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all_the_run_experiment_variants_using_multiprocessing.py
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all_the_run_experiment_variants_using_multiprocessing.py
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import multiprocessing
from multiprocessing import Pool, Process
import yaml
import argparse
import psutil
import os
from queue import Queue
from time import sleep
from rlkit.launchers.launcher_util import setup_logger, build_nested_variant_generator
# from exp_pool_fns.neural_process_v1 import exp_fn
def get_pool_function(exp_fn_name):
if exp_fn_name == 'neural_processes_v1':
from exp_pool_fns.neural_process_v1 import exp_fn
elif exp_fn_name == 'sac':
from exp_pool_fns.sac import exp_fn
return exp_fn
if __name__ == '__main__':
# # Arguments
# parser = argparse.ArgumentParser()
# parser.add_argument('-e', '--experiment', help='experiment specification file')
# args = parser.parse_args()
# with open(args.experiment, 'r') as spec_file:
# spec_string = spec_file.read()
# exp_specs = yaml.load(spec_string)
# # generating the variants
# vg_fn = build_nested_variant_generator(exp_specs)
# all_exp_args = []
# for i, variant in enumerate(vg_fn()):
# all_exp_args.append([variant, i])
# # setting up pool and cpu affinity
# num_total = len(all_exp_args)
# num_workers = min(exp_specs['meta_data']['num_workers'], num_total)
# cpu_range = exp_specs['meta_data']['cpu_range']
# num_available_cpus = cpu_range[1] - cpu_range[0] + 1
# num_cpu_per_worker = exp_specs['meta_data']['num_cpu_per_worker']
# assert num_cpu_per_worker * num_workers <= num_available_cpus
# affinity_Q = Queue()
# for i in range(int(num_available_cpus / num_cpu_per_worker)):
# affinity_Q.put(
# ','.join(
# map(
# str,
# [
# cpu_range[0] + num_cpu_per_worker * i + j
# for j in range(num_cpu_per_worker)
# ]
# )
# )
# )
# pool_function = get_pool_function(exp_specs['meta_data']['exp_fn_name'])
# running_process = {}
# args_idx = 0
# while (args_idx < len(all_exp_args)) or (len(running_process) > 0):
# if len(running_process) < num_workers:
# aff = affinity_Q.get()
# p = Process(target=pool_function, args=([all_exp_args[args_idx]]))
# args_idx += 1
# p.start()
# os.system("taskset -p -c %s %d" % (aff, p.pid))
# running_process[p] = aff
# new_running_process = {}
# for p, aff in running_process.items():
# N = p.join()
# if N is None:
# new_running_process[p] = aff
# else:
# del new_running_process[p]
# affinity_Q.put(aff)
# running_process = new_running_process
# print(running_process)
# sleep(2)
# # # # add the affinity_Q to the experiment params
# # # for a in all_exp_args:
# # # a.append(affinity_Q)
# # print(
# # '\n\n\n\n{}/{} experiments ran successfully!'.format(
# # sum(p.map(pool_function, all_exp_args)),
# # num_total
# # )
# # )
# Arguments
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--experiment', help='experiment specification file')
args = parser.parse_args()
with open(args.experiment, 'r') as spec_file:
spec_string = spec_file.read()
exp_specs = yaml.load(spec_string)
# generating the variants
vg_fn = build_nested_variant_generator(exp_specs)
all_exp_args = []
for i, variant in enumerate(vg_fn()):
all_exp_args.append([variant, i])
# setting up pool and cpu affinity
num_total = len(all_exp_args)
num_workers = min(exp_specs['meta_data']['num_workers'], num_total)
# cpu_range = exp_specs['meta_data']['cpu_range']
# num_available_cpus = cpu_range[1] - cpu_range[0] + 1
# num_cpu_per_worker = exp_specs['meta_data']['num_cpu_per_worker']
# assert num_cpu_per_worker * num_workers <= num_available_cpus
# m = multiprocessing.Manager()
# affinity_Q = m.Queue()
# for i in range(int(num_available_cpus / num_cpu_per_worker)):
# affinity_Q.put(
# [
# cpu_range[0] + num_cpu_per_worker * i + j
# for j in range(num_cpu_per_worker)
# ]
# )
p = Pool(num_workers)
pool_function = get_pool_function(exp_specs['meta_data']['exp_fn_name'])
# # add the affinity_Q to the experiment params
# for a in all_exp_args:
# a.append(affinity_Q)
print(
'\n\n\n\n{}/{} experiments ran successfully!'.format(
sum(p.map(pool_function, all_exp_args)),
num_total
)
)
# if __name__ == '__main__':
# # Arguments
# parser = argparse.ArgumentParser()
# parser.add_argument('-e', '--experiment', help='experiment specification file')
# args = parser.parse_args()
# with open(args.experiment, 'r') as spec_file:
# spec_string = spec_file.read()
# exp_specs = yaml.load(spec_string)
# # generating the variants
# vg_fn = build_nested_variant_generator(exp_specs)
# all_exp_args = []
# for i, variant in enumerate(vg_fn()):
# all_exp_args.append([variant, i])
# # setting up pool and cpu affinity
# num_total = len(all_exp_args)
# num_workers = min(exp_specs['meta_data']['num_workers'], num_total)
# cpu_range = exp_specs['meta_data']['cpu_range']
# num_available_cpus = cpu_range[1] - cpu_range[0] + 1
# num_cpu_per_worker = exp_specs['meta_data']['num_cpu_per_worker']
# assert num_cpu_per_worker * num_workers <= num_available_cpus
# m = multiprocessing.Manager()
# affinity_Q = m.Queue()
# for i in range(int(num_available_cpus / num_cpu_per_worker)):
# affinity_Q.put(
# [
# cpu_range[0] + num_cpu_per_worker * i + j
# for j in range(num_cpu_per_worker)
# ]
# )
# p = Pool(num_workers)
# pool_function = get_pool_function(exp_specs['meta_data']['exp_fn_name'])
# # add the affinity_Q to the experiment params
# for a in all_exp_args:
# a.append(affinity_Q)
# print(
# '\n\n\n\n{}/{} experiments ran successfully!'.format(
# sum(p.map(pool_function, all_exp_args)),
# num_total
# )
# )