[DOCS] final update of relnotes (#21108)
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Karol Blaszczak
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# OpenVINO Releease Notes {#openvino_release_notes}
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# OpenVINO Release Notes {#openvino_release_notes}
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@sphinxdirective
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The Intel® Distribution of OpenVINO™ toolkit is an open-source solution for
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optimizing and deploying AI inference in domains such as computer vision,
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automatic speech recognition, natural language processing, recommendation
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systems, and generative AI. With its plug-in architecture, OpenVINO enables
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developers to write once and deploy anywhere. We are proud to announce the
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release of OpenVINO 2023.2 introducing a range of new features, improvements,
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and deprecations aimed at enhancing the developer experience.
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The Intel® Distribution of OpenVINO™ toolkit is an open-source solution for optimizing
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and deploying AI inference in domains such as computer vision,automatic speech
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recognition, natural language processing, recommendation systems, and generative AI.
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With its plug-in architecture, OpenVINO enables developers to write once and deploy
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anywhere. We are proud to announce the release of OpenVINO 2023.2 introducing a range
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of new features, improvements, and deprecations aimed at enhancing the developer
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experience.
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2023.2
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##########
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New and changed in 2023.2
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###########################
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Summary of major features and improvements
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++++++++++++++++++++++++++++++++++++++++++++
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* More Generative AI coverage and framework integrations to minimize code changes:
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* More Generative AI coverage and framework integrations to minimize code changes.
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* **Expanded model support for direct PyTorch model conversion** - automatically convert
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additional models directly from PyTorch or execute via ``torch.compile`` with OpenVINO
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as the backend.
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* **New and noteworthy models supported** - we have enabled models used for chatbots,
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instruction following, code generation, and many more, including prominent models
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instruction following, code generation, and many more, including prominent models
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like Llava, chatGLM, Bark (text to audio) and LCM (Latent Consistency Models, an
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optimized version of Stable Diffusion).
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* **Easier optimization and conversion of Hugging Face models** - compress LLM models
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to int8 with the Hugging Face Optimum command line interface and export models
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to the OpenVINO IR format.
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* **OpenVINO is now available on Conan**, a package manager which allows more seamless
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to Int8 with the Hugging Face Optimum command line interface and export models to
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the OpenVINO IR format.
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* **OpenVINO is now available on Conan** - a package manager which allows more seamless
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package management for large scale projects for C and C++ developers.
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* Broader Large Language Model (LLM) support and more model compression techniques
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* Broader Large Language Model (LLM) support and more model compression techniques.
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* Accelerate inference for LLM models on Intel® CoreTM CPU and iGPU with
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the use of int8 model weight compression.
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* Accelerate inference for LLM models on Intel® CoreTM CPU and iGPU with the
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use of Int8 model weight compression.
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* Expanded model support for dynamic shapes for improved performance on GPU.
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* Preview support for int4 model format is now included. Int4 optimized model
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* Preview support for Int4 model format is now included. Int4 optimized model
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weights are now available to try on Intel® Core™ CPU and iGPU, to accelerate
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models like Llama 2 and chatGLM2.
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* The following int4 model compression formats are supported for inference
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* The following Int4 model compression formats are supported for inference
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in runtime:
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* Generative Pre-training Transformer Quantization (GPTQ); with GPTQ-compressed
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models, you can access them through the Hugging Face repositories.
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* Native int4 compression through Neural Network Compression Framework (NNCF).
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* Native Int4 compression through Neural Network Compression Framework (NNCF).
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* More portability and performance to run AI at the edge, in the cloud, or locally.
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@ -55,36 +55,36 @@ Summary of major features and improvements
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Support Change and Deprecation Notices
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++++++++++++++++++++++++++++++++++++++++++
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* The OpenVINO™ Development Tools package (pip install openvino-dev) is currently being
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deprecated and will be removed from installation options and distribution channels
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with 2025.0. To learn more, refer to the
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* The OpenVINO™ Development Tools package (pip install openvino-dev) is deprecated
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and will be removed from installation options and distribution channels with
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2025.0. To learn more, refer to the
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:doc:`OpenVINO Legacy Features and Components page <openvino_legacy_features>`.
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To ensure optimal performance, install the OpenVINO package (pip install openvino),
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To ensure optimal performance, install the OpenVINO package (pip install openvino),
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which includes essential components such as OpenVINO Runtime, OpenVINO Converter,
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and Benchmark Tool.
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and Benchmark Tool.
