Openvino Versions Save

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 2023.1.0.dev20230623 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2023.1.0.dev20230623
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2023.1.0.dev20230623

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

Major Features and Improvements Summary

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

• This 2022.3.1 LTS release provides functional bug fixes and minor capability changes for the previous 2022.3 Long-Term Support (LTS) release, enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence.
• Intel® Movidius ™ VPU-based products are supported in this release.

You can find OpenVINO™ toolkit 2022.3 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2022.3.1
• OpenVINO™ Development tools: pip install openvino-dev==2022.3.1

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-lts/2022-3.html

Summary of major features and improvements

• More integrations, minimizing code changes
  • Now you can load TensorFlow and TensorFlow Lite models directly in OpenVINO Runtime and OpenVINO Model Server. Models are converted automatically. For maximum performance, it is still recommended to convert to OpenVINO Intermediate Representation or IR format before loading the model. Additionally, we’ve introduced a similar functionality with PyTorch models as a preview feature where you can convert PyTorch models directly without needing to convert to ONNX.
  • Support for Python 3.11
  • NEW: C++ developers can now install OpenVINO runtime from Conda Forge​
  • NEW: ARM processors are now supported in CPU plug-in, including dynamic shapes, full processor performance, and broad sample code/notebook coverage. Officially validated for Raspberry Pi 4 and Apple® Mac M1/M2
  • Preview: A new Python API has been introduced to allow developers to convert and optimize models directly from Python scripts
• Broader model support and optimizations
  • Expanded model support for generative AI: CLIP, BLIP, Stable Diffusion 2.0, text processing models, transformer models (i.e. S-BERT, GPT-J, etc.), and others of note: Detectron2, Paddle Slim, RNN-T, Segment Anything Model (SAM), Whisper, and YOLOv8 to name a few.
  • Initial support for dynamic shapes on GPU - you no longer need to change to static shapes when leveraging the GPU which is especially important for NLP models.
  • Neural Network Compression Framework (NNCF) is now the main quantization solution. You can use it for both post-training optimization and quantization-aware training. Try it out: pip install nncf
• Portability and performance​
  • CPU plugin now offers thread scheduling on 12th gen Intel® Core and up. You can choose to run inference on E-cores, P-cores, or both, depending on your application’s configurations. It is now possible to optimize for performance or for power savings as needed. ​
  • NEW: Default Inference Precision - no matter which device you use, OpenVINO will default to the format that enables its optimal performance. For example, FP16 for GPU or BF16 for 4th Generation Intel® Xeon®. You no longer need to convert the model beforehand to specific IR precision, and you still have the option of running in accuracy mode if needed.​
  • Model caching on GPU is now improved with more efficient model loading/compiling.

You can find OpenVINO™ toolkit 2023.0 release here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install openvino==2023.0.0
• OpenVINO™ Development tools: pip install openvino-dev==2023.0.0

Release Notes are available here: https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino/2023-0.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 2023.0.0.dev20230427 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2023.0.0.dev20230427
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2023.0.0.dev20230427

Release notes is available here: https://docs.openvino.ai/nightly/prerelease_information.html 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 2023.0.0.dev20230407 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2023.0.0.dev20230407
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2023.0.0.dev20230407

Release notes is available here: https://docs.openvino.ai/nightly/prerelease_information.html 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 2023.0.0.dev20230217 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2023.0.0.dev20230217
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2023.0.0.dev20230217

Release notes is available here: https://docs.openvino.ai/nightly/prerelease_information.html 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 2023.0.0.dev20230119 pre-release version here:

• Download archives* with OpenVINO™ Runtime for C/C++
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2023.0.0.dev20230119
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2023.0.0.dev20230119

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

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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