kaspermunch · October 19, 2020 11:55 · kaspermunch · Jun 17, 2024
diff --git a/python_multiprocessing_slurm.py b/python_multiprocessing_slurm.py
 import os
 from multiprocessing import Pool, cpu_count

 # function you want to run in parallel:
 def myfunction(a, b):
  return a + b

 # list of tuples to serve as arguments to function:
 args = [(1, 2), (9, 11), (6, 2)]

 # number of cores you have allocated for your slurm task:
 number_of_cores = int(os.environ['SLURM_CPUS_PER_TASK'])
 # number_of_cores = cpu_count() # if not on the cluster you should do this instead

 # multiprocssing pool to distribute tasks to:
 with Pool(number_of_cores) as pool:
    # distribute computations and collect results:
    results = pool.starmap(myfunction, args)
	import os
	from multiprocessing import Pool, cpu_count

	# function you want to run in parallel:
	def myfunction(a, b):
	return a + b

	# list of tuples to serve as arguments to function:
	args = [(1, 2), (9, 11), (6, 2)]

	# number of cores you have allocated for your slurm task:
	number_of_cores = int(os.environ['SLURM_CPUS_PER_TASK'])
	# number_of_cores = cpu_count() # if not on the cluster you should do this instead

	# multiprocssing pool to distribute tasks to:
	with Pool(number_of_cores) as pool:
	# distribute computations and collect results:
	results = pool.starmap(myfunction, args)
No results found