企业做网站400电话作用,wordpress积分与奖励,网站建设开题报告设计,ui参考网站在Ubuntu上安装CUDA和cuDNN以及验证安装步骤
本教程详细介绍了如何在Ubuntu操作系统上安装CUDA#xff08;NVIDIA的并行计算平台#xff09;和cuDNN#xff08;深度神经网络库#xff09;#xff0c;以及如何验证安装是否成功。通过按照这些步骤操作#xff0c;您将能够…在Ubuntu上安装CUDA和cuDNN以及验证安装步骤
本教程详细介绍了如何在Ubuntu操作系统上安装CUDANVIDIA的并行计算平台和cuDNN深度神经网络库以及如何验证安装是否成功。通过按照这些步骤操作您将能够配置您的系统以利用GPU加速深度学习和其他计算密集型任务。此外还包括如何设置环境变量和编译运行示例代码以验证CUDA和cuDNN的正常运行。
安装 CUDA通过网络仓库安装CUDA适用于Ubuntu配置环境变量验证安装安装 cuDNN验证 cuDNN
安装 CUDA
在安装CUDA之前我们需要进行一些预安装操作。首先您需要安装当前正在运行的内核的头文件和开发包。打开终端并执行以下命令
sudo apt-get install linux-headers-$(uname -r)接下来您需要删除过时的签名密钥
sudo apt-key del 7fa2af80通过网络仓库安装CUDA适用于Ubuntu
新的CUDA存储库的GPG公钥是3bf863cc。您可以通过cuda-keyring包或手动方法将其添加到系统中不建议使用apt-key命令。执行以下步骤
安装新的cuda-keyring包。根据您的系统版本替换$distro/$arch
wget https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb$distro/$arch 应该根据以下选项之一进行替换
ubuntu1604/x86_64适用于 Ubuntu 16.04 64位版本。ubuntu1804/cross-linux-sbsa适用于 Ubuntu 18.04 交叉编译版本SBSA 架构。ubuntu1804/ppc64el适用于 Ubuntu 18.04 64位 PowerPC 架构版本。 * ubuntu1804/sbsa适用于 Ubuntu 18.04 SBSA 架构版本。ubuntu1804/x86_64适用于 Ubuntu 18.04 64位版本。ubuntu2004/cross-linux-aarch64适用于 Ubuntu 20.04 交叉编译版本AArch64 架构。ubuntu2004/arm64适用于 Ubuntu 20.04 64位 ARM 架构版本。ubuntu2004/cross-linux-sbsa适用于 Ubuntu 20.04 交叉编译版本SBSA 架构。ubuntu2004/sbsa适用于 Ubuntu 20.04 SBSA 架构版本。ubuntu2004/x86_64适用于 Ubuntu 20.04 64位版本。ubuntu2204/sbsa适用于 Ubuntu 22.04 SBSA 架构版本。ubuntu2204/x86_64适用于 Ubuntu 22.04 64位版本。 根据您的Ubuntu版本和架构选择适当的替代项来执行相应的安装步骤。
更新Apt仓库缓存
sudo apt-get update安装 CUDA SDK: 您可以使用以下命令获取可用的CUDA包列表
cat /var/lib/apt/lists/*cuda*Packages | grep Package:或查看下方列表
Meta PackagePurposecudaInstalls all CUDA Toolkit and Driver packages. Handles upgrading to the next version of the cuda package when it’s released.cuda-12-2Installs all CUDA Toolkit and Driver packages. Remains at version 12.1 until an additional version of CUDA is installed.cuda-toolkit-12-2Installs all CUDA Toolkit packages required to develop CUDA applications. Does not include the driver.cuda-toolkit-12Installs all CUDA Toolkit packages required to develop applications. Will not upgrade beyond the 12.x series toolkits. Does not include the driver.cuda-toolkitInstalls all CUDA Toolkit packages required to develop applications. Handles upgrading to the next 12.x version of CUDA when it’s released. Does not include the driver.cuda-tools-12-2Installs all CUDA command line and visual tools.cuda-runtime-12-2Installs all CUDA Toolkit packages required to run CUDA applications, as well as the Driver packages.cuda-compiler-12-2Installs all CUDA compiler packages.cuda-libraries-12-2Installs all runtime CUDA Library packages.cuda-libraries-dev-12-2Installs all development CUDA Library packages.cuda-driversInstalls all Driver packages. Handles upgrading to the next version of the Driver packages when they’re released.
