OpenVINO Release Notes¶
The Intel® Distribution of OpenVINO™ toolkit is an open-source solution for optimizing and deploying AI inference in domains such as computer vision,automatic speech recognition, natural language processing, recommendation systems, and generative AI. With its plug-in architecture, OpenVINO enables developers to write once and deploy anywhere. We are proud to announce the release of OpenVINO 2023.2 introducing a range of new features, improvements, and deprecations aimed at enhancing the developer experience.
New and changed in 2023.2¶
Summary of major features and improvements¶
More Generative AI coverage and framework integrations to minimize code changes.
Expanded model support for direct PyTorch model conversion - automatically convert additional models directly from PyTorch or execute via
torch.compilewith OpenVINO as the backend.
New and noteworthy models supported - we have enabled models used for chatbots, instruction following, code generation, and many more, including prominent models like Llava, chatGLM, Bark (text to audio) and LCM (Latent Consistency Models, an optimized version of Stable Diffusion).
Easier optimization and conversion of Hugging Face models - compress LLM models to Int8 with the Hugging Face Optimum command line interface and export models to the OpenVINO IR format.
OpenVINO is now available on Conan - a package manager which allows more seamless package management for large scale projects for C and C++ developers.
Broader Large Language Model (LLM) support and more model compression techniques.
Accelerate inference for LLM models on Intel® CoreTM CPU and iGPU with the use of Int8 model weight compression.
Expanded model support for dynamic shapes for improved performance on GPU.
Preview support for Int4 model format is now included. Int4 optimized model weights are now available to try on Intel® Core™ CPU and iGPU, to accelerate models like Llama 2 and chatGLM2.
The following Int4 model compression formats are supported for inference in runtime:
Generative Pre-training Transformer Quantization (GPTQ); with GPTQ-compressed models, you can access them through the Hugging Face repositories.
Native Int4 compression through Neural Network Compression Framework (NNCF).
More portability and performance to run AI at the edge, in the cloud, or locally.
In 2023.1 we announced full support for ARM architecture, now we have improved performance by enabling FP16 model formats for LLMs and integrating additional acceleration libraries to improve latency.
Support Change and Deprecation Notices¶
The OpenVINO™ Development Tools package (pip install openvino-dev) is deprecated and will be removed from installation options and distribution channels with 2025.0. To learn more, refer to the OpenVINO Legacy Features and Components page. To ensure optimal performance, install the OpenVINO package (pip install openvino), which includes essential components such as OpenVINO Runtime, OpenVINO Converter, and Benchmark Tool.
Deployment Manager is deprecated and will be removed in the 2024.0 release.
Accuracy Checker is deprecated and will be discontinued with 2024.0.
Post-Training Optimization Tool (POT) is deprecated and will be discontinued with 2024.0.
Model Optimizer is deprecated and will be fully supported up until the 2025.0 release. Model conversion to the OpenVINO format should be performed through OpenVINO Model Converter, which is part of the PyPI package. Follow the Model Optimizer to OpenVINO Model Converter transition guide for smoother transition. Known limitations are TensorFlow model with TF1 Control flow and object detection models. These limitations relate to the gap in TensorFlow direct conversion capabilities which will be addressed in upcoming releases.
PyTorch 1.13 support is deprecated in Neural Network Compression Framework (NNCF)
Intel® Gaussian & Neural Accelerator (Intel® GNA) will be deprecated in a future release. We encourage developers to use 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 will be discontinued with 2024.0.
Python 3.7 support has been discontinued.
OpenVINO™ Development Tools¶
List of components and their changes:¶
OpenVINO Model Converter tool now supports the original framework shape format.
Added data-free Int4 weight compression support for LLMs in OpenVINO IR with
Improved quantization time of LLMs with NNCF PTQ API for
Added support for SmoothQuant and ChannelAlighnment algorithms in NNCF HyperParameter Tuner for automatic optimization of their hyperparameters during quantization.
Added quantization support for the
IFoperation of models in OpenVINO format to speed up such models.
NNCF Post-training Quantization for PyTorch backend is now supported with
nncf.quantize()and the common implementation of quantization algorithms.
Added support for PyTorch 2.1. PyTorch 1.13 support has been deprecated.
OpenVINO™ Runtime (previously known as Inference Engine)¶
Operations for reference implementations updated from legacy API to API 2.0.
Symbolic transformation introduced the ability to remove Reshape operations surrounding MatMul operations.
OpenVINO Python API
Better support for the
openvino.propertiessubmodule, which now allows the use of properties directly, without additional parenthesis. Example use-case:
Added missing properties:
Improved error propagation on imports from OpenVINO package.