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* Tools:
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* :doc:`Deployment Manager <openvino_docs_install_guides_deployment_manager_tool>`
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is currently being deprecated and will be removed in the 2024.0 release.
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* Accuracy Checker is being deprecated and will be discontinued with 2024.0.
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* Post-Training Optimization Tool (POT) is being deprecated and will be
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is deprecated and will be removed in the 2024.0 release.
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* Accuracy Checker is deprecated and will be discontinued with 2024.0.
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* Post-Training Optimization Tool (POT) is deprecated and will be
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discontinued with 2024.0.
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* Model Optimizer is being deprecated and will be fully supported until the 2025.0
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release. Model conversion to the OpenVINO IR format should be performed through
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* Model Optimizer is deprecated and will be fully supported up until the 2025.0
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release. Model conversion to the OpenVINO format should be performed through
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OpenVINO Model Converter, which is part of the PyPI package. Follow the
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:doc:`Model Optimizer to OpenVINO Model Converter transition <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`
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guide for smoother transition. Known limitations are TensorFlow model with
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TF1 Control flow and object detection models. These limitations relate to the
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gap in TensorFlow direct conversion capabilities which will be addressed in
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upcoming releases.
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* Deprecated support for PyTorch 1.13 in Neural Network Compression Framework (NNCF).
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guide for smoother transition. Known limitations are TensorFlow model with
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TF1 Control flow and object detection models. These limitations relate to
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the gap in TensorFlow direct conversion capabilities which will be addressed
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in upcoming releases.
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* PyTorch 1.13 support is deprecated in Neural Network Compression Framework (NNCF)
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* Runtime:
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* Intel® Gaussian & Neural Accelerator (Intel® GNA) is being deprecated, the
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GNA plugin will be discontinued with 2024.0.
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* Intel® Gaussian & Neural Accelerator (Intel® GNA) will be deprecated in a future
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release. We encourage developers to use the Neural Processing Unit (NPU) for
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low powered systems like Intel® Core™ Ultra or 14th generation and beyond.
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* OpenVINO C++/C/Python 1.0 APIs will be discontinued with 2024.0.
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* Python 3.7 support has been discontinued.
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@ -98,16 +98,14 @@ List of components and their changes:
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now supports the original framework shape format.
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* `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__
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* Added data-free INT4 weight compression support for LLMs in OpenVINO IR with
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* Added data-free Int4 weight compression support for LLMs in OpenVINO IR with
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``nncf.compress_weights()``.
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* Preview feature was added to compress model weights to NF4 of LLMs in OpenVINO IR
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with ``nncf.compress_weights()``.
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* Improved quantization time of LLMs with NNCF PTQ API for ``nncf.quantize()``
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and ``nncf.quantize_with_accuracy_control()``.
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* Added support for SmoothQuant and ChannelAlighnment algorithms in NNCF HyperParameter
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Tuner for automatic optimization of their hyperparameters during quantization.
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* Added quantization support for the `IF` operation of models in OpenVINO IR
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to speed up such models.
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* Added quantization support for the ``IF`` operation of models in OpenVINO format
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to speed up such models.
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* NNCF Post-training Quantization for PyTorch backend is now supported with
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``nncf.quantize()`` and the common implementation of quantization algorithms.
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* Added support for PyTorch 2.1. PyTorch 1.13 support has been deprecated.
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@ -118,7 +116,8 @@ OpenVINO™ Runtime (previously known as Inference Engine)
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* OpenVINO Common
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* Operations for reference implementations updated from legacy API to API 2.0.
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* Symbolic transformation introduced the ability to remove Reshape operations surrounding MatMul operations.
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* Symbolic transformation introduced the ability to remove Reshape operations
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surrounding MatMul operations.
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* OpenVINO Python API
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@ -130,30 +129,31 @@ OpenVINO™ Runtime (previously known as Inference Engine)
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* AUTO device plug-in (AUTO)
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* Provided additional option to improve performance of cumulative throughput
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(or MULTI), where part of CPU resources can be reserved for GPU inference
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when GPU and CPU are both used for inference (using ov::hint::enable_cpu_pinning(true)).
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This avoids the performance issue of CPU resource contention where there is
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not enough CPU resources to schedule tasks for GPU
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* o Provided additional option to improve performance of cumulative throughput
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(or MULTI), where part of CPU resources can be reserved for GPU inference
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when GPU and CPU are both used for inference (using ``ov::hint::enable_cpu_pinning(true)``).
