import json
import logging
import multiprocessing as mp
import os
import pickle
import threading
import time
from collections import OrderedDict
from tqdm.auto import tqdm
from .reporter import DistStatusReporter, FakeReporter
from .resource import DistributedResource
from .scheduler import TaskScheduler
from ..core import Task
from ..core.decorator import _autotorch_method
from ..searcher import BaseSearcher
from ..searcher.searcher_factory import searcher_factory
from ..utils import save, load, mkdir
__all__ = ['FIFOScheduler']
logger = logging.getLogger(__name__)
[docs]class FIFOScheduler(TaskScheduler):
r"""Simple scheduler that just runs trials in submission order.
Parameters
----------
train_fn : callable
A task launch function for training. Note: please add the `@autotorch_method` decorater to the original function.
args : object (optional)
Default arguments for launching train_fn.
resource : dict
Computation resources. For example, `{'num_cpus':2, 'num_gpus':1}`
searcher : str or object
Autotorch searcher. For example, autotorch.searcher.self.argsRandomSampling
time_attr : str
A training result attr to use for comparing time.
Note that you can pass in something non-temporal such as
`training_epoch` as a measure of progress, the only requirement
is that the attribute should increase monotonically.
reward_attr : str
The training result objective value attribute. As with `time_attr`, this may refer to any objective value.
Stopping procedures will use this attribute.
dist_ip_addrs : list of str
IP addresses of remote machines.
Examples
--------
>>> import numpy as np
>>> import autotorch as at
>>> @at.args(
... lr=at.Real(1e-3, 1e-2, log=True),
... wd=at.Real(1e-3, 1e-2))
>>> def train_fn(args, reporter):
... print('lr: {}, wd: {}'.format(args.lr, args.wd))
... for e in range(10):
... dummy_accuracy = 1 - np.power(1.8, -np.random.uniform(e, 2*e))
... reporter(epoch=e, accuracy=dummy_accuracy, lr=args.lr, wd=args.wd)
>>> scheduler = at.scheduler.FIFOScheduler(train_fn,
... resource={'num_cpus': 2, 'num_gpus': 0},
... num_trials=20,
... reward_attr='accuracy',
... time_attr='epoch')
>>> scheduler.run()
>>> scheduler.join_jobs()
>>> scheduler.get_training_curves(plot=True)
"""
def __init__(self, train_fn, args=None, resource=None,
searcher=None, search_options=None,
checkpoint='./exp/checkpoint.ag',
resume=False, num_trials=None,
time_out=None, max_reward=1.0, time_attr='epoch',
reward_attr='accuracy',
visualizer='none', dist_ip_addrs=None):
super().__init__(dist_ip_addrs)
if resource is None:
resource = {'num_cpus': 1, 'num_gpus': 0}
self.resource = resource
if searcher is None:
searcher = 'random' # Default: Random searcher
if isinstance(searcher, str):
kwargs = search_options.copy() if search_options else dict()
kwargs['configspace'] = train_fn.cs
self.searcher: BaseSearcher = searcher_factory(searcher, **kwargs)
else:
assert isinstance(searcher, BaseSearcher)
self.searcher: BaseSearcher = searcher
assert isinstance(train_fn, _autotorch_method)
self.train_fn = train_fn
self.args = args if args else train_fn.args
# meta data
self.metadata = {
'search_space': train_fn.kwspaces,
'search_strategy': searcher,
'stop_criterion': {'time_limits': time_out, 'max_reward': max_reward},
'resources_per_trial': resource
}
self.num_trials = num_trials
self.time_out = time_out
self.max_reward = max_reward
self._checkpoint = checkpoint
self._time_attr = time_attr
self._reward_attr = reward_attr
self.visualizer = visualizer.lower()
self.log_lock = mp.Lock()
self.training_history = OrderedDict()
self.config_history = OrderedDict()
if resume:
if os.path.isfile(checkpoint):
self.load_state_dict(load(checkpoint))
else:
msg = f'checkpoint path {checkpoint} is not available for resume.'
