I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each.
For small to moderate size models, the 12 GB of the Titan X is usually enough for 2–3 people to run training concurrently on the same GPU. If the models are small enough that a single model does not take full advantage of all the computational units of the GPU, this can actually result in a speedup compared with running one training process after the other. Even in cases where the concurrent access to the GPU does slow down the individual training time, it is still nice to have the flexibility of having multiple users simultaneously train on the GPU.
The problem with TensorFlow is that, by default, it allocates the full amount of available GPU memory when it is launched. Even for a small two-layer neural network, I see that all 12 GB of the GPU memory is used up.
Is there a way to make TensorFlow only allocate, say, 4 GB of GPU memory, if one knows that this is enough for a given model?
You can set the fraction of GPU memory to be allocated when you construct a tf.Session by passing a tf.GPUOptions as part of the optional config argument:
# Assume that you have 12GB of GPU memory and want to allocate ~4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
The per_process_gpu_memory_fraction acts as a hard upper bound on the amount of GPU memory that will be used by the process on each GPU on the same machine. Currently, this fraction is applied uniformly to all of the GPUs on the same machine; there is no way to set this on a per-GPU basis.
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
https://github.com/tensorflow/tensorflow/issues/1578
For TensorFlow 2.0 and 2.1 (docs):
import tensorflow as tf
tf.config.gpu.set_per_process_memory_growth(True)
For TensorFlow 2.2+ (docs):
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
The docs also list some more methods:
Set environment variable TF_FORCE_GPU_ALLOW_GROWTH to true.
Use tf.config.experimental.set_virtual_device_configuration to set a hard limit on a Virtual GPU device.
Here is an excerpt from the Book Deep Learning with TensorFlow
In some cases it is desirable for the process to only allocate a subset of the available memory, or to only grow the memory usage as it is needed by the process. TensorFlow provides two configuration options on the session to control this. The first is the allow_growth option, which attempts to allocate only as much GPU memory based on runtime allocations, it starts out allocating very little memory, and as sessions get run and more GPU memory is needed, we extend the GPU memory region needed by the TensorFlow process.
1) Allow growth: (more flexible)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config, ...)
The second method is per_process_gpu_memory_fraction option, which determines the fraction of the overall amount of memory that each visible GPU should be allocated. Note: No release of memory needed, it can even worsen memory fragmentation when done.
2) Allocate fixed memory:
To only allocate 40% of the total memory of each GPU by:
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
session = tf.Session(config=config, ...)
Note:
That's only useful though if you truly want to bind the amount of GPU memory available on the TensorFlow process.
For Tensorflow version 2.0 and 2.1 use the following snippet:
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu_devices[0], True)
For prior versions , following snippet used to work for me:
import tensorflow as tf
tf_config=tf.ConfigProto()
tf_config.gpu_options.allow_growth=True
sess = tf.Session(config=tf_config)
All the answers above assume execution with a sess.run() call, which is becoming the exception rather than the rule in recent versions of TensorFlow.
When using the tf.Estimator framework (TensorFlow 1.4 and above) the way to pass the fraction along to the implicitly created MonitoredTrainingSession is,
opts = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
conf = tf.ConfigProto(gpu_options=opts)
trainingConfig = tf.estimator.RunConfig(session_config=conf, ...)
tf.estimator.Estimator(model_fn=...,
config=trainingConfig)
Similarly in Eager mode (TensorFlow 1.5 and above),
opts = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
conf = tf.ConfigProto(gpu_options=opts)
tfe.enable_eager_execution(config=conf)
Edit: 11-04-2018
As an example, if you are to use tf.contrib.gan.train, then you can use something similar to bellow:
tf.contrib.gan.gan_train(........, config=conf)
You can use
TF_FORCE_GPU_ALLOW_GROWTH=true
in your environment variables.
In tensorflow code:
bool GPUBFCAllocator::GetAllowGrowthValue(const GPUOptions& gpu_options) {
const char* force_allow_growth_string =
std::getenv("TF_FORCE_GPU_ALLOW_GROWTH");
if (force_allow_growth_string == nullptr) {
return gpu_options.allow_growth();
}
Tensorflow 2.0 Beta and (probably) beyond
The API changed again. It can be now found in:
tf.config.experimental.set_memory_growth(
device,
enable
)
Aliases:
tf.compat.v1.config.experimental.set_memory_growth
tf.compat.v2.config.experimental.set_memory_growth
References:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/config/experimental/set_memory_growth
https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth
See also:
Tensorflow - Use a GPU: https://www.tensorflow.org/guide/gpu
for Tensorflow 2.0 Alpha see: this answer
All the answers above refer to either setting the memory to a certain extent in TensorFlow 1.X versions or to allow memory growth in TensorFlow 2.X.
