I write a program to train my data (images with trainNetwork). I use a computer with the GPU nvidia quadpro M4000 and RAM 64GB to train. I write a short code here:
opts=trainingOptions('sgdm','InitialLearnRate',1e-3,'MaxEpochs',200,'MiniBatchSize',64,'Plots','training-progress', 'ExecutionEnvironment', 'auto');
net=trainNetwork(trainingData,layers,opts);
As description on Mathworks site, the trainNetwork will choose the GPU when it is available. Although I tried to change ExecutionEnvironment to gpu, it still run on CPU only. How to force the trainNetwork run on GPU?
Related
I'm working on a virtual machine on a remote server and I want to train a neural network on it but I don't have GPUs to use in this VM. is it possible to train the net on this VM using CPU only? and if that the case, does it work with a large dataset or that will be a problem?
I have used Tensorflow for training a deep neural network. I have used it with GPU and CPU only. The rest of my response is in context of Tensorflow.
Please be aware that Convolution Neural Nets are generally more resource hungry than standard regular feed forward neural networks because CNNs deal with much higher dimensional data. If you are not working deep CNNs then you may be all right to use CPU to and restrict to smaller datasets.
In my scenario, initially I was training with CPU only and then moved on to GPU mode because of speed improvements.
Example of speed
I was able to train the entire MNIST with when using GPU in under 15 minutes. Training on CPU was much slower but you can still learn by cutting down the on the size of the training data set.
Tensorflow with GPU
https://www.tensorflow.org/install/gpu
You will need to go through all the installation steps. This involves not only installing Tensorflow but also CUDA libraries.
What is CUDA?
CUDA is the specification developed by NVIDIA for programming with GPU. They provide their native libraries which talk to the underlying hardware.
https://docs.nvidia.com/cuda/
How to use TensorFlow GPU?
I am trying to get into deep learning using the neural network library in matlab. A good starting step seems to be training an autoencoder. In that respect, it would be good to see whether I am getting the msot out of my gpu.
In this connection, When I run
tic
autoenc1 = trainAutoencoder(allSets,5,...
'L2WeightRegularization',0.001,...
'SparsityRegularization',1,...
'SparsityProportion',0.2,...
'DecoderTransferFunction','logsig',...
'useGPU',true)
toc
I get "Elapsed time is 19.680823 seconds.".
However, not using the gpu (setting 'useGPU' to false) it only takes 8.272708 seconds.
I am puzzled by this, since I am assuming that using the gpu for neural networks will speed things up? Does anyone know of any way to check whether matlab and cuda are properly interfacing, or see how matlab is actually using the resources?
I have cuda 8.1 installed, and am using a GeForce GTX 960M (compute capability 5.0). The matlab version is 2016b.
EDIT: as has been pointed out, there is as of yet no cuda 8.1. What I do have is 8.0, and cudnn 5.1.
As pointed out in the comments, performing computations on the GPU is not necessarily faster. Instead, the impact on performance depends on the additional overhead of data conversion and transfer.
Usually, the overhead can be influenced via the batch size, but the trainAutoencoder function does not provide that option.
For general measurement and improvement of GPU performance in MATLAB, see this link.
how can I run this example in my pc? I don't have Nvidia graphic cards so I cannot use Cuda in Matlab.
I need to do it with Matlab because half of the my code is written in Matlab and all variables are in Matlab format.
My PC has ATI Radeon HD 4530 graphic card.
I read this page, but it is still confusing to understand which one is suitable.
Update1: I want to Train a deep neural network for image classification. A task similar to this example.
Update2: When I run the code mentioned in Update1, it gives me following error:
There is a problem with the CUDA driver or with this GPU device. Be sure that you have a supported GPU and that the
latest driver is installed.
Error in nnet.internal.cnn.SeriesNetwork/activations (line 48)
output = gpuArray(data);
Error in SeriesNetwork/activations (line 269)
YChannelFormat = predictNetwork.activations(X, layerID);
Error in DeepLearningImageClassificationExample (line 262)
trainingFeatures = activations(convnet, trainingSet, featureLayer, ...
Caused by:
The CUDA driver could not be loaded. The library name used was 'nvcuda.dll'. The error was:
The specified module could not be found.
Yes you can. You will have to create DLL's and use OpenCL. Look into S-Functions and Mex.
Check the documentation
There are third party tools that you may be able to use. I personally have never tried it.
Possible Tool
MatConvNet ->
Work both on CPU and GPU.
MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs. Many pre-trained CNNs for image classification, segmentation, face recognition, and text detection are available.
Another option: Caffe in general and Openmp variant of caffe in particular support Matlab and work both on CPU and GPU
I'm using theano to implement a convolution neural network. My CPU RAM is 32G and GPU RAM is 2G, but the data is also very big -- almost 5G training data.
When the program is running, the computer seems to be frozen and each operation is really slow, even didn't respond. And the CPU mode seems to be at least 2x faster than GPU mode.
Is there any way to speed up the GPU convolutional neural network?
Make sure to use Theano 0.7 with cudnn, this speed up convolution heavily:
http://deeplearning.net/software/theano/library/sandbox/cuda/dnn.html
In order to use GPU accelleration first thing you need to install CUDA.
On the level of Theano configuration(Theano flags/TheanoRC) there are few ways you can speed-up your model with GPU:
Specify usage of GPU "device = gpu"
Enable Cuda memory allocation (CnMem) "cnmem = 0.75"
Enable CUDNN optimization "optimizer = cudnn"
You can read more about Theano config here
I work on an audio processing project that needs to do a lot of basic computations (+, -, *) like a FFT (Fast Fourier Transform) calculation.
We're considering using a graphics card to accelerate these computations. But we don't know if this is the best solution. Our desired solution needs to be a good computation system costing less than $500.
We use Matlab programming, and we have a sound card acquisition which have to be plug in the system.
Do you know a solution other than graphics card + motherboard to do lot of calculus?
You can use the free Matlab CUDA library to perform the computations on the GPU. $500 will give you a very decent NVIDIA GPU. Beware that GPU's have limited video memory and will run out of memory with large data volumes even faster than Matlab.
I have benchmarked an 8core intel CPU against an 8800 Nvidia GPU (128streams) with GPUMat , for 512Kb datasets the GPU spun out at the same speed as the 8 core intel at 2Ghz, including transfer times to the GPU memory. For serious GPU work I recommend a dedicated card compared to the one you are using to drive the monitor. Use the motherboard cheapie intel video to drive the monitor and pass the array computes to the Nvidia.
Parallel Computing Toolbox from MathWorks now includes GPU support. In particular, elementwise operations and arithmetic are supported, as well as 1- and 2-dimensional FFTs (along with a whole bunch of other stuff to support hand-written CUDA code if you have that). If you're interested in performing calculations in double-precision, the recent Tesla and Quadro branded cards will give you the best performance.
Here's a trivial example showing how you might use the GPU in MATLAB using Parallel Computing Toolbox:
gA = gpuArray( rand(1000) );
gB = fft( 1 + gA * 3 );