【备忘】Fastdeploy编译中遇到nvcc fatal : Unsupported gpu architecture ‘compute_35‘的解决

原创 2024-04-30 21:09:38 2.1k 阅读 AI写同款
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解决nvcc fatal : Unsupported gpu architecture ‘compute_30‘在darknet的编译中的问题 解决nvcc fatal : Unsupported gpu architecture 'compute_30'在darknet的编译中的问题 阅读详情
AI摘要

文章讲述了在使用FastDeploy时遇到的编译错误，涉及CUDA不支持compute_35架构的问题，解决方案是修改FastDeploy的cuda.cmake文件，移除不兼容的GPU架构设置。

Fastdeploy编译中遇到nvcc fatal : Unsupported gpu architecture ‘compute_35‘的解决

背景

使用Paddle的Fastdeploy，安装过程中需要对C++ SDK进行编译，编译过程中报标题所述的错误。后来在github上找到了解决办法。

环境

  • GPU: RTX3060Ti
  • Ubuntu 2204
  • cuda 12.1.1
  • TensorRT-8.6.1.6
  • opencv 4.7
  • Fastdeploy develop，commit id = cd0ee79c91d4ed1103abdc65ff12ccadd23d0827

复现路径

  1. 安装cuda-12.1.1（官网下载步骤及链接）
  2. 安装opencv, 到github官网git clone下来，手动编译，资料csdn很多就不贴了。
  3. 安装TensorRT。按照Paddle官网要求，CUDA 工具包 12.0 配合 cuDNN v8.9.1, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.6.1.6（官网链接提供了，tar包的，解压后设置一下路径就可以了，但下载需要nvidia developer帐号登录，免费注册）
  4. 安装FastDeploy，我按照的是这个教程，其中以下cmake选项有几处问题需要手动修改。
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
mkdir build && cd build
cmake .. -DENABLE_ORT_BACKEND=ON \
         -DENABLE_PADDLE_BACKEND=ON \
         -DENABLE_OPENVINO_BACKEND=ON \
         -DENABLE_TRT_BACKEND=ON \
         -DWITH_GPU=ON \
         -DTRT_DIRECTORY=/Paddle/TensorRT-8.4.1.5 \  # TensorRT的路径要根据你刚才解压的Tar包进行修改
         -DCUDA_DIRECTORY=/usr/local/cuda \
         -DCMAKE_INSTALL_PREFIX=${PWD}/compiled_fastdeploy_sdk \
         -DENABLE_VISION=ON \
         -DOPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4 \  # 如果你是源码编译再make install的话，就不用改
         -DENABLE_TEXT=ON
make -j12
make install
  • 注意点1 cmake的选项需要调整，具体见上面。
  • 注意点2，个人认为就是Fastdeploy的问题，问题如下，留意那一堆的nvcc fatal。其中的compute_35其实就是老的计算架构SM_35，我的显卡是SM_86，不应该出现此问题。
make -j16
[  3%] Built target extern_onnxruntime
[  6%] Built target extern_paddle_inference
[  8%] Built target extern_fast_tokenizer
[ 10%] Built target extern_paddle2onnx
[ 21%] Built target yaml-cpp
[ 21%] Built target yaml-cpp-parse
[ 22%] Built target yaml-cpp-read
[ 23%] Built target yaml-cpp-sandbox
Consolidate compiler generated dependencies of target fastdeploy
[ 23%] Building CUDA object CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/common/cuda/adaptive_pool2d_kernel.cu.o
[ 23%] Building CUDA object CMakeFiles/fastdeploy.dir/fastdeploy/function/cuda_cast.cu.o
[ 23%] Building CUDA object CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/grid_sample_3d.cu.o
[ 24%] Building CUDA object CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/voxelize_op.cu.o
[ 24%] Building CUDA object CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/iou3d_nms_kernel.cu.o
[ 25%] Building CUDA object CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/centerpoint_postprocess_op.cu.o
[ 25%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/contrib/yolov5cls/preprocessor.cc.o
[ 25%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/contrib/resnet.cc.o
[ 25%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/contrib/yolov5cls/yolov5cls.cc.o
[ 26%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/ppcls/model.cc.o
[ 26%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/ppcls/postprocessor.cc.o
[ 26%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/ppcls/preprocessor.cc.o
[ 27%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/ppshitu/ppshituv2_rec_postprocessor.cc.o
[ 28%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/ppshitu/ppshituv2_rec.cc.o
[ 28%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/contrib/yolov5cls/postprocessor.cc.o
nvcc fatal   : Unsupported gpu architecture 'compute_35'
nvcc fatal   : Unsupported gpu architecture 'compute_35'
nvcc fatal   : Unsupported gpu architecture 'compute_35'
nvcc fatal   : Unsupported gpu architecture 'compute_35'
nvcc fatal   : Unsupported gpu architecture 'compute_35'
[ 28%] Building CXX object CMakeFiles/fastdeploy.dir/fastdeploy/vision/classification/ppshitu/ppshituv2_rec_preprocessor.cc.o
nvcc fatal   : Unsupported gpu architecture 'compute_35'
make[2]: *** [CMakeFiles/fastdeploy.dir/build.make:496：CMakeFiles/fastdeploy.dir/fastdeploy/function/cuda_cast.cu.o] 错误 1
make[2]: *** 正在等待未完成的任务....
make[2]: *** [CMakeFiles/fastdeploy.dir/build.make:510：CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/common/cuda/adaptive_pool2d_kernel.cu.o] 错误 1
make[2]: *** [CMakeFiles/fastdeploy.dir/build.make:706：CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/grid_sample_3d.cu.o] 错误 1
make[2]: *** [CMakeFiles/fastdeploy.dir/build.make:734：CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/voxelize_op.cu.o] 错误 1
make[2]: *** [CMakeFiles/fastdeploy.dir/build.make:720：CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/iou3d_nms_kernel.cu.o] 错误 1
make[2]: *** [CMakeFiles/fastdeploy.dir/build.make:692：CMakeFiles/fastdeploy.dir/fastdeploy/runtime/backends/paddle/ops/centerpoint_postprocess_op.cu.o] 错误 1
make[1]: *** [CMakeFiles/Makefile2:310：CMakeFiles/fastdeploy.dir/all] 错误 2
make: *** [Makefile:156：all] 错误 2

问题的解决

解决的方法也很简单，修改FastDeploy/cmake/cuda.cmake文件即可。

if(NOT WITH_GPU)
  return()
endif()

# This is to eliminate the CMP0104 warnings from cmake 3.18+.
# Instead of setting CUDA_ARCHITECTURES, we will set CMAKE_CUDA_FLAGS.
set(CMAKE_CUDA_ARCHITECTURES OFF)

if(BUILD_ON_JETSON)
  set(fd_known_gpu_archs "53 62 72")
  set(fd_known_gpu_archs10 "53 62 72")
else()
  message("Using New Release Strategy - All Arches Packge")
#  set(fd_known_gpu_archs "35 50 52 60 61 70 75 80 86") #原来
#  set(fd_known_gpu_archs10 "35 50 52 60 61 70 75")		#原来

  set(fd_known_gpu_archs "50 52 60 61 70 75 80 86")  #修改
  set(fd_known_gpu_archs10 "50 52 60 61 70 75")		 #修改

  set(fd_known_gpu_archs11 "50 60 61 70 75 80")
endif()

######################################################################################
# A function for automatic detection of GPUs installed  (if autodetection is enabled)
# Usage:
#   detect_installed_gpus(out_variable)

文件开头包含 “fd_known_gpu_archs” 和 “fd_known_gpu_archs10” 两个地方，删除35后，make即可通过。

[100%] Linking CUDA device code CMakeFiles/fastdeploy.dir/cmake_device_link.o
[100%] Linking CXX shared library libfastdeploy.so
[100%] Built target fastdeploy
[100%] Built target patchelf_paddle_inference
标签
#paddle #人工智能
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nvcc fatal : Unsupported gpu architecture ‘compute_20‘ CMake Error at pcl_gpu_utils_generated_repack 因为在安装pcl1 .8前安装了cuda9.0后出现了以下报错。（之前用另外一台没有cuda的环境下安装可以编译通过） 主要错误摘录： nvcc fatal : Unsupported gpu architecture ‘compute_20’ CMake Error at pcl_gpu_utils_generated_repacks.cu.o.cmake:209 (message): Error generating Unsupported gpu architecture ‘compute_20’ C 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

vs2019报错nvcc fatal : Unsupported gpu architecture ‘compute_30‘ error MSB3721:

本人使用的是GTX 1050Ti， cuda 11.0，opencv3.4，vs2019 编译darknet： 解决方法： 右击项目–>属性–>CUDA C/C++ -->Device 修改Code Generation为：compute_35,sm_35 如果不同型号的显卡的小伙伴可以试着尝试更改为darknet/Makefile 21行之后的几个算力参数 ...

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RuntimeError: Error compiling objects for extension 和nvcc fatal : Unsupported gpu architecture ‘c

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caffe编译过程中的错误： nvcc fatal : Unsupported gpu architecture ‘compute_20‘

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darknet_ros编译时出现nvcc fatal : Unsupported gpu architecture ‘compute_30‘

今天在ros中编译包的时候出现下图的错误。

wtlll的博客 751

nvcc fatal : Unsupported gpu architecture ‘compute_30‘

nvcc fatal : Unsupported gpu architecture 'compute_30'

weixin_46028606的博客 503

opencv编译时警告vncc warning: The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’

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