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Alessandro de Oliveira Faria 65a3f6ff45 - Update to 2025.4.1 
* Preview: Mixture of Experts (MoE) models optimized for CPUs and 
  GPUs, validated for GPT-OSS 20B model. How to convert model:
  optimum-cli export openvino -m "openai/gpt-oss-20b" out_dir 
  --weight-format int4
* Fixed issue ID 174531: Accuracy regression of 
  Mistral-7b-instruct-v0.2 and Mistral-7b-instruct-v0.3 
  on all devices when executed with OpenVINO GenAI. 
  As a workaround, use the IR converted with OpenVINO 2025.3.
* Fixed issue ID 176777: Using the callback parameter with the 
  Python API call generate() in Text2ImagePipeline, 
  Image2ImagePipeline, InpaintingPipeline may cause the process 
  to hang. As a workaround, do not use the callback parameter. 
  C++ implementations was not affected.
* Resolved an issue in the NPU plugin where the Level Zero (L0)
  context was implemented as a static global object and only 
  destroyed during DLL unload, even after unload_plugin() 
  was called. This behavior prevented the driver from 
  spawning threads required for certain optimizations and 
  features.

OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=47
2026-01-07 20:47:17 +00:00

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-------------------------------------------------------------------
Sun Dec 28 02:04:43 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Update to 2025.4.0
* Preview: Mixture of Experts (MoE) models optimized for CPUs and
GPUs, validated for GPT-OSS 20B model. How to convert model:
optimum-cli export openvino -m "openai/gpt-oss-20b" out_dir
--weight-format int4
* Fixed issue ID 174531: Accuracy regression of
Mistral-7b-instruct-v0.2 and Mistral-7b-instruct-v0.3
on all devices when executed with OpenVINO GenAI.
As a workaround, use the IR converted with OpenVINO 2025.3.
* Fixed issue ID 176777: Using the callback parameter with the
Python API call generate() in Text2ImagePipeline,
Image2ImagePipeline, InpaintingPipeline may cause the process
to hang. As a workaround, do not use the callback parameter.
C++ implementations was not affected.
* Resolved an issue in the NPU plugin where the Level Zero (L0)
context was implemented as a static global object and only
destroyed during DLL unload, even after unload_plugin()
was called. This behavior prevented the driver from
spawning threads required for certain optimizations and
features.
-------------------------------------------------------------------
Tue Dec 2 22:43:52 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Update to 2025.4.0
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported:
+ On CPUs & GPUs: Qwen3-Embedding-0.6B, Qwen3-Reranker-0.6B,
Mistral-Small-24B-Instruct-2501.
+ On NPUs: Gemma-3-4b-it and Qwen2.5-VL-3B-Instruct.
* Preview: Mixture of Experts (MoE) models optimized for CPUs
and GPUs, validated for Qwen3-30B-A3B.
* GenAI pipeline integrations: Qwen3-Embedding-0.6B and
Qwen3-Reranker-0.6B for enhanced retrieval/ranking, and
Qwen2.5VL-7B for video pipeline.
- Broader LLM model support and more model compression
techniques
* The Neural Network Compression Framework (NNCF) ONNX backend
now supports INT8 static post-training quantization (PTQ)
and INT8/INT4 weight-only compression to ensure accuracy
parity with OpenVINO IR format models. SmoothQuant algorithm
support added for INT8 quantization.
* Accelerated multi-token generation for GenAI, leveraging
optimized GPU kernels to deliver faster inference, smarter
KV-cache reuse, and scalable LLM performance.
* GPU plugin updates include improved performance with prefix
caching for chat history scenarios and enhanced LLM accuracy
with dynamic quantization support for INT8.
- More portability and performance to run AI at the edge, in the
cloud, or locally.
* Announcing support for Intel® Core Ultra Processor Series 3.
* Encrypted blob format support added for secure model
deployment with OpenVINO GenAI. Model weights and artifacts
are stored and transmitted in an encrypted format, reducing
risks of IP theft during deployment. Developers can deploy
with minimal code changes using OpenVINO GenAI pipelines.
* OpenVINO™ Model Server and OpenVINO™ GenAI now extend
support for Agentic AI scenarios with new features such as
output parsing and improved chat templates for reliable
multi-turn interactions, and preview functionality for the
Qwen3-30B-A3B model. OVMS also introduces a preview for
audio endpoints.
