临淄网站建设多少钱,建设工程公司企业简介,网站建设7个基,网站建设应对客户问题的话术系列文章目录
TensorRT英伟达官方示例解析#xff08;一#xff09; TensorRT英伟达官方示例解析#xff08;二#xff09; TensorRT英伟达官方示例解析#xff08;三#xff09; 文章目录 系列文章目录前言一、参考资料二、配置系统环境三、00-MNISTData四、01-SimpleD…系列文章目录
TensorRT英伟达官方示例解析一 TensorRT英伟达官方示例解析二 TensorRT英伟达官方示例解析三 文章目录 系列文章目录前言一、参考资料二、配置系统环境三、00-MNISTData四、01-SimpleDemo4.1 Makefile4.2 main.cpp4.3 main.py 总结 前言 一、参考资料
官方示例https://github.com/NVIDIA/trt-samples-for-hackathon-cn/tree/master 官方视频https://www.bilibili.com/video/BV1jj411Z7wG/?spm_id_from333.337.search-card.all.clickvd_sourcece674108fa2e19e5322d710724193487 MINST数据集下载地址http://yann.lecun.com/exdb/mnist/
二、配置系统环境
先安装所需的依赖包在cookbook下面
pip install -r requirements.txtLinux–Tensorrt环境配置: https://blog.csdn.net/m0_70420861/article/details/135700049 Window–TensorRT环境配置: https://blog.csdn.net/m0_70420861/article/details/135658922?spm1001.2014.3001.5502
1. 添加python环境变量–方法一
查看python环境变量
echo $PYTHONPATH如果没有找到 TensorRT 的安装路径请将其添加到环境变量中。例如在 Linux 或 macOS 中您可以在命令行中执行以下命令
export PYTHONPATH$PYTHONPATH:/root/TensorRT-8.6.1.6记得替换成自己TensorRT的安装路径
2. 添加python环境变量–方法二
要将环境变量永久添加到 ~/.bashrc 或 ~/.bash_profile 文件中可以使用以下方法推荐
vi ~/.bashrc编辑.bashrc文件在文件末尾添加export PYTHONPATH$PYTHONPATH:/root/TensorRT-8.6.1.6 在终端中执行以下命令使更改生效
source ~/.bashrc三、00-MNISTData
loadMnistData.py
#
# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the License);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
## http://yann.lecun.com/exdb/mnist/, https://storage.googleapis.com/cvdf-datasets/mnist/import gzipimport cv2
import numpy as npclass MnistData():def __init__(self, dataPath, isOneHotFalse, randomSeed97):with open(dataPath train-images-idx3-ubyte.gz, rb) as f:self.trainImage self.extractImage(f)with open(dataPath train-labels-idx1-ubyte.gz, rb) as f:self.trainLabel self.extractLabel(f)with open(dataPath t10k-images-idx3-ubyte.gz, rb) as f:self.testImage self.extractImage(f)with open(dataPath t10k-labels-idx1-ubyte.gz, rb) as f:self.testLabel self.extractLabel(f, isOneHotisOneHot)self.isOneHot isOneHotif self.isOneHot:self.trainLabel self.convertToOneHot(self.trainLabel)self.testLabel self.convertToOneHot(self.testLabel)else:self.trainLabel self.trainLabel.astype(np.float32)self.testLabel self.testLabel.astype(np.float32)np.random.seed(randomSeed)def getBatch(self, batchSize, isTrain):if isTrain:index np.random.choice(len(self.trainImage), batchSize, True)return self.trainImage[index], self.trainLabel[index]else:index np.random.choice(len(self.testImage), batchSize, True)return self.testImage[index], self.testLabel[index]def read4Byte(self, byteStream):dt np.dtype(np.uint32).newbyteorder()return np.frombuffer(byteStream.read(4), dtypedt)[0]def extractImage(self, f):print(Extracting, f.name)with gzip.GzipFile(fileobjf) as byteStream:if self.read4Byte(byteStream) ! 2051:raise ValueError(Failed reading file!)nImage self.read4Byte(byteStream)rows self.read4Byte(byteStream)cols self.read4Byte(byteStream)buf byteStream.read(rows * cols * nImage)return np.frombuffer(buf, dtypenp.uint8).astype(np.float32).reshape(nImage, rows, cols, 1) / 255def extractLabel(self, f, isOneHotFalse, nClass10):print(Extracting, f.name)with gzip.GzipFile(fileobjf) as byteStream:if self.read4Byte(byteStream) ! 