选择你需要的包进行安装这里选择 cuda-11.8
sudo apt-get install cuda-11-8此安装包中包含显卡驱动安装过程中会让你输入密码请记住该密码后面重启电脑进入 Perform MOK managment 会使用到。 安装完成后重新启动系统
sudo reboot配置 Perform MOK managment 选择 Enroll MOK 注册- 选择 Continue - 选择 Enroll the key - 选择 Yes - 键入步骤3中输入的密码-选择 Reboot 重启电脑完成英伟达显卡驱动安装。
配置环境变量
使用 vim 编辑 ~/.bashrc 文件。
sudo vim ~/.bashrc在文件结尾添加以下内容
export PATH/usr/local/cuda-11.8/bin${PATH::${PATH}}
export LD_LIBRARY_PATH/usr/local/cuda-11.8/lib64\${LD_LIBRARY_PATH::${LD_LIBRARY_PATH}}${PATH::${PATH}} 是一个用于设置环境变量的 Bash Shell 中的特殊语法。它的作用是在添加新路径到环境变量时确保如果原始变量在这种情况下是 $PATH已经包含一些路径那么新路径会添加在原有路径的末尾而且它们之间会用冒号 : 分隔。 具体来说${PATH::${PATH}} 的含义是 如果 $PATH 已经定义非空那么它会在新路径之前加上一个冒号 :然后再添加新路径。 如果 $PATH 未定义或为空那么它只会添加新路径不会加冒号。 这个语法的目的是确保在向 $PATH 添加新路径时保持路径之间用冒号分隔以确保环境变量的正确格式。这在很多环境变量的设置中都很有用因为它避免了路径之间缺少分隔符而导致的错误。 LD_LIBRARY_PATH 是一个环境变量用于指定动态链接器dynamic linker在运行可执行文件时搜索共享库文件动态链接库或共享对象文件的路径。在 Linux 和类Unix系统中共享库文件包含在各种程序中允许多个程序共享相同的库从而减少内存占用并提高系统的效率。 刷新配置 在终端中运行以下命令以使新的环境变量设置生效
source ~/.bashrc验证安装
首先我们需要安装一些CUDA示例所需的第三方库。这些示例通常会在构建过程中检测所需的库但如果未检测到您需要手动安装它们。打开终端并执行以下命令
sudo apt-get install g freeglut3-dev build-essential libx11-dev \libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev libfreeimage-dev完成第三方库依赖安装后从 github 下载 https://github.com/nvidia/cuda-samples 源代码。
下载完成后可以使用以下命令编译
cd cuda-sample
sudo make注意切换到你安装 cuda 版本的分支这里是 v11.8。 可以完成整个编译那么说明安装过程没有问题了。
在源代码目录执行 ./bin/x86_64/linux/release/deviceQuery 命令,结果如下所示
cheungxiongweiroot:~/Source/cuda-samples$ ./bin/x86_64/linux/release/deviceQuery
./bin/x86_64/linux/release/deviceQuery Starting...CUDA Device Query (Runtime API) version (CUDART static linking)Detected 1 CUDA Capable device(s)Device 0: NVIDIA GeForce RTX 4060 Laptop GPUCUDA Driver Version / Runtime Version 12.2 / 11.8CUDA Capability Major/Minor version number: 8.9Total amount of global memory: 7940 MBytes (8325824512 bytes)
MapSMtoCores for SM 8.9 is undefined. Default to use 128 Cores/SM
MapSMtoCores for SM 8.9 is undefined. Default to use 128 Cores/SM(024) Multiprocessors, (128) CUDA Cores/MP: 3072 CUDA CoresGPU Max Clock rate: 2250 MHz (2.25 GHz)Memory Clock rate: 8001 MhzMemory Bus Width: 128-bitL2 Cache Size: 33554432 bytesMaximum Texture Dimension Size (x,y,z) 1D(131072), 2D(131072, 65536), 3D(16384, 16384, 16384)Maximum Layered 1D Texture Size, (num) layers 1D(32768), 2048 layersMaximum Layered 2D Texture Size, (num) layers 2D(32768, 32768), 2048 layersTotal amount of constant memory: 65536 bytesTotal amount of shared memory per block: 49152 bytesTotal shared memory per multiprocessor: 102400 bytesTotal number of registers available per block: 65536Warp size: 32Maximum number of threads per multiprocessor: 1536Maximum number of threads per block: 1024Max dimension size of a thread block (x,y,z): (1024, 1024, 64)Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)Maximum memory pitch: 2147483647 bytesTexture alignment: 512 bytesConcurrent copy and kernel execution: Yes with 2 copy engine(s)Run time limit on kernels: YesIntegrated GPU sharing Host Memory: NoSupport host page-locked memory mapping: YesAlignment requirement for Surfaces: YesDevice has ECC support: DisabledDevice supports Unified Addressing (UVA): YesDevice supports Managed Memory: YesDevice supports Compute Preemption: YesSupports Cooperative Kernel Launch: YesSupports MultiDevice Co-op Kernel Launch: YesDevice PCI Domain ID / Bus ID / location ID: 0 / 1 / 0Compute Mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) deviceQuery, CUDA Driver CUDART, CUDA Driver Version 12.2, CUDA Runtime Version 11.8, NumDevs 1
Result PASS安装 cuDNN
安装 cuDNN库和 cuDNN 示例
sudo apt-get install libcudnn8${cudnn_version}-1${cuda_version}
sudo apt-get install libcudnn8-dev${cudnn_version}-1${cuda_version}
sudo apt-get install libcudnn8-samples${cudnn_version}-1${cuda_version}根据以下内容进行替换: ${cudnn_version} is 8.9.4.* ${cuda_version} is cuda12.2 or cuda11.8
使用以下命令查找与 cuDNN 版本 “libcudnn8” 相关的软件包信息
cat /var/lib/apt/lists/*cuda*Packages | grep ./libcudnn8输出结果如下所示
cheungxiongweiroot:~/cudnn_samples_v8/mnistCUDNN$ cat /var/lib/apt/lists/*cuda*Packages | grep ./libcudnn8
Filename: ./libcudnn8_8.5.0.96-1cuda11.7_amd64.deb
Filename: ./libcudnn8-dev_8.5.0.96-1cuda11.7_amd64.deb
Filename: ./libcudnn8_8.6.0.163-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.6.0.163-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.7.0.84-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.7.0.84-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.8.0.121-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.8.0.121-1cuda12.0_amd64.deb
Filename: ./libcudnn8-dev_8.8.0.121-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.8.0.121-1cuda12.0_amd64.deb
Filename: ./libcudnn8_8.8.1.3-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.8.1.3-1cuda12.0_amd64.deb
Filename: ./libcudnn8-dev_8.8.1.3-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.8.1.3-1cuda12.0_amd64.deb
Filename: ./libcudnn8_8.9.0.131-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.9.0.131-1cuda12.1_amd64.deb
Filename: ./libcudnn8-dev_8.9.0.131-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.9.0.131-1cuda12.1_amd64.deb
Filename: ./libcudnn8_8.9.1.23-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.9.1.23-1cuda12.1_amd64.deb
Filename: ./libcudnn8-dev_8.9.1.23-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.9.1.23-1cuda12.1_amd64.deb
Filename: ./libcudnn8-samples_8.9.1.23-1cuda11.8_amd64.deb
Filename: ./libcudnn8-samples_8.9.1.23-1cuda12.1_amd64.deb
Filename: ./libcudnn8_8.9.2.26-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.9.2.26-1cuda12.1_amd64.deb
Filename: ./libcudnn8-dev_8.9.2.26-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.9.2.26-1cuda12.1_amd64.deb
Filename: ./libcudnn8-samples_8.9.2.26-1cuda11.8_amd64.deb
Filename: ./libcudnn8-samples_8.9.2.26-1cuda12.1_amd64.deb
Filename: ./libcudnn8_8.9.3.28-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.9.3.28-1cuda12.1_amd64.deb
Filename: ./libcudnn8-dev_8.9.3.28-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.9.3.28-1cuda12.1_amd64.deb
Filename: ./libcudnn8-samples_8.9.3.28-1cuda11.8_amd64.deb
Filename: ./libcudnn8-samples_8.9.3.28-1cuda12.1_amd64.deb
Filename: ./libcudnn8_8.9.4.25-1cuda11.8_amd64.deb
Filename: ./libcudnn8_8.9.4.25-1cuda12.2_amd64.deb
Filename: ./libcudnn8-dev_8.9.4.25-1cuda11.8_amd64.deb
Filename: ./libcudnn8-dev_8.9.4.25-1cuda12.2_amd64.deb
Filename: ./libcudnn8-samples_8.9.4.25-1cuda11.8_amd64.deb
Filename: ./libcudnn8-samples_8.9.4.25-1cuda12.2_amd64.deb这里选择最新的 cudnn 8.9.4.25和 cuda 11.8 进行替换替换后的完整指令如下所示
sudo apt-get install libcudnn88.9.4.25-1cuda11.8
sudo apt-get install libcudnn8-dev8.9.4.25-1cuda11.8
sudo apt-get install libcudnn8-samples8.9.4.25-1cuda11.8验证 cuDNN
要验证 cuDNN 是否已安装并正常运行请编译 /usr/src/cudnn_samples_v8 目录中的 mnistCUDNN 示例。
复制 cuDNN 示例到当前用户目录
cp -r /usr/src/cudnn_samples_v8/ $HOME移动到 cuDNN 示例目录中
cd $HOME/cudnn_samples_v8/mnistCUDNN编译 cuDNN mnisiCUDNN 示例
$make clean make如报错没有找到 FreeImage.h 文件请执行 sudo apt-get install libfreeimage-dev 指令安装该依赖。 运行 mnistCUDNN 示例 ./mnistCUDNN如果 cuDNN 在您的 Linux 系统上正确安装并编译运行您将看到类似以下内容的消息
heungxiongweiroot:~/cudnn_samples_v8/mnistCUDNN$ ./mnistCUDNN
Executing: mnistCUDNN
cudnnGetVersion() : 8904 , CUDNN_VERSION from cudnn.h : 8904 (8.9.4)
Host compiler version : GCC 11.4.0There are 1 CUDA capable devices on your machine :
device 0 : sms 24 Capabilities 8.9, SmClock 2250.0 Mhz, MemSize (Mb) 7940, MemClock 8001.0 Mhz, Ecc0, boardGroupID0
Using device 0Testing single precision
Loading binary file data/conv1.bin
Loading binary file data/conv1.bias.bin
Loading binary file data/conv2.bin
Loading binary file data/conv2.bias.bin
Loading binary file data/ip1.bin
Loading binary file data/ip1.bias.bin
Loading binary file data/ip2.bin
Loading binary file data/ip2.bias.bin
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.010240 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.010240 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.018432 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.032992 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.047104 time requiring 2057744 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.051200 time requiring 184784 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 128848 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 128000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.049152 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.051200 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.058368 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.063648 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.065536 time requiring 128000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.130112 time requiring 128848 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Resulting weights from Softmax:
0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000
Loading image data/three_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.007328 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.010240 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.011264 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.024576 time requiring 2057744 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.025600 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.026624 time requiring 178432 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 128848 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 128000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.025376 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.030720 time requiring 128848 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.036864 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.051200 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.063488 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.065536 time requiring 128000 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006 Result of classification: 1 3 5Test passed!Testing half precision (math in single precision)
Loading binary file data/conv1.bin
Loading binary file data/conv1.bias.bin
Loading binary file data/conv2.bin
Loading binary file data/conv2.bias.bin
Loading binary file data/ip1.bin
Loading binary file data/ip1.bias.bin
Loading binary file data/ip2.bin
Loading binary file data/ip2.bias.bin
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 4608 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 28800 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.011264 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.021504 time requiring 28800 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.022592 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.025600 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.033792 time requiring 2057744 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.074752 time requiring 4608 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 1536 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 64000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.031744 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.040960 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.051168 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.060416 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.064512 time requiring 64000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.069632 time requiring 1536 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Resulting weights from Softmax:
0.0000001 1.0000000 0.0000001 0.0000000 0.0000563 0.0000001 0.0000012 0.0000017 0.0000010 0.0000001
Loading image data/three_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 4608 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 28800 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.009216 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.012288 time requiring 28800 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.021312 time requiring 184784 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.023552 time requiring 4608 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.024352 time requiring 178432 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.029696 time requiring 2057744 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnGetConvolutionForwardAlgorithm_v7 ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 1536 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 64000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.025600 time requiring 2450080 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.035840 time requiring 4656640 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.051200 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.060416 time requiring 1433120 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.064512 time requiring 64000 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.065536 time requiring 1536 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory
^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000714 0.0000000 0.0000000 0.0000000 0.0000000
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006 Result of classification: 1 3 5Test passed!
