Source code for autotorch.scheduler.fifo

import json
import logging
import multiprocessing as mp
import os
import pickle
import threading
import time
from collections import OrderedDict

from 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.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,, 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.join_jobs() >>> scheduler.get_training_curves(plot=True) """ def __init__(self, train_fn, args=None, resource=None, searcher=None, search_options=None, checkpoint='./exp/', 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)'Starting Experiments')'Num of Finished Tasks is {self.num_finished_tasks}')'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() 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.join_jobs() >>> scheduler.get_training_curves(plot=True) .. image:: """ 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:'Saving Training Curve in {filename}') plt.savefig(filename) if plot:
[docs] def state_dict(self, destination=None): """Returns a dictionary containing a whole state of the Scheduler Examples -------- >>>, '') """ 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('')) """ 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}')

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