The method tf.config.experimental.set_memory_growth indeed works for allowing dynamic growth during the allocation/preprocessing. Nevertheless one may like to allocate from the start a specific-upper limit GPU memory.
The logic behind allocating a specific GPU memory would also be to prevent OOM memory during training sessions. For example, if one trains while opening video-memory consuming Chrome tabs/any other video consumption process, the tf.config.experimental.set_memory_growth(gpu, True) could result in OOM errors thrown, hence the necessity of allocating from the start more memory in certain cases.
The recommended and correct way in which to allot memory per GPU in TensorFlow 2.X is done in the following manner:
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
# Restrict TensorFlow to only allocate 1GB of memory on the first GPU
try:
tf.config.experimental.set_virtual_device_configuration(
gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)]
Shameless plug: If you install the GPU supported Tensorflow, the session will first allocate all GPUs whether you set it to use only CPU or GPU. I may add my tip that even you set the graph to use CPU only you should set the same configuration(as answered above:) ) to prevent the unwanted GPU occupation.
And in an interactive interface like IPython and Jupyter, you should also set that configure, otherwise, it will allocate all memory and leave almost none for others. This is sometimes hard to notice.
If you're using Tensorflow 2 try the following:
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
For Tensorflow 2.0 this this solution worked for me. (TF-GPU 2.0, Windows 10, GeForce RTX 2070)
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# allocate 60% of GPU memory
from keras.backend.tensorflow_backend import set_session
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.6
set_session(tf.Session(config=config))
this code has worked for me:
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.InteractiveSession(config=config)
Well I am new to tensorflow, I have Geforce 740m or something GPU with 2GB ram, I was running mnist handwritten kind of example for a native language with training data containing of 38700 images and 4300 testing images and was trying to get precision , recall , F1 using following code as sklearn was not giving me precise reults. once i added this to my existing code i started getting GPU errors.
TP = tf.count_nonzero(predicted * actual)
TN = tf.count_nonzero((predicted - 1) * (actual - 1))
FP = tf.count_nonzero(predicted * (actual - 1))
FN = tf.count_nonzero((predicted - 1) * actual)
prec = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * prec * recall / (prec + recall)
plus my model was heavy i guess, i was getting memory error after 147, 148 epochs, and then I thought why not create functions for the tasks so I dont know if it works this way in tensrorflow, but I thought if a local variable is used and when out of scope it may release memory and i defined the above elements for training and testing in modules, I was able to achieve 10000 epochs without any issues, I hope this will help..
i tried to train unet on voc data set but because of huge image size, memory finishes. i tried all the above tips, even tried with batch size==1, yet to no improvement. sometimes TensorFlow version also causes the memory issues. try by using
pip install tensorflow-gpu==1.8.0
I am trying to run the following python script from my anaconda prompt:
python object_tracker.py --video test.mp4 --model yolov4 --dont_show
This comes directly from the AI Guys yolov4-deepsort repository (https://github.com/theAIGuysCode/yolov4-deepsort). It is code for an object tracker so its very computation heavy and running it on longer videos take hours. A computer with and Nvidia Graphics card has become available to me and I want to use GPU to speed up the process but I'm not sure how.
Python OpenCV uses NumPy for computation and NumPy runs on CPU. You can convert NumPy arrays to Pytorch tensors and can run your code on GPU. A simple idea is
N = 8000
np.random.seed(42)
nA = np.random.rand(N,N).astype(np.float32)
nB = np.random.rand(N,N).astype(np.float32)
nC = nA.dot(nB) # numpy dot product runs on CPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'PyTorch set to {device}') # cuda when available
tA = torch.from_numpy(nA).float().to(device)
tB = torch.from_numpy(nB).to(device)
tC = torch.mm(tA,tB) # torch matrix multiplication aka dot product runs on GPU
PS:
Just came across this
https://medium.com/swlh/understanding-torchvision-functionalities-for-pytorch-391273299dc9
seems you can do it in a better way with torchvision.
I am running a deep learning script but I am not an expert. My task is to run multiple GPUs for data training. However, I have trouble specifying GPUs. Here are the steps of my confusion.
