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https://github.com/wassname/ray.git
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Richard Liaw
125 lines
3.8 KiB
Python
Executable File
125 lines
3.8 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import argparse
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import random
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import ray
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from ray.tune import Trainable, run
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from ray.tune.schedulers import PopulationBasedTraining
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class PBTBenchmarkExample(Trainable):
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"""Toy PBT problem for benchmarking adaptive learning rate.
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The goal is to optimize this trainable's accuracy. The accuracy increases
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fastest at the optimal lr, which is a function of the current accuracy.
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The optimal lr schedule for this problem is the triangle wave as follows.
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Note that many lr schedules for real models also follow this shape:
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best lr
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^
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| /\
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| / \
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| / \
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| / \
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------------> accuracy
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In this problem, using PBT with a population of 2-4 is sufficient to
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roughly approximate this lr schedule. Higher population sizes will yield
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faster convergence. Training will not converge without PBT.
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"""
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def _setup(self, config):
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self.lr = config["lr"]
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self.accuracy = 0.0 # end = 1000
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def _train(self):
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midpoint = 100 # lr starts decreasing after acc > midpoint
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q_tolerance = 3 # penalize exceeding lr by more than this multiple
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noise_level = 2 # add gaussian noise to the acc increase
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# triangle wave:
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# - start at 0.001 @ t=0,
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# - peak at 0.01 @ t=midpoint,
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# - end at 0.001 @ t=midpoint * 2,
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if self.accuracy < midpoint:
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optimal_lr = 0.01 * self.accuracy / midpoint
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else:
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optimal_lr = 0.01 - 0.01 * (self.accuracy - midpoint) / midpoint
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optimal_lr = min(0.01, max(0.001, optimal_lr))
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# compute accuracy increase
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q_err = max(self.lr, optimal_lr) / min(self.lr, optimal_lr)
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if q_err < q_tolerance:
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self.accuracy += (1.0 / q_err) * random.random()
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elif self.lr > optimal_lr:
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self.accuracy -= (q_err - q_tolerance) * random.random()
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self.accuracy += noise_level * np.random.normal()
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self.accuracy = max(0, self.accuracy)
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return {
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"mean_accuracy": self.accuracy,
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"cur_lr": self.lr,
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"optimal_lr": optimal_lr, # for debugging
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"q_err": q_err, # for debugging
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"done": self.accuracy > midpoint * 2,
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}
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def _save(self, checkpoint_dir):
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return {
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"accuracy": self.accuracy,
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"lr": self.lr,
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}
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def _restore(self, checkpoint):
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self.accuracy = checkpoint["accuracy"]
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def reset_config(self, new_config):
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self.lr = new_config["lr"]
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return True
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing")
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args, _ = parser.parse_known_args()
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if args.smoke_test:
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ray.init(num_cpus=2) # force pausing to happen for test
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else:
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ray.init()
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pbt = PopulationBasedTraining(
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time_attr="training_iteration",
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metric="mean_accuracy",
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mode="max",
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perturbation_interval=20,
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hyperparam_mutations={
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# distribution for resampling
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"lr": lambda: random.uniform(0.0001, 0.02),
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# allow perturbations within this set of categorical values
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"some_other_factor": [1, 2],
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})
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run(
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PBTBenchmarkExample,
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name="pbt_test",
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scheduler=pbt,
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reuse_actors=True,
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verbose=False,
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stop={
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"training_iteration": 2000,
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},
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num_samples=4,
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config={
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"lr": 0.0001,
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# note: this parameter is perturbed but has no effect on
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# the model training in this example
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"some_other_factor": 1,
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})
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