Example 3 - Local and Parallel (using processes)ΒΆ

Getting closer to a distributed setup, this examples shows how to connect a nameserver, an optimizer and several workers running in different processes. This would also allow true parallelism if the workers do all the computation in Python, such that the thread based paralelization of example 2 would not work.

import logging

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')


if args.worker:
    w = MyWorker(sleep_interval = 0.5, nameserver='',run_id='example3')

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

# 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.

# 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']))

Total running time of the script: ( 0 minutes 0.000 seconds)

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