I'm running an AWS EC2 g2.2xlarge instance with Ubuntu 14.04 LTS. I'd like to observe the GPU utilization while training my TensorFlow models. I get an error trying to run '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's 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 these instructions to install CUDA 7 and cuDNN:
$sudo apt-get -q2 update
$sudo apt-get upgrade
$sudo reboot
=======================================================================
Post reboot, update the initramfs by running '$sudo update-initramfs -u'
Now, please edit the /etc/modprobe.d/blacklist.conf file to blacklist nouveau. Open the file in an editor and insert 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 from the file.
Now install the build essential tools and update the initramfs and reboot again as below:
$sudo apt-get install linux-{headers,image,image-extra}-$(uname -r) build-essential
$sudo update-initramfs -u
$sudo reboot
========================================================================
Post reboot, run the following commands to install 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
========================================================================
Now that the system has come up, verify the installation by running the following.
$sudo modprobe nvidia
$sudo nvidia-smi -q | head`enter code here`
You should see the output like 'nvidia.png'.
Now run the following commands. $
cd ~/NVIDIA_CUDA-7.0_Samples/1_Utilities/deviceQuery
$make
$./deviceQuery
However, 'nvidia-smi' still doesn't show GPU activity while Tensorflow is training models:
ubuntu@ip-10-0-1-48:~$ 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's features.
%quickref -> Quick reference.
help -> Python's 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 |
+-----------------------------------------------------------------------------+
I solved "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver" on my ASUS laptop with GTX 950m and Ubuntu 18.04 by disabling Secure Boot Control from BIOS.