. Advertisement .
..3..
. Advertisement .
..4..
I get the error: cuda_error_out_of_memory when I try to run the program below:
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:951] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.81GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:972] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1041] Creating TensorFlow device (/gpu:0) -> (device:0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
E tensorflow/stream_executor/cuda/cuda_driver.cc:965] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
Iter 20, Minibatch Loss= 40491.636719
...+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.27 Driver Version: 367.27
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M.
|===============================+======================+======================|
| 0 GeForce GTX 1080 Off | 0000:01:00.0 Off | N/A |
| 40% 61C P2 46W / 180W | 8107MiB / 8111MiB | 96% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 1080 Off | 0000:02:00.0 Off | N/A |
| 0% 40C P0 40W / 180W | 0MiB / 8113MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
│
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 22932 C python 8105MiB |
+-----------------------------------------------------------------------------++-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.27 Driver Version: 367.27
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M.
|===============================+======================+======================|
| 0 GeForce GTX 1080 Off | 0000:01:00.0 Off | N/A |
| 40% 61C P2 46W / 180W | 7793MiB / 8111MiB | 99% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 1080 Off | 0000:02:00.0 Off | N/A |
| 0% 40C P0 40W / 180W | 0MiB / 8113MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
│
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 22932 C python 7791MiB |
+-----------------------------------------------------------------------------+The error appears the system notifies as follows:
CUDA_ERROR_OUT_OF_MEMORYI tried to solve it with another sample. I got the reference in the community forum, but it still returned an invalid result. If someone knows the solution, please give me the support. Thanks!
The cause:
Tensorflow tries to assign a portion of the GPU RAM per process by default in order to save costs for memory management. Due to another process is using the GPU, so this fails and gives the CUDA OUT OF MEMORY warnings.
Solution:
allow_growth = Truecan make your memory usage smaller but it reduces the efficiency if you don’t use it exactly.Another way is manually deleting the GPU memory before each time operating by using nvidia-smi to check the memory usage of GPU:
However, this way is not effective if the GPU is being used in other process. If you come across such a situation, let’s use this command:
You will receive the following output:
You may get the PID for the process which is managing the memory of GPU, for example 24154.
Let’s exclude it by the following command:
Finally, you need to substitute the appropriate PID for MY_PID.
This issue occurred to me when I tried to run Keras/Tensorflow again after the first attempt was unsuccessful. The GPU memory cannot be allocated again because it is still being allocated. This was resolved by closing all python processes using the GPU or opening a new terminal window and running them again.