I'm getting this error although I did not compile Matconvnet with GPU support.
Does anyone else know this error?
May it be a Matconvnet-version issue?
Ok, so the source code I was calling, had an explicit flag to call the GPU, I switched it off, now it is working...
Related
I met this issue when I running my python file in linux.
I searched some answers in google like use the code below:
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
The system can be running but without ant out information. Actually I found the 2 measn ignore all information while 1 means give all information like error or normal output.
because I need to use GPU so my original code is:
os.environ["CUDA_VISIBLE_DEVICES"]="0"
But if I keep the code my output would be an error information like the title.
How can I do? and of course I need use the GPU and the codes can be running in colab which indicated that my code has no problem.
some guys even said uninstall tensorboard...that must a wrong way.
Or should I download tensorflow gpu not tensorflow in m,y virtual enviroment? when I USE THE tensorflow gpu version, the error is core dumped.
If when forcing the os.environ["CUDA_VISIBLE_DEVICES"]="0" doesn't work, then this means that your tensorflow gpu installation did not succeed. You must ensure you have the right combination of TensorFlow + CUDA + CUDNN. That is why you get the error, because due to improper versions/installation TF falls back on CPU.
Currently trying to run a basic similarity search via FAISS with reproducible code from that link. However, every time I run the code in the following venues, I have these problems:
Jupyter notebook - kernel crashes
VS Code - receive "Illegal Instruction" message in the terminal with no further documentation
I've got similar code working in Kaggle, so I suppose the problem is with my particular setup.
Based on the print statements, it appears that the error occurs during the call of the .search method. Because of how vague this error is, I've not been able to find much information on the problem. It seems that some people mentioned older processors may have a problem (AVX/AVX2 flags being the culprit?), though admittedly I didn't quite understand the connections.
Problem: Can I get some help understanding this error, and if possible, a potential solution?
Current setup:
WSL2
VSCODE (v. 1.49.0)
Jupyter-client (v. 6.1.7)
Jupyter-core (v. 4.6.3)
FAISS-cpu (v. 1.6.3)
Numpy (v. 1.19.2)
Older machine (AMD FX-8350 with 16GB RAM)
For anyone that runs across this error, the problem (in my case) was that my CPU was old enough that it doesn't support AVX2. To determine this, I used this SO post.
Once I ran the code in Colab or on a newer machine, all was well.
I had exported couple of functions in a kernel module through EXPORT_SYMBOL.
To my surprise one of the functions was not showing up in /proc/kallsyms.
The weird part is that if I change the name of the exported function, it shows up!
Please note:
1. I have CONFIG_KALLSYMS enabled in the kernel. Also, I'm exporting a function, not variables.
2. The function was not defined as static.
3. Rebooting the machine and retrying did not help.
Though I've worked around the problem by changing the name, what bothers me is that I still do not understand why the problem occurred in the first place.
I would appreciate if someone could provide pointers to at least how to go about debugging this sort of problem.
If it helps, I'm using kernel 2.6.32 with CONFIG_KALLSYMS enabled and CONFIG_KALLSYMS_ALL disabled.
Thanks in advance!
I'm running a small CUDA application: the QuickSort benchmark algorithm (see here). I have a dual system with a NVIDIA 660GTX (device 0) and 8600GTS (device 1).
Under Windows 8 and Visual Studio, the application compiles and runs flawlessly on device 0. Under Linux (Ubuntu 12.04 LTS), the app compiles with nvcc and gcc but suddenly stops in its tracks, returning a (unspecified launch failure).
I have two issues:
After this error, my GPU cannot perform some other operations, e.g., running the SDK example bandwidhtTest blocks when it performs the first data transfer, but running deviceQuery continues to perform well. How can I reset my GPU? I've already tried the cudaDeviceReset() method but it doesn't help
How can I find what is going wrong under linux? Has someone a clue or seen this before?
Thanks in advance for your help!
Using the nvidia-smi utility you can reset the GPU if it is compatible
To my knowledge and experience, (unspecified launch failure) usually referees to segmentation fault. Have you specified the right GPU to use? Try to use cuda-memcheck to see if there is any memory out of bound scenario.
From my experience XID 31 was always caused by accessing bad pointer (aka Memory access violation).
I'd first pursue this trail. Run your application with cuda memcheck. Like that cuda-memcheck you_app args to your app and see if it finds any wrong memory accesses.
Also try stepping though the code with cuda-gdb or Nsight Eclipse Edition.
I've found that using
cuda-memcheck -b ...
prevents the device from locking up.
I downloaded the tight vnc source code from its website. Now I am trying to use gdb on its executable. The debugger successfully adds breakpoints on functions but when I try to step through the function it says :
Single Stepping until exit from function func, which has no line number information
I think it is due to the fact that the compilation wasnt done with correct flags. I am trying to search the configuration files to understand how to enable it, but haven't been able to so far. I am not acquainted with Imakefiles etc. Maybe someone who has done this previously can help ?
Using gnu GCC and GDB on an ubuntu machine
You should compile with the -g flag.
If you are trying to learn the code, I would recommend "-g -O0". That will turn off the optimizer - gcc optimization can make it confusing to step through code.