2049:raise ValueError(Failed reading file!)nLabel self.read4Byte(byteStream)buf byteStream.read(nLabel)return np.frombuffer(buf, dtypenp.uint8)def convertToOneHot(self, labelIndex, nClass10):nLabel labelIndex.shape[0]res np.zeros((nLabel, nClass), dtypenp.float32)offset np.arange(nLabel) * nClassres.flat[offset labelIndex] 1return resdef saveImage(self, count, outputPath, isTrain):if self.isOneHot:returnimage, label ([self.testImage, self.testLabel], [self.trainImage, self.trainLabel])[isTrain]for i in range(min(count, 10000)):cv2.imwrite(outputPath str(i).zfill(5) - str(label[i]) .jpg, (image[i] * 255).astype(np.uint8))
用于处理MNIST数据集的类MnistData。加载MNIST数据集的图像和标签并提供获取批量数据的方法。在初始化时会读取训练集和测试集的图像和标签文件并将它们存储在trainImage、trainLabel、testImage和testLabel属性中。如果指定了isOneHot为True则标签将被转换为one-hot编码形式。 getBatch方法用于获取指定大小的批量数据。如果isTrain为True则从训练集中随机选择指定数量的样本否则从测试集中选择。 extractImage和extractLabel方法用于解析MNIST数据集文件。它们读取文件的魔数和其他元数据然后解析图像和标签数据。 (对于图像数据文件train-images-idx3-ubyte.gz和t10k-images-idx3-ubyte.gz文件的前4个字节是魔数用于验证文件类型。接下来的4个字节表示图像数量nImage接着的4个字节表示每张图像的行数rows再接着的4个字节表示每张图像的列数cols。之后的字节表示实际的图像数据。) convertToOneHot方法用于将标签索引转换为one-hot编码形式。 saveImage方法用于将图像保存到指定路径。这个方法只在isOneHot为False时才有效因为one-hot编码的标签无法直接保存为图像文件名。
extractMnistData.py
import sysimport loadMnistDatanTrain int(sys.argv[1]) if len(sys.argv) 1 and sys.argv[1].isdigit() else 3000
nTest int(sys.argv[2]) if len(sys.argv) 2 and sys.argv[2].isdigit() else 500mnist loadMnistData.MnistData(./, isOneHotFalse)
mnist.saveImage(nTrain, ./train/, True) # 60000 images in total
mnist.saveImage(nTest, ./test/, False) # 10000 images in total
导入了loadMnistData模块并定义了两个变量nTrain和nTest用于指定训练和测试样本的数量。接下来它使用loadMnistData.MnistData类加载MNIST数据集并将数据保存为图像文件。saveImage方法用于将指定数量的图像保存到指定目录中,总共有60000张训练图像和10000张测试图像。
首先下载MINST数据集放到该项目根目录下 MINST数据集下载地址http://yann.lecun.com/exdb/mnist/
python extractMnistData.py四、01-SimpleDemo
进入SimpleDemo,文件夹里有四个文件逐个解释
4.1 Makefile
Makefile文件用于管理C和Python代码的编译和构建。
include ../../include/Makefile.incSOURCE_CPP $(shell find . -name *.cpp 2/dev/null)
SOURCE_PY $(shell find . -name *.py 2/dev/null)
OBJ $(shell find . -name *.o 2/dev/null)
DEP $(OBJ:.o.d)
TARGET_EXE $(SOURCE_CPP:.cpp.exe)-include $(DEP)all: $(TARGET_EXE)%.exe: %.o$(NVCC) $(CCFLAG) $(LDFLAG) -o $ $%.o: %.cpp$(NVCC) $(CCFLAG) $(INCLUDE) -M -MT $ -o $(:.o.d) $$(NVCC) $(CCFLAG) $(INCLUDE) -Xcompiler -fPIC -o $ -c $.PHONY: test
test:make cleanmakepython3 $(SOURCE_PY)rm -rf ./*.plan./$(TARGET_EXE).PHONY: clean
clean:rm -rf ./*.d ./*.o ./*.so ./*.exe ./*.plan
include …/…/include/Makefile.inc: 包含一个名为Makefile.inc的文件该文件位于上级目录的include子目录中。Makefile.inc通常包含一些全局配置和共享的变量和指令。
然后让我们查看一下Makefile.inc,定义了一些变量用于指定编译和链接的相关选项。
CUDA_PATH /usr/local/cuda
NVCC $(CUDA_PATH)/bin/nvcc
TRT_INC_PATH /usr/include/x86_64-linux-gnu
TRT_LIB_PATH /usr/lib/x86_64-linux-gnu
GENCODE -gencodearchcompute_60,codesm_60 -gencodearchcompute_61,codesm_61 -gencodearchcompute_70,codesm_70 -gencodearchcompute_75,codesm_75 -gencodearchcompute_80,codesm_80 -gencodearchcompute_86,codesm_86 -gencodearchcompute_89,codesm_89
