openvino

Major Features and Improvements Summary

This is a Long-Term Support (LTS) release. LTS releases are released every year and supported for 2 years (1 year of bug fixes, and 2 years for security patches). Read Intel® Distribution of OpenVINO™ toolkit Long-Term Support (LTS) Policy  v.2 to get details.

• 2022.3 LTS release provides functional bug fixes, and capability changes for the previous 2022.2 release. This new release empowers developers with new performance enhancements, more deep learning models, more device portability and higher inferencing performance with less code changes.
• Broader model and hardware support – Optimize & deploy with ease across an expanded range of deep learning models including NLP, and access AI acceleration across an expanded range of hardware.
  • Full support for 4th Generation Intel® Xeon® Scalable processor family (code name Sapphire Rapids) for deep learning inferencing workloads from edge to cloud.
  • Full support for Intel’s discrete graphics cards, such as Intel® Data Center GPU Flex Series, and Intel® Arc™ GPU for DL inferencing workloads in the intelligent cloud, edge, and media analytics workloads.
  • Improved performance when leveraging throughput hint on CPU plugin for 12th and 13th Generation Intel® Core™ processor family (code named Alder Lake and Raptor Lake).
  • Enhanced “Cumulative throughput” and selection of compute modes added to AUTO functionality, enabling multiple accelerators (e.g. multiple GPUs) to be used at once to maximize inferencing performance.
• Expanded model coverage - Optimize & deploy with ease across an expanded range of deep learning models.
  • Broader support for NLP models and use cases like text to speech and voice recognition.
  • Continued performance enhancements for computer vision models Including StyleGAN2, Stable Diffusion, PyTorch RAFT and YOLOv7.
  • Significant quality and model performance improvements on Intel GPUs compared to the previous OpenVINO toolkit release.
  • New Jupyter notebook tutorials for Stable Diffusion text-to-image generation, YOLOv7 optimization and 3D Point Cloud Segmentation.
• Improved API and More Integrations – Easier to adopt and maintain code. Requires fewer code changes, aligns better with frameworks, & minimizes conversion
  • Preview of TensorFlow Front End – Load TensorFlow models directly into OpenVINO Runtime and easily export OpenVINO IR format without offline conversion. New “–use_new_frontend” flag enables this preview – see further details below in Model Optimizer section of release notes.
  • NEW: Hugging Face Optimum Intel – Gain the performance benefits of OpenVINO (including NNCF) when using Hugging Face Transformers. Initial release supports PyTorch models.
  • Intel® oneAPI Deep Neural Network Library (oneDNN) has been updated to 2.7 for further refinements and significant improvements in performance for the latest Intel CPU and GPU processors.
  • Introducing C API 2.0, to support new features introduced in OpenVINO API 2.0, such as dynamic shapes with CPU, pre-processing and post-process API, unified property definition and usage. The new C API 2.0 shares the same library files as the 1.0 API, but with a different header file.
• Note: Intel® Movidius ™ VPU based products are not supported in this release, but will be added back in a future OpenVINO 2022.3.1 LTS update. In the meantime, for support on those products please use OpenVINO 2022.1.
• Note: Macintosh* computers using the M1* processor can now install OpenVINO and use the OpenVINO ARM* Device Plug-in on OpenVINO 2022.3 LTS and later. This plugin is community supported; no support is provided by Intel and it doesn't fall under the LTS 2-year support policy. Learn more here: https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_supported_plugins_ARM_CPU.html

You can find OpenVINO™ toolkit 2022.3 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2022.3.0
• OpenVINO™ Development tools: pip install openvino-dev==2022.3.0

Release documentation is available here: https://docs.openvino.ai/2022.3/

Release Notes are available here: https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino-2022-3-lts-relnotes.html

NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.3.0.dev20221125 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.3.0.dev20221125
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.3.0.dev20221125

Release documentation is available here: https://docs.openvino.ai/nightly/

NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.3.0.dev20221103 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.3.0.dev20221103
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.3.0.dev20221103

Release documentation is available here: https://docs.openvino.ai/nightly/

* - sha256 sums for archives

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Major Features and Improvements Summary

In this standard release, we’ve fine-tuned our largest update (2022.1) in 4 years to include support for Intel’s latest CPUs and discrete GPUs for more AI innovation and opportunity.

