# PBT Function Example¶

```#!/usr/bin/env python

import numpy as np
import argparse
import json
import os
import random

import ray
from ray import tune
from ray.tune.schedulers import PopulationBasedTraining

def pbt_function(config, checkpoint_dir=None):
"""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.
"""
lr = config["lr"]
accuracy = 0.0  # end = 1000
start = 0
if checkpoint_dir:
with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
accuracy = state["acc"]
start = state["step"]

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,
for step in range(start, 100):
if accuracy < midpoint:
optimal_lr = 0.01 * accuracy / midpoint
else:
optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
optimal_lr = min(0.01, max(0.001, optimal_lr))

# compute accuracy increase
q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
if q_err < q_tolerance:
accuracy += (1.0 / q_err) * random.random()
elif lr > optimal_lr:
accuracy -= (q_err - q_tolerance) * random.random()
accuracy += noise_level * np.random.normal()
accuracy = max(0, accuracy)

if step % 3 == 0:
with tune.checkpoint_dir(step=step) as checkpoint_dir:
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"acc": accuracy, "step": start}))

tune.report(
mean_accuracy=accuracy,
cur_lr=lr,
optimal_lr=optimal_lr,  # for debugging
q_err=q_err,  # for debugging
done=accuracy > midpoint * 2,  # this stops the training process
)

def run_tune_pbt():
pbt = PopulationBasedTraining(
time_attr="training_iteration",
perturbation_interval=4,
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],
},
)

analysis = tune.run(
pbt_function,
name="pbt_test",
scheduler=pbt,
verbose=False,
metric="mean_accuracy",
mode="max",
stop={
"training_iteration": 30,
},
num_samples=8,
fail_fast=True,
config={
"lr": 0.0001,
# note: this parameter is perturbed but has no effect on
# the model training in this example
"some_other_factor": 1,
},
)

print("Best hyperparameters found were: ", analysis.best_config)

if __name__ == "__main__":
parser = argparse.ArgumentParser()
"--smoke-test", action="store_true", help="Finish quickly for testing"
)
type=str,
default=None,
required=False,
help="The address of server to connect to if using Ray Client.",
)
args, _ = parser.parse_known_args()
if args.smoke_test:
ray.init(num_cpus=2)  # force pausing to happen for test
else: