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C++ Sample Usage Guide

【免费下载链接】geGE（Graph Engine）是面向昇腾的图编译器和执行器，提供了计算图优化、多流并行、内存复用和模型下沉等技术手段，加速模型执行效率，减少模型内存占用。 GE 提供对 PyTorch、TensorFlow 前端的友好接入能力，并同时支持 onnx、pb 等主流模型格式的解析与编译。项目地址: https://gitcode.com/cann/ge

Directory Structure

├── CMakeLists.txt cmake configuration file ├── README.md Sample usage guide ├── config │ ├── model_generator.py Script to generate models required for test cases │ ├── add_func_multi.json Configuration file for multi-func used in test cases │ ├── add_func_multi_control.json Configuration file for multi-func used in test cases │ ├── add_func.json Configuration file for add FunctionPp used in test cases │ ├── add_graph.json Configuration file for add GraphPp used in test cases │ ├── data_flow_deploy_info.json Configuration file to specify node deployment location in test cases │ └── invoke_func.json Configuration file for udf calling nn used in test cases ├── node_builder.h Common methods to construct FunctionPp and GraphPp ├── sample_base.cpp This sample demonstrates basic DataFlow API graph building, including construction and execution of UDF, GraphPp, and UDF executing NN inference types of nodes ├── sample_timebatch.cpp This sample demonstrates TimeBatch usage method ├── sample_countbatch.cpp This sample demonstrates CountBatch usage method ├── sample_tensorflow.cpp This sample demonstrates TensorFlow graph construction DataFlow node method ├── sample_multifunc.cpp This sample demonstrates multi-func calling method ├── sample_exception.cpp This sample demonstrates enabling exception reporting method └── sample_perf.cpp This sample tests Feed and Fetch interface performance

Environment Requirements

• Reference Environment Preparation to download and install driver/firmware/CANN software packages;
• Model generation script model_generator.py in config directory depends on tensorflow, need to install through pip3 install tensorflow.

Program Compilation

# Execute tensorflow original model generation script in config directory: python3 config/model_generator.py # After execution completes, generate add.pb model in config directory source {HOME}/Ascend/cann/set_env.sh # "{HOME}/Ascend" is CANN software package installation directory, replace according to actual installation path. mkdir build cd build cmake .. && make -j 64 cd ../output

Run Samples

numa_config.json file configuration reference in the following text: numa_config field description and sample

# Optional export ASCEND_GLOBAL_LOG_LEVEL=3 #0 debug 1 info 2 warn 3 error Default error level if not set # Required source {HOME}/Ascend/cann/set_env.sh # "{HOME}/Ascend" is CANN software package installation directory, replace according to actual installation path. export RESOURCE_CONFIG_PATH=xxx/xxx/xxx/numa_config.json ./sample_base ./sample_timebatch ./sample_countbatch ./sample_tensorflow ./sample_multifunc ./sample_exception ./sample_perf # unset this environment variable to prevent affecting non-dflow test cases unset RESOURCE_CONFIG_PATH

【免费下载链接】geGE（Graph Engine）是面向昇腾的图编译器和执行器，提供了计算图优化、多流并行、内存复用和模型下沉等技术手段，加速模型执行效率，减少模型内存占用。 GE 提供对 PyTorch、TensorFlow 前端的友好接入能力，并同时支持 onnx、pb 等主流模型格式的解析与编译。项目地址: https://gitcode.com/cann/ge