logger.exception(msg)
raise FileExistsError(msg)
[docs] def run(self, **kwargs):
"""Run multiple number of trials
"""
start_time = time.time()
num_trials = kwargs.get('num_trials', self.num_trials)
time_out = kwargs.get('time_out', self.time_out)
logger.info('Starting Experiments')
logger.info(f'Num of Finished Tasks is {self.num_finished_tasks}')
logger.info(f'Num of Pending Tasks is {num_trials - self.num_finished_tasks}')
tbar = tqdm(range(self.num_finished_tasks, num_trials))
for _ in tbar:
if time_out and time.time() - start_time >= time_out \
or self.max_reward and self.get_best_reward() >= self.max_reward:
break
self.schedule_next()
[docs] def save(self, checkpoint=None):
"""Save Checkpoint
"""
if checkpoint is None:
if self._checkpoint is None:
logger.warning("Checkpointing is disabled")
else:
checkpoint = self._checkpoint
if checkpoint is not None:
with self.LOCK:
mkdir(os.path.dirname(checkpoint))
save(self.state_dict(), checkpoint)
[docs] def schedule_next(self):
"""Schedule next searcher suggested task
"""
# Allow for the promotion of a previously chosen config. Also,
# extra_kwargs contains extra info passed to both add_job and to
# get_config (if no config is promoted)
# request resource first
resources = DistributedResource(**self.resource)
FIFOScheduler.RESOURCE_MANAGER._request(resources)
config, extra_kwargs = self._promote_config()
if config is None:
# No config to promote: Query next config to evaluate from searcher
config = self.searcher.get_config(**extra_kwargs)
extra_kwargs['new_config'] = True
else:
# This is not a new config, but a paused one which is now promoted
extra_kwargs['new_config'] = False
task = Task(
self.train_fn,
{'args': self.args, 'config': config},
resources
)
self.add_job(task, **extra_kwargs)
[docs] def run_with_config(self, config):
"""Run with config for final fit.
It launches a single training trial under any fixed values of the hyperparameters.
For example, after HPO has identified the best hyperparameter values based on a hold-out dataset,
one can use this function to retrain a model with the same hyperparameters on all the available labeled data
(including the hold out set). It can also returns other objects or states.
"""
task = Task(
self.train_fn,
{'args': self.args, 'config': config},
DistributedResource(**self.resource)
)
reporter = FakeReporter()
task.args['reporter'] = reporter
return self.run_job(task)
def _dict_from_task(self, task):
if isinstance(task, Task):
return {'TASK_ID': task.task_id, 'Config': task.args['config']}
else:
assert isinstance(task, dict)
return {'TASK_ID': task['TASK_ID'], 'Config': task['Config']}
[docs] def add_job(self, task, **kwargs):
"""Adding a training task to the scheduler.
Args:
task (:class:`autotorch.scheduler.Task`): a new training task
Relevant entries in kwargs:
- bracket: HB bracket to be used. Has been sampled in _promote_config
- new_config: If True, task starts new config eval, otherwise it promotes
a config (only if type == 'promotion')
Only if new_config == False:
- config_key: Internal key for config
- resume_from: config promoted from this milestone
- milestone: config promoted to this milestone (next from resume_from)
"""
cls = FIFOScheduler
if not task.resources.is_ready:
cls.RESOURCE_MANAGER._request(task.resources)
# reporter
reporter = DistStatusReporter(remote=task.resources.node)
task.args['reporter'] = reporter
# Register pending evaluation
self.searcher.register_pending(task.args['config'])
# main process
job = cls._start_distributed_job(task, cls.RESOURCE_MANAGER)
# reporter thread
rp = threading.Thread(
target=self._run_reporter,
args=(task, job, reporter, self.searcher),
daemon=False
)
rp.start()
task_dict = self._dict_from_task(task)
task_dict.update({'Task': task, 'Job': job, 'ReporterThread': rp})
# checkpoint thread
if self._checkpoint is not None:
def _save_checkpoint_callback(fut):
self._cleaning_tasks()
self.save()
job.add_done_callback(_save_checkpoint_callback)
with self.LOCK:
self.scheduled_tasks.append(task_dict)
def _clean_task_internal(self, task_dict):
task_dict['ReporterThread'].join()
def _run_reporter(self, task, task_job, reporter, searcher):
last_result = None
while not task_job.done():
reported_result = reporter.fetch()
if 'traceback' in reported_result:
logger.exception(reported_result['traceback'])
reporter.move_on()
break
if reported_result.get('done', False):
reporter.move_on()
break
self._add_training_result(
task.task_id, reported_result, config=task.args['config'])
reporter.move_on()
last_result = reported_result
if last_result is not None:
last_result['done'] = True
searcher.update(
config=task.args['config'],
reward=last_result[self._reward_attr],
**last_result
)
def _promote_config(self):
"""
Provides a hook in schedule_next, which allows to promote a config
which has been selected and partially evaluated previously.
