I am trying to use a GPU(NVidia Cuda) in image processing using deep learning.
I have two questions.
The first one is:
My CPU usage (69%) is more than my GPU's (17%) during the training time. I would like to know if the GPU is working or not. If not or if it is a problem, how can I solve it?
The second one is:
CUDA out-of-memory problem. I have already decreased the batch size to 32 and smaller and smaller. And then I have already used torch.cuda.empty_cache() code. However, it doesn't work.
Related
I am running TensorFlow on a machine which has two GPUs, each with 3 GB memory. My batch size is only 2GB, and so can fit on one GPU. Is there any point in training with both GPUs (using CUDA_VISIBLE_DEVICES)? If I did, how would TensorFlow distribute the training?
With regards to memory: I assume that you mean that one data batch is 2GB. However, Tensorflow also requires memory to store variables as well as hidden layer results etc. (to compute gradients). For this reason it also depends on your specific model whether or not the memory will be enough. Your best bet would be to just try with one GPU and see if the program crashes due to memory errors.
With regards to distribution: Tensorflow doesn't do this automatically at all. Each op is placed on some device. By default, if you have any number of GPUs available, all GPU-compatible ops will be placed on the first GPU and the rest on the CPU. This is despite Tensorflow reserving all memory on all GPUs by default.
You should have a look at the GPU guide on the Tensorflow website. The most important thing is that you can use the with tf.device context manager to place ops on other GPUs. Using this, the idea would be to split your batch into X chunks (X = number of GPUs) and define your model on each device, each time taking the respective chunk as input and making sure to reuse variables.
If you are using tf.Estimator, there is some information in this question. It is very easy to do distributed execution here using just two simple wrappers, but I personally haven't been able to use it successfully (pretty slow and crashes randomly with a segfault).
Solved this problem myself. It was because there were too much images in the celeba dataset and my dataloader was so inefficient. The dataloading took too much time and caused the low speed.
But still, this could not explain why the code was running on the cpu while the gpu memory was also taken up. After all I just transfer to pytorch.
My environment: windows10, cuda 9.0, cudnn 7.0.5, tensorflow-gpu 1.8.0.
I am working a cyclegan model. At first, it worked fine with my toy dataset, and could run on gpu without main problem(though the first 10 iterations took extremely long time, which means it might be running on cpu).
I later tried celeba dataset, only changed the folder name to load the data(I loaded data to the memory all at once, then use my own next_batch function and feed_dict to train the model). Then the problem arose: the GPU memory was still taken according to GPU-Z, but the GPU-load is low(less than 10%), and the training speed is very slow(took more than 10 times than normal), which means the code was running on CPU.
Would anyone please give me some advise? Any help is appreciated, thanks.
What is the batch size that you were trying? If it's too low (something like 2-8) for a small model, the memory consumed will not be much. It all depends on your batch size, the number of parameters in your model, etc. It also depends on the model architecture and how much of the model has components that can be run in parallel. Maybe try increasing your batch size and re-running it?
I have a very large deep neural network. when I try to run it on GPU I get "OOM when allocating". but when I mask the GPU and run on CPU it works (about 100x slower when comparing small model).
My question is if there is any mechanism in tenosrflow that would enable me to run the model on GPU. I assume the CPU uses virtual memory so it can allocates as much as he likes and move between cache/RAM/Disk (thrashing).
is there something similiar on Tensorflow with GPU? that would help me even if it will be 10x slower than regular GPU run
Thanks
GPU memory is currently not extensible (Till something like PASCAL is available)
I have installed tensorflow-gpu on Linux Mint 18. My graphics card is a GT 740m. The deviceQuery and bandwidthTest for CUDA and the MNISTsample for cudnn scripts pass (referred here and here).
Tensorflow does use the GPU (e.g. following these instructions works, and memory and processing utilization of the GPU increases when running programes), but the performance is rather… mediocre.
For instance running the script shown on this site the GPU is only about twice as fast as the CPU. Certainly a nice improvement, but not "really, really fast", as is stated on the site. Another example: Using vgg16 with Keras to classify 100 images, each about 300x200 pixels takes around 30 seconds.
Is there anything I might do to increase the performance, or can I not expect anything better?
for search queries: slow,
I trained a neural network using a GPU (1080 ti). The training speed on GPU is far better than using CPU.
Currently, I want to serve this model using TensorFlow Serving. I just interested to know if using GPU in the serving process has a same impact on performance?
Since the training apply on batches but inferencing (serving) uses asynchronous requests, do you suggest using GPU in serving a model using TensorFlow serving?
You still need to do a lot of tensor operations on the graph to predict something. So GPU still provides performance improvement for inference. Take a look at this nvidia paper, they have not tested their stuff on TF, but it is still relevant:
Our results show that GPUs provide state-of-the-art inference
performance and energy efficiency, making them the platform of choice
for anyone wanting to deploy a trained neural network in the field. In
particular, the Titan X delivers between 5.3 and 6.7 times higher
performance than the 16-core Xeon E5 CPU while achieving 3.6 to 4.4
times higher energy efficiency.
The short answer is yes, you'll get roughly the same speedup for running on the GPU after training. With a few minor qualifications.
You're running 2 passes over the data in training, which all happens on the GPU, during the feedforward inference you're doing less work, so there will be more time spent transferring data to the GPU memory relative to computations than in training. This is probably a minor difference though. And you can now asynchronously load the GPU if that's an issue (https://github.com/tensorflow/tensorflow/issues/7679).
Whether you'll actually need a GPU to do inference depends on your workload. If your workload isn't overly demanding you might get away with using the CPU anyway, after all, the computation workload is less than half, per sample, so consider the number of requests per second you'll need to serve and test out whether you overload your CPU to achieve that. If you do, time to get the GPU out!