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Project description
GPUQueue
A very simple GPU tool - To run multiple jobs with assigned (limited) GPU resources
It provides very simple and basic function of dynamically utilize given GPUs with a large job array.
Examples
from gpu_queue import JobSubmitter
job_array = [
"python -c 'import os, time;print(\"GPU num utilized\",os.environ[\"CUDA_VISIBLE_DEVICES\"]);time.sleep(3)'",
"python -c 'import os, time;print(\"GPU num utilized\",os.environ[\"CUDA_VISIBLE_DEVICES\"]);time.sleep(2)'",
"python -c 'import os, time;print(\"GPU num utilized\",os.environ[\"CUDA_VISIBLE_DEVICES\"]);time.sleep(0.5)'",
"python -c 'import os, time;print(\"GPU num utilized\",os.environ[\"CUDA_VISIBLE_DEVICES\"]);time.sleep(0.5)'",
"python -c 'import os, time;print(\"GPU num utilized\",os.environ[\"CUDA_VISIBLE_DEVICES\"]);time.sleep(3)'",
"python -c 'import os, time;print(\"GPU num utilized\",os.environ[\"CUDA_VISIBLE_DEVICES\"]);time.sleep(1)'",
]
J = JobSubmitter(job_array, [0, 1, 2])
J.submit_jobs()
6 jobs has been saved
GPU num utilized 0
GPU num utilized 2
GPU num utilized 1
GPU num utilized 2
GPU num utilized 2
GPU num utilized 1
all jobs has been run
sucessful jobs: 4
failed jobs: 0
This script can be used to automatically identify the GPU that has been released by a newly-ended program.
gpuqueue
can be directly used in the bash interface, see bash_demo.sh
for more details.
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