openvino/docs/resources/prerelease_information.md

Pre-release Information

@sphinxdirective

To ensure you can test OpenVINO's upcoming features even before they are officially released, OpenVINO developers continue to roll out pre-release software. On this page you can find a general changelog for each version published under the current cycle.

.. note::

These versions are pre-release software and have not undergone full validation or qualification. OpenVINO™ toolkit pre-release is:

• NOT to be incorporated into production software/solutions.
• NOT subject to official support.
• Subject to change in the future.
• Introduced to allow early testing and get early feedback from the community.

.. dropdown:: OpenVINO Toolkit 2023.0.0.dev20230407 :open: :animate: fade-in-slide-down :color: primary

• Enabled remote tensor in C API 2.0 (accepting tensor located in graph memory)

• Introduced model caching on GPU. Model Caching, which reduces First Inference Latency (FIL), is extended to work as a single method on both CPU and GPU plug-ins.

• Added the post-training Accuracy-Aware Quantization mechanism for OpenVINO IR. By using this mechanism the user can define the accuracy drop criteria and NNCF will consider it during the quantization.

• Migrated the CPU plugin to OneDNN 3.1.

• Enabled CPU fall-back for the AUTO plugin - in case of run-time failure of networks on accelerator devices, CPU is used.

• Now, AUTO supports the option to disable CPU as the initial acceleration device to speed up first-inference latency.

• Implemented ov::hint::inference_precision, which enables running network inference independently of the IR precision. The default mode is FP16, it is possible to infer in FP32 to increase accuracy.

• Optimized performance on dGPU with Intel oneDNN v3.1, especially for transformer models.

• Enabled dynamic shapes on iGPU and dGPU for Transformer(NLP) models. Not all dynamic models are enabled but model coverage will be expanded in following releases.

• Improved performance for Transformer models for NLP pipelines on CPU.

• Extended support to the following models:

  • Enabled MLPerf RNN-T model.
  • Enabled Detectron2 MaskRCNN.
  • Enabled OpenSeeFace models.
  • Enabled Clip model.
  • Optimized WeNet model.

Known issues:

• OpenVINO-dev wheel does not contain the benchmark_app package

.. dropdown:: OpenVINO Toolkit 2023.0.0.dev20230217 :animate: fade-in-slide-down :color: secondary

OpenVINO™ repository tag: 2023.0.0.dev20230217 <https://github.com/openvinotoolkit/openvino/releases/tag/2023.0.0.dev20230217>__

• Enabled PaddlePaddle Framework 2.4
• Preview of TensorFlow Lite Front End – Load models directly via “read_model” into OpenVINO Runtime and export OpenVINO IR format using Model Optimizer or “convert_model”
• PyTorch Frontend is available as an experimental feature which will allow you to convert PyTorch models, using convert_model Python API directly from your code without the need to export to the ONNX format. Model coverage is continuously increasing. Feel free to start using the option and give us feedback.
• Model Optimizer now uses the TensorFlow Frontend as the default path for conversion to IR. Known limitations compared to the legacy approach are: TF1 Loop, Complex types, models requiring config files and old python extensions. The solution detects unsupported functionalities and provides fallback. To force using the legacy frontend --use_legacy_fronted can be specified.
• Model Optimizer now supports out-of-the-box conversion of TF2 Object Detection models. At this point, same performance experience is guaranteed only on CPU devices. Feel free to start enjoying TF2 Object Detection models without config files!
• Introduced new option ov::auto::enable_startup_fallback / ENABLE_STARTUP_FALLBACK to control whether to use CPU to accelerate first inference latency for accelerator HW devices like GPU.
• New FrontEndManager register_front_end(name, lib_path) interface added, to remove “OV_FRONTEND_PATH” env var (a way to load non-default frontends).

@endsphinxdirective