https://software.intel.com/en-us/articles/transitioning-from-intel-movidius-neural-compute-sdk-to-openvino-toolkit 7. 模型优化器Developer Guide: https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer 8. 推理引擎Developer Guide: https://software.intel.com/en-us/articles/OpenVINO-InferEng...
网络模型在导入至TensorRT后会进行一系列的优化，主要优化内容如下图所示 TensorRT官网下载地址：https://developer.nvidia.com/zh-cn/tensorrt 开发者指南：https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html Github地址：https://github.com/NVIDIA/TensorRT MediaPipe MediaPipe 是一款由 Google...
Enhanced Low-Precision Pipeline to Accelerate Inference with OpenVINO™ toolkit. Developer Guide: Model Optimization with the OpenVINO™ Toolkit. Evaluating QA: Metrics, Predictions, and the Null Response. SW/HW configuration Framework configuration:ONNXRuntime, Optimum-Intel [NNCF] Application configur...
用于推理调整的有用文档: 推理引擎开发人员指南Inference Engine Developer Guide 推理引擎 API 参考Inference Engine API References 推理代码示例Inference Code Samples 应用演示Application Demos 训练后优化工具指南Post-Training Optimization Tool Guide 深度学习工作台指南Deep Learning Workbench Guide 英特尔媒体SDKIntel M...
运行OpenVINO™ Notebooks 仓库的具体安装指南在这里:https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-installation-guide 运行这个llm-chatbot 的代码示例,需要安装以下必要的依赖包。 选择推理的模型 由于我们在 Jupyter Notebook 演示中提供了一组由 OpenVINO™ 支持的 多语种的大预言...
开发者指南:https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html Github地址:https://github.com/NVIDIA/TensorRT MediaPipe MediaPipe 是一款由 Google Research 开发并开源的多媒体机器学习模型应用框架。在谷歌,一系列重要产品,如 YouTube、Google Lens、ARCore、Google Home 以及 Nest,都已深度...
git config --global user.name userName git config --global user.email userEmail 分支53 标签65 Sebastian Golebiewski[DOCS] Updating samples in GenAI Guide - p...ab9030f10小时前 17649 次提交 提交 .github [GHA] Set checkout timeout (#27995) ...
运行OpenVINO™ Notebooks 仓库的具体安装指南在这里:https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-installation-guide 运行这个llm-chatbot 的代码示例,需要安装以下必要的依赖包。 选择推理的模型 由于我们在 Jupyter Notebook 演示中提供了一组由 OpenVINO™ 支持的 多语种的大预言...
The OpenVINO™ Plugin architecture is described in the OpenVINO™ Developer Guide for Inference Engine Plugin Library. The source files are located under runtime/plugin. The three main components of the runtime plugin are the Plugin class, the Executable Network class, and the Inference Request...
You can also look at OpenVINO developer guide located here: https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/python/docs/code_examples.md#before-start-layout-of-the-project I hope that I have explained everything, @andrey-churkin can we close this issue? PS for actual docu...