Note: This release intended for developers that prefer the very latest features and leading performance. Standard releases will continue to be made available three to four times a year. Long Term Support (LTS) releases are released every year and supported for 2 years (1 year of bug fixes, and 2 years for security patches). Read Intel® Distribution of OpenVINO™ toolkit Long-Term Support (LTS) Policy to get details. For the latest LTS release visit our selector tool.

• Broader model and hardware support - Optimize & deploy with ease across an expanded range of deep learning models including NLP, and access AI acceleration across an expanded range of hardware.

  • NEW: Support for Intel 13th Gen Core Processor for desktop (code named Raptor Lake).
  • NEW: Preview support for Intel’s discrete graphics cards, Intel® Data Center GPU Flex Series and Intel® Arc™ GPU for DL inferencing workloads in intelligent cloud, edge and media analytics workloads. Hundreds of models enabled.
  • NEW: Test your model performance with preview support for Intel 4th Generation Xeon® processors (code named Sapphire Rapids).
  • Broader support for NLP models and use cases like text to speech and voice recognition. Reduced memory consumption when using Dynamic Input Shapes on CPU. Improved efficiency for NLP applications.

• Frameworks Integrations – More options that provide minimal code changes to align with your existing frameworks

  • OpenVINO Execution Provider for ONNX Runtime gives ONNX Runtime developers more choice for performance optimizations by making it easy to add OpenVINO with minimal code changes.
  • NEW: Accelerate PyTorch models with ONNX Runtime using OpenVINO™ integration with ONNX Runtime for PyTorch (OpenVINO™ Torch-ORT). Now PyTorch developers can stay within their framework and benefit from OpenVINO performance gains.
  • OpenVINO Integration with TensorFlow now supports more deep learning models with improved inferencing performance.

  • NOTE: The above frameworks integrations are not included in the install packages. Please visit the respective github links for more information. These products are intended for those who have not yet installed native OpenVINO

• More portability and performance - See a performance boost straight away with automatic device discovery, load balancing & dynamic inference parallelism across CPU, GPU, and more.

  • NEW: Introducing new performance hint (”Cumulative throughput”) in AUTO device, enabling multiple accelerators (e.g. multiple GPUs) to be used at once to maximize inferencing performance.
  • NEW: Introducing Intel® FPGA AI Suite support which enables real-time, low-latency, and low-power deep learning inference in this easy-to-use package  

    • NOTE: The Intel® FPGA AI Suite is not included in our distribution packages, please request information here to learn more.

You can find OpenVINO™ toolkit 2022.2 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2022.2.0
• OpenVINO™ Development tools: pip install openvino-dev==2022.2.0

Release documentation is available here: https://docs.openvino.ai/2022.2/

Release Notes are available here: https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino-relnotes.html

NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.2.0.dev20220829 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.2.0.dev20220829
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.2.0.dev20220829

Release documentation is available here: https://docs.openvino.ai/nightly/

* - sha256 sums for archives

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Minor updates and bug fixes for specific use cases and scenarios.

This release provides functional bug fixes and capability updates from the previous release 2022.1 that enable developers

Note: This is a standard release intended for developers that prefer the very latest version of OpenVINO. Standard releases will continue to be made available three to four times a year. Long Term Support (LTS) releases are also available. A new LTS version is released every year and is supported for 2 years (1 year of bug fixes, and 2 years for security patches). Visit Intel® Distribution of OpenVINO™ toolkit Long-Term Support (LTS) Policy to get details on the latest LTS releases.

