I tried to setup a very simple Mnist example with an Estimator.
First I used the estimator's deprecated fit() parameters x, y and batch_size. This executed very fast and utilized about 100% of my GPU while not effecting the CPU much (about 10% utilization). So it worked as expected.
Because the x, y and batch_size parameters are deprecated, I wanted to use the input_fn parameter for the fit() function. To build the input_fn, I used a tf.slice_input_producer and batched it with tf.train.batch. This is my code https://gist.github.com/andreas-eberle/11f650fca0dce4c9d3d6c0955145e80d. You should be able to just run it with tensorflow 1.0.
My problem is that the training now runs very slow and only utilizes about 30 % of my GPU (shown in nvidia-smi).
I also tried to increase the queue capacity of the slice_input_producer and to increase the number of threads used for batching. However, this only helped to get to about 45% of GPU utilization and resulted in a 100 % GPU utilization.
What am I doing wrong? Is there a better way for feeding the inputs and batching them? I do not want to create the batches manually (creating subarrays of the numpy input array) because I want to use this example for a more complex input queue where I'll be reading and preprocessing the images in the graph.
I don't think my hardware should be the problem:
List item
Windows 10
NVidia GTX 960M
i7-6700HQ
32 GB RAM
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I'm trying to train a small TF2.x model on 4 GPUs (AWS g4dn.12xlarge) that takes both dense and sparse tensors as its input. Once I tried without sparse features and just used dense features, my distributed training code worked well without any performance degradation. After including the sparse features, however, I found numerous unexpected chunks on the TensorBoard Profiler's trace_viewer.
Attached the profiler screenshot.
The main problem is that, although it seems all the GPUs computes their given batches well, there is a large timespan between a pair of computation blocks on the host side. There are 17x4 of EagerExecute:DeserializeSparse with the terminal ops of _Send input 0 from /job:localhost/replica:0/task:0/device:GPU:{gpu_number} to /job:localhost/replica:0/task:0/device:CPU:0. Here, 17 is the number of sparse features that the model receives, and 4 is the num of GPUs being utilized. Plus, tons of MemcpyD2H (small pink blocks at the screen shot) are occupying each GPU, not parallelized. That large period of time is about x6 of the actual forward pass.
Below is how the model treats sparse tensor inputs:
def call(self, inputs: tf.sparse.SparseTensor):
with tf.device("\cpu:0"):
x = self.hash_inputs_from_static_hash_table(inputs)
x = self.embedding_lookup_sparse(x)
return self.prediction_head(x)
The data can never be big (batch size = 128 per replica, sparse feature embedding dimension is <10), and I tried to move all sparse-related operations to CPU not to burden GPUs, but the problem persists just as the same as I didn't move those ops to CPU manually.
I want to know why those chunks appear after the GPU computations, and hopefully remove them to fully benefit from distributed training with multiple GPUs.
Seems like I'm still missing something that can be optimized and this situation might not that unique in distributed training, so asking for help for broader audience.
I am training a sparse logistic regression model on tensorflow. This problem is specifically about the inference part. I am trying to benchmark inference on on cpu and gpu. I am using the Nvidia P100 gpu (4 dies) on my current GCE box. I am new to gpu so sorry for naive questions.
The model is pretty big ~54k operation (is it considered big compared to dnn or imagenet models ? ) . When i log device placement , i only see gpu:0 being used , and rest of them unused ? I don't do any device placement during training , but during inference i want it to optimally place and use gpu.
Few things i observed : my input node placehoolder (feed_dict) is placed on cpu, so i assume my data is being copied from cpu to gpu ? how does feed_dict exactly work behind the scene ?
1) How can i place my data on which i want to run prediction directly on gpu ? Note : my training runs on distributed cpu with multiple terabytes so i cannot have constant or variable directly in my graph during training , but my inference i can definitely have small batches of data that i would directly like to place on gpu. Are there ways i can achieve this ?
2) Since i am using P100 gpu , i think it has unified memory with host , is it possible to have zerocopy and directly have my data loaded into gpu ? How can i do this from python , java and c++ code. Currently i use feed_dict which from various google sources i think is not at all optimal .
