I want to use TensorFlow on Windows (Win 10) with a AMD GPU.
If I google, there are a lot discussions and sources but I just couldn't figure out what's the best way to do this at the moment.
Could someone write a short installation instruction that he thinks is the best and most up-to-date way of doing so?
Tensorflow officially only supports CUDA, which is a proprietary NVIDIA technology. There is one unofficial implementation using openCL here which could work, or you could try using Google colab
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I have been trying to install a GPU-based scientific software, that necessitated the need for PGI compilers. And since Google Colab has a GPU I would like to use that as a platform to install the software. But I'm not sure if PGI compiler is preinstalled on Google Colab. If it is then how can I check that?
Thanks in advance.
I am working on a GPU server from my college with the computing capability less than 3.0, Windows 7 Professional, 64-bit operating system and 48GB RAM. I have tried to install tensorflow earlier but then I got to know that my GPU cannot support it.
I now want to work on keras but as tensorflow is not there so will it work or not as I am also not able to import it?
I have to do video processing and have to work on big video datasets for Dynamic Sign Language Recognition. Can anyone suggest me what I can do to get going in the field of Deep Learning with such GPU server? Or if I want to work on CPU only, then will there be any problem in this field of video processing?
I also have an HP Probook 440 G4 Laptop with Windows 10 Pro so is it better than the GPU server I have or not?
I am totally new to this field and cannot find a way to work properly in it.
Your opinions are needed right now!
The 'dxdiag' information for my laptop is shown and .
Thanks in advance!
For Keras to work you need either Tensorflow or Theano. Your Laptop seems to have a GeForce 930M GPU. This card has a compute capability or 5.0 according to the NVIDIA documentation (https://developer.nvidia.com/cuda-gpus). So you are better off with your Laptop if my research was right.
I guess you will use CNN with your video processing and therefore I would advise you to use a GPU. You can run your code also on a CPU but training will be much slower since GPUs are made for parallel computing and CPUs are not (the big matrix multiplication profit a lot from the parallel computing).
Maybe you could try a cloud computing provider if you think training is too slow on your laptop
I consider using OpenCL in a consumer product which is currently under development.
Doing a small research I found that generally there is good support under Mac OSX. Linux support is also relatively good, but my target audience does not use Linux. It remains to check how well it is supported in Windows.
Regarding Windows I found OpenCL distribution which raises some concerns.
Do any of you have any experience with using OpenCL in consumer-oriented products under Windows? I am more interested in the GPU side of OpenCL, specifically driver support.
Just like CUDA or Stream, OpenCL needs to be supported by the driver. Most CUDA-capable GPUs support OpenCL with a somewhat up-to-date driver (CUDA 1.0 upwards).
In fact, if you compile with, say, CUDA SDK 4.1 your end users will need newer drivers than if you had used OpenCL.
Also, OpenCL is not bound to any GPU architecture. While this might be problematic for specifically designed algorithms, it shouldn't have a very high impact on normal end user programs.
At least with CUDA, you can only compile code optimized for the current known major version. Compiling OpenCL kernels on the end user machine might allow optimizations for newer binary specifications in the future.
The crashes the author in that questions reported for Nvidia OpenCL generally seem to happen a lot if resources are not freed properly. I've been seeing similar crashes until I fixed a leak that didn't release created kernels.
I'm not saying it's the only reason why it might crash, but apart from programmer errors it appears fairly stable to me.
AMD and NVidia both support OpenCL on most (all?) of their GPUs
Unfortunately Intel only supports it on the CPU which is a bit pointless and if you have to insist that the user has a separate GPU for your app you can also insist that they have an NVidia one and use CUDA. This has limited the uptake of OpenCL.
I have some OpenCL kernels that aren't doing what they should be, and I would love to debug them in Xcode. Is this possible?
If not, is there any way I can use printf() in my CPU-based kernels? When I use printf() in my kernels the OpenCL compiler always gives me a whole bunch of errors.
Casting the format string to const char * appears to fix this problem.
This works for me on Lion:
printf((char const *)"%d %d\n", dl, dll);
This has the error described above:
printf("%d %d\n", dl, dll);
Have you tried adding this pragma to enable printf?
#pragma OPENCL EXTENSION cl_amd_printf : enable
You might also want to try using Quartz Composer to test out your kernels. If you have access to the WWDC 2010 videos, I believe they show how to use Quartz Composer for rapid prototyping of OpenCL kernels in Sessions 416: "Harnessing OpenCL in Your Application" or 418: "Maximizing OpenCL Performance". There were also some good sessions on this during WWDC 2009 and 2008 that might also be available via ADC on iTunes.
Using Quartz Composer, you can quickly set up inputs and outputs for a kernel, then monitor the results in realtime. You can avoid the change-compile-test cycle because everything is compiled as you type. Syntax errors and the like will pop up as you change code, which makes it fairly easy to identify those.
I've used this tool to develop and test out OpenGL shaders, which have many things in common with OpenCL kernels.
Have you given the gDEBugger a try already? I think it's the only choice you have currently, for OpenCL debugging on the Mac.
Intel offers a printf in their new OpenCL 1.1 SDK, but that's only for Linux and Windows. Lion has OpenCL 1.1, but at least my Core 2 Duo does not support the printf extension.
AMD ist still developing their OpenCL tools, and the Nvidia Debugging tools are only for CUDA, as far as I understand.
How can I know which CPU can be programmed by OpenCL?
For example, the Pentium E5200.
Is there a way to know w/o running and querying it?
OpenCL compatibility can generally be determined by looking on the vendor's sites. AMD's APP SDK requires CPUs to support at least SSE2. They also have a list of currently supported ATI/AMD video cards.
The most official source is probably the Khronos conformance list:
http://www.khronos.org/conformance/adopters/conformant-products#opencl
For compatibility with the AMD APP SDK: http://developer.amd.com/gpu/AMDAPPSDK/pages/DriverCompatibility.aspx
For the NVIDIA, anything that supports CUDA should support their implementation of OpenCL:
http://www.nvidia.com/object/cuda_gpus.html
For compatibility with the Intel OpenCL SDK, look at:
https://software.intel.com/en-us/articles/opencl-code-builder-release-notes
Here is the list of conforming OpenCL products from the Khronos site:
http://www.khronos.org/conformance/adopters/conformant-products/
You got Intel OpenCL too http://software.intel.com/en-us/articles/intel-opencl-sdk/ for windows right now.
Just one more comment about Intel, Now they do not only support OpenCL under windows, but also linux. But it is part of a commercial SDK see https://software.intel.com/en-us/intel-media-server-studio.
Another alternative for OpenCL development under Linux is Beignet, an OpenCL source project maintain by Intel China.
http://www.freedesktop.org/wiki/Software/Beignet/
I have tested on linux and it works as per tutorial, however, the compiler they use is completely different from the one under the windows.
Well for the CPU, AMD's SDK is supposed to work on x86 (even on Intel's x86), so that will cover most of your options.
And for the GPU, I think almost all cards made in the last couple of years should run OpenCL kernels. I don't have of a particular list.
EDIT: Looks like AMD removed the original SDK pages with no replacement. There are unofficial mirrors for Windows and Linux, but I haven't tried them.