Component updates:

• OpenVINO runtime:
  • Added a way to unload TBB libraries upon OpenVINO library unloading - use ov::force_tbb_terminate option to ov::Core
  • Added a way to unload OpenVINO frontend libraries for cases when IR / ONNX / PDPD files are read in runtime. Users should call ov::shutdown once they finish to work with OpenVINO library to free all the resources.

You can find the OpenVINO™ 2022.1.1 release here:

• Download archives* with OpenVINO™ Runtime for C/C++Download archives* with OpenVINO™ Runtime for C/C++
• Github Repository

Limitations of this release:

• Windows OS, Linux and MacOS
• Intel® Movidius™ Myriad™ X plugin is not included
• The Intel® version of OpenCV will not be included, please visit this guide on how to use the community version
• For product specifications, please visit release notes on OpenVINO toolkit 2022.1

Major Features and Improvements Summary

This release is the biggest upgrade in 3.5 years! Read the release notes below for a summary of changes.

2022.1 release provides functional bug fixes, and capability changes for the previous 2021.4.2 LTS release. This new release empowers developers with new performance enhancements, more deep learning models, more device portability, and higher inferencing performance with fewer code changes.

Note: This is a standard release intended for developers that prefer the very latest features and leading performance. Standard releases will continue to be made available three to four times a year. Long Term Support (LTS) releases are also available. A new LTS version is released every year and is supported for 2 years (1 year of bug fixes, and 2 years for security patches). ead Intel® Distribution of OpenVINO™ toolkit Long-Term Support (LTS) Policy to get details. Latest LTS releases: 2020.x LTS and 2021.x LTS.

• Updated, cleaner API:

  • New OpenVINO API 2.0 was introduced. The API aligns OpenVINO inputs/outputs with frameworks. Input and output tensors use native framework layouts and element types. Old Inference Engine and nGraph APIs are available but will be deprecated in a future release down the road.

  • inference_engine, inference_engine_transformations, inferencengine_lp_transformations and ngraph libraries were merged to common openvino library. Other libraries were renamed. Please, use common ov:: namespace inside all OpenVINO components. See how to implement Inference Pipeline using OpenVINO API v2.0 for details.

  • Model Optimizer’s API parameters have been reduced to minimize complexity. Performance has been significantly improved for model conversion on ONNX models.

  • It’s highly recommended to migrate to API 2.0 because it already has additional features and this list will be extended later. The following list of additional features is supported by API 2.0:

    • Working with dynamic shapes. The feature is quite useful for best performance for Neural Language Processing (NLP) models, super-resolution models, and other which accepts dynamic input shapes. Note: Models compiled with dynamic shapes may show reduced performance and consume more memory than models configured with a static shape on the same input tensor size. Setting upper bounds to reshape the model for dynamic shapes or splitting the input into several parts is recommended.

    • Preprocessing of the model to add preprocessing operations to the inference models and fully occupy the accelerator and free CPU resources.

  • Read the Transition Guide for migrating to the new API 2.0.

• Portability and Performance:

  • New AUTO plugin self-discovers available system inferencing capacity based on model requirements, so applications no longer need to know its compute environment in advance.

  • The OpenVINO™ performance hints are the new way to configure the performance with portability in mind. The hints “reverse” the direction of the configuration in the right fashion: rather than map the application needs to the low-level performance settings, and keep an associated application logic to configure each possible device separately, the idea is to express a target scenario with a single config key and let the device to configure itself in response. As the hints are supported by every OpenVINO™ device, this is a completely portable and future-proof solution.

  • Automatic batching functionality via code hints automatically scale batch size based on XPU and available memory.

• Broader Model Support:

  • With Dynamic Input Shapes capabilities on CPU, OpenVINO will be able to adapt to multiple input dimensions in a single model providing more complete NLP support. Dynamic Shapes support on additional XPUs expected in a future dot release.

