Parscript: Parallel or distributed execution of jobs

Simple concept: write your jobs (shell, python, batch or anything!) in a txt file, one job one line!

python train.py --name=exp1 --param=1
python train.py --name=exp2 --param=2
python train.py --name=exp3 --param=3

And use python -m parscript.worker job-list.txt to run the jobs sequentially, or python -m parscript.dispatcher job-list.txt to run them in parallel or distributed fashion.

Usage:

• Specify the number of GPUs through -g (default 1)
• Specify the number of workers (per GPU) through -w (default 1)
• Reset the job counter through -r
• Directly edit job-list.txt after launched to add more jobs to it
• If one job fails, it will not affect other jobs and failed jobs will be recorded and reported at the end.
• Use -s to shutdown the machine after all jobs are finished (useful for running jobs on AWS).
• Specify which GPUs using CUDA_VISIBLE_DEVICES. You still need to set -g correspondingly for the dispatcher.

For example: python -m parscript.dispatcher job-list.txt -g 4 -w 2 means running 8 jobs at a time from the job list using 4 GPUs with 2 jobs on each GPU.

Hint: for complicated jobs, you might want to write a job/script generator.

Installation

pip install parscript

Useful bash aliases (add them to ~/.bash_aliases):

alias parworker='python -m parscript.worker'
alias pardispatch='python -m parscript.dispatcher'