I'm working on code that trains a relatively large RNN (128 cell LSTM and some added layers). The main process is maxing out a core on the CPU, and I'm wondering if this is normal or whether I can optimize it. During the training loop (session.run calls) it's using about 60-70% GPU load while using 100% CPU load on one core. Note that data sampling work is already being done concurrently on other cores, so it's just the updating of the model parameters. Is this regular for such applications in TensorFlow or should the CPU load be much lower, while using the full capacity of the GPU?
We don't have full documentation on it yet, but you can take a look at the profiling information to see if it gives you more of an idea of where the time is going:
https://github.com/tensorflow/tensorflow/issues/1824#issuecomment-225754659
I think RNN cell have two input, it must wait for those two direction input when traning data, in other word, it optimize parallelism don't as easy as CNN. You can use a big batch size to improve the GPU utilization rate, but maybe cause other problem like that paper On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.
Related
If the dataset is small enough to fit in the GPU memory, is it possible with Tensorflow to allocate it all initially and then do the training without having data transfers between CPU and GPU?
It seems to me that with tf.data this is not possible and the data transfer is not controlled by the programmer.
Analyzing the GPU workload during training, it reaches 75% with CIFAR10, but I would expect it to reach 100% being that the dataset fit in GPU memory.Also analyzing with Tensorboard I see that there are a lot of Send operations.
(I saw that there is a similar question quite old here, however at that time there was no tf.data yet)
curiously I just found out that my CPU is much faster for predictions.
Doing inference with GPU is much slower then with CPU.
I have tf.keras (tf2) NN model with a simple dense layer:
input = tf.keras.layers.Input(shape=(100,), dtype='float32')
X = X = tf.keras.layers.Dense(2)(input)
model = tf.keras.Model(input,X)
#also initiialized with weights from a file
weights = np.load("weights.npy", allow_pickle=True )
model.layers[-1].set_weights(weights)
scores = model.predict_on_batch(data)
For 100 samples doing predictions I get:
2 s for GPU
0.07 s for CPU (!)
I am using a simple geforce mx150 with 2gb
I also tried the predict_on_batch(x) as someone suggested this as it is more faster than just predict. But here it is of same time.
Refer: Why does keras model predict slower after compile?
Has anyone an idea, what is going on there? What could be an issue possibly?
Using the GPU puts a lot of overhead to load data on the GPU memory (through the relatively slow PCI bus) and to get the results back.
In order for the GPU to be more efficient than the CPU, the model must to be very big, have plenty of data and use algorithms that can run fully inside the GPU, without requiring partial results to be moved back to the CPU.
The optimal configuration depends on the quantity of memory and of cores inside your GPU, so you must do some tests, but the following rules apply:
Your NN must have at least >10k parameters, training data set must have at least 10k records. Otherwise your overhead will probably kill the performances of GPU
When you model.fit, use a large batch_size (pay attention, the default is only 32), possibly to contain your whole dataset, or at least a multiple of 1024. Do some test to find the optimum for you.
For some GPUs, it might help performing computations in float16 instead of float32. Follow this tutorial to see how to activate it.
If your GPU has specific Tensor Cores, in order to use efficiently its hardware, several data must be multiples of 8. In the preceding tutorial, see at the paragraph "Ensuring GPU Tensor Cores are used" what parameters must be changed and how. In general, it's a bad idea to use layers which contain a number of neurons not multiple of 8.
Some type of layers, namely RNNs, have an architecture which cannot be solved directly by the GPU. In this case, data must be moved constantly back and forth to CPU and the speed is lost. If a RNN is really needed, Tensorflow v2 has an implementation of the LSTM layer which is optimized for GPU, but some limitations on the parameters are present: see this thread and the documetation.
If you are training a Reinforcement Learning, activate an Experience Replay and use a memory buffer for the experience which is at least >10x your batch_size. This way, you will activate the NN training only when a big bunch of data is ready.
Deactivate as much verbosity as possible
If everything is set up correctly, you should be able to train your model faster with GPU than with CPU.
GPU is good if you have compute-intensive tasks (large models) due to the overhead of copying your data and results between the host and GPU. In your case, the model is very small. It means it will take you longer to copy data than to predict. Even if the CPU is slower than the GPU, you don't have to copy the data, so it's ultimately faster.
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'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.
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!