DEBUG_MACRO -UDEBUG
WARNING_MACRO -w
CUFLAG -stdc14 -O3 $(DEBUG_MACRO) -Xcompiler -fPIC $(GENCODE)
CCFLAG -stdc14 -O3 $(DEBUG_MACRO) -Xcompiler -fPIC -use_fast_math
SOFLAG -shared
INCLUDE -I. -I$(CUDA_PATH)/include -I$(TRT_INC_PATH)
INCLUDE -I../../include -I../../../include
LDFLAG -L$(CUDA_PATH)/lib64 -lcudart -L$(TRT_LIB_PATH) -lnvinfer
其中TRT_INC_PATH和TRT_LIB_PATH分别指定了TensorRT的头文件路径和库文件路径可以在这里进行设置。如果安装TensorRT时使用默认路径则上述设置应该是正确的。但是如果您安装TensorRT时使用了自定义路径则需要相应地更改这些路径。
查询TensorRT路径
sudo find / -name libnvinfer.so*所以应该将TRT_INC_PATH设置为/root/TensorRT-8.6.1.6/targets/x86_64-linux-gnu/include将TRT_LIB_PATH设置为/root/TensorRT-8.6.1.6/targets/x86_64-linux-gnu/lib 如果不确定自己的cuda安装路径是什么可以用下面的查询一般就只要改TRT_INC_PATH、TRT_LIB_PATH和CUDA_PATH
which nvccSOURCE_CPP $(shell find . -name ‘*.cpp’ 2/dev/null): 使用shell命令查找当前目录及其子目录下的所有.cpp文件并将它们的路径保存在变量SOURCE_CPP中。 SOURCE_PY $(shell find . -name ‘*.py’ 2/dev/null): 使用shell命令查找当前目录及其子目录下的所有.py文件并将它们的路径保存在变量SOURCE_PY中。 OBJ $(shell find . -name *.o 2/dev/null): 使用shell命令查找当前目录及其子目录下的所有.o文件并将它们的路径保存在变量OBJ中。 DEP $(OBJ:.o.d): 将OBJ中所有.o后缀的文件替换为.d后缀生成依赖关系文件的路径列表并将其保存在变量DEP中。 TARGET_EXE $(SOURCE_CPP:.cpp.exe): 将SOURCE_CPP中所有.cpp后缀的文件替换为.exe后缀生成可执行文件的路径列表并将其保存在变量TARGET_EXE中。 -include $(DEP): 包含所有依赖关系文件如果某个依赖关系文件不存在则忽略该文件而不产生错误。 all: $(TARGET_EXE): all是一个伪目标用于指定Makefile的默认目标。在本例中all目标会构建所有的可执行文件$(TARGET_EXE)。 %.exe: %.o: 定义了一个模式规则用于根据.o文件构建可执行文件。%表示通配符可以匹配任何字符串例如a.exe可以匹配a.o、b.exe可以匹配b.o等。
4.2 main.cpp
这是一个使用TensorRT API创建和执行推理引擎的示例程序 加载或创建推理引擎 程序首先检查是否存在名为 “model.plan” 的序列化引擎文件如果存在则加载该文件否则创建新的引擎。如果创建了新的引擎则将其序列化并保存到 “model.plan” 文件中。 创建执行上下文 使用ICudaEngine::createExecutionContext()创建一个推理上下文对象该对象可以用于执行推理。 设置输入数据 为输入缓冲区分配内存并将数据传输到设备GPU上。 执行推理 调用IExecutionContext::enqueueV3()方法执行推理。 获取输出数据 从设备GPU上复制输出数据并打印输出结果。
/** Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.** Licensed under the Apache License, Version 2.0 (the License);* you may not use this file except in compliance with the License.* You may obtain a copy of the License at** http://www.apache.org/licenses/LICENSE-2.0** Unless required by applicable law or agreed to in writing, software* distributed under the License is distributed on an AS IS BASIS,* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.* See the License for the specific language governing permissions and* limitations under the License.*/#include cookbookHelper.cuhusing namespace nvinfer1;const std::string trtFile {./model.plan};
static Logger gLogger(ILogger::Severity::kERROR);void run()
{ICudaEngine *engine nullptr;if (access(trtFile.c_str(), F_OK) 0){std::ifstream engineFile(trtFile, std::ios::binary);long int fsize 0;engineFile.seekg(0, engineFile.end);fsize engineFile.tellg();engineFile.seekg(0, engineFile.beg);std::vectorchar engineString(fsize);engineFile.read(engineString.data(), fsize);if (engineString.size() 0){std::cout Failed getting serialized engine! std::endl;return;}std::cout Succeeded getting serialized engine! std::endl;IRuntime *runtime {createInferRuntime(gLogger)};engine runtime-deserializeCudaEngine(engineString.data(), fsize);if (engine nullptr){std::cout Failed loading engine! std::endl;return;}std::cout Succeeded loading engine! std::endl;}else{IBuilder *builder createInferBuilder(gLogger);INetworkDefinition *network builder-createNetworkV2(1U int(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH));IOptimizationProfile *profile builder-createOptimizationProfile();IBuilderConfig *config builder-createBuilderConfig();config-setMemoryPoolLimit(MemoryPoolType::kWORKSPACE, 1 30);ITensor *inputTensor network-addInput(inputT0, DataType::kFLOAT, Dims32 {3, {-1, -1, -1}});profile-setDimensions(inputTensor-getName(), OptProfileSelector::kMIN, Dims32 {3, {1, 1, 1}});profile-setDimensions(inputTensor-getName(), OptProfileSelector::kOPT, Dims32 {3, {3, 4, 5}});profile-setDimensions(inputTensor-getName(), OptProfileSelector::kMAX, Dims32 {3, {6, 8, 10}});config-addOptimizationProfile(profile);IIdentityLayer *identityLayer network-addIdentity(*inputTensor);network-markOutput(*identityLayer-getOutput(0));IHostMemory *engineString builder-buildSerializedNetwork(*network, *config);if (engineString nullptr || engineString-size() 0){std::cout Failed building serialized engine! std::endl;return;}std::cout Succeeded building serialized engine! std::endl;IRuntime *runtime {createInferRuntime(gLogger)};engine runtime-deserializeCudaEngine(engineString-data(), engineString-size());if (engine nullptr){std::cout Failed building engine! std::endl;return;}std::cout Succeeded building engine! std::endl;std::ofstream engineFile(trtFile, std::ios::binary);if (!engineFile){std::cout Failed opening file to write std::endl;return;}engineFile.write(static_castchar *(engineString-data()), engineString-size());if (engineFile.fail()){std::cout Failed saving .plan file! std::endl;return;}std::cout Succeeded saving .plan file! std::endl;}long unsigned int nIO engine-getNbIOTensors();long unsigned int nInput 0;long unsigned int nOutput 0;std::vectorstd::string vTensorName(nIO);for (int i 0; i nIO; i){vTensorName[i] std::string(engine-getIOTensorName(i));nInput int(engine-getTensorIOMode(vTensorName[i].c_str()) TensorIOMode::kINPUT);nOutput int(engine-getTensorIOMode(vTensorName[i].c_str()) TensorIOMode::kOUTPUT);}IExecutionContext *context engine-createExecutionContext();context-setInputShape(vTensorName[0].c_str(), Dims32 {3, {3, 4, 5}});for (int i 0; i nIO; i){std::cout std::string(i nInput ? Input [ : Output[);std::cout i std::string(]- );std::cout dataTypeToString(engine-getTensorDataType(vTensorName[i].c_str())) std::string( );std::cout shapeToString(engine-getTensorShape(vTensorName[i].c_str())) std::string( );std::cout shapeToString(context-getTensorShape(vTensorName[i].c_str())) std::string( );std::cout vTensorName[i] std::endl;}std::vectorint vTensorSize(nIO, 0);for (int i 0; i nIO; i){Dims32 dim context-getTensorShape(vTensorName[i].c_str());int size 1;for (int j 0; j dim.nbDims; j){size * dim.d[j];}vTensorSize[i] size * dataTypeToSize(engine-getTensorDataType(vTensorName[i].c_str()));}std::vectorvoid *vBufferH {nIO, nullptr};std::vectorvoid * vBufferD {nIO, nullptr};for (int i 0; i nIO; i){vBufferH[i] (void *)new char[vTensorSize[i]];CHECK(cudaMalloc(vBufferD[i], vTensorSize[i]));}float *pData (float *)vBufferH[0];for (int i 0; i vTensorSize[0] / dataTypeToSize(engine-getTensorDataType(vTensorName[0].c_str())); i){pData[i] float(i);}for (int i 0; i nInput; i){CHECK(cudaMemcpy(vBufferD[i], vBufferH[i], vTensorSize[i], cudaMemcpyHostToDevice));}for (int i 0; i nIO; i){context-setTensorAddress(vTensorName[i].c_str(), vBufferD[i]);}context-enqueueV3(0);for (int i nInput; i nIO; i){CHECK(cudaMemcpy(vBufferH[i], vBufferD[i], vTensorSize[i], cudaMemcpyDeviceToHost));}for (int i 0; i nIO; i){printArrayInformation((float *)vBufferH[i], context-getTensorShape(vTensorName[i].c_str()), vTensorName[i], true, true);}for (int i 0; i nIO; i){delete[] (char *)vBufferH[i];CHECK(cudaFree(vBufferD[i]));}return;
}int main()
{CHECK(cudaSetDevice(0));run();run();return 0;
}
make之后生成main.exe和main.d文件说明编译成功再使用./main.exe直接运行。
make
./main.exe输出如下
4.3 main.py TensorRT C API 基础解析 https://blog.csdn.net/m0_70420861/article/details/135574423
ppt官方代码代码里面有下载地址 main.py 使用TensorRT进行推理的示例代码。它首先检查是否存在一个已序列化的网络文件如果存在则加载该文件并进行推理如果不存在则从头开始构建一个网络并将其序列化保存到文件中然后进行推理。
#
# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the License);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#import osimport numpy as np
import tensorrt as trt
from cuda import cudart# yapf:disabletrtFile ./model.plan
data np.arange(3 * 4 * 5, dtypenp.float32).reshape(3, 4, 5) # input data for inferencedef run():logger trt.Logger(trt.Logger.ERROR) # create Logger, avaiable level: VERBOSE, INFO, WARNING, ERRROR, INTERNAL_ERRORif os.path.isfile(trtFile): # load serialized network and skip building process if .plan file existedwith open(trtFile, rb) as f:engineString f.read()if engineString None:print(Failed getting serialized engine!)returnprint(Succeeded getting serialized engine!)else: # build a serialized network from scratchbuilder trt.Builder(logger) # create Buildernetwork builder.create_network(1 int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) # create Networkprofile builder.create_optimization_profile() # create Optimization Profile if using Dynamic Shape modeconfig builder.create_builder_config() # create BuidlerConfig to set meta data of the networkconfig.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 30) # set workspace for the optimization process (default value is total GPU memory)inputTensor network.add_input(inputT0, trt.float32, [-1, -1, -1]) # set inpute tensor for the networkprofile.set_shape(inputTensor.name, [1, 1, 1], [3, 4, 5], [6, 8, 10]) # set danamic range of the input tensorconfig.add_optimization_profile(profile) # add the Optimization Profile into the BuilderConfigidentityLayer network.add_identity(inputTensor) # here is only a identity transformation layer in our simple network, which the output is exactly equal to inputnetwork.mark_output(identityLayer.get_output(0)) # mark the output tensor of the networkengineString builder.build_serialized_network(network, config) # create a serialized networkif