I'm using keras with tensorflow backend on a computer with a nvidia Tesla K20c GPU. (CUDA 8)
I'm tranining a relatively simple Convolutional Neural Network, during training I run the terminal program nvidia-smi to check the GPU use. As you can see in the following output, the GPU utilization commonly shows around 7%-13%
My question is: during the CNN training shouldn't the GPU usage be higher? is this a sign of a bad GPU configuration or usage by keras/tensorflow?
nvidia-smi output
Could be due to several reasons but most likely you're having a bottleneck when reading the training data. As your GPU has processed a batch it requires more data. Depending on your implementation this can cause the GPU to wait for the CPU to load more data resulting in a lower GPU usage and also a longer training time.
Try loading all data into memory if it fits or use a QueueRunner which will make an input pipeline reading data in the background. This will reduce the time that your GPU is waiting for more data.
The Reading Data Guide on the TensorFlow website contains more information.
You should find the bottleneck:
On windows use Task-Manager> Performance to monitor how you are using your resources
On Linux use nmon, nvidia-smi, and htop to monitor your resources.
The most possible scenarios are:
If you have a huge dataset, take a look at the disk read/write rates; if you are accessing your hard-disk frequently, most probably you need to change they way you are dealing with the dataset to reduce number of disk access
Use the memory to pre-load everything as much as possible.
If you are using a restful API or any similar services, make sure that you do not wait much for receiving what you need. For restful services, the number of requests per second might be limited (check your network usage via nmon/Task manager)
Make sure you do not use swap space in any case!
Reduce the overhead of preprocessing by any means (e.g. using cache, faster libraries, etc.)
Play with the bach_size (however, it is said that higher values (>512) for batch size might have negative effects on accuracy)
The reason may be that your network is "relatively simple". I had a MNIST network with 60k training examples.
with 100 neurons in 1 hidden layer, CPU training was faster and GPU utilization on GPU training was around 10%
with 2 hidden layers, 2000 neurons each, GPU was significantly faster(24s vs 452s on CPU) and its utilization was around 39%
I have a pretty old PC (24GB DDR3-1333, i7 3770k) but a modern graphic card(RTX 2070 + SSDs if that matters) so there is a memory-GPU data transfer bottleneck.
I'm not yet sure how much room for improvement is here. I'd have to train a bigger network and compare it with better CPU/memory configuration + same GPU.
I guess that for smaller networks it doesn't matter that much anyway because they are relatively easy for the CPU.
Measuring GPU performance and utilization is not as straightforward as CPU or Memory. GPU is an extreme parallel processing unit and there are many factors. The GPU utilization number shown by nvidia-smi means what percentage of the time at least one gpu multiprocessing group was active. If this number is 0, it is a sign that none of your GPU is being utilized but if this number is 100 does not mean that the GPU is being used at its full potential.
These two articles have lots of interesting information on this topic:
https://www.imgtec.com/blog/a-quick-guide-to-writing-opencl-kernels-for-rogue/
https://www.imgtec.com/blog/measuring-gpu-compute-performance/
Low GPU utilization might be due to the small batch size. Keras has a habit of occupying the whole memory size whether, for example, you use batch size x or batch size 2x. Try using a bigger batch size if possible and see if it changes.
Related
We are trying to train our model for object recognition using tensorflow. Since there are too many images (100GB), I guess our current GPU server (1*2080Ti) could not work. We may need to purchase a more powerful one, but I do not sure how to estimate how much GPU memory we need. Is there some approach to estimate the requirements? thanks!
Your 2080Ti would do just fine for your task. The GPU memory for DL tasks are dependent on many factors such as number of trainable parameters in the network, size of the images you are feeding, batch size, floating point type (FP16 or FP32) and number of activations and etc. I think you get confused about loading all of the images to GPU memory at once. We do not do that, instead we use minibatches of different sizes to fit all of the images and params into memory. Throw any kind of network to your 2080Ti and adjust batch size then your training will run smoothly. You could go with your 2080Ti or can get another or two increase training speed. This blogpost provides beautiful insights about creating optimal DL environments.
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).
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!
I am trying to calibrate my expectations around a single laptop's ability to train a neural network. I am using tensorflow and keras and after about say 10 minutes, it crashes. I've seen killsignal 9 exit code 137, and I'm wondering if this is due to insufficient memory? Other times, when one-hot encoding using np_utils.to_categorical() I've seen the words memoryerror in the console, and that's it and my script crashes. This is just trying to transform the outputs into what a neural net expects before it even runs.
I have 6400 inputs and 1500 outputs and a small hidden layer of 100 nodes. Batch size 128.
That's it. It's not even deep. It crashes whether using an nvidia gpu or a 4 core cpu. For you pros, is my network too big to train on my system (i7 4 cores, 16gb ram, nvidia GT 750m , compute capability 3.0). Is my neural network considered a large one? I have 3 million samples, btw.
