Related
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 create and train a CNN model. But every time I run the code, the tensorflow is not utilising GPU instead it uses CPU. I have installed the latest version of tensorflow. Attaching the details below.
python => 3.9.5\
Tensorflow-GPU => 2.5.0\
CUDA => 11.3\
cuDNN => 8.2.1
While running I get the following output with a warning message. (Platform: VS code)
2021-07-28 15:35:13.163991: W tensorflow/core/common_runtime/bfc_allocator.cc:337] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.
System Performance
Output
Code is accessible here
Note: I had already tried adding the following code to activate gpu and it isn't working.
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
tf.config.set_visible_devices(gpus[0], 'GPU')
Output for suggestion:
Try this:
physical_devices = tf.config.list_physical_devices('GPU')
print("Num GPUs:", len(physical_devices))
Since your model is quite small it is possible that the GPU is so bottlenecked by the rest of your system that it can't use the full GPU.
On another note, Task Manager is not the best tool to check on the GPU load, if available use nvidia-smi.
To specifically do what the warning says I set it as env setting as follows
os.environ['TF_ENABLE_GPU_GARBAGE_COLLECTION'] = 'false'
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 have used tensorflow-gpu 1.13.1 in Ubuntu 18.04 with CUDA 10.0 on Nvidia GeForce RTX 2070 (Driver Version: 415.27).
Code like below was used to manage tensorflow memory usage. I have about 8Gb GPU memory, so tensorflow mustn't allocate more than 1Gb of GPU memory. But when I look on memory usage with nvidia-smi command, I see, that it uses ~1.5 Gb despite the fact that I restricted memory quantity with GPUOptions.
memory_config = tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.12))
memory_config.gpu_options.allow_growth = False
with tf.Session(graph=graph, config=memory_config) as sess:
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
Why is it going? And how I can avoid this or at least calculate memory needs for every session? I need to make strong restrictions for every process, because I have several paralell instances with different sessions, so I need to be sure, that there will no resource race
BTW, I have tried to set memory_config.gpu_options.allow_growth to False, but it affect nothing. Tensorflow is still allocate memory the same way independently from this flag value. And it's also seems strange
Solution
Try with gpu_options.allow_growth = True to see how much default memory is consumed in tf.Session creation. That memory will be always allocated regardless of values.
Based on your result, it should be somewhere less than 500MB. So if you want each process to truly have 1GB of memory each, calculate:
(1GB minus default memory)/total_memory
Reason
When you create a tf.Session, regardless of your configuration, Tensorflow device is created on GPU. And this device requires some minimum memory.
import tensorflow as tf
conf = tf.ConfigProto()
conf.gpu_options.allow_growth=True
session = tf.Session(config=conf)
Given allow_growth=True, there should be no gpu allocation. However in reality, it yields:
2019-04-05 18:44:43.460479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15127 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:03:00.0, compute capability: 6.0)
which occupies small fraction of memory (in my past experience, the amount differs by gpu models). NOTE: setting allow_growth occupies almost same memory as setting per_process_gpu_memory=0.00001, but latter won't be able to create session properly.
In this case, it is 345MB :
That is the offset you are experiencing. Let's take a look in case of per_process_gpu_memory:
conf = tf.ConfigProto()
conf.gpu_options.per_process_gpu_memory_fraction=0.1
session = tf.Session(config=conf)
Since the gpu has 16,276MB of memory, setting per_process_gpu_memory_fraction = 0.1 probably makes you think only about 1,627MB will be allocated. But the truth is:
1,971MB is allocated, which however coincides with sum of default memory (345MB) and expected memory (1,627MB).
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