• New Models with focus on NLP and a new category, Anomaly detection, and support for conversion and inference of select PaddlePaddle models:

  • Pre-trained Models: Anomaly segmentation focus on industrial inspection making Speech denoising trainable plus updates on speech recognition and speech synthesis

  • Combined Demo: Noise reduction + speech recognition + question answering + translation+ text to speech

  • Public Models: Focus on NLP ContextNet, Speech-Transformer, HiFi-GAN, Glow-TTS, FastSpeech2, and Wav2Vec

• Built with 12th Gen Intel® Core™ 'Alder Lake' in mind. Supports the hybrid architecture to deliver enhancements for high-performance inferencing on CPU & integrated GPU

You can find OpenVINO™ toolkit 2022.1 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2022.1.0
• OpenVINO™ Development tools: pip install openvino-dev==2022.1.0

Release documentation is available here: https://docs.openvino.ai/2022.1/

Release Notes are available here: https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino-relnotes.html

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.1.0.dev20220316 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.1.0.dev20220316
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.1.0.dev20220316

Release documentation is available here: https://docs.openvino.ai/nightly/

* - sha256 sums for archives

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NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.1.0.dev20220302 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.1.0.dev20220302
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.1.0.dev20220302

Release documentation is available here: https://docs.openvino.ai/nightly/

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NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.1.0.dev20220215 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.1.0.dev20220215
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.1.0.dev20220215

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NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

OpenVINO™ toolkit pre-release definition:

• It is introduced to get early feedback from the community.
• The scope and functionality of the pre-release version is subject to change in the future.
• Using the pre-release in production is strongly discouraged.

You can find OpenVINO™ toolkit 2022.1.0.dev20220131 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2022.1.0.dev20220131
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2022.1.0.dev20220131

* - sha256 sums for archives

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NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

This 2021.4.2 LTS release provides functional bug fixes, and minor capability changes for the previous 2021.4.1 Long-Term Support (LTS) release, enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence. To learn more about long-term support and maintenance, go to the Long Term Support Policy.

You can find OpenVINO™ toolkit 2021.4.2 release here:

• Download archives* with OpenVINO™ Runtime
• OpenVINO™ Runtime for Python: pip install openvino==2021.4.2
• OpenVINO™ Development tools: pip install openvino-dev==2021.4.2

Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

This 2021.4.1 LTS release provides functional bug fixes, and minor capability changes for the previous 2021.4 Long-Term Support (LTS) release, enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence. To learn more about long-term support and maintenance, go to the Long Term Support Policy.

You can find OpenVINO™ toolkit 2021.4.1 release here:

• Download archives* with OpenVINO™ Runtime
• OpenVINO™ Runtime for Python: pip install openvino==2021.4.1
• OpenVINO™ Development tools: pip install openvino-dev==2021.4.1

Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

What's New

• This new 2021.4 Long-Term Support (LTS) Release provides bug fixes, longer-term maintenance and support with a focus on stability and compatibility enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence. A new LTS version is released every year and supported for two years. For those developers that prefer the very latest features and leading performance, standard releases will continue to be made available 3-4 times a year. Read more about the long-term support and maintenance, go to the Long Term Support Policy.
• New Jupyter Notebooks, demos and support for additional public models to make development easier:
  • Ready-to-run Jupyter Notebooks with tutorials for converting TensorFlow and PyTorch models, image classification, segmentation, depth estimation, post-training quantization and more.
  • Audio Noise Suppression & Time Series Forecasting demos
  • Public Models: RCAN and IseeBetter (image super-resolution), Attention OCR (image text prediction), Tacotron 2 (text-to-speech) and ModNet (portrait/image matting)
• Time-to-first-inference latency performance enhancements: Initialization has been optimized on CPU and integrated GPU (iGPU), significantly improving performance at inferencing startup. Setting up inferencing always involves additional initialization time as the network is loaded and configured on the device, especially on GPUs due to their architecture. This setup time has been reduced significantly for many networks by doing more initialization work in parallel among other optimizations.
• Preview of OpenVINO ™ integration with TensorFlow: Although not a part of the 2021.4 LTS release, a new open source component called the OpenVINO™ integration with TensorFlow is available as a public preview. This component is designed for TensorFlow developers newly exploring OpenVINO™ toolkit to try it with minimal code changes, maximizing TensorFlow API compatibility. For highest performance, lowest memory footprint and complete hardware control, adopting native OpenVINO APIs continues to be the recommended approach.