3) Is there some tool or profiler i can use to see when i profile code like :
for epoch_step in epochs:
start_time = time.time()
for i in range(epoch_step):
result = session.run(output, feed_dict={input_example: records_batch})
end_time = time.time()
print("Batch {} epochs {} :time {}".format(batch_size, epoch_step, str(end_time - start_time)))
how much time is being spent on 1) cpu to gpu data transfer 2) session run overhead 3) gpu utilization (currently i use nvidia-smi periodically to monitor
4) kernel call overhead on cpu vs gpu (I assume each invokation of sess.run invokes 1 kernel call right ?
my current bench marking results :
CPU :
Batch size : 10
NumberEpochs TimeGPU TimeCPU
10 5.473 0.484
20 11.673 0.963
40 22.716 1.922
100 56.998 4.822
200 113.483 9.773
Batch size : 100
NumberEpochs TimeGPU TimeCPU
10 5.904 0.507
20 11.708 1.004
40 23.046 1.952
100 58.493 4.989
200 118.272 9.912
Batch size : 1000
NumberEpochs TimeGPU TimeCPU
10 5.986 0.653
20 12.020 1.261
40 23.887 2.530
100 59.598 6.312
200 118.561 12.518
Batch size : 10k
NumberEpochs TimeGPU TimeCPU
10 7.542 0.969
20 14.764 1.923
40 29.308 3.838
100 72.588 9.822
200 146.156 19.542
Batch size : 100k
NumberEpochs TimeGPU TimeCPU
10 11.285 9.613
20 22.680 18.652
40 44.065 35.727
100 112.604 86.960
200 225.377 174.652
Batch size : 200k
NumberEpochs TimeGPU TimeCPU
10 19.306 21.587
20 38.918 41.346
40 78.730 81.456
100 191.367 202.523
200 387.704 419.223
Some notable observations:
As batch size increase i see my gpu utilization increase (reaches to 100% for the only gpu it uses , is there a way i can tell tf to use other gpu too)
at batch size 200k is the only time i see my naive benchmarking shows gpu has minor gain as compared to cpu.
Increasing batch size for a given epoch has minimal effect on time both cpu and gpu until batch size <= 10k. But after that increasing batch size from 10k -> 100k -> 200k the time also increase quite fast i.e for a given epoch let us say 10 batch size 10, 100, 1k, 10k the cpu time and gpu time remain pretty stable ~5-7 sec for gpu and 0.48-0.96 sec for cpu (meaning that sess.run has much higher overhead than computing of graph themselves ?), but increasing batch size further the compute time increase at much faster rate i.e for epoch 10 100k->200k gputime increase from 11 -> 19 sec and cpu time also doubles , why so ? It seems for larger batch size even though i have just one sess.run , but internally it splits that into smaller batch and calls sess.run twice because epoch 20 batch size 100k matches more closely with epoch 10 batch 200k ..
How can i improve my inference further , i believe i am not usding all gpus optimally.
Are there any ideas around how can i benchmark better to get better breakdowns of time for cpu-> gpu transfer and actual speedup for graph computation from moving from cpu to gpu ?
Loading data better directly if possible zero copy into gpu ?
Can i place some nodes to gpu only during inference to get better performance ?
Ideas around quantization or optimizing inference graph ?
Any more ideas to improve gpu based inference . May be xla based optimization or tensrort ? i want to have high performance inference code to run these computations on gpu while the application server crunches on cpu.
One source of information are the TensorFlow docs on performance, including Optimizing for GPU and High Performance Models.
That said, those guides tend to target training more than batch inference, though certainly some of the principles still apply.
I will note that, unless you are using DistributionStrategy, TensorFlow will not automatically put ops on more than a single GPU (source).
In your particularly case, I don't believe GPUs are yet well-tuned to do the type of sparse operation required for your model, so I don't actually expect it to do that well on a GPU (if you log the device placement there's a chance the lookup is done on the CPU). A logistic regression model has only an (sparse) input layer and an output layer, so generally there are very few math ops. GPUs excel the most when they are doing lots of matrix multiplies, convolutions, etc.
Finally, I would encourage you to use TensorRT to optimize your graph, though for your particular model there's no guarantee it does much better.
I have a 4 GPU machine on which I run Tensorflow (GPU) with Keras. Some of my classification problems take several hours to complete.
nvidia-smi returns Volatile GPU-Util which never exceeds 25% on any of my 4 GPUs.
How can I increase GPU Util% and speed up my training?
If your GPU util is below 80%, this is generally the sign of an input pipeline bottleneck. What this means is that the GPU sits idle much of the time, waiting for the CPU to prepare the data:
What you want is the CPU to keep preparing batches while the GPU is training to keep the GPU fed. This is called prefetching:
Great, but if the batch preparation is still way longer than the model training, the GPU will still remain idle, waiting for the CPU to finish the next batch. To make the batch preparation faster we can parallelize the different preprocessing operations:
We can go even further by parallelizing I/O:
Now to implement this in Keras, you need to use the Tensorflow Data API with Tensorflow version >= 1.9.0. Here is an example:
Let's assume, for the sake of this example that you have two numpy arrays x and y. You can use tf.data for any type of data but this is simpler to understand.
def preprocessing(x, y):
# Can only contain TF operations
...
return x, y
dataset = tf.data.Dataset.from_tensor_slices((x, y)) # Creates a dataset object
dataset = dataset.map(preprocessing, num_parallel_calls=64) # parallel preprocessing
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(None) # Will automatically prefetch batches
....
model = tf.keras.model(...)
model.fit(x=dataset) # Since tf 1.9.0 you can pass a dataset object
tf.data is very flexible, but as anything in Tensorflow (except eager), it uses a static graph. This can be a pain sometimes but the speed up is worth it.
