Guillaume GARDET
134d683b86
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=11
260 lines
12 KiB
Plaintext
260 lines
12 KiB
Plaintext
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Sat Jun 22 12:01:23 UTC 2024 - Andreas Schwab <schwab@suse.de>
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- Add riscv-cpu-plugin subpackage
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Wed Jun 19 21:36:01 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
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- Update to 2024.2.0
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- More Gen AI coverage and framework integrations to minimize code
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changes
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* Llama 3 optimizations for CPUs, built-in GPUs, and discrete
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GPUs for improved performance and efficient memory usage.
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* Support for Phi-3-mini, a family of AI models that leverages
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the power of small language models for faster, more accurate
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and cost-effective text processing.
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* Python Custom Operation is now enabled in OpenVINO making it
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easier for Python developers to code their custom operations
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instead of using C++ custom operations (also supported).
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Python Custom Operation empowers users to implement their own
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specialized operations into any model.
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* Notebooks expansion to ensure better coverage for new models.
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Noteworthy notebooks added: DynamiCrafter, YOLOv10, Chatbot
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notebook with Phi-3, and QWEN2.
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- Broader Large Language Model (LLM) support and more model
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compression techniques.
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* GPTQ method for 4-bit weight compression added to NNCF for
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more efficient inference and improved performance of
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compressed LLMs.
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* Significant LLM performance improvements and reduced latency
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for both built-in GPUs and discrete GPUs.
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* Significant improvement in 2nd token latency and memory
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footprint of FP16 weight LLMs on AVX2 (13th Gen Intel® Core™
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processors) and AVX512 (3rd Gen Intel® Xeon® Scalable
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Processors) based CPU platforms, particularly for small
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batch sizes.
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- More portability and performance to run AI at the edge, in the
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cloud, or locally.
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* Model Serving Enhancements:
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* Preview: OpenVINO Model Server (OVMS) now supports
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OpenAI-compatible API along with Continuous Batching and
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PagedAttention, enabling significantly higher throughput
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for parallel inferencing, especially on Intel® Xeon®
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processors, when serving LLMs to many concurrent users.
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* OpenVINO backend for Triton Server now supports built-in
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GPUs and discrete GPUs, in addition to dynamic
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shapes support.
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* Integration of TorchServe through torch.compile OpenVINO
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backend for easy model deployment, provisioning to
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multiple instances, model versioning, and maintenance.
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* Preview: addition of the Generate API, a simplified API
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for text generation using large language models with only
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a few lines of code. The API is available through the newly
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launched OpenVINO GenAI package.
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* Support for Intel Atom® Processor X Series. For more details,
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see System Requirements.
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* Preview: Support for Intel® Xeon® 6 processor.
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- Support Change and Deprecation Notices
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* Using deprecated features and components is not advised.
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They are available to enable a smooth transition to new
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solutions and will be discontinued in the future.
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To keep using discontinued features, you will have to revert
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to the last LTS OpenVINO version supporting them. For more
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details, refer to the OpenVINO Legacy Features and
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Components page.
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* Discontinued in 2024.0:
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+ Runtime components:
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- Intel® Gaussian & Neural Accelerator (Intel® GNA).
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Consider using the Neural Processing Unit (NPU) for
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low-powered systems like Intel® Core™ Ultra or 14th
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generation and beyond.
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- OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API
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transition guide for reference).
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- All ONNX Frontend legacy API (known as ONNX_IMPORTER_API)
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- 'PerfomanceMode.UNDEFINED' property as part of the
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OpenVINO Python API
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+ Tools:
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- Deployment Manager. See installation and deployment
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guides for current distribution options.
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- Accuracy Checker.
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- Post-Training Optimization Tool (POT). Neural Network
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Compression Framework (NNCF) should be used instead.
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- A Git patch for NNCF integration with
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huggingface/transformers. The recommended approach
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is to use huggingface/optimum-intel for applying NNCF
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optimization on top of models from Hugging Face.
