Cuda Runtime version is insufficent for CUDA runtime version. All of my versions match!

Hi, im trying to install CUDA for using Tensrflow.

| NVIDIA-SMI 390.77                 Driver Version: 390.77                    |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|   0  GeForce GTX 970M    Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   49C    P8     7W /  N/A |    208MiB /  3024MiB |      4%      Default |
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|    0       951      G   /usr/lib/xorg/Xorg                           111MiB |
|    0      1160      G   /usr/bin/gnome-shell                          86MiB |
|    0      2633      G   /usr/lib/firefox/firefox                       2MiB |
|    0      3925      G   /usr/lib/firefox/firefox                       2MiB |
|    0      3963      G   /usr/lib/firefox/firefox                       2MiB |
|    0      4013      G   /usr/lib/firefox/firefox                       2MiB |

NVRM version: NVIDIA UNIX x86_64 Kernel Module 390.77 Tue Jul 10 18:28:52 PDT 2018
GCC version: gcc version 7.3.0 (Ubuntu 7.3.0-27ubuntu1~18.04)

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

bash: ./deviceQuery: No such file or directory

The thing is when I open Python on my tensorflow conda env and try initializing a tensorflow session i get the CUDA driver version is insufficient for CUDA runtime version. This is the tenth time I try this install, I made sure everything is just ritght and I’m clueless about this.


you should probably build the deviceQuery sample code before trying to run it. Follow the instructions in the linux install guide to verify your CUDA install.

If that passes, it may be that your conda install is pulling in a different version of cuda toolkit

Another possibility is that you installed the GPU driver from an incorrect source (such as an ubuntu source, instead of a NVIDIA installer/source)

Well I certainly did install my nvidia driver from ubuntu software&updates because every time i try to install from nvidia web I end up messing up the whole system. Anyway i compiled deviceQuery and this is the result

./deviceQuery Starting…

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

Detected 1 CUDA Capable device(s)

Device 0: “GeForce GTX 970M”
CUDA Driver Version / Runtime Version 9.1 / 9.0
CUDA Capability Major/Minor version number: 5.2
Total amount of global memory: 3025 MBytes (3171483648 bytes)
(10) Multiprocessors, (128) CUDA Cores/MP: 1280 CUDA Cores
GPU Max Clock rate: 1038 MHz (1.04 GHz)
Memory Clock rate: 2505 Mhz
Memory Bus Width: 192-bit
L2 Cache Size: 1572864 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: 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
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
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 = 9.1, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS

and I still get the same problem.