* NPU deployment is simplified with batch support, enabling
seamless model execution across Intel® Core Ultra
processors while eliminating driver dependencies. Models
are reshaped to batch_size=1 before compilation.
* The improved NVIDIA Triton Server* integration with
OpenVINO backend now enables developers to utilize Intel
GPUs or NPUs for deployment.
-------------------------------------------------------------------
Sun Sep 7 01:21:19 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Update to 2025.3.0
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported: Phi-4-mini-reasoning, AFM-4.5B,
Gemma-3-1B-it, Gemma-3-4B-it, and Gemma-3-12B,
* NPU support added for: Qwen3-1.7B, Qwen3-4B, and Qwen3-8B.
* LLMs optimized for NPU now available on OpenVINO Hugging
Face collection.
- Broader LLM model support and more model compression techniques
* The NPU plug-in adds support for longer contexts of up to
8K tokens, dynamic prompts, and dynamic LoRA for improved
LLM performance.
* The NPU plug-in now supports dynamic batch sizes by reshaping
the model to a batch size of 1 and concurrently managing
multiple inference requests, enhancing performance and
optimizing memory utilization.
* Accuracy improvements for GenAI models on both built-in
and discrete graphics achieved through the implementation
of the key cache compression per channel technique, in
addition to the existing KV cache per-token compression
method.
* OpenVINO™ GenAI introduces TextRerankPipeline for improved
retrieval relevance and RAG pipeline accuracy, plus
Structured Output for enhanced response reliability and
function calling while ensuring adherence to predefined
formats.
- More portability and performance to run AI at the edge,
in the cloud, or locally.
* Announcing support for Intel® Arc™ Pro B-Series
(B50 and B60).
* Preview: Hugging Face models that are GGUF-enabled for
OpenVINO GenAI are now supported by the OpenVINO™ Model
Server for popular LLM model architectures such as
DeepSeek Distill, Qwen2, Qwen2.5, and Llama 3.
This functionality reduces memory footprint and
simplifies integration for GenAI workloads.
* With improved reliability and tool call accuracy,
the OpenVINO™ Model Server boosts support for
agentic AI use cases on AI PCs, while enhancing
performance on Intel CPUs, built-in GPUs, and NPUs.
* int4 data-aware weights compression, now supported in the
Neural Network Compression Framework (NNCF) for ONNX
models, reduces memory footprint while maintaining
accuracy and enables efficient deployment in
resource-constrained environments.
-------------------------------------------------------------------
Wed Jun 25 01:09:14 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- openSUSE Leap 16.0 compatibility
-------------------------------------------------------------------
Tue Jun 24 05:10:06 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Remove openvino-gcc5-compatibility.patch file
-------------------------------------------------------------------
Tue Jun 24 02:54:10 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
Summary of major features and improvements  
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported on CPUs & GPUs: Phi-4,
Mistral-7B-Instruct-v0.3, SD-XL Inpainting 0.1, Stable
Diffusion 3.5 Large Turbo, Phi-4-reasoning, Qwen3, and
Qwen2.5-VL-3B-Instruct. Mistral 7B Instruct v0.3 is also
supported on NPUs.
* Preview: OpenVINO ™ GenAI introduces a text-to-speech
pipeline for the SpeechT5 TTS model, while the new RAG
backend offers developers a simplified API that delivers
reduced memory usage and improved performance.
* Preview: OpenVINO™ GenAI offers a GGUF Reader for seamless
integration of llama.cpp based LLMs, with Python and C++
pipelines that load GGUF models, build OpenVINO graphs,
and run GPU inference on-the-fly. Validated for popular models:
DeepSeek-R1-Distill-Qwen (1.5B, 7B), Qwen2.5 Instruct
(1.5B, 3B, 7B) & llama-3.2 Instruct (1B, 3B, 8B).
- Broader LLM model support and more model compression
techniques
* Further optimization of LoRA adapters in OpenVINO GenAI
for improved LLM, VLM, and text-to-image model performance
on built-in GPUs. Developers can use LoRA adapters to
quickly customize models for specialized tasks.
* KV cache compression for CPUs is enabled by default for
INT8, providing a reduced memory footprint while maintaining
accuracy compared to FP16. Additionally, it delivers
substantial memory savings for LLMs with INT4 support compared
to INT8.
