I am trying to compile this with cuda support: https://github.com/CharlesShang/DCNv2 for a Project. But everytime I try it gives me this error message:
/THC.h: No such file or directory
9 | #include <THC/THC.h>
I am using:
Arch Linux with kernel version 6.1.4
GTX 1080
python 3.6
pytorch 1.2.0
torchvision 0.4.0
cudatoolkit 10.0
gcc 7.5
I thought it might be incompatible cuda and gcc versions, but I tried multiple combinations and none of them worked. At the moment I am using cuda 10.0 with gcc version 7.5 as it should be compatible.
Any help is greatly appreciated.
I'm setting up a conda environment to use pytorch 1.4.0 (on Ubuntu 20.04.2), but getting the error message:
ValueError: Unknown CUDA arch (8.6) or GPU not supported
I know this has been asked before, but no answer fits my case. This answer suggests that the CUDA version is too old. However, I updated my CUDA version to the most recent, and get the same error message.
nvcc -V says I have CUDA 11 installed, and when I run nvidia-smi I get this info:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.84 Driver Version: 460.84 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
which, according to the NVIDIA docs, should work be compatible:
Another auxilliary question: What does the "8.6" in CUDA arch (8.6) represent?
Specific versions of PyTorch work only with specific versions of CUDA.
If you are using CUDA-11.1, you'll need a fairly recent version of PyTorch. You need to either upgrade your PyTorch, or downgrade your CUDA.
It seems you can grab PyTorch v1.4 for CUDA 10.0 from here:
pip install torch==1.4.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
Installed Nvidia CUDA 11
Got the cuDNN 8.0 (I think)
Added the directory to PATH
Installed TensorFlow through (pip install tensorflow-gpu)
But I still get this error
Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
TF 2.4 supports CUDA 11
TF 2.3 needs CUDA 10.1
Just install CUDA 10.1 (you can have more than one instalation)
I am very confused by the different CUDA versions shown by running which nvcc and nvidia-smi. I have both cuda9.2 and cuda10 installed on my ubuntu 16.04. Now I set the PATH to point to cuda9.2. So when I run
$ which nvcc
/usr/local/cuda-9.2/bin/nvcc
However, when I run
$ nvidia-smi
Wed Nov 21 19:41:32 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.72 Driver Version: 410.72 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| 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 106... Off | 00000000:01:00.0 Off | N/A |
| N/A 53C P0 26W / N/A | 379MiB / 6078MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1324 G /usr/lib/xorg/Xorg 225MiB |
| 0 2844 G compiz 146MiB |
| 0 15550 G /usr/lib/firefox/firefox 1MiB |
| 0 19992 G /usr/lib/firefox/firefox 1MiB |
| 0 23605 G /usr/lib/firefox/firefox 1MiB |
So am I using cuda9.2 as which nvcc suggests, or am I using cuda10 as nvidia-smi suggests? I saw this answer but it does not provide direct answer to the confusion, it just asks us to reinstall the CUDA Toolkit, which I already did.
CUDA has 2 primary APIs, the runtime and the driver API. Both have a corresponding version (e.g. 8.0, 9.0, etc.)
The necessary support for the driver API (e.g. libcuda.so on linux) is installed by the GPU driver installer.
The necessary support for the runtime API (e.g. libcudart.so on linux, and also nvcc) is installed by the CUDA toolkit installer (which may also have a GPU driver installer bundled in it).
In any event, the (installed) driver API version may not always match the (installed) runtime API version, especially if you install a GPU driver independently from installing CUDA (i.e. the CUDA toolkit).
The nvidia-smi tool gets installed by the GPU driver installer, and generally has the GPU driver in view, not anything installed by the CUDA toolkit installer.
Recently (somewhere between 410.48 and 410.73 driver version on linux) the powers-that-be at NVIDIA decided to add reporting of the CUDA Driver API version installed by the driver, in the output from nvidia-smi.
This has no connection to the installed CUDA runtime version.
nvcc, the CUDA compiler-driver tool that is installed with the CUDA toolkit, will always report the CUDA runtime version that it was built to recognize. It doesn't know anything about what driver version is installed, or even if a GPU driver is installed.
Therefore, by design, these two numbers don't necessarily match, as they are reflective of two different things.
If you are wondering why nvcc -V displays a version of CUDA you weren't expecting (e.g. it displays a version other than the one you think you installed) or doesn't display anything at all, version wise, it may be because you haven't followed the mandatory instructions in step 7 (prior to CUDA 11) (or step 6 in the CUDA 11 linux install guide) of the cuda linux install guide
Note that although this question mostly has linux in view, the same concepts apply to windows CUDA installs. The driver has a CUDA driver version associated with it (which can be queried with nvidia-smi, for example). The CUDA runtime also has a CUDA runtime version associated with it. The two will not necessarily match in all cases.
