OceanFFT | CUDA sampleのOceanFFTの移植、簡素版 | GPU library
kandi X-RAY | OceanFFT Summary
kandi X-RAY | OceanFFT Summary
1:Pointsのみの描画 2:法線を中心差分で計算 3:法線を解析的に計算 4:ライティング、視点固定、フレネル反射あり。(要修正) 5:Choppy Wave実装 6:泡(bubbles)実装.
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QUESTION
I am trying to get Tensorflow GPU support going in Python under Windows 10.
What does work;
Download and install Python v3.7.3
...ANSWER
Answered 2019-Apr-11 at 22:37For all those with the „DLL load failed“ problem under Windows 10/Python 3.6.x/RTX20xx.
The combination of CUDA 10.0 (not 10.1!), cuDNN 7.5.0 works fine for me (as of 12 April 2019). I also have Visual Studio 2015 installed (but not sure if needed).
Don‘t forget to add the location of the cuDNN *.dll file (it‘s the /bin/
dir in your CUDA dir) to your PATH.
If you have CUDA 10.1, just uninstall it, install 10.0, add the cuDNN files to the 10.0 dir, and reboot.
Tensorflow can be installed using pip install tensorflow-gpu
QUESTION
I'm using CUDA Toolkit 8 with Visual Studio Community 2015. When I try simple vector addition from NVidia's PDF manual (minus error checking which I don't have the *.h's for) it always comes back as undefined values, which means the output array was never filled. When I pre-fill it with 0's, that's all I get at the end.
Others have had this problem and some people are saying it's caused by compiling for the wrong compute capability. However, I am using an NVidia GTX 750 Ti, which is supposed to be Compute Capability 5. I have tried compiling for Compute Capability 2.0 (the minimum for my SDK) and 5.0.
I also cannot make any of the precompiled examples work, such as vectoradd.exe which says, "Failed to allocate device vector A (error code initialization error)!" And oceanfft.exe says, "Error unable to find GLSL vertex and fragment shaders!" which doesn't make sense because GLSL and fragment shading are very basic features.
My driver version is 361.43 and other apps such as Blender Cycles in CUDA mode and Stellarium work perfectly.
Here is the code that should work:
...ANSWER
Answered 2017-Jul-02 at 10:12This was apparently caused by using an incompatible driver version with the CUDA 8 toolkit. Installing the driver distributed with the version 8 toolkit solved thr problem.
[Answer assembled from comments and added as a community wiki entry to get the question off the unanswered queue for the CUDA tag]
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