* Optimizations for Intel® Core™ Ultra Processor Series 2
built-in GPUs and Intel® Arc™ B Series Graphics with the
Intel® XMX systolic platform to enhance the performance of
VLM models and hybrid quantized image generation models, as
well as improve first-token latency for LLMs through dynamic
quantization.
- More portability and performance to run AI at the edge, in the
cloud, or locally.
* Enhanced Linux* support with the latest GPU driver for
built-in GPUs on Intel® Core™ Ultra Processor Series 2
(formerly codenamed Arrow Lake H).
* Support for INT4 data-free weights compression for ONNX
models implemented in the Neural Network Compression
Framework (NNCF).
* NPU support for FP16-NF4 precision on Intel® Core™ 200V
Series processors for models with up to 8B parameters is
enabled through symmetrical and channel-wise quantization,
improving accuracy while maintaining performance efficiency.
Support Change and Deprecation Notices
- Discontinued in 2025:
* Runtime components:
+ The OpenVINO property of Affinity API is no longer
available. It has been replaced with CPU binding
configurations (ov::hint::enable_cpu_pinning).
+ The openvino-nightly PyPI module has been discontinued.
End-users should proceed with the Simple PyPI nightly repo
instead. More information in Release Policy. The
openvino-nightly PyPI module has been discontinued.
End-users should proceed with the Simple PyPI nightly repo
instead. More information in Release Policy.
* Tools:
+ The OpenVINO™ Development Tools package (pip install
openvino-dev) is no longer available for OpenVINO releases
in 2025.
+ Model Optimizer is no longer available. Consider using the
new conversion methods instead. For more details, see the
model conversion transition guide.
+ Intel® Streaming SIMD Extensions (Intel® SSE) are currently
not enabled in the binary package by default. They are
still supported in the source code form.
+ Legacy prefixes: l_, w_, and m_ have been removed from
OpenVINO archive names.
* OpenVINO GenAI:
+ StreamerBase::put(int64_t token)
+ The Bool value for Callback streamer is no longer accepted.
It must now return one of three values of StreamingStatus
enum.
+ ChunkStreamerBase is deprecated. Use StreamerBase instead.
* NNCF create_compressed_model() method is now deprecated.
nncf.quantize() method is recommended for
Quantization-Aware Training of PyTorch and TensorFlow models.
* OpenVINO Model Server (OVMS) benchmark client in C++
using TensorFlow Serving API.
- Deprecated and to be removed in the future:
* Python 3.9 is now deprecated and will be unavailable after
OpenVINO version 2025.4.
* openvino.Type.undefined is now deprecated and will be removed
with version 2026.0. openvino.Type.dynamic should be used
instead.
* APT & YUM Repositories Restructure: Starting with release
2025.1, users can switch to the new repository structure
for APT and YUM, which no longer uses year-based
subdirectories (like “2025”). The old (legacy) structure
will still be available until 2026, when the change will
be finalized. Detailed instructions are available on the
relevant documentation pages:
+ Installation guide - yum
+ Installation guide - apt
* OpenCV binaries will be removed from Docker images in 2026.
* Ubuntu 20.04 support will be deprecated in future OpenVINO
releases due to the end of standard support.
* “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.
* MacOS x86 is no longer recommended for use due to the
discontinuation of validation. Full support will be removed
later in 2025.
* The openvino namespace of the OpenVINO Python API has been
redesigned, removing the nested openvino.runtime module.
The old namespace is now considered deprecated and will be
discontinued in 2026.0.
-------------------------------------------------------------------
Wed May 21 14:43:02 UTC 2025 - Andreas Schwab <schwab@suse.de>
- Fix file list for riscv64
-------------------------------------------------------------------
Mon May 5 07:47:30 UTC 2025 - Dominique Leuenberger <dimstar@opensuse.org>
- Do not force GCC15 on Tumblewed just yet: follow the distro
default compiler, like any other package.