:return: config, extra_args
"""
config = None
extra_args = dict()
return config, extra_args
[docs] def get_best_config(self):
"""Get the best configuration from the finished jobs.
"""
return self.searcher.get_best_config()
[docs] def get_best_reward(self):
"""Get the best reward from the finished jobs.
"""
return self.searcher.get_best_reward()
def _add_training_result(self, task_id, reported_result, config=None):
with self.log_lock:
# Note: We store all of reported_result in training_history[task_id],
# not just the reward value.
if task_id in self.training_history:
self.training_history[task_id].append(reported_result)
else:
self.training_history[task_id] = [reported_result]
if config:
self.config_history[task_id] = config
[docs] def get_training_curves(self, filename=None, plot=False, use_legend=True):
"""Get Training Curves
Parameters
----------
filename : str
plot : bool
use_legend : bool
Examples
--------
>>> scheduler.run()
>>> scheduler.join_jobs()
>>> scheduler.get_training_curves(plot=True)
.. image:: https://github.com/zhanghang1989/AutoGluonWebdata/blob/master/doc/api/autogluon.1.png?raw=true
"""
if filename is None and not plot:
logger.warning('Please either provide filename or allow plot in get_training_curves')
import matplotlib.pyplot as plt
plt.ylabel(self._reward_attr)
plt.xlabel(self._time_attr)
plt.title("Performance vs Training-Time in each HPO Trial")
with self.log_lock:
for task_id, task_res in self.training_history.items():
rewards = [x[self._reward_attr] for x in task_res]
x = list(range(len(task_res)))
plt.plot(x, rewards, label=f'task {task_id}')
if use_legend:
plt.legend(loc='best')
if filename:
logger.info(f'Saving Training Curve in {filename}')
plt.savefig(filename)
if plot:
plt.show()
[docs] def state_dict(self, destination=None):
"""Returns a dictionary containing a whole state of the Scheduler
Examples
--------
>>> at.save(scheduler.state_dict(), 'checkpoint.ag')
"""
destination = super().state_dict(destination)
destination['searcher'] = pickle.dumps(self.searcher)
with self.log_lock:
destination['training_history'] = json.dumps(self.training_history)
if self.visualizer == 'mxboard' or self.visualizer == 'tensorboard':
destination['visualizer'] = json.dumps(self.mxboard._scalar_dict)
return destination
[docs] def load_state_dict(self, state_dict):
"""Load from the saved state dict.
Examples
--------
>>> scheduler.load_state_dict(at.load('checkpoint.ag'))
"""
super().load_state_dict(state_dict)
self.searcher = pickle.loads(state_dict['searcher'])
with self.log_lock:
self.training_history = json.loads(state_dict['training_history'])
if self.visualizer == 'mxboard' or self.visualizer == 'tensorboard':
self.mxboard._scalar_dict = json.loads(state_dict['visualizer'])
logger.debug(f'Loading Searcher State {self.searcher}')