1) How do I estimate the amount of memory required for my network? Is it 6400 (# inputs) * 1500 (#outputs) * 4 bytes (per parameter) = 38.4 gb? Can I see how much memory is being used in real time on a mac somewhere? I've used activity monitor and the memory pressure gauge is normal.
2) GPUs typically are maxing out at 8gb-12gb of RAM, whereas CPUs on desktops could easily have 64 gb. So if the memory requirements of my network exceed 8gb of RAM, would it be impossible to train on a single GPU?
3) what is the difference, especially memory wise, between batch_size and batch_training?
Thank you!
Your calculation was correct with the multiplication, with the exception, that you are dealing with mega bytes and not giga bytes. The actual requirement is 6400*100*4 + 100*1500*4, which should ~4 MB if you use the default float32. You multiply the layer sizes of two subsequent layers together, because every neuron is connected to every neuron in the subsequent layer. Then the whole memory requirement is multiplied by the batch size. This is why convolutional layers are used to train deep networks.
For gpu I am using nvidia-smi to monitor the memory requirements on linux. A google search gave me this for mac: https://phvu.net/2015/03/30/nvidia-smi-on-macos/. If the memory requirements exceed the GPU memory you can not train it on the gpu. You could train it on a cpu, but that will take ages.
There are multiple ways to train with a large training set. Normally generator are used to train on batches. This means only loading the parts of the training set you actually need (https://keras.io/getting-started/faq/#how-can-i-use-keras-with-datasets-that-dont-fit-in-memory).
Finding the memory requirements for your neural network not only depends on the size of the network or the number of parameters itself. For calculating the memory foot print of the neural network, one document that I always go to is the Stanford CS231n Convolutional Neural Networks for Visual Recognition course notes. Please take a look at the portion where they find the memory requirements for each and every layer of the network.
To add to that, batch size (the number of inputs per one batch) is a crucial factor in deciding the 'memory usage'. For example, in a newer NVIDIA P100 GPU, I can go as much as 2048 images per batch if I train a CIFAR10 model and less than 512 or 256 images if I train AlexNet on ImageNet dataset. The input size matters a lot, so does the batch size since the GPU memory need to account for the batch of inputs.
One way to test the batch size which works is to do nvidia-smi and see how much memory is used. Since doing it every now and then is boring, I usually do watch nvidia-smi in my Linux machine. In my MAC, I do not have a NVIDIA GPU installed so I seldom use these tricks. When I want to, I will write quick bash scripts like these:
while true; do nvidia-smi; sleep 0.5; clear; done
You can port install watch in Mac as well.
Also, two of my most favorite tools of all time are htop and dstat.
htop gives you a much better graphical interface to the famous top command in Linux. It gives you real-time information regarding your memory and processor usage, along with the different processes. If you give sudo access to htop, you can change the niceness and other parameters directly from the interface.
dstat gives you real time information about your I/O. In most cases, I will add two flags -d and -n to specify disk and network usage only.
Fortunately, htop can be brew installed on Mac by running:
brew install htop
dstat on the other hand is not directly available. Please look into ifstat or iostat for similar functionalities.
A screenshot of htop command in Mac.
How can we minimize the idle time of a GPU when training a network using Tensorflow ?
To do this :-
I used multiple Python threads to preprocess data and feed it to a tf.RandomShuffleQueue from where the TensorFlow took the data.
I thought that this will be more efficient than the feed_dict method.
However I still find on doing nvidia-smi that my GPU still goes from 100% utilization to 0% utilization and back to 100% quite often.
Since my network is large and the dataset is also large 12 million, any fruitful advice on speeding up would be very helpful.
Is my thinking that reading data directly from a tf.Queue is better than feed_dict correct ?
NOTE: I am using a 12 GB Titan X GPU (Maxwell architecture)
You are correct on assuming that feeding through a queue is better than feed_dict, for multiple reasons (mainly loading and preprocessing done on CPU, and not on the main thread). But one thing that can undermine this is if the GPU consume the data faster than it is loaded. You should therefore monitor the size of your queue to check if you have times where the queue size is 0.
If this is the case, I would recommand you to move your threading process into the graph, tensorflow as some nice mecanismes to allow batch loading (your loading batchs should be larger than your training batchs to maximise your loading efficiency, I personnaly use training batchs of 128 and loading batchs of 1024) in threads on CPU very efficiently. Moreover, you should place your queue on CPU and give it a large maximum size, you will be able to take advantage of the large size of RAM memory (I always have more than 16000 images loaded in RAM, waiting for training).
If you still have troubles, you should check tensorflow's performance guide:
https://www.tensorflow.org/guide/data_performance