README.md

Occupied GPUs

The program used to occupy GPUs.

Preparation

pip install -r requirements.txt

Note: Please make sure that cuda is avaliable.

Usage

Python Module

This repository has been packaged and published in PyPI. Please run the following command to install it.

pip install occupiedgpus

And happy to occupy GPUs:

python -m occupiedgpus.core --gpu-ids 0,1,2,3 --epochs 120 --options 0

where --options can be assigned 0 or 1 ( 0 means to occupy the GPU when it is not used, and 1 means to occupy the remaining GPU memory at any time).

Source Code

Clone this repository to your local and activate your Python environment.

git clone https://github.com/jinzcdev/occupied-gpus.git

single processing

To occupy the corresponding GPUs in single processing, run:

sh train.sh 0,1,2,3 [option]

or

chmod u+x ./train.sh
./train.sh 0,1,2,3 [option]

where 0,1,2,3 stands for the GPU0-3 to be occpied. [option] can be assigned 0 or 1 as mentioned above, and the default is 0.

multi-processing

To occupy GPUs faster with multi-processing, run in bash (NOT sh):

bash ./multi_train.sh 0,1,2,3 [option] [port]

where [option] can be assigned 0 or 1 as mentioned above, and the default is 0. The default [port] is 54886, and when a port conflict occurs, you may change the [port].

Note: If your pytorch version is less than 1.9.0, please replace torchrun with python -m torch.distributed.launch --use_env in multi_train.sh.

Run in the background

if you want to run the code in the background, run:

nohup sh train.sh ${GPU_IDS} &>> /dev/null &
or
nohup sh multi_train.sh ${GPU_IDS} &>> /dev/null &
or
nohup python -m occupiedgpus.core --gpu-ids ${GPU_IDS} --epochs 120 --options 0 &>> /dev/null &

replace ${GPU_IDS} with 0,1,...