Ubuntu 16.04+Nvidia GTX 1080+CUDA8.0 深度學習環境設定

Fresh drivers from upstream, currently shipping Nvidia.

## Current Status

We currently recommend: $nvidia-361$ , Nvidia’s current long lived branch.
For GeForce 8 and 9 series GPUs use $nvidia-340$
For GeForce 6 and 7 series GPUs use $nvidia-304$

## What we’re working on right now:

– Normal driver updates– Investigating how to bring this goodness to distro on a cadence.

## WARNINGS:

This PPA is currently in testing, you should be experienced with packaging before you dive in here. Give us a few days to sort out the kinks.

Volunteers welcome! See also: https://github.com/mamarley/nvidia-graphics-drivers/

http://www.ubuntu.com/download/desktop/contribute
更多資訊： https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa
按回車繼續或者 Ctrl+c 取消新增

New in CUDA 8

Pascal Architecture Support
Out of box performance improvements on Tesla P100, supports GeForce GTX 1080
Simplify programming using Unified memory on Pascal including support for large datasets, concurrent data access and atomics*
Optimize Unified Memory performance using new data migration APIs*
Faster Deep Learning using optimized cuBLAS routines for native FP16 computation
Developer Tools
Quickly identify latent system-level bottlenecks using the new critical path analysis feature
Improve productivity with up to 2x faster NVCC compilation speed
Tune OpenACC applications and overall host code using new profiling extensions
Libraries
Accelerate graph analytics algorithms with nvGRAPH
New cuBLAS matrix multiply optimizations for matrices with sizes smaller than 512 and for batched operation

sudo sh cuda_8.0.27_linux.run --tmpdir=/opt/temp/

Not enough space on parition mounted at /.Need 5091561472 bytes.

Disk space check has failed. Installation cannot continue.

Logging to /opt/temp//cuda_install_6583.log
Using more to view the EULA.
End User License Agreement
————————–

Preface——-

The following contains specific license terms and conditions
for four separate NVIDIA products. By accepting this
agreement, you agree to comply with all the terms and
conditions applicable to the specific product(s) included
herein.

Do you accept the previously read EULA?accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?(y)es/(n)o/(q)uit: n

Install the CUDA 8.0 Toolkit?(y)es/(n)o/(q)uit: y

Enter Toolkit Location[ default is /usr/local/cuda-8.0 ]:

Do you want to install a symbolic link at /usr/local/cuda?(y)es/(n)o/(q)uit: y

Install the CUDA 8.0 Samples?(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location[ default is /home/textminer ]:

Installing the CUDA Toolkit in /usr/local/cuda-8.0 …
Installing the CUDA Samples in /home/textminer …
Copying samples to /home/textminer/NVIDIA_CUDA-8.0_Samples now…
Finished copying samples.

===========
= Summary =
===========

Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/textminer

Please make sure that
– PATH includes /usr/local/cuda-8.0/bin
– LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin

Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.

***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing with the name of this run file:
sudo .run -silent -driver

Logfile is /opt/temp//cuda_install_6583.log

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

nvidia-smi

cd 1_Utilities/deviceQuerymake

“/usr/local/cuda-8.0″/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_20,code=sm_20 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_60,code=compute_60 -o deviceQuery.o -c deviceQuery.cpp
“/usr/local/cuda-8.0″/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_20,code=sm_20 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_60,code=compute_60 -o deviceQuery deviceQuery.o
mkdir -p ../../bin/x86_64/linux/release
cp 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: “GeForce GTX 1080”
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 8112 MBytes (8506179584 bytes)
(20) Multiprocessors, (128) CUDA Cores/MP: 2560 CUDA Cores
GPU Max Clock rate: 1835 MHz (1.84 GHz)
Memory Clock rate: 5005 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 2097152 bytes
Maximum 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 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max 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 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
<Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX 1080Result = PASS

cd ../../5_Simulations/nbody/make

./nbody -benchmark -numbodies=256000 -device=0

> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
gpuDeviceInit() CUDA Device [0]: “GeForce GTX 1080
> Compute 6.1 CUDA device: [GeForce GTX 1080]
number of bodies = 256000
256000 bodies, total time for 10 iterations: 2291.469 ms
= 286.000 billion interactions per second
= 5719.998 single-precision GFLOP/s at 20 flops per interaction

Ubuntu 14.04 安裝設定CUDA  http://www.linuxidc.com/Linux/2014-10/107501.htm

Ubuntu 12.04設定NVIDIA CUDA5.5實錄  http://www.linuxidc.com/Linux/2014-10/107502.htm

Ubuntu安裝Theano+CUDA  http://www.linuxidc.com/Linux/2014-10/107503.htm

關於Ubuntu 12.04 下 CUDA5.5 的安裝請參看如下連結 Ubuntu 12.04 安裝 CUDA-5.5

Caffe設定簡明教學 ( Ubuntu 14.04 / CUDA 7.5 / cuDNN 5.1 / OpenCV 3.1 ) http://www.linuxidc.com/Linux/2016-09/135016.htm

在Ubuntu 14.04上設定CUDA+Caffe+cuDNN+Anaconda+DIGITS  http://www.linuxidc.com/Linux/2016-11/136775.htm

Ubuntu16.04下CUDA8.0+Caffe安裝設定過程  http://www.linuxidc.com/Linux/2017-01/139313.htm

Ubuntu 14.04下CUDA8.0 + cuDNN v5 + Caffe  安裝設定 http://www.linuxidc.com/Linux/2017-01/139300.htm