I am using NVIDIA GeForce RTX 2070 Super with 24GB gpu memory in total . When I try to train a RES101 model using Pytorch, the cuda memory runs out during training time. I found that only 8GB gpu memory is saved for Pytorch. I wonder if I can increase the gpu memory allocated for it.
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My System Configuration : I’m working on a Yolov3 model with GeForce RTX 2080 Ti GPU with 11 GB GPU memory and Intel(R) Core™ i9-9900KF CPU with 6 core and 64GB of RAM.
When i inference on images, my average FPS is between 35 FPS and GPU utilization is 1128MiB(~1GB out of 11GB). I use opencv and load the yolo on the GPU
I am able to increase the volume of image inference by running the same setup in 6 instances and I am able to achieve 210 FPS and GPU utilisation is 6760MiB(~6GB out of 11GB). However this method requires me to separate the images and feed non duplicate entries to each of the 6 instances.
How do I run a single instance that utilises the full GPU so I can extract the best FPS from all the 11 GB of GPU.
This will help me to feed the images from multiple sources to a single instances therefore reducing the need for me to split and assign to different instances.
I'm trying to run a reinforcement learning algorithm using pytorch, but it keeps telling me that CUDA is out of memory. However, it seems that pytorch is only accessing a tiny amount of my GPU's memory.
RuntimeError: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 4.00 GiB total capacity; 3.78 MiB already allocated; 0 bytes free; 4.00 MiB reserved in total by PyTorch)
It's not that PyTorch is only accessing a tiny amount of GPU memory, but your PyTorch program accumulatively allocated tensors to the GPU memory, and that 2 MB tensor hits the limitation. Try to use a lower batch size or run the model with half-precision to save the GPU memory.
This command should work for PyTorch to access the GPU memory.
> import os os.environ["CUDA_VISIBLE_DEVICES"] = "1"
I'm trying to free up GPU memory after finishing using the model.
I checked the nvidia-smi before creating and trainning the model: 402MiB / 7973MiB
After creating and training the model, I checked again the GPU memory status with nvidia-smi: 7801MiB / 7973MiB
Now I tried to free up GPU memory with:
del model
torch.cuda.empty_cache()
gc.collect()
and checked again the GPU memory: 2361MiB / 7973MiB
As you can see not all the GPU memory was released (I expected to get 400~MiB / 7973MiB).
I can only relase the GPU memory via terminal (sudo fuser -v /dev/nvidia* and kill pid)
Is there a way to free up the GPU memory after I done using the model ?
This happens becauce pytorch reserves the gpu memory for fast memory allocation. To learn more about it, see pytorch memory management. To solve this issue, you can use the following code:
from numba import cuda
cuda.select_device(your_gpu_id)
cuda.close()
However, this comes with a catch. It closes the GPU completely. So, you can't start training without restarting everything.
Why does it occupy so much GPU memory when I use .cuda()? And how to reduce it? My poor 1660's GPU memory is so limited.
Execute--
torch.cuda.empty_cache()
It will reduce the GPU memory. If it does not reduce the memory, then restarting the kernel would solve the problem.
I am using Keras with tensorflow-gpu in backend, I don't have tensorflow (CPU - version) installed, all the outputs show GPU selected but tf is using CPU and system memory
when i run my code the output is: output_code
I even ran device_lib.list_local_device() and the output is: list_local_devices_output
After running the code I tried nvidia-smi to see the usage of gpu and the output is:
nvidia-smi output
Tensorflow-gpu = "1.12.0"
CUDA toolkit = "9.0"
cuDNN = "7.4.1.5"
Environment Variables contain:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin;
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\libnvvp;
C:\WINDOWS\system32;
C:\WINDOWS;
C:\WINDOWS\System32\Wbem;
C:\WINDOWS\System32\WindowsPowerShell\v1.0\;
C:\WINDOWS\System32\OpenSSH\;
C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;
D:\Anaconda3;D:\Anaconda3\Library\mingw-w64\bin
D:\Anaconda3\Library\usr\bin;
D:\Anaconda3\Library\bin;
D:\Anaconda3\Scripts;D:\ffmpeg\bin\;
But still when i check for memory usage in task manager the output is
CPU utilization 51%, RAM utilization 86%
GPU utilization 1%, GPU-RAM utilization 0%
Task_manager_Output
So, I think it is still using CPU instead of GPU.
System Configuration:
Windows-10 64 bit; IDE: Liclipse; Python: 3.6.5
It is using the GPU, as you can see in logs.
The problem is, that a lot of things can not be done on the GPU and as long your data is small and your complexity is low, you will end up with low GPU usage.
Maybe the batch_size is to low -> Increase until you run into OOM Errors
Your data loading is consuming a lot of time and your gpu has to wait (IO Reads)
Your RAM is to low and the application uses Disk as a fallback
Preprocsssing is to slow. If you are dealing with image try to compute everything as a generator or on the gpu if possible
You are using some operations, which are not GPU accelerated
Here is some more detailed explanation.