I'm using the deep learning framework Caffe on a Ubuntu 14.04 machine. I compiled CAFE with CPU_ONLY option, i.e. I disabled GPU and CUDA usage. I have an NVidia Quadro K2200 graphics card and CUDA version 5.5.
I would like to know if it is possible to use Caffe with CUDA enabled with my GPU. On NVidia page, it is written that Quadro K2200 has a compute capability of 5.0. Does it mean that I can use it with CUDA versions up to release 5.0? When it is possible to use Caffe with GPU-enabled with Quadro K2200, how can I choose the appropriate CUDA version for that?
CUDA version is not the same thing as Compute Capability. For one, CUDA is current (7.5 prerelease), while CC is only at 5.2. K2200 supports CC 5.0.
The difference:
CUDA version means the library/toolkit/SDK/etc version. You should always use the highest one available.
Compute Capability is your GPU's capability to perform certain instructions, etc. Every CUDA function has a minimum CC requirement. When you write a CUDA program, it's CC requirement is the maximum of the requirements of all the features you used.
That said, I've no idea what Caffe is, but a quick search shows they require CC of 2.0, so you should be good to go. CC 5.0 is pretty recent, so very few things won't work on it.
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.
I have a machine with an NVIDA GTX 1050 Ti GPU (compute capability 6.1), and am trying to profile a kernel in a program I built with CUDA 11.4. My OS distribution is Devuan GNU/Linux 4 Chimaera (~= Debian 11 Bullseye).
NSight Compute starts my program, and shows me API call after API call, but when I get to the first kernel launch, it gives me an error message in the Details column of the API call listing:
Error: Profiling is not supported on this device
Why? What's wrong with my device? Is it a permissions issue?
tl;dr: Nsight Compute no longer supports Pascal GPUs.
Nsight Compute used to support Pascal-microarchitecture GPUs (Compute Capability 6.x) - up until version 2019.5.1. Beginning with 2020, Nsight Compute dropped support for Pascal.
If you're wondering why that is - no reason or justification was given to my knowledge (see also the quote below). This is especially puzzling, or annoying, given the short period of time between the release of post-Pascal GPUs and this dropping of support (as little as 1.5 years if you look at consumer GTX cards).
On the other hand, you may still use the NVIDIA Visual Profiler tool with Pascal cards, so they did throw you entirely under the bus. And you can also download and use Nsight Computer 2019.5.1.
To quote an NVIDIA moderator's statement on the matter on the NVIDIA developer forums:
Pascal support was deprecated, then dropped from Nsight Compute after Nsight Compute 2019.5.1. The profiling tools that support Pascal in the CUDA Toolkit 11.1 and later are nvprof and visual profiler.
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 want to know if the latest CUDA version, which is 8.0, supports the GPUs in my computer, which are GeForce GTX 970 and Quadro K4200 (a dual-GPU system); I couldn't find the info online.
In general, how to find if a CUDA version, especially the newly released version, supports a specific Nvidia GPU?
Thanks!
In general, how to find if a CUDA version, especially the newly released version, supports a specific Nvidia GPU?
All CUDA versions from CUDA 7.0 to CUDA 8.0 support GPUs that have a compute capability of 2.0 or higher. Both of your GPUs are in this category.
Prior to CUDA 7.0, some older GPUs were supported also. You can find details of that here.
Note that CUDA 8.0 has announced that development for compute capability 2.0 and 2.1 is deprecated, meaning that support for these (Fermi) GPUs may be dropped in a future CUDA release.
In general, a list of currently supported CUDA GPUs and their compute capabilities is maintained by NVIDIA here although the list occasionally has omissions for very new GPUs just released.
I've successfully installed tensorflow (GPU) on Linux Ubuntu 16.04 and made some small changes in order to make it work with the new Ubuntu LTS release.
However, I thought (who knows why) that my GPU met the minimum requirement of a compute capability greater than 3.5. That was not the case since my GeForce 820M has just 2.1. Is there a way of making tensorflow GPU version working with my GPU?
I am asking this question since apparently there was no way of making tensorflow GPU version working on Ubuntu 16.04 but by searching the internet I found out that was not the case and indeed I made it almost work were it not for this unsatisfied requirement. Now I am wondering if this issue with GPU compute capability could be fixed as well.
Recent GPU versions of tensorflow require compute capability 3.5 or higher (and use cuDNN to access the GPU.
cuDNN also requires a GPU of cc3.0 or higher:
cuDNN is supported on Windows, Linux and MacOS systems with Pascal, Kepler, Maxwell, Tegra K1 or Tegra X1 GPUs.
Kepler = cc3.x
Maxwell = cc5.x
Pascal = cc6.x
TK1 = cc3.2
TX1 = cc5.3
Fermi GPUs (cc2.0, cc2.1) are not supported by cuDNN.
Older GPUs (e.g. compute capability 1.x) are also not supported by cuDNN.
Note that there has never been either a version of cuDNN or any version of TF that officially supported NVIDIA GPUs less than cc3.0. The initial version of cuDNN started out by requiring cc3.0 GPUs, and the initial version of TF started out by requiring cc3.0 GPUs.
Sep.2017 Update: No way to do that without problems and pains. I've tried hard all the ways and even apply below trick to force it run but finally I had to give up. If you are serious with Tensorflow just go ahead and buy 3.0 compute capability GPU.
This is a trick to force tensorflow run on 2.0 compute capability GPU (not officially):
Find the file in
Lib/site-packages/tensorflow/python/_pywrap_tensorflow_internal.pyd
(orLib/site-packages/tensorflow/python/_pywrap_tensorflow.pyd)
Open it with Notepad++ or something similar
Search for the first occurence of 3\.5.*5\.2 using regex
You see the 3.0 before 3.5*5.2, change it to 2.0
I changed as above and can do simple calculation with GPU, but get stuck with strange and unknown issues when try with practical projects(those projects run well with 3.0 compute capability GPU)
I found it how to install Tensorflow-gpu on a compute capability 2.1 NVIDIA GeForce 525M for python ,the trick is simple use a archived version of tensorflow, I used 1.9.0
The python command for package using PIP is
pip install tensorflow-gpu==1.9.0
and cuDNN version is 7.4.1