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.dev20230811 pre-release version here:

• Download archives* with OpenVINO™
• Install it via Conda: conda install -c "conda-forge/label/openvino_dev" openvino=2023.1.0.dev20230811
• OpenVINO™ Runtime for Python: pip install --pre openvino or pip install openvino==2023.1.0.dev20230811
• OpenVINO™ Development tools: pip install --pre openvino-dev or pip install openvino-dev==2023.1.0.dev20230811

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

What's Changed

• CPU runtime:
  • Enabled weights decompression support for Large Language models (LLMs). The implementation supports avx2 and avx512 HW targets for Intel® Core™ processors, improving performance in the latency mode (comparison: FP32 VS FP32+INT8 weights). For 4th Generation Intel® Xeon® Scalable Processors (formerly Sapphire Rapids) this INT8 decompression feature improves performance compared to pure BF16 inference. PRs: #18915, #19111
  • Reduced memory consumption of the ‘compile model’ stage by moving constant folding of Transpose nodes to the CPU Runtime side. PR: #18877
  • Set FP16 inference precision by default for non-convolution networks on ARM. Convolution networks will be executed in FP32. PRs: #19069, #19192, #19176
• GPU runtime: Added paddings for dynamic convolutions to improve performance for models like Stable-Diffusion v2.1, PR: #19001
• Python API:
  • Added the torchvision.transforms object to OpenVINO preprocessing. PR: #17934
  • All python tools related with OpenVINO are now available via single namespace, to improve user experience by better API readability. PR: #18157
• TensorFlow FE:
  • Added support for the TensorFlow 1 Checkpoint format. All native TensorFlow formats are now enabled.
  • Added support for 8 new operations:
    • MaxPoolWithArgmax #19085
    • UnravelIndex #19060
    • AdjustContrastv2 #19029
    • InvertPermutation #19020
    • CheckNumerics #19020
    • DivNoNan #19007
    • EnsureShape #19007
    • ShapeN #18913
• PyTorch FE:
  • Added support for 7 new operations. To know how to enjoy PyTorch models conversion follow Link
    • aten::concat #19101
    • aten::masked_scatter #19038
    • aten::linspace #18998
    • aten::view_as #18960
    • aten::std #18960
    • aten::outer #18903
    • aten::broadcast_to #18899

New openvino_notebooks

• 245-typo-detector English Typo Detection in sentences with OpenVINO™
• 247-code-language-id Identify the programming language used in an arbitrary code snippet
• 121-convert-to-openvino Learn OpenVINO model conversion API
• 244-named-entity-recognition Named entity recognition with OpenVINO™
• 246-depth-estimation-videpth Monocular Visual-Inertial Depth Estimation with OpenVINO™
• 248-stable-diffusion-xl Image generation with Stable Diffusion XL
• 249-oneformer-segmentation Universal segmentation with OneFormer

Fixed GitHub issues

• Fixed #18978 "Webassembly build fails" with PR #19005
• Fixed #18847 "Debugging OpenVINO Python GIL Error" with PR #18848
• Fixed #18465 "OpenVINO can't be built in an environment that has an 'ambient' oneDNN installation" with PR #18805

Acknowledgements Thanks for contributions from the OpenVINO developer community: @DmitriyValetov, @kai-waang

Full Changelog: 2023.1.0.dev20230728...2023.1.0.dev20230811