In most cases, if nvidia-smi reports a CUDA version that is numerically equal to or higher than the one reported by nvcc -V, this is not a cause for concern. That is a defined compatibility path in CUDA (newer drivers/driver API support "older" CUDA toolkits/runtime API). For example if nvidia-smi reports CUDA 10.2, and nvcc -V reports CUDA 10.1, that is generally not cause for concern. It should just work, and it does not necessarily mean that you "actually installed CUDA 10.2 when you meant to install CUDA 10.1"
If nvcc command doesn't report anything at all (e.g. Command 'nvcc' not found...) or if it reports an unexpected CUDA version, this may also be due to an incorrect CUDA install, i.e the mandatory steps mentioned above were not performed correctly. You can start to figure this out by using a linux utility like find or locate (use man pages to learn how, please) to find your nvcc executable. Assuming there is only one, the path to it can then be used to fix your PATH environment variable. The CUDA linux install guide also explains how to set this. You may need to adjust the CUDA version in the PATH variable to match your actual CUDA version desired/installed.
Similarly, when using docker, the nvidia-smi command will generally report the driver version installed on the base machine, whereas other version methods like nvcc --version will report the CUDA version installed inside the docker container.
Similarly, if you have used another installation method for the CUDA "toolkit" such as Anaconda, you may discover that the version indicated by Anaconda does not "match" the version indicated by nvidia-smi. However, the above comments still apply. Older CUDA toolkits installed by Anaconda can be used with newer versions reported by nvidia-smi, and the fact that nvidia-smi reports a newer/higher CUDA version than the one installed by Anaconda does not mean you have an installation problem.
Here is another question that covers similar ground. The above treatment does not in any way indicate that this answer is only applicable if you have installed multiple CUDA versions intentionally or unintentionally. The situation presents itself any time you install CUDA. The version reported by nvcc and nvidia-smi may not match, and that is expected behavior and in most cases quite normal.
nvcc is in the CUDA bin folder - as such check if the CUDA bin folder has been added to your $PATH.
Specifically, ensure that you have carried out the CUDA Post-Installation actions (e.g. from here):
Add the CUDA Bin to $PATH (i.e. add the following line to your ~/.bashrc)
export PATH=/usr/local/cuda-10.1/bin:/usr/local/cuda-10.1/NsightCompute-2019.1${PATH:+:${PATH}}
PS. Ensure the following two paths above, exist first: /usr/local/cuda-10.1/bin and /usr/local/cuda-10.1/NsightCompute-2019.1 (the NsightCompute path could have a slightly different ending depending on the version of Nsight compute installed...
Update $LD_LIBRARY_PATH (i.e. add the following line to your ~/bashrc).
export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
After this, both nvcc and nvidia-smi (or nvtop) report the same version of CUDA...
If you are using cuda 10.2 :
export PATH=/usr/local/cuda-10.2/bin:/opt/nvidia/nsight-compute/2019.5.0${PATH:+:${PATH}}
might help because when I checked, there was no directory for nsight-compute in cuda-10.2.
I am not sure if this was just the problem with me or else why wouldn't they mention it in the official documentation.
Adding onto Robert Crovella's answer...
The difference between the device driver and the runtime driver is that, with device driver you will be able to run compiled CUDA C code. That is, you can download CUDA powered applications and they will be able to successfully execute their code on your GPU.
Whereas, with the runtime driver you will be able to able to compile the CUDA C code, which then will be executed with the help of the device driver on your GPU.
Section 2.2.3 - Cuda Development Toolkit
nvidia-smi can show a “different CUDA version” from the one that is reported by nvcc. Because they are reporting two different things:
nvidia-smi shows that maximum available CUDA version support for a given GPU driver.
And the 2nd thing which nvcc -V reports is the CUDA version that is currently being used by the system.
In short
nvidia-smi shows the highest version of CUDA supported by your driver. nvcc -V shows the version of the current CUDA installation. As long as your driver-supported version is higher than your installed version, it's fine. You can even have several versions of CUDA installed at the same time.
I have installed cuda-8.0 and cudnn5.1 on CentOS. Then, when importing tensorflow (python 3.6), it gives the error as above.
I have already set symbol link as below in /etc/profile. Are there any guys who occurred this kind of problem?
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:$LD_LIBRARY_PATH
Also, what makes me confused is that, I run nvcc -V, it shows
Cuda compilation tools, release 8.0, V8.0.61
However, when I run ./deviceQuery in folder /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery, on device 0: "Tesla M40", it shows
CUDA Driver Version / Runtime Version 9.1 / 8.0
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = Tesla M40
Check your version of tensorflow using "pip3 list | grep tensorflow" If it is of version tensorflow-gpu (1.5.0) then the required cuda version is 9.0 and cuDNN v7.
Look into the following link for more details:
https://github.com/tensorflow/tensorflow/releases
Tensorflow installation guide needs to be updated.
I had the same problem. Tensorflow 1.5.0 is precompiled to CUDA 9.0 (which is outdated; Sept 2017).
The newest CUDA version is CUDA 9.1 (Dec. 2017) and sudo pip install tensorflow-gpu will not work with the newest CUDA 9.1. There are two solutions to the problem:
1.) Install CUDA 9.0 next to CUDA 9.1 (this worked for me)
2.) Build Tensorflow by yourself from the git source code
Either way do not forget to add the PATH variables to your operating system, otherwise you receive the error message stated in the question from your python interpreter.