I have Tensorflow 1.4 GPU version installed. Cuda8 is installed too.
I trained my pretty simple GAN network on MNIST data.
I have AMD FX 8320 CPU, 16Gb system memory and SSD hard drive.
It took about 17 seconds per epoch on GeForce 720 GPU with 1GB memory.
The training utilized about 25% of GPU and 99% of memory. CPU was loaded prettyhigh, close to 100%.
Then I inserted other video board with GeForce1050 Ti GPU and 4Gb memory instead of previous. The GPU was loaded only for 5-6%, memory was utilized for 93%.
But I still got about 17s per epoch and high load for CPU.
So maybe Tensorflow has some settings to utilize more GPU?
Or what is a cause of high CPU load and low GPU load?
If you are training a simple GAN network it is fairly likely that your old GPU was not the bottleneck in the first place. So, improving it had no effect. If the amount of work done per sess.run() call very small, the overheads (executing your Python code, copying the input data to GPU, starting and running the TensorFlow executor, scheduling all the operations to GPU, etc) can dominate your computation.
The only sure way of knowing what happens is to profile. You can take a look here https://www.tensorflow.org/performance/performance_guide as a starting point. The timeline tool it mentions can be fairly useful. See here for more details: Can I measure the execution time of individual operations with TensorFlow?.
Agree, for MNIST datasets, there are probably other bottlenecks in the system, not the GPU. I ran 2 side-by side TensorFlows,
Intel i7 4600M with NVIDIA Quadro K1100M GPU and 12 GB RAM, which is a 4th Gen Haswell Intel machine, and
Intel i5 8300U with No Cuda GPU and 16GB of RAM.
Basically 8th Gen Kaby Lake Intel CPU vs 4th Gen Intel, and I got:
4th Gen Intel chip with NVIDIA GPU:
311.5 sec, 315.9 sec, 313.0 sec to complete all 10 epocs on a MNIST run
8th Gen Intel chip with no GPU:
252.7 sec, 243.5 sec, 254.9 sec
So I'm running 20% faster with no GPU, just a newer generation of Intel chip.
Related
I am trying to do a matrix decomposition (or tucker decomposition on a tensor) in Tensorflow with GPU. I have tensorflow-gpu, my NVidia GPU has 4GB RAM. My problem is that my input matrix is huge, millions of rows and millions of columns and the size of the matrix is more than 5GB in memory. So each time Tensorflow gives me an out of memory (OOM) error. (If I turn off GPU, the whole process can run successfully in CPU using system RAM. Of course, the speed is slow.)
I did some research on Tensorflow and on NVidia CUDA lib. CUDA seems has a "unified memory" mechanism so the system RAM and GPU RAM share one address book. Yet no further details found.
I wonder if Tensorflow supports some memory sharing mechanism such that I can generate input in system RAM? (Since I want to use GPU to accelerate the calculations) And GPU can do the calculation piece by piece.
I use CPU xeon E5-1650 (3.2 GHz, 6Cores, 12 Threads) for training Tensorflow model.
But training is so slow...
If I will use desktop computer with typical CPU and 2 GPU GeForce GTX750 (2 Gb), it will be faster?
Using the GPU's will be faster. The only things to keep in mind are that the size of your model is then constrained by the memory of the GPUs and that you have to choose the right sets of version numbers and drivers, such that your GPU is supported.
I have an GTX 1050 ti (4GB) and i5 CPU, 8GB memory.
I successfully installed tensorflow-gpu with cuda driver on win10 and the test shows that tensorflow is actually using the gpu (snapshot):
However, when carrying out the training with CNN, while the GPU memory is always 100%, the GPU load is qualsi 0 with some spikes # 30%~70%:
Is it normal ?
EDIT: While the GPU occupation is qualsi 0 with spikes, the CPU load is fixed at 100% during the training.
EDIT2: I did read somewhere that the CPU could be high while GPU be low if there are a lot of operations of data copy between CPU and GPU. But I am using the official tensorflow object detection api for the training so I am totally unaware of the possible place in code.
What you see is normal behavior in most cases.
TensorFlow books the entire GPU memory initially.
The load on the GPU is dependent upon the data it is getting for processing.
If the data loading operation is slow, then most of the time GPU is waiting for data to get copied from disk to the GPU, and during that time it is not performing any work. That is what you see in your screen.
I am running deep learning CNN (4-CNN layers and 3 FNN layers) model (written in Keras with tensorflow as backend) on two different machines.
I have 2 machines (A: with a GTX 960 graphics GPU with 2GB memory & clock speed: 1.17 GHz and B: with a Tesla K40 computation GPU with 12GB memory & clock speed: 745MHz)
But when I run the CNN model on A:
Epoch 1/35
50000/50000 [==============================] - 10s 198us/step - loss: 0.0851 - acc: 0.2323
on B:
Epoch 1/35
50000/50000 [==============================] - 43s 850us/step - loss: 0.0800 - acc: 0.3110
The numbers are not even comparable. I am quite new to deep learning and running code on GPUs. Could someone please help me explain why the numbers are so different?
Dataset: CIFAR-10 (32x32 RGB images)
Model batch size: 128
Model number of parameters: 1.2M
OS: Ubuntu 16.04
Nvidia driver version: 384.111
Cuda version: 7.5, V7.5.17
Please let me know if you need any more data.
Edit 1: (adding CPU info)
Machine A (GTX 960): 8 cores - Intel(R) Core(TM) i7-6700 CPU # 3.40GHz
Machine B (Tesla K40c):8 cores - Intel(R) Xeon(R) CPU E5-2637 v4 # 3.50GHz
TL;DR: Measure again with a larger batch size.
Those results do not surprise me much. It's a common mistake to think that an expensive Tesla card (or a GPU for that matter) will automatically do everything faster. You have to understand how GPUs work in order to harness their power.
If you compare the base clock speeds of your devices, you will find that your Xeon CPU has the fastest one:
Nvidia K40c: 745MHz
Nvidia GTX 960: 1127MHz
Intel i7: 3400MHz
Intel Xeon: 3500MHz
This gives you a hint of the speeds at which these devices operate and gives a very rough estimate of how fast they can crunch numbers if they would only do one thing at a time, that is, with no parallelization.
So as you see, GPUs are not fast at all (for some definition of fast), in fact they're quite slow. Also note how the K40c is in fact slower than the GTX 960.
However, the real power of a GPU comes from its ability to process a lot of data simultaneously! If you now check again at how much parallelization is possible on these devices, you will find that your K40c is not so bad after all:
Nvidia K40c: 2880 cuda cores
Nvidia GTX 960: 1024 cuda cores
Intel i7: 8 threads
Intel Xeon: 8 threads
Again, these numbers give you a very rough estimate of how many things these devices can do simultaneously.
Note: I am severely simplifying things: In absolutely no way is a CPU core comparable to a cuda core! They are very very different things. And in no way can base clock frequencies be compared like this! It's just to give an idea of what's happening.
So, your devices needs to be able to process a lot of data in parallel in order to maximize their throughput. Luckily tensorflow already does this for you: It will automatically parallelize all those heavy matrix multiplications for maximum throughput. However this is only going to be fast if the matrices have a certain size. Your batch size is set to 128 which means that almost all of these matrices will have the first dimension set to 128. I don't know the details of your model, but if the other dimensions are not large either, then I suspect that most of your K40c is staying idle during those matrix multiplications. Try to increase the batch size and measure again. You should find that larger batch sizes will make the K40c faster in comparison with the GTX 960. The same should be true for increasing the model's capacity: increase the number of units in the fully-connected layers and the number of filters in the convolutional layers. Adding more layers will probably not help here. The output of the nvidia-smi tool is also very useful to see how busy a GPU really is.
Note however that changing the model's hyper-parameter and/or the batch size will of course have a huge impact on how the model is able to train successfully and naturally you might also hit memory limitations.
Perhaps if increasing the batch size or changing the model is not an option, you could also try to train two models on the K40c at the same time to make use of the idle cores. However I have never tried this, so it might not work at all.
I am using Nvidia Digits Box with GPU (Nvidia GeForce GTX Titan X) and Tensorflow 0.6 to train the Neural Network, and everything works. However, when I check the Volatile GPU Util using nvidia-smi -l 1, I notice that it's only 6%, and I think most of the computation is on CPU, since I notice that the process which runs Tensorflow has about 90% CPU usage. The result is the training process is very slow. I wonder if there are ways to make full usage of GPU instead of CPU to speed up the training process. Thanks!
I suspect you have a bottleneck somewhere (like in this github issue) -- you have some operation which doesn't have GPU implementation, so it's placed on CPU, and the GPU is idling because of data transfers. For instance, until recently reduce_mean was not implemented on GPU, and before that Rank was not implemented on GPU, and it was implicitly being used by many ops.
At one point, I saw a network from fully_connected_preloaded.py being slow because there was a Rank op that got placed on CPU, and hence triggering the transfer of entire dataset from GPU to CPU at each step.
To solve this I would first recommend upgrading to 0.8 since it had a few more ops implemented for GPU (reduce_prod for integer inputs, reduce_mean and others).
Then you can create your session with log_device_placement=True and see if there are any ops placed on CPU or GPU that would cause excessive transfers per step.
There are often ops in the input pipeline (such as parse_example) which don't have GPU implementations, I find it helpful sometimes to pin the whole input pipeline to CPU using with tf.device("/cpu:0"): block