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        "description": "# `GPUQueue` A very simple GPU tool - To run multiple jobs with assigned (limited) GPU resources\n\nIt provides very simple and basic function of dynamically utilize given GPUs with a large job array. It can be used to automatically identify the GPU that has been released by a newly-ended program.\n\n### Examples\n\n---\n`python` interface\n``` python\nfrom gpu_queue import JobSubmitter\n\njob_array = [\n    \"python -c 'import os, time;print(\\\"GPU num utilized\\\",os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]);time.sleep(3)'\",\n    \"python -c 'import os, time;print(\\\"GPU num utilized\\\",os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]);time.sleep(2)'\",\n    \"python -c 'import os, time;print(\\\"GPU num utilized\\\",os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]);time.sleep(0.5)'\",\n    \"python -c 'import os, time;print(\\\"GPU num utilized\\\",os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]);time.sleep(0.5)'\",\n    \"python -c 'import os, time;print(\\\"GPU num utilized\\\",os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]);time.sleep(3)'\",\n    \"python -c 'import os, time;print(\\\"GPU num utilized\\\",os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]);time.sleep(1)'\",\n]\n\nJ = JobSubmitter(job_array, [0, 1, 2])\nJ.submit_jobs()\n```\nOutput:\n```\n6 jobs has been saved\nGPU num utilized 0\nGPU num utilized 2\nGPU num utilized 1\nGPU num utilized 2\nGPU num utilized 2\nGPU num utilized 1\nall jobs has been run\nsucessful jobs: 6\n\nfailed jobs: 0\n```\n`gpuqueue` can be directly used in the bash   \n`Bash` interface\n```bash\n#!/usr/bin/env bash\n\n# example of typical machine learning hyper-parameter tuning \n# mean teacher for semi supervised learning\n\nsave_dir=cifar10/labeled_sample_4000/augment_img\nEMA_decay=0.999\n\ndeclare -a StringArray=(\n\"python classify_main.py Trainer.name=MeanTeacherTrainer Config=config/cifar_mt_config.yaml Trainer.save_dir=${save_dir}/meanteacherbaseline  RegScheduler.max_value=0  Trainer.EMA_decay=${EMA_decay}  \"\n\"python classify_main.py Trainer.name=MeanTeacherTrainer Config=config/cifar_mt_config.yaml Trainer.save_dir=${save_dir}/meanteacher_0.1      RegScheduler.max_value=0.1  Trainer.EMA_decay=${EMA_decay} \"\n\"python classify_main.py Trainer.name=MeanTeacherTrainer Config=config/cifar_mt_config.yaml Trainer.save_dir=${save_dir}/meanteacher_1        RegScheduler.max_value=1  Trainer.EMA_decay=${EMA_decay}  \"\n\"python classify_main.py Trainer.name=MeanTeacherTrainer Config=config/cifar_mt_config.yaml Trainer.save_dir=${save_dir}/meanteacher_10       RegScheduler.max_value=10  Trainer.EMA_decay=${EMA_decay} \"\n\"python classify_main.py Trainer.name=MeanTeacherTrainer Config=config/cifar_mt_config.yaml Trainer.save_dir=${save_dir}/meanteacher_20       RegScheduler.max_value=20  Trainer.EMA_decay=${EMA_decay} \"\n\"python classify_main.py Trainer.name=MeanTeacherTrainer Config=config/cifar_mt_config.yaml Trainer.save_dir=${save_dir}/meanteacher_50       RegScheduler.max_value=50  Trainer.EMA_decay=${EMA_decay} \"\n)\n# just using 0 and 1 gpus for those jobs\ngpuqueue \"${StringArray[@]}\" --available_gpus 0 1\n```\n\n\n---\n### install \n```bash\npip install gpuqueue\n```\n\n\n\n",
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