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NVIDIA-SMI failed because it could not contact the NVIDIA driver

I am running an instance of AWS EC2 g2.2xlarge with Ubuntu 14.04 LTS. I would like to observe the use of the GPU while teaching TensorFlow models. I get an error trying to start 'nvidia-smi'.

ubuntu@ip-10-0-1-213:/etc/alternatives$ cd /usr/lib/nvidia-375/bin
ubuntu@ip-10-0-1-213:/usr/lib/nvidia-375/bin$ ls
nvidia-bug-report.sh     nvidia-debugdump     nvidia-xconfig
nvidia-cuda-mps-control  nvidia-persistenced
nvidia-cuda-mps-server   nvidia-smi
ubuntu@ip-10-0-1-213:/usr/lib/nvidia-375/bin$ ./nvidia-smi
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.


ubuntu@ip-10-0-1-213:/usr/lib/nvidia-375/bin$ dpkg -l | grep nvidia 
ii  nvidia-346                                            352.63-0ubuntu0.14.04.1                             amd64        Transitional package for nvidia-346
ii  nvidia-346-dev                                        346.46-0ubuntu1                                     amd64        NVIDIA binary Xorg driver development files
ii  nvidia-346-uvm                                        346.96-0ubuntu0.0.1                                 amd64        Transitional package for nvidia-346
ii  nvidia-352                                            375.26-0ubuntu1                                     amd64        Transitional package for nvidia-375
ii  nvidia-375                                            375.39-0ubuntu0.14.04.1                             amd64        NVIDIA binary driver - version 375.39
ii  nvidia-375-dev                                        375.39-0ubuntu0.14.04.1                             amd64        NVIDIA binary Xorg driver development files
ii  nvidia-modprobe                                       375.26-0ubuntu1                                     amd64        Load the NVIDIA kernel driver and create device files
ii  nvidia-opencl-icd-346                                 352.63-0ubuntu0.14.04.1                             amd64        Transitional package for nvidia-opencl-icd-352
ii  nvidia-opencl-icd-352                                 375.26-0ubuntu1                                     amd64        Transitional package for nvidia-opencl-icd-375
ii  nvidia-opencl-icd-375                                 375.39-0ubuntu0.14.04.1                             amd64        NVIDIA OpenCL ICD
ii  nvidia-prime                                          0.6.2.1                                             amd64        Tools to enable NVIDIA Prime
ii  nvidia-settings                                       375.26-0ubuntu1                                     amd64        Tool for configuring the NVIDIA graphics driver
ubuntu@ip-10-0-1-213:/usr/lib/nvidia-375/bin$ lspci | grep -i nvidia
00:03.0 VGA compatible controller: NVIDIA Corporation GK104GL [GRID K520] (rev a1)
ubuntu@ip-10-0-1-213:/usr/lib/nvidia-375/bin$ 

$ inxi -G
Graphics:  Card-1: Cirrus Logic GD 5446 
           Card-2: NVIDIA GK104GL [GRID K520] 
           X.org: 1.15.1 driver: N/A tty size: 80x24 Advanced Data: N/A out of X

$  lspci -k | grep -A 2 -E "(VGA|3D)"
00:02.0 VGA compatible controller: Cirrus Logic GD 5446
    Subsystem: XenSource, Inc. Device 0001
    Kernel driver in use: cirrus
00:03.0 VGA compatible controller: NVIDIA Corporation GK104GL [GRID K520] (rev a1)
    Subsystem: NVIDIA Corporation Device 1014
00:1f.0 Unassigned class [ff80]: XenSource, Inc. Xen Platform Device (rev 01)

I followed the instructions for installing CUDA 7 and cuDNN:

$sudo apt-get -q2 update
$sudo apt-get upgrade
$sudo reboot

==================================================== ======================

After rebooting, update initramfs by running '$ sudo update-initramfs -u'

Now edit the file /etc/modprobe.d/blacklist.conf to the blacklist. Open the file in the editor and paste the following lines at the end of the file.

blacklist nouveau blacklist lbm-nouveau options nouveau modeset = 0 alias nouveau off alias lbm-nouveau off

Save and exit the file.

initramfs , :

$sudo apt-get install linux-{headers,image,image-extra}-$(uname -r) build-essential
$sudo update-initramfs -u
$sudo reboot

=============================================== =========================

Nvidia.

$sudo wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run
$sudo chmod 700 ./cuda_7.0.28_linux.run
$sudo ./cuda_7.0.28_linux.run
$sudo update-initramfs -u
$sudo reboot

=============================================== =========================

, , , .

$sudo modprobe nvidia
$sudo nvidia-smi -q | head`enter code here`

"nvidia.png".

. $

cd ~/NVIDIA_CUDA-7.0_Samples/1_Utilities/deviceQuery
$make
$./deviceQuery

, "nvidia-smi" - GPU, Tensorflow - :

ubuntu@ip-10-0-1-48:~$ ipython
Python 2.7.11 |Anaconda custom (64-bit)| (default, Dec  6 2015, 18:08:32) 
Type "copyright", "credits" or "license" for more information.