I set multiple GPUs by
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["TF_MIN_GPU_MULTIPROCESSOR_COUNT"]="2"
os.environ["CUDA_VISIBLE_DEVICES"]= "5,6"
print("# of GPU available:", len(tf.config.list_physical_devices('GPU')))
# of GPU available: 2
when I start the model creation, I receive this error, which I did not receive when using only ONE gpu.
tf.random.set_seed(42)
model_unet = binary_unet(256,256,6)
ResourceExhaustedError: OOM when allocating tensor with shape[3,3,64,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:TruncatedNormal]
I thought I could set one GPU first (for step 2) by making the cuda_visible_devices to be the ONE gpu wanted, and specify multiple GPUs (after step 2) by making the cuda_visible_devices to be multiple GPUs. But then, tensorflow couldn't recognize multiple GPUs that, for example:
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["TF_MIN_GPU_MULTIPROCESSOR_COUNT"]="2"
os.environ["CUDA_VISIBLE_DEVICES"]= "5,6"
print("# of GPU available:", len(tf.config.list_physical_devices('GPU')))
# of GPU available: 1
Note the number of GPUs available becomes 1. This will stick around unless I restart the kernel and clear output. Actually, I need to restart the kernel and clear output between step 1 and 2 as well, to make sure only 1 GPU is available and step 2 doesn't fail. But I can't just restart and clear everything because I am going to use previous outputs to run epochs.
I believe some potential solutions are: 1) make step 2 (creating a unet model) run with multiple GPUs; 2) somehow clear the logs in tensorflow without having to restart the kernel that I can create model with 1 GPU but train data/run epoch with multiple GPUs. But I have no idea how to do this. Could someone help?
I am using cuda in pytorch framwework in linux server with multiple cuda devices.
The problem is that
eventhough I specified certain gpus that can be shown,
the program keeps using only first gpu.
(But other program works fine and other specified gpus are allocated well.
because of that, I think it is not nvidia or system problem.
nvidia-smi shows all gpus well and there's no problem.
I didn't have problem with allocating gpus with below codes before (except when the system is not working)
)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBILE_DEVICES"] = str(args.gpu)
I wrote that before running main function.
and it works fine for other programs in same system.
I printed that args.gpu variable, and could see that the value is not "0".
Have you tried something like this?
device = torch.device("cuda:0,1" if torch.cuda.is_available() else "cpu") ## specify the GPU id's, GPU id's start from 0.
model = CreateModel()
model= nn.DataParallel(model,device_ids = [0, 1])
model.to(device)
let me know about this
I coded both Python and C++ version of Caffe forward classification scripts to test Caffe's inference performance. The model is trained already. And the results are quite similar, GPU utils is not full enough.
My settings:
1. Card: Titan XP, 12GB
2. Model: InceptionV3
3. Img size: 3*299*299
When batch_size set to 40, GRAM usage can reach 10GB, but the GPU utils can just reach 77%~79%, both for Python and C++. So the performance is about 258 frames/s.
In my scripts, I loaded the image, preprocess it, load it into the input layer, and then repeat the net_.forward() operation. According to my understanding, this won't cause any Mem copy ops, so ideally should maximally pull up the GPU utils. But I can only reach no more than 80%.
In the C++ Classification Tutorial, I found below phrase:
Use multiple classification threads to ensure the GPU is always fully utilized and not waiting for an I/O blocked CPU thread.
So I tried to use the multi-thread compiled OpenBLAS, and under CPU backend, actually more CPU is involved to do the forwarding, but no use for the GPU backend. Under the GPU backend, the CPU utils will be fixed to about 100%.
Then I even tried to reduce the batch_size to 20, and start two classification processes in two terminals. The result is, GRAM usage increases to 11GB, but the GPU utils decrease to 64%~66%. Finally, the performance decreases to around 200 frames/s.
Has anyone encountered this problem? I'm really confused.
Any opinion is welcome.
Thanks,
As I had observed, the GPU util is decreased with,
1) low PCI express mode resnet-152(x16)-90% > resnet-152(x8)-80% > resnet-152(x4)-70%
2) large model - VGG-100%(x16) ; ResNet-50(x16)-95~100% ; ResNet-152(x16) - 90%
In addition, if I turn off cuDNN, the GPU Util is always 100%.
So I think there is some problem related with cuDNN, but I don't know more about the problem.
NVCaffe is somewhat better, and MXNet can utilize GPU 100% (resnet-152; x4).