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- Support for Apache MXNet, Caffe, and Kaldi model formats.
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Conversion to ONNX may be used as a solution.
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* Deprecated and to be removed in the future:
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+ The OpenVINO™ Development Tools package (pip install
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openvino-dev) will be removed from installation options
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and distribution channels beginning with OpenVINO 2025.0.
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+ Model Optimizer will be discontinued with OpenVINO 2025.0.
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Consider using the new conversion methods instead. For
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more details, see the model conversion transition guide.
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+ OpenVINO property Affinity API will be discontinued with
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OpenVINO 2025.0. It will be replaced with CPU binding
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configurations (ov::hint::enable_cpu_pinning).
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+ OpenVINO Model Server components:
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+ “auto shape” and “auto batch size” (reshaping a model in
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runtime) will be removed in the future. OpenVINO’s dynamic
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shape models are recommended instead.
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+ A number of notebooks have been deprecated. For an
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up-to-date listing of available notebooks, refer to the
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OpenVINO™ Notebook index (openvinotoolkit.github.io).
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-------------------------------------------------------------------
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Thu May 9 22:56:53 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
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- Fix sample source path in build script:
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* openvino-fix-build-sample-path.patch
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- Update to 2024.1.0
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- More Generative AI coverage and framework integrations to
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minimize code changes.
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* Mixtral and URLNet models optimized for performance
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improvements on Intel® Xeon® processors.
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* Stable Diffusion 1.5, ChatGLM3-6B, and Qwen-7B models
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optimized for improved inference speed on Intel® Core™
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Ultra processors with integrated GPU.
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* Support for Falcon-7B-Instruct, a GenAI Large Language Model
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(LLM) ready-to-use chat/instruct model with superior
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performance metrics.
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* New Jupyter Notebooks added: YOLO V9, YOLO V8
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Oriented Bounding Boxes Detection (OOB), Stable Diffusion
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in Keras, MobileCLIP, RMBG-v1.4 Background Removal, Magika,
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TripoSR, AnimateAnyone, LLaVA-Next, and RAG system with
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OpenVINO and LangChain.
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- Broader Large Language Model (LLM) support and more model
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compression techniques.
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* LLM compilation time reduced through additional optimizations
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with compressed embedding. Improved 1st token performance of
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LLMs on 4th and 5th generations of Intel® Xeon® processors
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with Intel® Advanced Matrix Extensions (Intel® AMX).
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* Better LLM compression and improved performance with oneDNN,
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INT4, and INT8 support for Intel® Arc™ GPUs.
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* Significant memory reduction for select smaller GenAI
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models on Intel® Core™ Ultra processors with integrated GPU.
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- More portability and performance to run AI at the edge,
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in the cloud, or locally.
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* The preview NPU plugin for Intel® Core™ Ultra processors
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is now available in the OpenVINO open-source GitHub
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repository, in addition to the main OpenVINO package on PyPI.
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* The JavaScript API is now more easily accessible through
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the npm repository, enabling JavaScript developers’ seamless
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access to the OpenVINO API.
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* FP16 inference on ARM processors now enabled for the
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Convolutional Neural Network (CNN) by default.
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- Support Change and Deprecation Notices
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* Using deprecated features and components is not advised. They
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are available to enable a smooth transition to new solutions
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and will be discontinued in the future. To keep using
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Discontinued features, you will have to revert to the last
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LTS OpenVINO version supporting them.
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* For more details, refer to the OpenVINO Legacy Features
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and Components page.
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* Discontinued in 2024.0:
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+ Runtime components:
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- Intel® Gaussian & Neural Accelerator (Intel® GNA).
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Consider using the Neural Processing Unit (NPU)
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for low-powered systems like Intel® Core™ Ultra or
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14th generation and beyond.
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- OpenVINO C++/C/Python 1.0 APIs (see 2023.3 API
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transition guide for reference).