IPython 4.1.2 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython features.
%quickref -> Quick reference.
help      -> Python own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import tensorflow as tf 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.7.5 locally



ubuntu@ip-10-0-1-48:~$ nvidia-smi
Thu Mar 30 05:45:26 2017       
+------------------------------------------------------+                       
| NVIDIA-SMI 346.46     Driver Version: 346.46         |                       
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID K520           Off  | 0000:00:03.0     Off |                  N/A |
| N/A   35C    P0    38W / 125W |     10MiB /  4095MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
+19
10

, "NVIDIA-SMI , NVIDIA" ASUS GTX 950m Ubuntu 18.04, Secure Boot Control BIOS.

+23

Ubuntu 16.04 ( Linux 4.14) Google Compute Engine K80. 4.14 . Linux 4.14 4.15:

Step 1:
Check the existing kernel of your Ubuntu Linux:

uname -a

Step 2:

Ubuntu maintains a website for all the versions of kernel that have 
been released. At the time of this writing, the latest stable release 
of Ubuntu kernel is 4.15. If you go to this 
link: http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/, you will 
see several links for download.

Step 3:

Download the appropriate files based on the type of OS you have. For 64 
bit, I would download the following deb files:

wget http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/linux-headers-
4.15.0-041500_4.15.0-041500.201802011154_all.deb
wget http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/linux-headers-
4.15.0-041500-generic_4.15.0-041500.201802011154_amd64.deb
wget http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/linux-image-
4.15.0-041500-generic_4.15.0-041500.201802011154_amd64.deb

Step 4:

Install all the downloaded deb files:

sudo dpkg -i *.deb

Step 5:
Reboot your machine and check if the kernel has been updated by:
uname -a

, , , nvidia-smi .

+6

, NVIDIA:

sudo ubuntu-drivers

:

sudo apt install

+4

AWS DeepAMI P2 , Nvidia GPU torch tenorflow. ,

nvcc --version

apt install nvidia-cuda-toolkit

, .

+4

@Heapify , .

1: Ubuntu Linux:

uname -a

2:

Ubuntu - , . Ubuntu - 4.15. : http://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/, .

3:

. 64- deb:

// UP-TO-DATE 2019-03-18
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/linux-headers-4.15.0-041500_4.15.0-041500.201802011154_all.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/linux-headers-4.15.0-041500-generic_4.15.0-041500.201802011154_amd64.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v4.15/linux-image-4.15.0-041500-generic_4.15.0-041500.201802011154_amd64.deb

4:

deb:

sudo dpkg -i *.deb

5:

, :

uname -aenter code here
+1

NVIDIA 367.57 CUDA 7.5 Tensorflow g2.2xlarge Ubuntu 14.04LTS. NVIDIA---367_367.57.orig.tar

GRID K520 , :

ubuntu@ip-10-0-1-70:~$ nvidia-smi
Sat Apr  1 18:03:32 2017       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.57                 Driver Version: 367.57                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID K520           Off  | 0000:00:03.0     Off |                  N/A |
| N/A   39C    P8    43W / 125W |   3800MiB /  4036MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0      2254    C   python                                        3798MiB |
+-----------------------------------------------------------------------------+

ubuntu@ip-10-0-1-70:~/NVIDIA_CUDA-7.0_Samples/1_Utilities/deviceQuery$ ./deviceQuery 
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GRID K520"
  CUDA Driver Version / Runtime Version          8.0 / 7.0
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 4036 MBytes (4232052736 bytes)
  ( 8) Multiprocessors, (192) CUDA Cores/MP:     1536 CUDA Cores
  GPU Max Clock rate:                            797 MHz (0.80 GHz)
  Memory Clock rate:                             2500 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 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:                     No
  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 / 0 / 3
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 7.0, NumDevs = 1, Device0 = GRID K520
Result = PASS
0

, .

sudo apt-get update
sudo apt-get install --no-install-recommends nvidia-384 libcuda1-384 nvidia-opencl-icd-384
sudo reboot

→ https://deeptalk.lambdalabs.com/t/nvidia-smi-has-failed-because-it-couldnt-communicate-with-the-nvidia-driver/148

0

.

Kubernetes Google Cloud Tesla K-80 GPU.

, , : https://cloud.google.com/kubernetes-engine/docs/how-to/gpus

:

  1. NVIDIA GPU . :

COS:

kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml

UBUNTU:

kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/ubuntu/daemonset-preloaded.yaml

, . , .

  1. nvidia/cuda: 10.1-base-ubuntu16.04

  2. GPU! , . yaml :

    resources:
  limits:
    nvidia.com/gpu: 1
0

NVIDIA, , , DKMS. , . - , IPMI/. DKMS, dkms autoinstall . , - . dkms autoinstall , modprobe - ( DKMS).

0
source

Try pulling out the NVIDIA graphics card and reinstalling it.

-1
source

Source: https://habr.com/ru/post/1673044/

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