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timdim
timdim

Reputation: 1291

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

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?

Upvotes: 126

Views: 376662

Answers (14)

Olney1
Olney1

Reputation: 762

Expanding on the Python solution above, you can get further detail on the memory being cleared and print the outcome:

import torch
import gc

def print_gpu_memory():
    allocated = torch.cuda.memory_allocated() / (1024**2)
    cached = torch.cuda.memory_reserved() / (1024**2)
    print(f"Allocated: {allocated:.2f} MB")
    print(f"Cached: {cached:.2f} MB")

# Before clearing the cache
print("Before clearing cache:")
print_gpu_memory()

# Clearing cache
gc.collect()
torch.cuda.empty_cache()

# After clearing the cache
print("\nAfter clearing cache:")
print_gpu_memory()

Upvotes: 1

George Pearse
George Pearse

Reputation: 358

Normally I just use nvidia-smi, but for some problems it's not enough (something still in cuda memory)

The nvidia-smi kill all is:

nvidia-smi | grep 'python' | awk '{ print $5 }' | xargs -n1 kill -9

If you're still hitting unexpected memory errors or similar problems then try:

sudo fuser -v /dev/nvidia* | cut -d' ' -f2- | sudo xargs -n1 kill -9

Upvotes: 7

arnaudlenain
arnaudlenain

Reputation: 36

If all of this does not work, I found another answer here:

How to kill process on GPUs with PID in nvidia-smi using keyword?

nvidia-smi | grep 'python' | awk '{ print $X }' | xargs -n1 kill -9

Note that X (in the 'awk' expression) corresponds to the Xth column of your nvidia-smi command. If your nvidia-smi command looks like this, you should then replace X by 5.

Upvotes: 0

AbuAli
AbuAli

Reputation: 131

to kill all processess on GPU:

sudo fuser -v /dev/nvidia* -k

Upvotes: 10

user20068036
user20068036

Reputation: 71

If you have the problem that after killing one process the next starts (Comment)- like for example when you have a bash script that calls multiple python scripts and you want to kill them but can't find its PID you can use ps -ef where you'll find the PID of your "problematic" process and also its PPID (parent PID). Use kill PPID or kill -9 PPID or sudo kill PPID to stop the processes.

Upvotes: 0

kor
kor

Reputation: 1

I just started a new terminal and closed the old one and it worked out pretty well for me.

Upvotes: -2

zahid hasan
zahid hasan

Reputation: 35

For OS: UBUNTU 20.04 In the terminal type

nvtop

If the direct killing of consuming activity doesn't work then find and note the exact number of activity PID with most GPU usage.

sudo kill PID -number

Upvotes: 1

Antoine Viallon
Antoine Viallon

Reputation: 394

One can also use nvtop, which gives an interface very similar to htop, but showing your GPU(s) usage instead, with a nice graph. You can also kill processes directly from here.

Here is a link to its Github : https://github.com/Syllo/nvtop

NVTOP interface

Upvotes: 16

talonmies
talonmies

Reputation: 72349

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

Upvotes: 19

Lukas
Lukas

Reputation: 427

for the ones using python:

import torch, gc
gc.collect()
torch.cuda.empty_cache()

Upvotes: 21

Ashiq Imran
Ashiq Imran

Reputation: 2281

First type 

nvidia-smi

then select the PID that you want to kill

sudo kill -9 PID

Upvotes: 77

Kenan
Kenan

Reputation: 14094

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.

Upvotes: 215

ailihong
ailihong

Reputation: 137

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

Upvotes: 12

Matthias Winkelmann
Matthias Winkelmann

Reputation: 16394

on macOS (/ OS X), if someone else is having trouble with the OS apparently leaking memory:

  • https://github.com/phvu/cuda-smi is useful for quickly checking free memory
  • Quitting applications seems to free the memory they use. Quit everything you don't need, or quit applications one-by-one to see how much memory they used.
  • If that doesn't cut it (quitting about 10 applications freed about 500MB / 15% for me), the biggest consumer by far is WindowServer. You can Force quit it, which will also kill all applications you have running and log you out. But it's a bit faster than a restart and got me back to 90% free memory on the cuda device.

Upvotes: 5

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