I have the base image for my application built with nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04.I have to run that application in the cluster which is having cuda version
NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2.
My application is not giving me right prediction results for the GPU trained model(it is returning the base score as prediction output).However, it is able to return accurate prediction results for the CPU-trained model.so, I am speculating it as the CUDA version incompatibility issue between the two. I want to know that whether CUDA version 11.2 works well with application that is complied with CUDA 10.2 or not..
Yes, it is possible for an application compiled with CUDA 10.2 to run in an environment that has CUDA 11.2 installed. This is part of the CUDA compatibility model/system.
Otherwise, there isn't enough information in this question to diagnose why your application is behaving the way you describe. For that, SO expects a minimal reproducible example.
Related
Can anyone please help me to understand about NVIDIA devices serias 30 with Ampere architecture and compatible CUDA versions?
From here and from all over the net I understand that in CUDA toolkit v11 support for Ampere was added :
https://forums.developer.nvidia.com/t/can-rtx-3080-support-cuda-10-1/155849
What I don't understand is how it make sense with this :
https://docs.nvidia.com/cuda/ampere-compatibility-guide/index.html
Section
"1.3.1. Applications Built Using CUDA Toolkit 10.2 or Earlier"
So 🤷♂️ is it posible or not with CUDA 10.1 ?
Thanks you very very much 🙏
Note the sentence
CUDA applications built using CUDA Toolkit versions 2.1 through 10.2 are compatible with NVIDIA Ampere architecture based GPUs as long as they are built to include PTX versions
(emphasis mine)
Plus the explanation in the section above.
When a CUDA application launches a kernel on a GPU, the CUDA Runtime determines the compute capability of the GPU in the system and uses this information to find the best matching cubin or PTX version of the kernel. If a cubin compatible with that GPU is present in the binary, the cubin is used as-is for execution. Otherwise, the CUDA Runtime first generates compatible cubin by JIT-compiling 1 the PTX and then the cubin is used for the execution. If neither compatible cubin nor PTX is available, kernel launch results in a failure.
In effect: The CUDA toolkit remains ABI-compatible between 2.1 and 11. Therefore an application built for an old version will continue to load at runtime. The CUDA runtime will then detect that your kernels are built for a version that is not compatible with Ampere. So it will take the PTX and compile a new version at runtime.
As note in comments, only a current driver is required on the production system for this to work.
What is the minimum Computation Capability required by the latest PyTorch version?
I have Nvidia Geforce 820M with computation capability 2.1. How can I run PyTorch models on my GPU (if it doesn't support naturally)
Looking at this page, PyTorch (even the somewhat oldest versions) support CUDA upwards from version 7.5. Whereas, looking at this page, CUDA 7.5 requires minimum Compute Capability 2.0. So, on paper, your machine should support some older version of PyTorch which allows CUDA 7.5 or preferably 8.0 (as of writing this answer, the latest version uses minimum CUDA 9.2).
However, PyTorch also requires cuDNN. So, cuDNN 6.0 works for CUDA 7.5. But cuDNN 6.0 requires Compute Capability of 3.0. So, mostly, PyTorch won't work on your machine. (Thanks for pointing out the cuDNN part Robert Crovella)
I have a Cuda application that was built with Cuda Toolkit 9.0 and running fine on Jetson TX2 board.
I now have a Jetson Xavier board, flashed with Jetpack 4 that installs Cuda Toolkit 10.0 (only 10.0 is available).
What do I need to do if I want to run the same application on Xavier?
Nvidia documentation suggests that as long as I specify the correct target hardware when running nvcc, I should be able to run on future hardwares thanks to JIT compilation. But does this hold for different versions of Cuda toolkit (9 vs 10)?
In theory (and note I don't have access to a Xavier board to test anything), you should be able to run a cross compiled CUDA 9 application (and that might mean both ARM and GPU architecture settings) on a CUDA 10 host.
What you will need to make sure is that you either statically link or copy all the CUDA runtime API library components you require with your application on the Xavier board. Note that there is still an outside chance that those libraries might lack the necessary GPU and ARM features to run correctly on a Xavier system, or more subtle issues like libC incompatibility. That you will have to test for yourself.
Since newer drivers ship with the CUDA runtime (I can choose 9.1 or 9.2 in the drivers download page) my question is: should my library (which uses a CUDA kernel internally) be shipped with -lcudart_static?
I had issues launching kernels compiled with 9.2 on systems which used 9.1 CUDA drivers. What's the most 'compatible' way of ensuring my library will run everywhere a recent CUDA driver is installed? (I'm already compiling for a virtual architecture)
Since newer drivers ship with the CUDA runtime (I can choose 9.1 or 9.2 in the drivers download page)
No, that's incorrect. That choice in the drivers download page is related to the fact that each CUDA version has a minimum required driver version associated with it. It does not mean that the driver ships with the CUDA runtime (stated another way, the driver does not install libcudart.so on linux and never has - with some careful experimentation on a clean install, you can prove this to yourself.)
Some additional comments:
-lcudart_static is actually the default for current/recent versions of nvcc. You can discover this by reading the nvcc manual. Therefore, by default, your executable, when compiled/built with nvcc should already be statically linked to the CUDA runtime library corresponding to the version of nvcc that you are using for compilation. The reason you might need to specify this or something like this is if you are building an application with e.g. the gnu toolchain (on linux) rather than nvcc.
The purpose of static linking to the CUDA runtime library is, as you surmise, so that an application can be built in such a way that it does not need an installation of the CUDA toolkit to run properly. It only needs a machine with a proper GPU driver install.
The most compatible way to ensure that an application will run on a range of machines with a range of GPU driver installs is to compile your application using the oldest CUDA toolkit required to meet the needs of the earliest GPU driver in the range you intend to cover. Again, you can refer to the table here.
I recently upgraded from msvc 2005 to 2015.
I have compiled my code with revision 4.2 of cuda toolkit for year. I'm now learning the hard way that there is no forward compatibility betweend visual and cuda, however Google shows that some trick exists to force the compilation (messing up with .props and .targets files).
From what I understand, cuda 4.2 is a no-go. nvcc seems to have an hardcoded check on the msvc revision.
My questions are:
is there a way to compile with cuda 5.x or 6.x?
worst case scenario is that I have to update to cuda 7.5, does it even work?
Thanks for your help.
Update: CUDA 8RC supports VS2015 Update 1 officially (not update 2).
For CUDA toolkits prior to CUDA 8RC, none officially list MSVC 2015 as a supported environment, including CUDA 7.5 (the most recent production toolkit, at the moment).
For recent CUDA toolkits, the official support matrix is given in the windows getting started guide or installation guide which you would have to review for each toolkit version, to find the support for that version.
Since support for a VS version means that the CUDA toolkit will make changes to the VS environment (e.g. installing CUDA build customization rules, what you refer to as "messing with .props and .targets") and also provide appropriate project definition files for each of the cuda sample projects, if you wanted to work around this, you would have to duplicate those functions yourself. There might be non-standard ways to do this, but you would be operating in unsupported territory.
CUDA 8 is the first version to support MSVC 2015, including the community edition (with the exception of cross compiling). At the time of writing this, CUDA 8 is available as a release candidate if you are signed up for the NVIDIA "Accelerated Computing Developer Program".