37c4e1817aAccepting request 1235692 from science:machinelearning
factory
Ana Guerrero
2025-01-09 14:06:03 +00:00
6f04946ce8- openvino-onnx-ml-defines.patch and openvino-remove-npu-compile-tool.patchhas been removed as it is no longer needed in this version. - Update to 2024.4.0 - Summary of major features and improvements * OpenVINO 2024.6 release includes updates for enhanced stability and improved LLM performance. * Introduced support for Intel® Arc™ B-Series Graphics (formerly known as Battlemage). * Implemented optimizations to improve the inference time and LLM performance on NPUs. * Improved LLM performance with GenAI API optimizations and bug fixes. - 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 macOS x86_64 debug bins will no longer be provided with the OpenVINO toolkit, starting with OpenVINO 2024.5. + Python 3.8 is no longer supported, starting with OpenVINO 2024.5. + As MxNet doesn’t support Python version higher than 3.8, according to the MxNet PyPI project, it is no longer supported by OpenVINO, either. + Discrete Keem Bay support is no longer supported, starting with OpenVINO 2024.5. + Support for discrete devices (formerly codenamed Raptor Lake) is no longer available for NPU.
devel
Guillaume GARDET2025-01-07 17:14:03 +00:00
ae427f9e36Accepting request 1229915 from science:machinelearning
Ana Guerrero
2024-12-10 22:50:04 +00:00
8d5f5ac9c6- fix build on tumbleweed * currently openvino does not support protobuf v22 or newerGuillaume GARDET2024-12-10 17:24:18 +00:00
0a7c9bb146Accepting request 1208543 from science:machinelearning
Ana Guerrero
2024-10-17 16:39:32 +00:00
fd89371cd6- Temporarily inserted gcc-13 in Tumbleweed/Factory/Slowroll: Because there is an incompatibility of the source code of the level-zero library and npu module with gcc-14. I am working with Intel on tests to return to native gcc. - Update to 2024.4.0 - Summary of major features and improvements * More Gen AI coverage and framework integrations to minimize code changes + Support for GLM-4-9B Chat, MiniCPM-1B, Llama 3 and 3.1, Phi-3-Mini, Phi-3-Medium and YOLOX-s models. + Noteworthy notebooks added: Florence-2, NuExtract-tiny Structure Extraction, Flux.1 Image Generation, PixArt-α: Photorealistic Text-to-Image Synthesis, and Phi-3-Vision Visual Language Assistant. * Broader Large Language Model (LLM) support and more model compression techniques. + OpenVINO™ runtime optimized for Intel® Xe Matrix Extensions (Intel® XMX) systolic arrays on built-in GPUs for efficient matrix multiplication resulting in significant LLM performance boost with improved 1st and 2nd token latency, as well as a smaller memory footprint on Intel® Core™ Ultra Processors (Series 2). + Memory sharing enabled for NPUs on Intel® Core™ Ultra Processors (Series 2) for efficient pipeline integration without memory copy overhead. + Addition of the PagedAttention feature for discrete GPUs* enables a significant boost in throughput for parallel inferencing when serving LLMs on Intel® Arc™ Graphics or Intel® Data Center GPU Flex Series. * More portability and performance to run AI at the edge, in the cloud, or locally. + OpenVINO™ Model Server now comes with production-quality support for OpenAI-compatible API which enables i significantly higher throughput for parallel inferencing on Intel® Xeon® processors when serving LLMs to many concurrent users. + Improved performance and memory consumption with prefix caching, KV cache compression, and other optimizations for serving LLMs using OpenVINO™ Model Server. + Support for Python 3.12. - 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 macOS x86_64 debug bins will no longer be provided with the OpenVINO toolkit, starting with OpenVINO 2024.5. + Python 3.8 is now considered deprecated, and it will not be available beyond the 2024.4 OpenVINO version. + dKMB support is now considered deprecated and will be fully removed with OpenVINO 2024.5 + Intel® Streaming SIMD Extensions (Intel® SSE) will be supported in source code form, but not enabled in the binary package by default, starting with OpenVINO 2025.0 + The openvino-nightly PyPI module will soon be discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy. + 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. + A number of notebooks have been deprecated. For an up-to-date listing of available notebooks, refer to the OpenVINO™ Notebook index (openvinotoolkit.github.io).Guillaume GARDET2024-10-17 06:21:51 +00:00
fbf5e530a9- Add Leap15 build - Remove comment lines in the spec file that cause the insertion of extra lines during a commitGuillaume GARDET2024-10-03 06:36:01 +00:00
