openvinotoolkit/openvino 2024.1.0

on GitHub

Summary of major features and improvements  

• More Generative AI coverage and framework integrations to minimize code changes.

  • Mixtral and URLNet models optimized for performance improvements on Intel® Xeon® processors.
  • Stable Diffusion 1.5, ChatGLM3-6B, and Qwen-7B models optimized for improved inference speed on Intel® Core™ Ultra processors with integrated GPU.
  • Support for Falcon-7B-Instruct, a GenAI Large Language Model (LLM) ready-to-use chat/instruct model with superior performance metrics.
  • New Jupyter Notebooks added: YOLO V9, YOLO V8 Oriented Bounding Boxes Detection (OOB), Stable Diffusion in Keras, MobileCLIP, RMBG-v1.4 Background Removal, Magika, TripoSR, AnimateAnyone, LLaVA-Next, and RAG system with OpenVINO and LangChain.

• Broader Large Language Model (LLM) support and more model compression techniques.

  • LLM compilation time reduced through additional optimizations with compressed embedding. Improved 1st token performance of LLMs on 4th and 5th generations of Intel® Xeon® processors with Intel® Advanced Matrix Extensions (Intel® AMX).
  • Better LLM compression and improved performance with oneDNN, INT4, and INT8 support for Intel® Arc™ GPUs.
  • Significant memory reduction for select smaller GenAI models on Intel® Core™ Ultra processors with integrated GPU.

• More portability and performance to run AI at the edge, in the cloud, or locally.

  • The preview NPU plugin for Intel® Core™ Ultra processors is now available in the OpenVINO open-source GitHub repository, in addition to the main OpenVINO package on PyPI.
  • The JavaScript API is now more easily accessible through the npm repository, enabling JavaScript developers’ seamless access to the OpenVINO API.
  • FP16 inference on ARM processors now enabled for the Convolutional Neural Network (CNN) by default.

Support Change and Deprecation Notices

• Using deprecated features and components is not advised. They are available to enable a smooth transition to new solutions and will be discontinued in the future. To keep using Discontinued features, you will have to revert to the last LTS OpenVINO version supporting them.
  For more details, refer to the OpenVINO Legacy Features and Components page.
• Discontinued in 2024.0:
  • Runtime components:
    • Intel® Gaussian & Neural Accelerator (Intel® GNA). Consider using 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 (see 2023.3 API transition guide for reference).
    • All ONNX Frontend legacy API (known as ONNX_IMPORTER_API)
    • 'PerfomanceMode.UNDEFINED' property as part of the OpenVINO Python API
  • Tools:
    • Deployment Manager. See installation and deployment guides for current distribution options.
    • Accuracy Checker.
    • Post-Training Optimization Tool (POT). Neural Network Compression Framework (NNCF) should be used instead.
    • A Git patch for NNCF integration with huggingface/transformers. The recommended approach is to use huggingface/optimum-intel for applying NNCF optimization on top of models from Hugging Face.
    • Support for Apache MXNet, Caffe, and Kaldi model formats. Conversion to ONNX may be used as a solution.
• Deprecated and to be removed in the future:
  • The OpenVINO™ Development Tools package (pip install openvino-dev) will be removed from installation options and distribution channels beginning with OpenVINO 2025.0.
  • Model Optimizer will be discontinued with OpenVINO 2025.0. Consider using the new conversion methods instead. For more details, see the model conversion transition guide.
  • OpenVINO property Affinity API will be discontinued with OpenVINO 2025.0. It will be replaced with CPU binding configurations (ov::hint::enable_cpu_pinning).
  • OpenVINO Model Server components:
    • “auto shape” and “auto batch size” (reshaping a model in runtime) will be removed in the future. OpenVINO’s dynamic shape models are recommended instead.

You can find OpenVINO™ toolkit 2024.1 release here:

• Download archives* with OpenVINO™
• Install it via Conda: conda install -c conda-forge openvino=2024.1.0
• OpenVINO™ for Python: pip install openvino==2024.1.0

Acknowledgements

Thanks for contributions from the OpenVINO developer community:
@LucaTamSapienza
@AsakusaRinne
@awayzjj
@MonalSD
@siddhant-0707
@qxprakash
@FredBill1
@Pranshu-S
@vshampor
@PRATHAM-SPS
@inbasperu
@linzs148
@chux0519
@ccinv
@Vishwa44
@rghvsh
@Aryan8912
@BHbean
@Vladislav-Denisov
@MeeCreeps
@YaritaiKoto
@Godwin-T
@mory91
@Bepitic
@akiseakusa
@kuanxian1
@himanshugupta11002
@mengbingrock

Release documentation is available here: https://docs.openvino.ai/2024
Release Notes are available here: https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino/2024-1.html