-------------------------------------------------------------------
Sat May 3 19:19:07 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- openvino-gcc5-compatibility.patch to resolve incompatibility
in gcc5
-------------------------------------------------------------------
Thu May 1 01:06:52 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Added gcc-14
-------------------------------------------------------------------
Mon Apr 14 06:52:03 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Update to 2025.1.0
- Downgrade from gcc13-c++ to 12 due to incompatibility in tbb
compilation. This was due to C++ libraries (using libstdc++)
resulting in the error: libtbb.so.12: undefined reference to
`__cxa_call_terminate@CXXABI_1.3.15'
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported: Phi-4 Mini, Jina CLIP v1, and Bce
Embedding Base v1.
* OpenVINO™ Model Server now supports VLM models, including
Qwen2-VL, Phi-3.5-Vision, and InternVL2.
* OpenVINO GenAI now includes image-to-image and inpainting
features for transformer-based pipelines, such as Flux.1 and
Stable Diffusion 3 models, enhancing their ability to generate
more realistic content.
* Preview: AI Playground now utilizes the OpenVINO Gen AI backend
to enable highly optimized inferencing performance on AI PCs.
- Broader LLM model support and more model compression techniques
* Reduced binary size through optimization of the CPU plugin and
removal of the GEMM kernel.
* Optimization of new kernels for the GPU plugin significantly
boosts the performance of Long Short-Term Memory (LSTM) models,
used in many applications, including speech recognition,
language modeling, and time series forecasting.
* Preview: Token Eviction implemented in OpenVINO GenAI to reduce
the memory consumption of KV Cache by eliminating unimportant
tokens. This current Token Eviction implementation is
beneficial for tasks where a long sequence is generated, such
as chatbots and code generation.
* NPU acceleration for text generation is now enabled in
OpenVINO™ Runtime and OpenVINO™ Model Server to support the
power-efficient deployment of VLM models on NPUs for AI PC use
cases with low concurrency.
- More portability and performance to run AI at the edge, in the
cloud, or locally.
* Additional LLM performance optimizations on Intel® Core™ Ultra
200H series processors for improved 2nd token latency on
Linux.
* Enhanced performance and efficient resource utilization with
the implementation of Paged Attention and Continuous Batching
by default in the GPU plugin.
* Preview: The new OpenVINO backend for Executorch will enable
accelerated inference and improved performance on Intel
hardware, including CPUs, GPUs, and NPUs.
-------------------------------------------------------------------
Tue Mar 4 00:38:30 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Disabled JAX plugin beta.
-------------------------------------------------------------------
Sun Feb 9 03:36:41 UTC 2025 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Update to 2025.0.0
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported: Qwen 2.5, Deepseek-R1-Distill-Llama-8B,
DeepSeek-R1-Distill-Qwen-7B, and DeepSeek-R1-Distill-Qwen-1.5B,
FLUX.1 Schnell and FLUX.1 Dev
* Whisper Model: Improved performance on CPUs, built-in GPUs,
and discrete GPUs with GenAI API.
* Preview: Introducing NPU support for torch.compile, giving
developers the ability to use the OpenVINO backend to run the
PyTorch API on NPUs. 300+ deep learning models enabled from
the TorchVision, Timm, and TorchBench repositories..
- Broader Large Language Model (LLM) support and more model
compression techniques.
* Preview: Addition of Prompt Lookup to GenAI API improves 2nd
token latency for LLMs by effectively utilizing predefined
prompts that match the intended use case.
* Preview: The GenAI API now offers image-to-image inpainting
functionality. This feature enables models to generate
realistic content by inpainting specified modifications and
seamlessly integrating them with the original image.
* Asymmetric KV Cache compression is now enabled for INT8 on
CPUs, resulting in lower memory consumption and improved 2nd
token latency, especially when dealing with long prompts that
require significant memory. The option should be explicitly
specified by the user.
- More portability and performance to run AI at the edge, in the
cloud, or locally.
* Support for the latest Intel® Core™ Ultra 200H series
processors (formerly codenamed Arrow Lake-H)
* Integration of the OpenVINO ™ backend with the Triton
Inference Server allows developers to utilize the Triton
server for enhanced model serving performance when deploying
on Intel CPUs.
* Preview: A new OpenVINO ™ backend integration allows
developers to leverage OpenVINO performance optimizations
directly within Keras 3 workflows for faster AI inference on
CPUs, built-in GPUs, discrete GPUs, and NPUs. This feature is
available with the latest Keras 3.8 release.
