示例 3 - 本地和并行 (使用进程)

进一步接近分布式设置，本示例展示了如何连接一个命名服务器、一个优化器和运行在不同进程中的多个工作者。如果工作者在 Python 中完成所有计算，这也会实现真正的并行性，从而使得示例 2 中基于线程的并行化无法工作。

import logging
logging.basicConfig(level=logging.INFO)

import argparse

import hpbandster.core.nameserver as hpns
import hpbandster.core.result as hpres

from hpbandster.optimizers import BOHB as BOHB
from hpbandster.examples.commons import MyWorker



parser = argparse.ArgumentParser(description='Example 3 - Local and Parallel Execution.')
parser.add_argument('--min_budget',   type=float, help='Minimum budget used during the optimization.',    default=9)
parser.add_argument('--max_budget',   type=float, help='Maximum budget used during the optimization.',    default=243)
parser.add_argument('--n_iterations', type=int,   help='Number of iterations performed by the optimizer', default=4)
parser.add_argument('--n_workers', type=int,   help='Number of workers to run in parallel.', default=2)
parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true')

args=parser.parse_args()



if args.worker:
    w = MyWorker(sleep_interval = 0.5, nameserver='127.0.0.1',run_id='example3')
    w.run(background=False)
    exit(0)

# Start a nameserver (see example_1)
NS = hpns.NameServer(run_id='example3', host='127.0.0.1', port=None)
NS.start()


# Run an optimizer (see example_2)
bohb = BOHB(  configspace = MyWorker.get_configspace(),
                      run_id = 'example3',
                      min_budget=args.min_budget, max_budget=args.max_budget
               )
res = bohb.run(n_iterations=args.n_iterations, min_n_workers=args.n_workers)

# Step 4: Shutdown
# After the optimizer run, we must shutdown the master and the nameserver.
bohb.shutdown(shutdown_workers=True)
NS.shutdown()

# Step 5: Analysis
# Each optimizer returns a hpbandster.core.result.Result object.
# It holds informations about the optimization run like the incumbent (=best) configuration.
# For further details about the Result object, see its documentation.
# Here we simply print out the best config and some statistics about the performed runs.
id2config = res.get_id2config_mapping()
incumbent = res.get_incumbent_id()

all_runs = res.get_all_runs()

print('Best found configuration:', id2config[incumbent]['config'])
print('A total of %i unique configurations where sampled.' % len(id2config.keys()))
print('A total of %i runs where executed.' % len(res.get_all_runs()))
print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget))
print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget))
print('The run took  %.1f seconds to complete.'%(all_runs[-1].time_stamps['finished'] - all_runs[0].time_stamps['started']))

脚本总运行时间： ( 0 minutes 0.000 seconds)