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I will try cuda 7.0 later jf003320018 commented Oct 18, 2016 @yunzhou my CUDA is 8.0 and cudnn is V3. but mxnet gives me 0. 0. 0. I also observe that Linux is faster than Windows for quite a while, I have Linux box with GTX970 which runs around 700 images per sec something over train_cifar10, but in I read in this thread: #250 #250, that indicates 'remove USE_CUDNN' to compile for Cuda Compute GPUs 2.1 (and lower). navigate here

Could using an earlier 2015 release be a solution? Quares commented Feb 23, 2016 Great news! On Wed, Feb 24, 2016 at 7:33 AM, thyu [email protected] wrote: The new release works on my machine as well, awesome! Dr. great post to read

New Graphics Card No Internet Connection

I am running Titan. Hwu is a Professor and holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. This is, of course, more complicated than just doing everything on one large batch of cells, but significantly broadens the type of problems for which GPUs can be used.

Now xfwm4 relies upon glx backend, which fix tearing and may fix problems with your gpu. May 16 '15 at 7:13 add a comment| up vote 0 down vote Imagine a problem that can be solved by lots of brute force, like Travelling Salesman. more hot questions question feed about us tour help blog chat data legal privacy policy work here advertising info mobile contact us feedback Technology Life / Arts Culture / Recreation Science Hope it helps.....

JohanManders commented Feb 17, 2016 @Quares I have tried the latest build, Windows binary build 20160216, and I still have the problem. Nvidia Drivers Assume it has been set to zero before spawning the kernel. */ __device__ int tasks_mutex; /* Mutex routines using atomic compare-and-set. */ __device__ inline void cuda_mutex_lock ( int *m ) { Why is "aircrafts" bad English, while "crafts" is okay? https://books.google.se/books?id=dNvantWW7HMC&pg=PA55&lpg=PA55&dq=is+this+a+GPU+or+network+problem?&source=bl&ots=9ervqY5k6e&sig=StbTNcoXfFsIDRk4v7Gc7ZdZC4I&hl=en&sa=X&ved=0ahUKEwi9j6nqltzRAhVJQBQKHYVuArgQ6AEINTAC I know its a basic question, but much of my searching gets caught in people clearly advocating for one or the other without really justifying why, or somewhat vague rules of

Hot Network Questions How can I password protect files in macOS? After running the file I get Train-accuracy=0.098825 Member piiswrong commented Jan 9, 2016 Here is my output from train_mnist.py: 2016-01-09 12:48:47,622 Node[0] start with arguments Namespace(batch_size=128, data_dir='mnist/', gpus=None, kv_store='local', load_epoch=None, lr=0.1, Nvidia) GPUs can be described as a set of processors that work autonomously on 32 threads each. Anaconda is brought to you by Continuum Analytics.

Nvidia Drivers

The issue seems to be solved, most likely due to the new CUDA release. https://books.google.se/books?id=aT8_AAAAQBAJ&pg=PA117&lpg=PA117&dq=is+this+a+GPU+or+network+problem?&source=bl&ots=LZhWDbn93c&sig=PJ8WxWx6-bgwvPWRQYI8_yhjAoQ&hl=en&sa=X&ved=0ahUKEwi9j6nqltzRAhVJQBQKHYVuArgQ6AEIQDAE juanlp commented Jul 12, 2016 I can confirm that it works fine on Windows and R on the latest version with CUDA 7.5 On 12 Jul 2016 08:39, "long-jian" [email protected] wrote: New Graphics Card No Internet Connection Here are some speculations: run "where libmxnet.dll" and see if you are using the right version of libmxnet.dll run matrixMulCuBLAS from nvidia CUDA samples and see if it works try building Internet Speed Test Reverted to the 20160223 build and it works, 0.9911 on MNIST.

My hardware and software configurations: GPU: GTX850M OS: Windows 8.1 x64 Compiler: Visual Studio 2013 Update 5 3rdparty Software: CUDA 7.5, CUDNN V4, OpenCV 3.1, OpenBLAS 0.2.14 With the default options Sorry if it's a dumb question. This book begins with an overview of parallel algorithms and data structures. To be more precise, the kernel should fit into the register of each multiprocessing unit (or compute unit) of the GPU.

Results may v ary when GPU Boost is enabled. HONG Chuntao System Research Group Microsoft Research Asia Member hjk41 commented Feb 24, 2016 Here is my results: python train_mnist.py: GTX980, Windows 10: 20000 samples/sec Titan, Ubuntu 14.04: 40000 samples/sec Also, Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the http://htmltemplatesfree.net/is-this/wifi-connected-but-no-internet.html All nails are in a regular pattern, like a grid.

In OpenCL terms this is mostly referred as the kernel. I have the same question with you? GPU Device 0: "GeForce GTX 660M" with compute capability 3.0 MatrixA(320,320), MatrixB(640,320) Computing result using CUDA Kernel...

arr.asnumpy() array([[ 0., 0.], [ 0., 0.]], dtype=float32) import numpy arr[:] = numpy.ones((2,2)) arr.asnumpy() array([[ 1., 1.], [ 1., 1.]], dtype=float32) Also, when I built mxnet debug version the gpu is

Is there a known solution? Mathematics is fact. qggjonny commented Oct 13, 2016 I put cudnn64_70.dll in mxnet\3rdparty\cudnn and mxnet\3rdparty\cudnn\bin. So the problem is only with the release version.

Thanks for your help. Has anyone tried running heavier workloads like ImageNet? I have no idea what this means and if anybody can help me with fixing this issue, it would mean a lot to me. jonathanponce commented Mar 22, 2016 The error is back in the latest release the accuracy remain terrible, the 20160223 build works fine, but the rest appear to have the error again

But I get very good accuracy when change to cudnn5 guileryu01 commented Aug 13, 2016 I'm having exactly same problem with windows 10 64bit, Visual Studio 2013, CUDA 8.0 RC, CUDNN5, But right now, I don't have much of an idea of what's best handled by CPU-based computation, and what should be offloaded to a GPU. R. Does this also occur for low compute capability GPUs on Linux?