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This avoids the performance issue of CPU resource contention where there
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is not enough CPU resources to schedule tasks for GPU
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(`PR #19214 <https://github.com/openvinotoolkit/openvino/pull/19214>`__).
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* CPU
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* Introduced support of GPTQ quantized INT4 models, with improved performance
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compared to INT8 weight compressed or FP16 models. In the CPU plugin,
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* Introduced support of GPTQ quantized Int4 models, with improved performance
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compared to Int8 weight-compressed or FP16 models. In the CPU plugin,
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the gain in performance is achieved by FullyConnected acceleration with
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4bit weight decompression
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(`PR #20607 <https://github.com/openvinotoolkit/openvino/pull/20607>`__).
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* Improved performance of INT8 weight-compressed large language models on
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* Improved performance of Int8 weight-compressed large language models on
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some platforms, such as 13th Gen Intel Core
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(`PR #20607 <https://github.com/openvinotoolkit/openvino/pull/20607>`__).
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* Further reduced memory consumption of select large language models on
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CPU platforms with AMX and AVX512 ISA, by eliminating extra memory copy
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with unified weight layout in matrix multiplication operator
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CPU platforms with AMX and AVX512 ISA, by eliminating extra memory copy
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with a unified weight layout
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(`PR #19575 <https://github.com/openvinotoolkit/openvino/pull/19575>`__).
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* Fixed performance issue observed in 2023.1 release on selected Xeon CPU
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platform with improved thread workload partitioning matching L2 cache
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utilization for operator like inner_product
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* Fixed performance issue observed in 2023.1 release on select Xeon CPU
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platform with improved thread workload partitioning matching L2 cache
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utilization
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(`PR #20436 <https://github.com/openvinotoolkit/openvino/pull/20436>`__).
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* Extended support of configuration (enable_cpu_pinning) on Windows
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platforms to allow fine-grain control on CPU resource used for inference
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@ -165,19 +165,17 @@ OpenVINO™ Runtime (previously known as Inference Engine)
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* GPU
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* Enhanced inference performance for Large Language Models:
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* Introduced int8 weight compression to boost LLM performance. (`PR #19548 <https://github.com/openvinotoolkit/openvino/pull/19548>`__).
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* Implemented int4 GPTQ weight compression for improved LLM performance.
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* Optimized constant weights for LLMs, resulting in better memory usage
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and faster model loading.
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* Optimized gemm (general matrix multiply) and fc (fully connected) for
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enhanced performance on iGPU. (`PR #19780 <https://github.com/openvinotoolkit/openvino/pull/19780>`__).
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* Completed GPU plugin migration to API 2.0.
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* Optimized PVC platform for enhanced performance (`PR #19767 <https://github.com/openvinotoolkit/openvino/pull/19767>`__).
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* Added dynamic model support using loop operator.
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* Added support for oneDNN 3.3 version.
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* Enhanced inference performance for Large Language Models.
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* Introduced int8 weight compression to boost LLM performance.
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(`PR #19548 <https://github.com/openvinotoolkit/openvino/pull/19548>`__).
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* Implemented Int4 GPTQ weight compression for improved LLM performance.
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* Optimized constant weights for LLMs, resulting in better memory usage
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and faster model loading.
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* Optimized gemm (general matrix multiply) and fc (fully connected) for
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enhanced performance on iGPU.
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(`PR #19780 <https://github.com/openvinotoolkit/openvino/pull/19780>`__).
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* Completed GPU plugin migration to API 2.0.
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* Added support for oneDNN 3.3 version.
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* Model Import Updates
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@ -187,30 +185,16 @@ OpenVINO™ Runtime (previously known as Inference Engine)
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`PR #19903 <https://github.com/openvinotoolkit/openvino/pull/19903>`__
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* Supported TF 2.14.
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`PR #20385 <https://github.com/openvinotoolkit/openvino/pull/20385>`__
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* New operations supported.
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* Fixes:
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* Attributes handling for CTCLoss operation.
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`PR #20775 <https://github.com/openvinotoolkit/openvino/pull/20775>`__
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* Attributes handling for CumSum operation.
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`PR #20680 <https://github.com/openvinotoolkit/openvino/pull/20680>`__
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* PartitionedCall fix for number of external and internal inputs mismatch.
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`PR #20680 <https://github.com/openvinotoolkit/openvino/pull/20680>`__
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* Preserving input and output tensor names for conversion of models from memory.