You can find OpenVINO™ toolkit 2021.4 release here:

• Download archives* with OpenVINO™ Runtime
• OpenVINO™ Runtime for Python: pip install openvino==2021.4.0
• OpenVINO™ Development tools: pip install openvino-dev==2021.4.0

Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

This release provides bug fixes for the previous 2020.3 Long-Term Support (LTS) release, a new release type that provides longer-term maintenance and support with a focus on stability and compatibility. Read more about the support details: Long Term Support Release

You can find OpenVINO™ toolkit 2020.3.2 release here:

• Download archives* with OpenVINO™ Runtime

Release notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2020-3-lts-relnotes.html

What's New

• Upgrade to the latest version for new capabilities and performance improvements.
• Introduces a preview of Conditional Compilation (available in open-source distribution) which enables a significant reduction to the binary footprint of the runtime components (Inference Engine linked into applications) for particular models.
• Introducing support for the 3rd Gen Intel® Xeon® Scalable platform (code-named Ice Lake), which delivers advanced performance, security, efficiency, and built-in AI acceleration to handle unique workloads and more powerful AI.
• New pre-trained models and support for public models to streamline development:
  • Pre-trained Models: machine-translation, person-vehicle-bike-detection, text-recognition and text-to-speech.
  • Public Models: aclnet-int8 (sound_classification), deblurgan-v2 (image_processing), fastseg-small and fastseg-large (semantic segmentation) and more.
• Developer tools now available as Python wheel packages for Windows*, Linux*, and macOS* for easy package installation and upgrades (pip install openvino-dev)

You can find OpenVINO™ toolkit 2021.3 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2021.3.0
• OpenVINO™ Development tools: pip install openvino-dev==2021.3.0

Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

What's New

• Integrates the Deep Learning Workbench with the Intel® DevCloud for the Edge as a Beta release. Graphically analyze models using the Deep Learning Workbench on the Intel® DevCloud for the Edge (instead of a local machine only) to compare, visualize and fine-tune a solution against multiple remote hardware configurations.
• Introduces support for Red Hat Enterprise Linux (RHEL) 8.2.
• Introduces per-channel quantization support in the Model Optimizer for models quantized with TensorFlow Quantization-Aware Training containing per-channel quantization for weights, which improves performance by model compression and latency reduction.
• Pre-trained models and support for public models to streamline development:
  • Public Models: Yolov4 (for object detection), AISpeech (for speech recognition), and DeepLabv3 (for semantic segmentation)
  • Pre-trained Models: Human Pose Estimation (update), Formula Recognition Polynomial Handwritten (new), Machine Translation (update), Common Sign Language Recognition (New), and Text-to-Speech (new)
• New OpenVINO™ Security Add-on, which controls access to model(s) through secure packaging and execution. Based on KVM Virtual machines and Docker* containers and compatible with the OpenVINO™ Model Server, this new add-on enables packaging for flexible deployment and controlled model access.
• PyPI project moved from openvino-python to openvino, and 2021.1 version to be removed in the default view. The specific version is still available for users depending on this exact version by using openvino-python==2021.1

You can find OpenVINO™ toolkit 2021.2 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2021.2

Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

What's New

• This release provides bug fixes for the previous 2020.3 Long-Term Support (LTS) release, a new release type that provides longer-term maintenance and support with a focus on stability and compatibility. Read more about the support details: Long Term Support Release
• Based on v.2020.3 LTS, the v.2020.3.1 LTS release includes security and functionality bug fixes, and minor capability changes.
• Includes improved support for 11th Generation Intel® Core™ Processor (formerly codenamed Tiger Lake), which includes Intel® Iris® Xe Graphics and Intel® DL Boost instructions.
• Intel® Distribution of OpenVINO™ toolkit 2020.3.X LTS releases will continue to support Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. For questions about next-generation programmable deep-learning solutions based on FPGAs, talk to your sales representative or contact us to get the latest FPGA updates.

You can find OpenVINO™ toolkit 2020.3.1 release here:

• Download archives* with OpenVINO™ Runtime

Release notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2020-3-lts-relnotes.html

What's New

• Introducing a major release in October 2020 (v.2021). You are highly encouraged to upgrade to this version because there it introduces new and important capabilities, as well as breaking changes and backward-incompatible changes.
• Support for TensorFlow 2.2.x. Introduces official support for models trained in the TensorFlow 2.2.x framework.
• Support for the Latest Hardware. Introduces official support for 11th Generation Intel® Core™ Processor Family for Internet of Things (IoT) Applications (formerly codenamed Tiger Lake) including new inference performance enhancements with Iris® Xe Graphics and Intel® DL Boost instructions, as well as Intel® Gaussian & Neural Accelerators 2.0 for low-power speech processing acceleration.
• Going Beyond Vision. Enables end-to-end capabilities to leverage the Intel® Distribution of OpenVINO™ toolkit for workloads beyond computer vision, which include audio, speech, language, and recommendation, with new pre-trained models, support for public models, code samples and demos, and support for non-vision workloads in OpenVINO™ toolkit DL Streamer.
• Coming in Q4 2020: (Beta Release) Integration of DL Workbench and the Intel® DevCloud for the Edge. Developers can now graphically analyze models using the DL Workbench on Intel® DevCloud for the Edge (instead of a local machine only) to compare, visualize and fine-tune a solution against multiple remote hardware configurations.
• OpenVINO™ Model Server. An add-on to the Intel® Distribution of OpenVINO™ toolkit and a scalable microservice, which provides a gRPC or HTTP/REST endpoint for inference, makes it easier to deploy models in cloud or edge server environments. It is now implemented in C++ to enable reduced container footprint (for example, less than 500MB) and deliver higher throughput and lower latency.
• Now available through Gitee* and PyPI* distribution methods. You are encouraged to choose from the distribution methods and download.

You can find OpenVINO™ toolkit 2021.1 release here:

• Download archives* with OpenVINO™ Runtime

Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

What's New

• Improves performance while maintaining accuracy close to full precision (for example, FP32 data type) by introducing support for the Bfloat16 data type for inferencing using the 3rd generation Intel® Xeon® Scalable processor (formerly code-named Cooper Lake).
• Increases accuracy when layers have varying bit-widths by extending the Post-Training Optimization Tool to support mixed-precision quantization.
• Allows greater compatibility of models by supporting directly reading Open Neural Network Exchange (ONNX*) model format to the Inference Engine.
  • For users looking to take full advantage of Intel® Distribution of OpenVINO™ toolkit, it is recommended to follow the native workflow of using the Intermediate Representation from the Model Optimizer as input to the Inference Engine.
  • For users looking to more easily take a converted model in ONNX model format (for example, PyTorch to ONNX using torch.onnx), they are now able to input the ONNX format directly to the Inference Engine to run models on Intel architecture.
• Enables initial support for TensorFlow* 2.2.0 for computer vision use cases.
• Enables users to connect to and profile multiple remote hosts; collect and store data in one place for further analysis by extending the Deep Learning Workbench with remote profiling capability.

You can find OpenVINO™ toolkit 2020.4 release here:

• Download archives* with OpenVINO™ Runtime

Release notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html