89541f3922Accepting request 1196392 from science:machinelearning
Dominique Leuenberger
2024-08-28 19:30:51 +00:00
74f2524437The specification file update was automatically generated by the osc command.Guillaume GARDET2024-08-28 06:11:03 +00:00
3ac7c6a6fe- Update to 2024.3.0 - Summary of major features and improvements * More Gen AI coverage and framework integrations to minimize code changes + OpenVINO pre-optimized models are now available in Hugging Face making it easier for developers to get started with these models. * Broader Large Language Model (LLM) support and more model compression techniques. + Significant improvement in LLM performance on Intel discrete GPUs with the addition of Multi-Head Attention (MHA) and OneDNN enhancements. * More portability and performance to run AI at the edge, in the cloud, or locally. + Improved CPU performance when serving LLMs with the inclusion of vLLM and continuous batching in the OpenVINO Model Server (OVMS). vLLM is an easy-to-use open-source library that supports efficient LLM inferencing and model serving. + Ubuntu 24.04 long-term support (LTS), 64-bit (Kernel 6.8+) (preview support) - 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. + A number of notebooks have been deprecated. For an up-to-date listing of available notebooks, refer to the OpenVINO™ Notebook index (openvinotoolkit.github.io).Christian Goll2024-08-20 13:01:12 +00:00
be4505d5d1Accepting request 1183106 from science:machinelearning
Ana Guerrero
2024-06-25 21:08:00 +00:00
94fa725fc2Accepting request 1181952 from science:machinelearning
Ana Guerrero
2024-06-21 14:03:48 +00:00
580d68900e- Update to 2024.2.0 - More Gen AI coverage and framework integrations to minimize code changes * Llama 3 optimizations for CPUs, built-in GPUs, and discrete GPUs for improved performance and efficient memory usage. * Support for Phi-3-mini, a family of AI models that leverages the power of small language models for faster, more accurate and cost-effective text processing. * Python Custom Operation is now enabled in OpenVINO making it easier for Python developers to code their custom operations instead of using C++ custom operations (also supported). Python Custom Operation empowers users to implement their own specialized operations into any model. * Notebooks expansion to ensure better coverage for new models. Noteworthy notebooks added: DynamiCrafter, YOLOv10, Chatbot notebook with Phi-3, and QWEN2. - Broader Large Language Model (LLM) support and more model compression techniques. * GPTQ method for 4-bit weight compression added to NNCF for more efficient inference and improved performance of compressed LLMs. * Significant LLM performance improvements and reduced latency for both built-in GPUs and discrete GPUs. * Significant improvement in 2nd token latency and memory footprint of FP16 weight LLMs on AVX2 (13th Gen Intel® Core™ processors) and AVX512 (3rd Gen Intel® Xeon® Scalable Processors) based CPU platforms, particularly for small batch sizes. - More portability and performance to run AI at the edge, in the cloud, or locally. * Model Serving Enhancements: * Preview: OpenVINO Model Server (OVMS) now supports OpenAI-compatible API along with Continuous Batching and PagedAttention, enabling significantly higher throughput for parallel inferencing, especially on Intel® Xeon® processors, when serving LLMs to many concurrent users. * OpenVINO backend for Triton Server now supports built-in GPUs and discrete GPUs, in addition to dynamic shapes support. * Integration of TorchServe through torch.compile OpenVINO backend for easy model deployment, provisioning to multiple instances, model versioning, and maintenance. * Preview: addition of the Generate API, a simplified API for text generation using large language models with only a few lines of code. The API is available through the newly launched OpenVINO GenAI package. * Support for Intel Atom® Processor X Series. For more details, see System Requirements. * Preview: Support for Intel® Xeon® 6 processor. - 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. + A number of notebooks have been deprecated. For an up-to-date listing of available notebooks, refer to the OpenVINO™ Notebook index (openvinotoolkit.github.io).Guillaume GARDET2024-06-20 13:47:15 +00:00
812f79c477Accepting request 1173894 from science:machinelearning
Ana Guerrero
2024-05-14 11:38:33 +00:00
Ana Guerrero
Guillaume GARDET
Dominique Leuenberger
Christian Goll
Atri Bhattacharya