When loading a dataset into the GPU for training, would a Pytorch NN load the entire dataset or just the batch?
I have a 33GB dataset that fits comfortably on my normal RAM (64GB) but i only have a 16GB of GPU RAM (T4). As long as Pytorch only loads one batch at a time into the GPU, that should work fine without any memory problems?
You can load one batch of data at a time into GPU. You should use data loader to fetch a batch of data and also initialize a torch device instance to use GPU.
You can check the following tutorial. It uses data loader to get data as batches and load them to GPU by using torch device.
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Related
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?
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 using Stanford crf classifier in java. when i train the classifier on small data up to 40,000 words, it works fine, but when i increase the training data and try to train it on 170000 words, the program gets stuck only after two or three iterations.The case is same even if i provide heap space up to 4GB to the program. i am using : edu.stanford.nlp.ie.crf.CRFClassifier library.
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
My MATLAB code uses the GPU to create raw data(25 seconds). Then in MATLAB, this raw data gets processed into scaler quantities that can be fed into an objective function(15 seconds).
Is it possible for a MATLAB particle swarm optimization code to start retrieving the next batch of raw data while the current raw data is processed?
Thank you.
You mean essentially work load distribution and timing. MatLab does have a ability to perform tasks in parallel by using SPMD:
http://www.mathworks.nl/help/distcomp/spmd.html
You might be able to first retrieve a set from your GPU and then start processing the data using MatLab AND retrieving the next set simultaneously using the SPMD statement.