ImportError: libcuda.so.1: cannot open shared object file
Question:
When I run my code with TensorFlow directly, everything is normal.
However, when I run it in a screen window, I get the following error.
ImportError: libcuda.so.1: cannot open shared object file: No such file or directory
I have tried the command:
source /etc/profile
But it doesn’t work.
Cause I use ssh to connect to the servers, the screen is necessary.
How can I fix it?
Answers:
Try to put libcuda.so.1 path to LD_LIBRARY_PATH environment variable.
example:
export LD_LIBRARY_PATH=/path/of/libcuda.so.1:$LD_LIBRARY_PATH
Background
libcuda.so.1 is the library for interacting with the CUDA driver (as opposed to CUDA’s "Runtime API", for which you need libcudart.so.*.
Now, it’s quite possible to have the CUDA Toolkit properly installed, without the driver being properly installed. And this error could be the result of building a (non-statically-linked) CUDA application in this situation.
Alternatively, it could be the case that there’s some misconfiguration of the library search path – because normally, libcuda.so.* are supposed to be installed in some directory on that path!
So, what’s on that search path? As explained here, it is:
- directories from
$LD_LIBRARY_PATH
- directories from
/etc/ld.so.conf
/lib/usr/lib
A typical scenario would be for /etc/ld.so.conf to add, say, /usr/lib/x86_64-linux-gnu; and for libcuda.so.* to be there.
Bottom line
Here’s what you should do:
- Make sure a(n up-to-date) CUDA driver has been properly installed. If it hasn’t, download and install it, problem solved.
- Locate the
libcuda.so.1 file (e.g. using locate). If it’s been placed somewhere weird that’s not in the library search path – act as in step 1.
- If you wanted the driver library installed someplace weird, then add that path to your user’s
$LD_LIBRARY_PATH.
Steps to follow:
Find libcuda.so.1:
echo $LD_LIBRARY_PATH #path
sudo find /usr/ -name 'libcuda.so.*' #version
Then add to $LD_LIBRARY_PATH, in my case /usr/local/cuda-10.0/compat, with the following command, in terminal:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.0/compat
As my condition, I develop in docker container environment, I do the following steps:
- Confirm your docker container have run with nvidia gpu
- Find libcuda.so.1:
sudo find /usr/ -name 'libcuda.so.*'
- Then add to
$LD_LIBRARY_PATH, in my case /usr/local/cuda-11.5/compat, with the following command, in terminal:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.5/compat
When I run my code with TensorFlow directly, everything is normal.
However, when I run it in a screen window, I get the following error.
ImportError: libcuda.so.1: cannot open shared object file: No such file or directory
I have tried the command:
source /etc/profile
But it doesn’t work.
Cause I use ssh to connect to the servers, the screen is necessary.
How can I fix it?
Try to put libcuda.so.1 path to LD_LIBRARY_PATH environment variable.
example:
export LD_LIBRARY_PATH=/path/of/libcuda.so.1:$LD_LIBRARY_PATH
Background
libcuda.so.1 is the library for interacting with the CUDA driver (as opposed to CUDA’s "Runtime API", for which you need libcudart.so.*.
Now, it’s quite possible to have the CUDA Toolkit properly installed, without the driver being properly installed. And this error could be the result of building a (non-statically-linked) CUDA application in this situation.
Alternatively, it could be the case that there’s some misconfiguration of the library search path – because normally, libcuda.so.* are supposed to be installed in some directory on that path!
So, what’s on that search path? As explained here, it is:
- directories from
$LD_LIBRARY_PATH - directories from
/etc/ld.so.conf /lib/usr/lib
A typical scenario would be for /etc/ld.so.conf to add, say, /usr/lib/x86_64-linux-gnu; and for libcuda.so.* to be there.
Bottom line
Here’s what you should do:
- Make sure a(n up-to-date) CUDA driver has been properly installed. If it hasn’t, download and install it, problem solved.
- Locate the
libcuda.so.1file (e.g. usinglocate). If it’s been placed somewhere weird that’s not in the library search path – act as in step 1. - If you wanted the driver library installed someplace weird, then add that path to your user’s
$LD_LIBRARY_PATH.
Steps to follow:
Find libcuda.so.1:
echo $LD_LIBRARY_PATH #path
sudo find /usr/ -name 'libcuda.so.*' #version
Then add to $LD_LIBRARY_PATH, in my case /usr/local/cuda-10.0/compat, with the following command, in terminal:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.0/compat
As my condition, I develop in docker container environment, I do the following steps:
- Confirm your docker container have run with nvidia gpu
- Find libcuda.so.1:
sudo find /usr/ -name 'libcuda.so.*' - Then add to
$LD_LIBRARY_PATH, in my case/usr/local/cuda-11.5/compat, with the following command, in terminal:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.5/compat