AUTO device plug-in (AUTO)
o Provided additional option to improve performance of cumulative throughput (or MULTI), where part of CPU resources can be reserved for GPU inference when GPU and CPU are both used for inference (using
ov::hint::enable_cpu_pinning(true)). This avoids the performance issue of CPU resource contention where there is not enough CPU resources to schedule tasks for GPU (PR #19214).
Introduced support of GPTQ quantized Int4 models, with improved performance compared to Int8 weight-compressed or FP16 models. In the CPU plugin, the gain in performance is achieved by FullyConnected acceleration with 4bit weight decompression (PR #20607).
Improved performance of Int8 weight-compressed large language models on some platforms, such as 13th Gen Intel Core (PR #20607).
Further reduced memory consumption of select large language models on CPU platforms with AMX and AVX512 ISA, by eliminating extra memory copy with a unified weight layout (PR #19575).
Fixed performance issue observed in 2023.1 release on select Xeon CPU platform with improved thread workload partitioning matching L2 cache utilization (PR #20436).
Extended support of configuration (enable_cpu_pinning) on Windows platforms to allow fine-grain control on CPU resource used for inference workload, by binding inference thread to CPU cores (PR #19418).
Optimized YoloV8n and YoloV8s model performance for BF16/FP32 precision.
Optimized Falcon model on 4th Gen Intel® Xeon® Scalable Processors.
Enabled support for FP16 inference precision on ARM.
Enhanced inference performance for Large Language Models.
Introduced int8 weight compression to boost LLM performance. (PR #19548).
Implemented Int4 GPTQ weight compression for improved LLM performance.
Optimized constant weights for LLMs, resulting in better memory usage and faster model loading.
Optimized gemm (general matrix multiply) and fc (fully connected) for enhanced performance on iGPU. (PR #19780).
Completed GPU plugin migration to API 2.0.
Added support for oneDNN 3.3 version.
Model Import Updates
TensorFlow Framework Support
PyTorch Framework Support
Supported Int4 GPTQ models.
New operations supported.
ONNX Framework Support
Added support for ONNX version 1.14.1 (PR #18359)
OpenVINO Model Server¶
Introduced an extension of the KServe gRPC API, enabling streaming input and output for servables with Mediapipe graphs. This extension ensures the persistence of Mediapipe graphs within a user session, improving processing performance. This enhancement supports stateful graphs, such as tracking algorithms, and enables the use of source calculators. (see additional documentation)
Mediapipe framework has been updated to the version 0.10.3.
model_api used in the openvino inference Mediapipe calculator has been updated and included with all its features.
Added a demo showcasing gRPC streaming with Mediapipe graph. (see here)
Added parameters for gRPC quota configuration and changed default gRPC channel arguments to add rate limits. It will minimize the risks of impact of the service from uncontrolled flow of requests.
Updated python clients requirements to match wide range of python versions from 3.6 to 3.11
Learn more about the changes in https://github.com/openvinotoolkit/model_server/releases
Jupyter Notebook Tutorials¶
The following notebooks have been updated or newly added:
LaBSE Cross-lingual Books Alignment With Transformers
LLM chatbot Create LLM-powered Chatbot
Updated to include Int4 weight compression and Zephyr 7B model
Bark Text-to-Speech Text-to-Speech generation using Bark
LLaVA Multimodal Chatbot Visual-language assistant with LLaVA
BLIP-Diffusion - Subject-Driven Generation Subject-driven image generation and editing using BLIP Diffusion
DeciDiffusion Image generation with DeciDiffusion
Fast Segment Anything Object segmentations with FastSAM
QR Code Monster Generate creative QR codes with ControlNet QR Code Monster
Würstchen Text-to-image generation with Würstchen
Distil-Whisper Automatic speech recognition using Distil-Whisper and OpenVINO™
Added optimization support (8-bit quantization, weight compression) by NNCF for the following notebooks:
Included in This Release¶
The Intel® Distribution of OpenVINO™ toolkit is available for downloading in three types of operating systems: Windows, Linux, and macOS.
OpenVINO (Inference Engine) C++ Runtime
Unified API to integrate the inference with application logic
OpenVINO (Inference Engine) Headers
Intel® OpenVINO™ Distribution License (Version May 2021)
OpenVINO (Inference Engine) Pythion API
OpenVINO (Inference Engine) Samples
Samples that illustrate OpenVINO C++/ Python API usage
[Deprecated] Deployment manager
The Deployment Manager is a Python* command-line tool that
creates a deployment package by assembling the model, IR files,
your application, and associated dependencies into a runtime
package for your target device.
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