All of the following is under a venv but I tried without it and still get the same result.
I’m using Ubuntu-20.04.
I start with
import tensorflow as tf
and I get the following warning:
2020-09-15 13:27:32.929958: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
although I installed the cuda drivers:
$ /usr/local/cuda-11.0/samples/bin/x86_64/linux/release/deviceQuery
/usr/local/cuda-11.0/samples/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 MX150"
CUDA Driver Version / Runtime Version 11.1 / 11.0
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 2048 MBytes (2147483648 bytes)
( 3) Multiprocessors, (128) CUDA Cores/MP: 384 CUDA Cores
GPU Max Clock rate: 1532 MHz (1.53 GHz)
Memory Clock rate: 3004 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 524288 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 1 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 supports Managed Memory: Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: 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 = 11.1, CUDA Runtime Version = 11.0, NumDevs = 1
Result = PASS
Should I try to install cuda driver v10.1 that tensorflow can’t find? Seems wrong to me.
The output of find /usr -name *libcudart*:
/usr/local/cuda-11.0/doc/man/man7/libcudart.7
/usr/local/cuda-11.0/doc/man/man7/libcudart.so.7
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudart.so.11.0.221
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudart_static.a
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudart.so.11.0
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudart.so