Guillaume GARDET
580d68900e
- 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).
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=9
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name: openvino
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version: 2024.2.0
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mtime: 1716981469
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commit: 5c0f38f83f62fdabcdc980fa6dc3ed1ea16c8a05
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