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`PR #19690 <https://github.com/openvinotoolkit/openvino/pull/19690>`__
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* 5D case for FusedBatchNorm.
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`PR #19904 <https://github.com/openvinotoolkit/openvino/pull/19904>`__
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* PyTorch Framework Support
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* Supported INT4 GPTQ models
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* Supported Int4 GPTQ models.
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* New operations supported.
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* ONNX Framework Support
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* Added support for ONNX version 1.14.1 `PR #18359 <https://github.com/openvinotoolkit/openvino/pull/18359>`__
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* New operations supported.
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* Added support for ONNX version 1.14.1
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(`PR #18359 <https://github.com/openvinotoolkit/openvino/pull/18359>`__)
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OpenVINO Ecosystem
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@ -219,12 +203,13 @@ OpenVINO Ecosystem
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OpenVINO Model Server
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--------------------------
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* Introduced an extension of the KServe gRPC API, enabling streaming input and
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output for servables with Mediapipe graphs. This extension ensures the persistence
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of Mediapipe graphs within a user session, improving processing performance.
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This enhancement supports stateful graphs, such as tracking algorithms, and
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enables the use of source calculators.
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(`see additional documentation <https://github.com/openvinotoolkit/model_server/blob/main/docs/streaming_endpoints.md>`__)
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Introduced an extension of the KServe gRPC API, enabling streaming input and
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output for servables with Mediapipe graphs. This extension ensures the persistence
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of Mediapipe graphs within a user session, improving processing performance.
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This enhancement supports stateful graphs, such as tracking algorithms, and
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enables the use of source calculators.
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(`see additional documentation <https://github.com/openvinotoolkit/model_server/blob/main/docs/streaming_endpoints.md>`__)
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* Mediapipe framework has been updated to the version 0.10.3.
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* model_api used in the openvino inference Mediapipe calculator has been updated
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and included with all its features.
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@ -247,7 +232,7 @@ Jupyter Notebook Tutorials
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* `LLM chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/254-llm-chatbot>`__
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Create LLM-powered Chatbot
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* Updated to include INT4 weights compression and Zephyr 7B model
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* Updated to include Int4 weight compression and Zephyr 7B model
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* `Bark Text-to-Speech <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/256-bark-text-to-audio>`__
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Text-to-Speech generation using Bark
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@ -288,43 +273,40 @@ Jupyter Notebook Tutorials
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Known issues
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++++++++++++++++++++++++++++++++++++++++++++
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| ID - 118179
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| Component - Python API, Plugins
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| Description:
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| When input byte sizes are matching, inference methods accept incorrect inputs
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in copy mode (share_inputs=False). Example: [1, 4, 512, 512] is allowed when
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[1, 512, 512, 4] is required by the model.
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| Workaround:
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| Pass inputs which shape and layout match model ones.
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| ID - 124181
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| Component - CPU plugin
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| Description:
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| On CPU platform with L2 cache size less than 256KB, such as i3 series of 8th
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Gen Intel CORE platforms, some models may hang during model loading.
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| Workaround:
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| Rebuild the software from OpenVINO master or use the next OpenVINO release.
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| ID - 121959
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| Component - CPU plugin
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| Description:
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| During inference using latency hint on selected hybrid CPU platforms
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(such as 12th or 13th Gen Intel CORE), there is a sporadic occurrence of
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increased latency caused by the operating system scheduling of P-cores or
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E-cores during OpenVINO initialization.
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| Workaround:
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| This will be fixed in the next OpenVINO release.
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| ID - 123101
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| Component - GPU plugin
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| Description:
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| Hung up of GPU plugin on A770 Graphics (dGPU) in case of
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large batch size (1750).
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| Workaround:
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| Decrease the batch size, wait for fixed driver released.
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| **ID - 118179**
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| *Component* - Python API, Plugins
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| *Description:*
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| When input byte sizes are matching, inference methods accept incorrect inputs
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in copy mode (share_inputs=False). Example: [1, 4, 512, 512] is allowed when
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[1, 512, 512, 4] is required by the model.
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| *Workaround:*
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| Pass inputs which shape and layout match model ones.
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| **ID - 124181**
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| *Component* - CPU plugin
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| *Description:*
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| On CPU platform with L2 cache size less than 256KB, such as i3 series of 8th
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Gen Intel CORE platforms, some models may hang during model loading.
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| *Workaround:*
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| Rebuild the software from OpenVINO master or use the next OpenVINO release.