To go further, you can have a look at the performance guide and the Tensorflow data guide.
I've got similar issue - the memory of all the GPUs were allocated by Keras, but Volatile was around 0% and training was taking almost the same amount of time as on CPU. I was using ImageDataGenerator, which turned out to be a bottleneck. When I increased the number of workers in fit_generator method from default value 1 to all available CPUs, then the training time dropped rapidly.
You can also load the data to the memory and then use flow method to prepare batches with augmented images.
I have been trying to use Google's RNN based seq2seq model.
I have been training a model for text summarization and am feeding in a textual data approximately of size 1GB. The model quickly fills up my entire RAM(8GB), starts filling up even the swap memory(further 8GB) and crashes post which I have to do a hard shutdown.
The configuration of my LSTM network is as follows:
model: AttentionSeq2Seq
model_params:
attention.class: seq2seq.decoders.attention.AttentionLayerDot
attention.params:
num_units: 128
bridge.class: seq2seq.models.bridges.ZeroBridge
embedding.dim: 128
encoder.class: seq2seq.encoders.BidirectionalRNNEncoder
encoder.params:
rnn_cell:
cell_class: GRUCell
cell_params:
num_units: 128
dropout_input_keep_prob: 0.8
dropout_output_keep_prob: 1.0
num_layers: 1
decoder.class: seq2seq.decoders.AttentionDecoder
decoder.params:
rnn_cell:
cell_class: GRUCell
cell_params:
num_units: 128
dropout_input_keep_prob: 0.8
dropout_output_keep_prob: 1.0
num_layers: 1
optimizer.name: Adam
optimizer.params:
epsilon: 0.0000008
optimizer.learning_rate: 0.0001
source.max_seq_len: 50
source.reverse: false
target.max_seq_len: 50
I tried decreasing the batch size from 32 to 16, but it still did not help. What specific changes should I make in order to prevent my model from taking up the entirety of RAM and crashing? (Like decreasing data size, decreasing number of stacked LSTM cells, further decreasing batch size etc)
My system runs Python 2.7x, TensorFlow version 1.1.0, and CUDA 8.0. The system has an Nvidia Geforce GTX-1050Ti(768 CUDA cores) with 4GB of memory, and the system has 8GB of RAM and a further 8GB of swap memory.
You model looks pretty small. The only thing kind of big is the train data. Please check to make sure your get_batch() function has no bugs. It is possible that each batch you are actually loading the whole data set for training, in case there is a bug there.
In order to quickly prove this, just cut down your training data size to something very small (such as 1/10 of current size) and see if that helps. Note that it should not help because you are using mini batch. But if that resolve the problem, fix your get_batch() function.
Setup
I have a network, one whose parameter is a large-embedding matrix (3Million X 300 sized), say embed_mat.
During training, for each mini-batch, I only update a small subset of the vectors from embed_mat (max 15000 vectors) which are chosen using the embedding_lookup op. I am using the Adam optimizer to train my model.
As I cannot store this embed_mat in the GPU, due to its size, I define it under CPU (say /cpu:0) device, but the rest of the parameters of the model, the optimizer etc. are defined under a GPU (say, gpu:/0) device.
Questions
I see that my GPU usage is very minimal (200 MB), which suggests all my training is happening on the CPU. What I expected was that the result of the embedding_lookup is copied to the GPU and all my training happens there. Am I doing something wrong.
The training time is very largely affected by the size (num_vectors) of the embedding matrix which doesn't seem correct to me. In any mini-batch, I only update my network parameters and the vectors I looked up (~15000), so the training time should, if at all, grow sub-linearly with the size of the embedding matrix.
Is there a way to automatically and seamlessly split up my embed_mat to multiple GPUs for faster training?
I suspect the Adam Optimizer for this. Looks like because the embed_mat is on the CPU, all training is happening on the CPU. Is this correct?
Try visualizing on tensorboard where each of your ops is placed. In the "graph" tab you can color by "device". Ideally the embedding variable, the embedding lookup, and the embedding gradient update should be in the CPU, while most other things should be in the GPU.