/
launch_plot_results.py
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/
launch_plot_results.py
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# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import logging
from pathlib import Path
from syne_tune.backend import LocalBackend
from syne_tune.experiments import load_experiment
from syne_tune.optimizer.baselines import RandomSearch
from syne_tune import Tuner, StoppingCriterion
from syne_tune.config_space import randint
from examples.training_scripts.height_example.train_height import (
METRIC_ATTR,
METRIC_MODE,
MAX_RESOURCE_ATTR,
)
if __name__ == "__main__":
logging.getLogger().setLevel(logging.INFO)
random_seed = 31415927
max_steps = 100
n_workers = 4
config_space = {
MAX_RESOURCE_ATTR: max_steps,
"width": randint(0, 20),
"height": randint(-100, 100),
}
entry_point = str(
Path(__file__).parent
/ "training_scripts"
/ "height_example"
/ "train_height.py"
)
trial_backend = LocalBackend(entry_point=entry_point)
# Random search without stopping
scheduler = RandomSearch(
config_space, mode=METRIC_MODE, metric=METRIC_ATTR, random_seed=random_seed
)
stop_criterion = StoppingCriterion(max_wallclock_time=20)
tuner = Tuner(
trial_backend=trial_backend,
scheduler=scheduler,
n_workers=n_workers,
stop_criterion=stop_criterion,
results_update_interval=5,
tuner_name="plot-results-demo",
metadata={"description": "just an example"},
)
tuner.run()
# shows how to print the best configuration found from the tuner and retrains it
trial_id, best_config = tuner.best_config()
tuning_experiment = load_experiment(tuner.name)
# prints the best configuration found from experiment-results
print(f"best result found: {tuning_experiment.best_config()}")
# plots the best metric over time
tuning_experiment.plot()
# plots values found by all trials over time
tuning_experiment.plot_trials_over_time()