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|
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| **ID - 121959**
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| *Component* - CPU plugin
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| *Description:*
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| During inference using latency hint on selected hybrid CPU platforms
|
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(such as 12th or 13th Gen Intel CORE), there is a sporadic occurrence of
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increased latency caused by the operating system scheduling of P-cores or
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E-cores during OpenVINO initialization.
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| *Workaround:*
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| This will be fixed in the next OpenVINO release.
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|
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| **ID - 123101**
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| *Component* - GPU plugin
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| *Description:*
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| Hung up of GPU plugin on A770 Graphics (dGPU) in case of
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large batch size (1750).
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| *Workaround:*
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| Decrease the batch size, wait for fixed driver released.
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Included in This Release
|
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+++++++++++++++++++++++++++++++++++++++++++++
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|
|
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|
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@ -1,5 +1,5 @@
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# System Requirements {#system_requirements}
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@sphinxdirective
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@ -70,14 +70,14 @@ GPU
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Distribution of OpenVINO™ toolkit package.
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* A chipset that supports processor graphics is required for Intel® Xeon®
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processors. Processor graphics are not included in all processors. See
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`Product Specifications <https://ark.intel.com/>`__
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for information about your processor.
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`Product Specifications <https://ark.intel.com/>`__
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for information about your processor.
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* Although this release works with Ubuntu 20.04 for discrete graphic cards,
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Ubuntu 20.04 is not POR for discrete graphics drivers, so OpenVINO support
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is limited.
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* The following minimum (i.e., used for old hardware) OpenCL™ driver's versions
|
||||
were used during OpenVINO internal validation: 22.43 for Ubuntu 22.04, 21.48
|
||||
for Ubuntu 20.04 and 21.49 for Red Hat Enterprise Linux 8.
|
||||
for Ubuntu 20.04 and 21.49 for Red Hat Enterprise Linux 8.
|
||||
|
||||
NPU and GNA
|
||||
#############################
|
||||
|
|
@ -118,11 +118,12 @@ Operating systems and developer environment
|
|||
Build environment components:
|
||||
|
||||
* Python* 3.8-3.11
|
||||
* Intel® HD Graphics Driver. Required for inference on GPU.
|
||||
* `Intel® HD Graphics Driver <https://downloadcenter.intel.com/product/80939/Graphics-Drivers>`__
|
||||
required for inference on GPU
|
||||
* GNU Compiler Collection and CMake are needed for building from source:
|
||||
|
||||
* GNU Compiler Collection (GCC) 7.5 and above
|
||||
* CMake* 3.10 or higher
|
||||
* `GNU Compiler Collection (GCC) <https://www.gnu.org/software/gcc/>`__ 7.5 and above
|
||||
* `CMake <https://cmake.org/download/>`__ 3.10 or higher
|
||||
|
||||
Higher versions of kernel might be required for 10th Gen Intel® Core™ Processors,
|
||||
11th Gen Intel® Core™ Processors, 11th Gen Intel® Core™ Processors S-Series Processors,
|
||||
|
|
@ -137,10 +138,11 @@ Operating systems and developer environment
|
|||
|
||||
Build environment components:
|
||||
|
||||
* Microsoft Visual Studio* 2019
|
||||
* CMake 3.10 or higher
|
||||
* Python* 3.8-3.11
|
||||
* Intel® HD Graphics Driver (Required only for GPU).
|
||||
* `Microsoft Visual Studio 2019 <https://visualstudio.microsoft.com/vs/older-downloads/>`__
|
||||
* `CMake <https://cmake.org/download/>`__ 3.10 or higher
|
||||
* `Python* 3.8-3.11 <http://www.python.org/downloads/>`__
|
||||
* `Intel® HD Graphics Driver <https://downloadcenter.intel.com/product/80939/Graphics-Drivers>`__
|
||||
required for inference on GPU
|
||||
|
||||
.. tab-item:: macOS
|
||||
|
||||
|
|
@ -148,9 +150,9 @@ Operating systems and developer environment
|
|||
|
||||
Build environment components:
|
||||
|
||||
* Xcode* 10.3
|
||||
* Python 3.8-3.11
|
||||
* CMake 3.10 or higher
|
||||
* `Xcode* 10.3 <https://developer.apple.com/xcode/>`__
|
||||
* `Python* 3.8-3.11 <http://www.python.org/downloads/>`__
|
||||
* `CMake <https://cmake.org/download/>`__ 3.10 or higher
|
||||
|
||||
.. tab-item:: DL frameworks versions:
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue