NOTE: The CUDA Samples are not meant for performance measurements.
Supports MultiDevice Co-op Kernel Launch: Noĭevice PCI Domain ID / Bus ID / location ID: 0 / 1 / 0ĭeviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime Version = 9.1, NumDevs = 1 Support host page-locked memory mapping: Yesĭevice supports Unified Addressing (UVA): Yes Max dimension size of a grid size (x,y,z): ( 2147483647, 65535, 65535)Ĭoncurrent copy and kernel execution: Yes with 1 copy engine(s) Max dimension size of a thread block (x,y,z): ( 1024, 1024, 64) Maximum number of threads per block: 1024 Maximum number of threads per multiprocessor: 2048 Total number of registers available per block: 65536 Total amount of shared memory per block: 49152 bytes Total amount of constant memory: 65536 bytes Maximum Layered 2D Texture Size, ( num) layers 2D=( 16384, 16384), 2048 layers Maximum Layered 1D Texture Size, ( num) layers 1D=( 16384), 2048 layers ( 5) Multiprocessors, ( 128) CUDA Cores/MP: 640 CUDA Cores Total amount of global memory: 2004 MBytes ( 2101870592 bytes) deviceQueryĬUDA Device Query (Runtime API) version (CUDART static linking)ĬUDA Driver Version / Runtime Version 9.1 / 9.1ĬUDA Capability Major/Minor version number: 5.0
找到 samples,一般在 home目录下 cd ~/NVIDIA_CUDA- 9.1_Samples/ Graham Inggs <> nvidia-cuda-toolkit (9.1.+-+ $ nvcc -V nvcc: NVIDIA ( R) Cuda compiler driver Copyright ( c) 2005 -2017 NVIDIA Corporation Built on Fri_Nov_3_21 :07 :56_CDT_2017 Cuda compilation tools, release 9.
I copied together as much as possible from the original Nvidia Dockerfiles, making changes only where necessary.
are/jetbrains-toolbox/jetbrains-toolbox 5MiB | bin/bash This gist contains step by step instructions to install cuda v9.0 and cudnn 7.3 in ubuntu 18.04 steps verify the system has a cuda-capable gpu download and install the nvidia cuda toolkit and cudnn setup environmental variables verify the installation to verify your gpu is cuda enable check lspci grep -i nvidia gcc compiler is required for. Docker image for Ubuntu 18.04 with cuda 9.0 As Nvidia does not provide Docker images for the combination of older cuda versions with never versions of Ubuntu, I made this image. | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr.
zshrc) echo 'export PATH=/usr/local/cuda-9.1/bin:$PATH' > ~/.zshrcĮcho 'export LD_LIBRARY_PATH=/usr/local/cuda-9.1/lib64:$LD_LIBRARY_PATH' > ~/.zshrc Prepare/Download CUDA installtion packages Download the runfile installation packager from CUDA website legacy download, download base installer (cuda84.81linux-run for Ubuntu 16.04) and four patch file (cuda9.0.)/ Installation chmod +x cuda84.81linux-run sudo.
NVRM version: NVIDIA UNIX x86_64 Kernel Module 390.48 Thu Mar 22 00: 42: Instructions for several deep learning frameworks are also given (TensorFlow, Theano, Chainer) as well as OpenCV 3.4 Installation Install 16.Ubuntu18.04默认 GCC-7.3.0,由于 CUDA未支持 GCC-7,所以需要安装低版本的 5或者 to keep the current choice, or type selection number: It uses Ubuntu 16.04 as there are still some incompatibilities with 18.04, as well as CUDA 9.0 and cuDNN 7.3
Instructions have been collected from many sources plus additional debugging required when updating the software of one of the machines used for deep learning at the lab. The following are a set of reference instructions (no warranties) to install a machine learning server.