engineString None:print(Failed building serialized engine!)returnprint(Succeeded building serialized engine!)with open(trtFile, wb) as f: # write the serialized netwok into a .plan filef.write(engineString)print(Succeeded saving .plan file!)engine trt.Runtime(logger).deserialize_cuda_engine(engineString) # create inference Engine using Runtimeif engine None:print(Failed building engine!)returnprint(Succeeded building engine!)nIO engine.num_io_tensors # since TensorRT 8.5, the concept of Binding is replaced by I/O Tensor, all the APIs with binding in their name are deprecatedlTensorName [engine.get_tensor_name(i) for i in range(nIO)] # get a list of I/O tensor names of the engine, because all I/O tensor in Engine and Excution Context are indexed by name, not binding number like TensorRT 8.4 or beforenInput [engine.get_tensor_mode(lTensorName[i]) for i in range(nIO)].count(trt.TensorIOMode.INPUT) # get the count of input tensor#nOutput [engine.get_tensor_mode(lTensorName[i]) for i in range(nIO)].count(trt.TensorIOMode.OUTPUT) # get the count of output tensorcontext engine.create_execution_context() # create Excution Context from the engine (analogy to a GPU context, or a CPU process)context.set_input_shape(lTensorName[0], [3, 4, 5]) # set actual size of input tensor if using Dynamic Shape modefor i in range(nIO):print([%2d]%s- % (i, Input if i nInput else Output), engine.get_tensor_dtype(lTensorName[i]), engine.get_tensor_shape(lTensorName[i]), context.get_tensor_shape(lTensorName[i]), lTensorName[i])bufferH [] # prepare the memory buffer on host and devicebufferH.append(np.ascontiguousarray(data))for i in range(nInput, nIO):bufferH.append(np.empty(context.get_tensor_shape(lTensorName[i]), dtypetrt.nptype(engine.get_tensor_dtype(lTensorName[i]))))bufferD []for i in range(nIO):bufferD.append(cudart.cudaMalloc(bufferH[i].nbytes)[1])for i in range(nInput): # copy input data from host buffer into device buffercudart.cudaMemcpy(bufferD[i], bufferH[i].ctypes.data, bufferH[i].nbytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice)for i in range(nIO):context.set_tensor_address(lTensorName[i], int(bufferD[i])) # set address of all input and output data in device buffercontext.execute_async_v3(0) # do inference computationfor i in range(nInput, nIO): # copy output data from device buffer into host buffercudart.cudaMemcpy(bufferH[i].ctypes.data, bufferD[i], bufferH[i].nbytes, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost)for i in range(nIO):print(lTensorName[i])print(bufferH[i])for b in bufferD: # free the GPU memory buffer after all workcudart.cudaFree(b)if __name__ __main__:os.system(rm -rf ./*.plan)run() # create a serialized network of TensorRT and do inferencerun() # load a serialized network of TensorRT and do inference
python main.py如果报以下错误
pip install cuda-python -i https://mirrors.aliyun.com/pypi/simple/输出结果 会生成一个model.plan文件
注意安装 TensorRT Python 包是使用 TensorRT 的关键步骤之一如果报tensorrt模块未找到就是没装。 可以用以下命令进行安装
pip install tensorrt这个快一点
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorrt总结
TensorRT英伟达官方示例解析 00 和 01 后续更新
