  * openvino-fix-build-sample-path.patch
- Update to 2024.1.0
- More Generative AI coverage and framework integrations to
  minimize code changes.
  * Mixtral and URLNet models optimized for performance 
    improvements on Intel® Xeon® processors.
  * Stable Diffusion 1.5, ChatGLM3-6B, and Qwen-7B models 
    optimized for improved inference speed on Intel® Core™
    Ultra processors with integrated GPU.
  * Support for Falcon-7B-Instruct, a GenAI Large Language Model
    (LLM) ready-to-use chat/instruct model with superior
    performance metrics.
  * New Jupyter Notebooks added: YOLO V9, YOLO V8
    Oriented Bounding Boxes Detection (OOB), Stable Diffusion 
    in Keras, MobileCLIP, RMBG-v1.4 Background Removal, Magika, 
    TripoSR, AnimateAnyone, LLaVA-Next, and RAG system with 
    OpenVINO and LangChain.
- Broader Large Language Model (LLM) support and more model
  compression techniques.
  * LLM compilation time reduced through additional optimizations
    with compressed embedding. Improved 1st token performance of
    LLMs on 4th and 5th generations of Intel® Xeon® processors 
    with Intel® Advanced Matrix Extensions (Intel® AMX).
  * Better LLM compression and improved performance with oneDNN,
    INT4, and INT8 support for Intel® Arc™ GPUs.
  * Significant memory reduction for select smaller GenAI
    models on Intel® Core™ Ultra processors with integrated GPU.
- More portability and performance to run AI at the edge, 
  in the cloud, or locally.
  * The preview NPU plugin for Intel® Core™ Ultra processors
    is now available in the OpenVINO open-source GitHub 
    repository, in addition to the main OpenVINO package on PyPI.
  * The JavaScript API is now more easily accessible through
    the npm repository, enabling JavaScript developers’ seamless 
    access to the OpenVINO API.
  * FP16 inference on ARM processors now enabled for the 
    Convolutional Neural Network (CNN) by default.
- Support Change and Deprecation Notices
  * Using deprecated features and components is not advised. They
    are available to enable a smooth transition to new solutions 
    and will be discontinued in the future. To keep using 
    Discontinued features, you will have to revert to the last 
    LTS OpenVINO version supporting them.
  * For more details, refer to the OpenVINO Legacy Features 
    and Components page.
  * Discontinued in 2024.0:
    + Runtime components:
      - Intel® Gaussian & Neural Accelerator (Intel® GNA).
        Consider using the Neural Processing Unit (NPU) 
        for low-powered systems like Intel® Core™ Ultra or
        14th generation and beyond.
      - OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API 
        transition guide for reference).
      - All ONNX Frontend legacy API (known as 
        ONNX_IMPORTER_API)
      - 'PerfomanceMode.UNDEFINED' property as part of
         the OpenVINO Python API
    + Tools:
      - Deployment Manager. See installation and deployment
        guides for current distribution options.
      - Accuracy Checker.
      - Post-Training Optimization Tool (POT). Neural Network
        Compression Framework (NNCF) should be used instead.
      - A Git patch for NNCF integration with 
        huggingface/transformers. The recommended approach
        is to use huggingface/optimum-intel for applying 
        NNCF optimization on top of models from Hugging 
        Face.
      - Support for Apache MXNet, Caffe, and Kaldi model 
        formats. Conversion to ONNX may be used as 
        a solution.
  * Deprecated and to be removed in the future:
    + The OpenVINO™ Development Tools package (pip install
      openvino-dev) will be removed from installation options
      and distribution channels beginning with OpenVINO 2025.0.
    + Model Optimizer will be discontinued with OpenVINO 2025.0.
      Consider using the new conversion methods instead. For 
      more details, see the model conversion transition guide.
    + OpenVINO property Affinity API will be discontinued with 
      OpenVINO 2025.0. It will be replaced with CPU binding 
      configurations (ov::hint::enable_cpu_pinning).
    + OpenVINO Model Server components:
      - “auto shape” and “auto batch size” (reshaping a model
        in runtime) will be removed in the future. OpenVINO’s
        dynamic shape models are recommended instead.

-------------------------------------------------------------------
Tue Apr 23 18:57:17 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>

- License update: play safe and list all third party licenses as
  part of the License tag.

-------------------------------------------------------------------
Tue Apr 23 12:42:32 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>

- Switch to _service file as tagged Source tarball does not
  include `./thirdparty` submodules.
- Update openvino-fix-install-paths.patch to fix python module
  install path.
- Enable python module and split it out into a python subpackage
  (for now default python3 only).
- Explicitly build python metadata (dist-info) and install it
  (needs simple sed hackery to support "officially" unsupported
  platform ppc64le).
- Specify ENABLE_JS=OFF to turn off javascript bindings as
  building these requires downloading npm stuff from the network.
- Build with system pybind11.
- Bump _constraints for updated disk space requirements.
- Drop empty %check section, rpmlint was misleading when it
  recommended adding this.

-------------------------------------------------------------------
Fri Apr 19 08:08:02 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>

- Numerous specfile cleanups:
  * Drop redundant `mv` commands and use `install` where
    appropriate.
  * Build with system protobuf.
  * Fix Summary tags.
  * Trim package descriptions.
  * Drop forcing CMAKE_BUILD_TYPE=Release, let macro default
    RelWithDebInfo be used instead.
  * Correct naming of shared library packages.
  * Separate out libopenvino_c.so.* into own shared lib package.
  * Drop rpmlintrc rule used to hide shlib naming mistakes.
  * Rename Source tarball to %{name}-%{version}.EXT pattern.
  * Use ldconfig_scriptlet macro for post(un).
- Add openvino-onnx-ml-defines.patch -- Define ONNX_ML at compile
  time when using system onnx to allow using 'onnx-ml.pb.h'
  instead of 'onnx.pb.h', the latter not being shipped with
  openSUSE's onnx-devel package (gh#onnx/onnx#3074).
- Add openvino-fix-install-paths.patch: Change hard-coded install
  paths in upstream cmake macro to standard Linux dirs.
- Add openvino-ComputeLibrary-include-string.patch: Include header
  for std::string.
- Add external devel packages as Requires for openvino-devel.
- Pass -Wl,-z,noexecstack to %build_ldflags to avoid an exec stack
  issue with intel CPU plugin.
- Use ninja for build.
- Adapt _constraits file for correct disk space and memory
  requirements.
- Add empty %check section.

-------------------------------------------------------------------
Mon Apr 15 03:18:33 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>

- Initial package
- Version 2024.0.0
- Add openvino-rpmlintrc.