#!/usr/bin/env python
import numpy as np
import argparse
import random
import ray
from ray.tune import Trainable, run
from ray.tune.schedulers import PopulationBasedTraining
class PBTBenchmarkExample(Trainable):
"""Toy PBT problem for benchmarking adaptive learning rate.
The goal is to optimize this trainable's accuracy. The accuracy increases
fastest at the optimal lr, which is a function of the current accuracy.
The optimal lr schedule for this problem is the triangle wave as follows.
Note that many lr schedules for real models also follow this shape:
best lr
^
| /\
| / \
| / \
| / \
------------> accuracy
In this problem, using PBT with a population of 2-4 is sufficient to
roughly approximate this lr schedule. Higher population sizes will yield
faster convergence. Training will not converge without PBT.
"""
def setup(self, config):
self.lr = config["lr"]
self.accuracy = 0.0 # end = 1000
def step(self):
midpoint = 100 # lr starts decreasing after acc > midpoint
q_tolerance = 3 # penalize exceeding lr by more than this multiple
noise_level = 2 # add gaussian noise to the acc increase
# triangle wave:
# - start at 0.001 @ t=0,
# - peak at 0.01 @ t=midpoint,
# - end at 0.001 @ t=midpoint * 2,
if self.accuracy < midpoint:
optimal_lr = 0.01 * self.accuracy / midpoint
else:
optimal_lr = 0.01 - 0.01 * (self.accuracy - midpoint) / midpoint
optimal_lr = min(0.01, max(0.001, optimal_lr))
# compute accuracy increase
q_err = max(self.lr, optimal_lr) / min(self.lr, optimal_lr)
if q_err < q_tolerance:
self.accuracy += (1.0 / q_err) * random.random()
elif self.lr > optimal_lr:
self.accuracy -= (q_err - q_tolerance) * random.random()
self.accuracy += noise_level * np.random.normal()
self.accuracy = max(0, self.accuracy)
return {
"mean_accuracy": self.accuracy,
"cur_lr": self.lr,
"optimal_lr": optimal_lr, # for debugging
"q_err": q_err, # for debugging
"done": self.accuracy > midpoint * 2,
}
def save_checkpoint(self, checkpoint_dir):
return {
"accuracy": self.accuracy,
"lr": self.lr,
}
def load_checkpoint(self, checkpoint):
self.accuracy = checkpoint["accuracy"]
def reset_config(self, new_config):
self.lr = new_config["lr"]
self.config = new_config
return True
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
if args.smoke_test:
ray.init(num_cpus=2) # force pausing to happen for test
else:
ray.init()
pbt = PopulationBasedTraining(
time_attr="training_iteration",
metric="mean_accuracy",
mode="max",
perturbation_interval=20,
hyperparam_mutations={
# distribution for resampling
"lr": lambda: random.uniform(0.0001, 0.02),
# allow perturbations within this set of categorical values
"some_other_factor": [1, 2],
})
run(
PBTBenchmarkExample,
name="pbt_test",
scheduler=pbt,
reuse_actors=True,
checkpoint_freq=20,
verbose=False,
stop={
"training_iteration": 200,
},
num_samples=8,
config={
"lr": 0.0001,
# note: this parameter is perturbed but has no effect on
# the model training in this example
"some_other_factor": 1,
})