- Support Change and Deprecation Notices
* Now deprecated:
+ Legacy prefixes l_, w_, and m_ have been removed from
OpenVINO archive names.
+ The runtime namespace for Python API has been marked as
deprecated and designated to be removed for 2026.0. The
new namespace structure has been delivered, and migration
is possible immediately. Details will be communicated
through warnings andvia documentation.
+ NNCF create_compressed_model() method is deprecated.
nncf.quantize() method is now recommended for
Quantization-Aware Training of PyTorch and
TensorFlow models.
-------------------------------------------------------------------
Sun Dec 29 03:41:47 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- 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.
-------------------------------------------------------------------
Tue Dec 10 15:50:41 UTC 2024 - Giacomo Comes <gcomes.obs@gmail.com>
- fix build on tumbleweed
* currently openvino does not support protobuf v22 or newer
-------------------------------------------------------------------
Tue Oct 15 00:56:54 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- 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).
-------------------------------------------------------------------
Wed Oct 2 20:56:59 UTC 2024 - Giacomo Comes <gcomes.obs@gmail.com>
- Add Leap15 build
- Remove comment lines in the spec file that cause the insertion
of extra lines during a commit
-------------------------------------------------------------------
Sat Aug 10 01:41:06 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Remove NPU Compile Tool
* openvino-remove-npu-compile-tool.patch
- 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.
- 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).
-------------------------------------------------------------------
Sat Jun 22 12:01:23 UTC 2024 - Andreas Schwab <schwab@suse.de>
- Add riscv-cpu-plugin subpackage
-------------------------------------------------------------------
Wed Jun 19 21:36:01 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- 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).
-------------------------------------------------------------------
Thu May 9 22:56:53 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- 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.
-------------------------------------------------------------------
Tue Apr 23 18:57:17 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>
- License update: play safe and list all third party licenses as
part of the License tag.
-------------------------------------------------------------------
Tue Apr 23 12:42:32 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>
- Switch to _service file as tagged Source tarball does not
include `./thirdparty` submodules.
- Update openvino-fix-install-paths.patch to fix python module
install path.
- Enable python module and split it out into a python subpackage
(for now default python3 only).
- Explicitly build python metadata (dist-info) and install it
(needs simple sed hackery to support "officially" unsupported
platform ppc64le).
- Specify ENABLE_JS=OFF to turn off javascript bindings as
building these requires downloading npm stuff from the network.
- Build with system pybind11.
- Bump _constraints for updated disk space requirements.
- Drop empty %check section, rpmlint was misleading when it
recommended adding this.
-------------------------------------------------------------------
Fri Apr 19 08:08:02 UTC 2024 - Atri Bhattacharya <badshah400@gmail.com>
- Numerous specfile cleanups:
* Drop redundant `mv` commands and use `install` where
appropriate.
* Build with system protobuf.
* Fix Summary tags.
* Trim package descriptions.
* Drop forcing CMAKE_BUILD_TYPE=Release, let macro default
RelWithDebInfo be used instead.
* Correct naming of shared library packages.
* Separate out libopenvino_c.so.* into own shared lib package.
* Drop rpmlintrc rule used to hide shlib naming mistakes.
* Rename Source tarball to %{name}-%{version}.EXT pattern.
* Use ldconfig_scriptlet macro for post(un).
- Add openvino-onnx-ml-defines.patch -- Define ONNX_ML at compile
time when using system onnx to allow using 'onnx-ml.pb.h'
instead of 'onnx.pb.h', the latter not being shipped with
openSUSE's onnx-devel package (gh#onnx/onnx#3074).
- Add openvino-fix-install-paths.patch: Change hard-coded install
paths in upstream cmake macro to standard Linux dirs.
- Add openvino-ComputeLibrary-include-string.patch: Include header
for std::string.
- Add external devel packages as Requires for openvino-devel.
- Pass -Wl,-z,noexecstack to %build_ldflags to avoid an exec stack
issue with intel CPU plugin.
- Use ninja for build.
- Adapt _constraits file for correct disk space and memory
requirements.
- Add empty %check section.
-------------------------------------------------------------------
Mon Apr 15 03:18:33 UTC 2024 - Alessandro de Oliveira Faria <cabelo@opensuse.org>
- Initial package
- Version 2024.0.0
- Add openvino-rpmlintrc.
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