问题
I have a laptop with a GeForce 940 MX. I want to get Tensorflow up and running on the gpu. I installed everything from their tutorial page, now when I import Tensorflow, I get
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:119] Couldn't open CUDA library libcuda.so.1. LD_LIBRARY_PATH:
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: workLaptop
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: Permission denied: could not open driver version path for reading: /proc/driver/nvidia/version
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1092] LD_LIBRARY_PATH:
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1093] failed to find libcuda.so on this system: Failed precondition: could not dlopen DSO: libcuda.so.1; dlerror: libnvidia-fatbinaryloader.so.367.57: cannot open shared object file: No such file or directory
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
>>>
after which I think it just switches to running on the cpu.
EDIT: After I nuked everything , started from scratch. Now I get this:
>>> import tensorflow
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:119] Couldn't open CUDA library libcuda.so.1. LD_LIBRARY_PATH: :/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: workLaptop
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: Permission denied: could not open driver version path for reading: /proc/driver/nvidia/version
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1092] LD_LIBRARY_PATH: :/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1093] failed to find libcuda.so on this system: Failed precondition: could not dlopen DSO: libcuda.so.1; dlerror: libnvidia-fatbinaryloader.so.367.57: cannot open shared object file: No such file or directory
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
回答1:
libcuda.so.1 is a symlink to a file that is specific to the version of your NVIDIA drivers. It may be pointing to the wrong version or it may not exist.
# See where the link is pointing.
ls /usr/lib/x86_64-linux-gnu/libcuda.so.1 -la
# My result:
# lrwxrwxrwx 1 root root 19 Feb 22 20:40 \
# /usr/lib/x86_64-linux-gnu/libcuda.so.1 -> ./libcuda.so.375.39
# Make sure it is pointing to the right version.
# Compare it with the installed NVIDIA driver.
nvidia-smi
# Replace libcuda.so.1 with a link to the correct version
cd /usr/lib/x86_64-linux-gnu
sudo ln -f -s libcuda.so.<yournvidia.version> libcuda.so.1
Now in the same way, make another symlink from libcuda.so.1 to a link of the same name in your LD_LIBRARY_PATH directory.
You may also find that you need to create a link to libcuda.so.1 in /usr/lib/x86_64-linux-gnu named libcuda.so
回答2:
In case anyone still encounters this. First make sure to add the --runtime=nvidia parameter in order to run your container.
docker run --runtime=nvidia -t tensorflow/serving:latest-gpu
where tensorflow/serving:latest-gpu is the name of the docker image.
回答3:
In the case I just solved, it was updating the GPU driver to the latest and installing the cuda toolkit. First, the ppa was added and GPU driver installed:
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-390
After adding the ppa, it showed options for driver versions, and 390 was the latest 'stable' version that was shown.
Then install the cuda toolkit:
sudo apt install nvidia-cuda-toolkit
Then reboot:
sudo reboot
It updated the drivers to a newer version than the 390 originally installed in the first step (it was 410; this was a p2.xlarge instance on AWS).
回答4:
If you use Tensorflow on docker, you must have nvidia-docker 2.0 version. Otherwise there will be errors loading libcuda.so.1. Link to the tutorial with installation on Ubuntu 18.04: https://medium.com/@sh.tsang/docker-tutorial-5-nvidia-docker-2-0-installation-in-ubuntu-18-04-cb80f17cac65
来源：https://stackoverflow.com/questions/41890549/tensorflow-cannot-open-libcuda-so-1