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- All ONNX Frontend legacy API (known as
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ONNX_IMPORTER_API)
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- 'PerfomanceMode.UNDEFINED' property as part of
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the OpenVINO Python API
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+ Tools:
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- Deployment Manager. See installation and deployment
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guides for current distribution options.
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- Accuracy Checker.
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- Post-Training Optimization Tool (POT). Neural Network
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Compression Framework (NNCF) should be used instead.
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- A Git patch for NNCF integration with
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huggingface/transformers. The recommended approach
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is to use huggingface/optimum-intel for applying
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NNCF optimization on top of models from Hugging
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Face.
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- Support for Apache MXNet, Caffe, and Kaldi model
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formats. Conversion to ONNX may be used as
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a solution.
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* Deprecated and to be removed in the future:
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+ The OpenVINO™ Development Tools package (pip install
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openvino-dev) will be removed from installation options
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and distribution channels beginning with OpenVINO 2025.0.
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+ Model Optimizer will be discontinued with OpenVINO 2025.0.
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Consider using the new conversion methods instead. For
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more details, see the model conversion transition guide.
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+ OpenVINO property Affinity API will be discontinued with
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OpenVINO 2025.0. It will be replaced with CPU binding
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configurations (ov::hint::enable_cpu_pinning).
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+ OpenVINO Model Server components:
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- “auto shape” and “auto batch size” (reshaping a model
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in runtime) will be removed in the future. OpenVINO’s
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dynamic shape models are recommended instead.
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-------------------------------------------------------------------
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Tue Apr 23 18:57:17 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>
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- License update: play safe and list all third party licenses as
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part of the License tag.
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-------------------------------------------------------------------
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Tue Apr 23 12:42:32 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>
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- Switch to _service file as tagged Source tarball does not
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include `./thirdparty` submodules.
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- Update openvino-fix-install-paths.patch to fix python module
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install path.
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- Enable python module and split it out into a python subpackage
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(for now default python3 only).
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- Explicitly build python metadata (dist-info) and install it
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(needs simple sed hackery to support "officially" unsupported
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platform ppc64le).
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- Specify ENABLE_JS=OFF to turn off javascript bindings as
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building these requires downloading npm stuff from the network.
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- Build with system pybind11.
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- Bump _constraints for updated disk space requirements.
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- Drop empty %check section, rpmlint was misleading when it
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recommended adding this.
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-------------------------------------------------------------------
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Fri Apr 19 08:08:02 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>
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- Numerous specfile cleanups:
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* Drop redundant `mv` commands and use `install` where
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appropriate.
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* Build with system protobuf.
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* Fix Summary tags.
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* Trim package descriptions.
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* Drop forcing CMAKE_BUILD_TYPE=Release, let macro default
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RelWithDebInfo be used instead.
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* Correct naming of shared library packages.
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* Separate out libopenvino_c.so.* into own shared lib package.
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* Drop rpmlintrc rule used to hide shlib naming mistakes.
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* Rename Source tarball to %{name}-%{version}.EXT pattern.
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* Use ldconfig_scriptlet macro for post(un).
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- Add openvino-onnx-ml-defines.patch -- Define ONNX_ML at compile
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time when using system onnx to allow using 'onnx-ml.pb.h'
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instead of 'onnx.pb.h', the latter not being shipped with
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openSUSE's onnx-devel package (gh#onnx/onnx#3074).
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- Add openvino-fix-install-paths.patch: Change hard-coded install
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paths in upstream cmake macro to standard Linux dirs.
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- Add openvino-ComputeLibrary-include-string.patch: Include header
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for std::string.
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- Add external devel packages as Requires for openvino-devel.
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- Pass -Wl,-z,noexecstack to %build_ldflags to avoid an exec stack
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issue with intel CPU plugin.
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- Use ninja for build.
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- Adapt _constraits file for correct disk space and memory
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requirements.
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- Add empty %check section.
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-------------------------------------------------------------------
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Mon Apr 15 03:18:33 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
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- Initial package
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- Version 2024.0.0
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- Add openvino-rpmlintrc.
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