How can I flush GPU memory using CUDA (physical reset is unavailable)

clear gpu memory tensorflow
nvidia-smi --gpu-reset
how to clear gpu memory windows 10
cuda out of memory clean cache
torch cuda cache clear
clear gpu memory mac
colab clear gpu memory
caffe clear gpu memory

My CUDA program crashed during execution, before memory was flushed. As a result, device memory remained occupied.

I'm running on a GTX 580, for which nvidia-smi --gpu-reset is not supported.

Placing cudaDeviceReset() in the beginning of the program is only affecting the current context created by the process and doesn't flush the memory allocated before it.

I'm accessing a Fedora server with that GPU remotely, so physical reset is quite complicated.

So, the question is - Is there any way to flush the device memory in this situation?

Although it should be unecessary to do this in anything other than exceptional circumstances, the recommended way to do this on linux hosts is to unload the nvidia driver by doing

$ rmmod nvidia 

with suitable root privileges and then reloading it with

$ modprobe nvidia

If the machine is running X11, you will need to stop this manually beforehand, and restart it afterwards. The driver intialisation processes should eliminate any prior state on the device.

This answer has been assembled from comments and posted as a community wiki to get this question off the unanswered list for the CUDA tag

How can I flush GPU memory using CUDA (physical reset - php, How can I flush GPU memory using CUDA (physical reset is unavailable) - cuda. reset(gpudev) resets the GPU device and clears its memory of gpuArray and CUDAKernel data. The GPU device identified by gpudev remains the selected device, but all gpuArray and CUDAKernel objects in MATLAB representing data on that device are invalid.

check what is using your GPU memory with

sudo fuser -v /dev/nvidia*

Your output will look something like this:

                     USER        PID  ACCESS COMMAND
/dev/nvidia0:        root       1256  F...m  Xorg
                     username   2057  F...m  compiz
                     username   2759  F...m  chrome
                     username   2777  F...m  chrome
                     username   20450 F...m  python
                     username   20699 F...m  python

Then kill the PID that you no longer need on htop or with

sudo kill -9 PID.

In the example above, Pycharm was eating a lot of memory so I killed 20450 and 20699.

How can I flush GPU memory using CUDA (physical reset , How can I flush GPU memory using CUDA (physical reset is unavailable). Question. My CUDA program crashed during execution, before memory was flushed. The GPU will run out of memory in one of the training steps. Expected behavior It would be much more convenient if the fast.ai library automatically frees the GPU memory after GPU runs out of memory and aborts the training, just like what it does after the training successfully completes.

First type

nvidia-smi

then select the PID that you want to kill

sudo kill -9 PID

Tensorflow or cuda not giving back gpu memory after session closes , How can I flush GPU memory using CUDA (physical reset is unavailable). nvidia-​smi --gpu-reset tensorflow clear gpu memory cuda out of memory clean cache Step 1: check what is using your GPU memory with sudo fuser -v /dev/nvidia* USER PID ACCESS COMMAND /dev/nvidia0: ubuntu 2167 F…m python /dev/nvidia1: ubuntu 2167 F…m python

I also had the same problem, and I saw a good solution in quora, using

sudo kill -9 PID.

see https://www.quora.com/How-do-I-kill-all-the-computer-processes-shown-in-nvidia-smi

Kill Nvidia GPU process in Ubuntu – Beeren Sahu, I am using: Ubuntu 16.04 tf = 1.5.0 from pip install tensorflow-gpu CUDA -can-i-​flush-gpu-memory-using-cuda-physical-reset-is-unavailable ] Reset GPU & Clear its Memory. Learn more about gpu, gpu ram, gpu memory, cuda, cuda device, gpu device MATLAB, Parallel Computing Toolbox

Clearing GPU Memory - PyTorch - Beginner (2018), To free memory of GPU from unwanted processes we may follow the /how-can-​i-flush-gpu-memory-using-cuda-physical-reset-is-unavailable  Gpu properties say's 85% of memory is full. Nothing flush gpu memory except numba.cuda.close() but won't allow me to use my gpu again. The only way to clear it is restarting kernel and rerun my code. I'm looking for any script code to add my code allow me to use my code in for loop and clear gpu in every loop. Part of my code :

nvidia-smi - NVIDIA System Management , The GPU memory jumped from 350MB to 700MB, going on with the tutorial and Or, we can free this memory without needing to restart the kernel. Thanks @​sam2! torch.cuda.empty_cache() worked for me :slight_smile:. I am tying to install tensorflow correctly and I am getting memory allocation erros. I am using: Ubuntu 16.04 tf = 1.5.0 from pip install tensorflow-gpu CUDA 9.0 CUDNN 7.0.5 starting python in a command terminal and running the following

Reset GPU & Clear its Memory - MATLAB Answers, Flags can be combined with comma e.g. "MEMORY,ECC". Can be used to clear double bit ECC errors or recover hung GPU. report serial numbers, which match the ids physically printed on each board. After each reboot compute mode will reset to "DEFAULT". "EXCLUSIVE_PROCESS" was added in CUDA 4.0. By using our site, ailihong. Unregistered. Apparently, this user prefers to keep an air of mystery about them. 10 How can I flush GPU memory using CUDA

Learn more about gpu, gpu ram, gpu memory, cuda, cuda device, gpu device MATLAB, Parallel I'm sure there must be a way to reset the GPU in MATLAB, and if one of the methods I've all CUDA-capable devices are busy or unavailable. What is the best way to free the GPU memory using numba CUDA? Background: I have a pair of GTX 970s; I access these GPUs using python threading; My problem, while massively parallel, is very memory intensive. So I break up the work between the GPUs based on their free memory, usually this means that each gpu will get used a bunch of times.

Comments
  • "As a result, device memory remains occupied" - How do you know this to be true?
  • Although nvidia-smi --gpu-reset is not available, I can still get some information with nvidia-smi -q. In most fields it gives 'N/A', but some information is useful. Here is the relevant output: Memory Usage Total : 1535 MB Used : 1227 MB Free : 307 MB
  • If you have root access, you can unload and reload the nvidia driver.
  • If you do ps -ef |grep 'whoami' and the results show any processes that appear to be related to your crashed session, kill those. (the single quote ' should be replaced with backtick ` )
  • Have you tried sudo rmmod nvidia?
  • cannot process the above command, error says, CUDA in use. So killed the PID using the solution provided by stackoverflow.com/a/46597252/3503565. Its works for me
  • Thank you! For some reason, I had a process hogging all my VRAM, not showing on nvidia-smi.
  • I need to use this a lot when running deep learning in different jupyter notebooks. The only issue is knowing exactly which PID is which. Any tips on this?
  • is chrome --- the google chrome browser? if so what business does it have using a gpu?
  • @josh I kill them one at a time making a mental note of the COMMAND.
  • @kRazzyR - It uses it for speeding up computations, I assume, for rendering graphics, but maybe also other things. This did cause me a lot of issues when I install Nvidia drivers, CUDA and cudnn. I had to turn a lot of it off. See here.
  • Worked a treat when I accidentally opened and loaded two different jupyter notebooks with VGG16. Warning: it kills the notebooks. I guess you could pick one to free up some memory for the other but I dont know how you select the PID for a given notebook.