A toolkit for optimizing and deploying AI inference
https://github.com/openvinotoolkit/openvino
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
- Developed at science:machinelearning
- Sources inherited from project openSUSE:Factory
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Source Files
Filename | Size | Changed |
---|---|---|
_constraints | 0000000206 206 Bytes | |
_service | 0000000585 585 Bytes | |
openvino-2024.1.0.obscpio | 0865282063 825 MB | |
openvino-ComputeLibrary-include-string.patch | 0000000546 546 Bytes | |
openvino-fix-build-sample-path.patch | 0000000656 656 Bytes | |
openvino-fix-install-paths.patch | 0000002931 2.86 KB | |
openvino-onnx-ml-defines.patch | 0000000489 489 Bytes | |
openvino-rpmlintrc | 0000000237 237 Bytes | |
openvino.changes | 0000007342 7.17 KB | |
openvino.obsinfo | 0000000100 100 Bytes | |
openvino.spec | 0000014724 14.4 KB |
Latest Revision
Ana Guerrero (anag+factory)
accepted
request 1173894
from
Guillaume GARDET (Guillaume_G)
(revision 2)
- Fix sample source path in build script: * openvino-fix-build-sample-path.patch - Update to 2024.1.0 - 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.
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