def __init__(self, fn):
self.fn = fn
self._config = {}
nthreads_per_trial = get_cpu_count()
ngpus_per_trial = get_gpu_count()
self._config['scheduler'] = self._config.get('scheduler', 'fifo')
self._config['scheduler_options'] = {
'resource': {
'num_cpus': nthreads_per_trial,
'num_gpus': ngpus_per_trial
},
'num_trials': self._config.get('num_trials', 2),
'time_out': self._config.get('time_limits', 60 * 60),
'time_attr': 'epoch',
'reward_attr': 'accuracy',
'searcher': self._config.get('searcher', 'random'),
'search_options': self._config.get('search_options', None)
}
if self._config['scheduler'] == 'hyperband':
self._config['scheduler_options'].update({
'searcher':
'random',
'max_t':
self._config.get('epochs', 50),
'grace_period':
self._config.get('grace_period', 100)
})
def __init__(self, config=None, estimator=None, logger=None):
super(ImageClassification, self).__init__()
self._fit_summary = {}
self._logger = logger if logger is not None else logging.getLogger(
__name__)
self._logger.setLevel(logging.INFO)
# cpu and gpu setting
cpu_count = get_cpu_count()
gpu_count = get_gpu_count()
# default settings
if not config:
if gpu_count < 1:
self._logger.info(
'No GPU detected/allowed, using most conservative search space.'
)
config = LiteConfig()
else:
config = DefaultConfig()
config = config.asdict()
else:
if not config.get('dist_ip_addrs', None):
ngpus_per_trial = config.get('ngpus_per_trial', gpu_count)
if ngpus_per_trial < 1:
self._logger.info(
'No GPU detected/allowed, using most conservative search space.'
)
default_config = LiteConfig()
else:
default_config = DefaultConfig()
config = default_config.merge(config,
allow_new_key=True).asdict()
# adjust cpu/gpu resources
if not config.get('dist_ip_addrs', None):
nthreads_per_trial = config.get('nthreads_per_trial', cpu_count)
if nthreads_per_trial > cpu_count:
nthreads_per_trial = cpu_count
ngpus_per_trial = config.get('ngpus_per_trial', gpu_count)
if ngpus_per_trial > gpu_count:
ngpus_per_trial = gpu_count
self._logger.warning(
"The number of requested GPUs is greater than the number of available GPUs."
"Reduce the number to %d", ngpus_per_trial)
else:
raise ValueError(
'Please specify `nthreads_per_trial` and `ngpus_per_trial` '
'given that dist workers are available')
# additional configs
config['num_workers'] = nthreads_per_trial
config['gpus'] = [int(i) for i in range(ngpus_per_trial)]
if config['gpus']:
config['batch_size'] = config.get('batch_size', 8) * len(
config['gpus'])
self._logger.info(
'Increase batch size to %d based on the number of gpus %d',
config['batch_size'], len(config['gpus']))
config['seed'] = config.get('seed', np.random.randint(32, 767))
self._config = config
# scheduler options
self.search_strategy = config.get('search_strategy', 'random')
self.scheduler_options = {
'resource': {
'num_cpus': nthreads_per_trial,
'num_gpus': ngpus_per_trial
},
'checkpoint': config.get('checkpoint', 'checkpoint/exp1.ag'),
'num_trials': config.get('num_trials', 2),
'time_out': config.get('time_limits', 60 * 60),
'resume': (len(config.get('resume', '')) > 0),
'visualizer': config.get('visualizer', 'none'),
'time_attr': 'epoch',
'reward_attr': 'acc_reward',
'dist_ip_addrs': config.get('dist_ip_addrs', None),
'searcher': self.search_strategy,
'search_options': config.get('search_options', None)
}
if self.search_strategy == 'hyperband':
self.scheduler_options.update({
'searcher':
'random',
'max_t':
config.get('epochs', 50),
'grace_period':
config.get('grace_period',
config.get('epochs', 50) // 4)
})
def __init__(self, config=None, logger=None):
super(ObjectDetection, self).__init__()
self._fit_summary = {}
self._logger = logger if logger is not None else logging.getLogger(__name__)
self._logger.setLevel(logging.INFO)
# cpu and gpu setting
cpu_count = get_cpu_count()
gpu_count = get_gpu_count()
# default settings
if not config:
if gpu_count < 1:
self._logger.info('No GPU detected/allowed, using most conservative search space.')
config = LiteConfig()
else:
config = DefaultConfig()
config = config.asdict()
else:
if not config.get('dist_ip_addrs', None):
ngpus_per_trial = config.get('ngpus_per_trial', gpu_count)
if ngpus_per_trial > gpu_count:
ngpus_per_trial = gpu_count
if ngpus_per_trial < 1:
self._logger.info('No GPU detected/allowed, using most conservative search space.')
default_config = LiteConfig()
else:
default_config = DefaultConfig()
config = default_config.merge(config, allow_new_key=True).asdict()
# adjust cpu/gpu resources
if not config.get('dist_ip_addrs', None):
nthreads_per_trial = config.get('nthreads_per_trial', cpu_count)
if nthreads_per_trial > cpu_count:
nthreads_per_trial = cpu_count
ngpus_per_trial = config.get('ngpus_per_trial', gpu_count)
if ngpus_per_trial > gpu_count:
ngpus_per_trial = gpu_count
self._logger.warning(
"The number of requested GPUs is greater than the number of available GPUs."
"Reduce the number to %d", ngpus_per_trial)
else:
raise ValueError('Please specify `nthreads_per_trial` and `ngpus_per_trial` '
'given that dist workers are available')
# fix estimator-transfer relationship
estimator = config.get('estimator', None)
transfer = config.get('transfer', None)
if estimator is not None and transfer is not None:
if isinstance(estimator, ag.Space):
estimator = estimator.data
elif isinstance(estimator, str):
estimator = [estimator]
if isinstance(transfer, ag.Space):
transfer = transfer.data
elif isinstance(transfer, str):
transfer = [transfer]
valid_transfer = []
for e in estimator:
for t in transfer:
if e in t:
valid_transfer.append(t)
if not valid_transfer:
raise ValueError(f'No matching `transfer` model for {estimator}')
if len(valid_transfer) == 1:
config['transfer'] = valid_transfer[0]
else:
config['transfer'] = ag.Categorical(*valid_transfer)
# additional configs
config['num_workers'] = nthreads_per_trial
config['gpus'] = [int(i) for i in range(ngpus_per_trial)]
config['seed'] = config.get('seed', np.random.randint(32,767))
self._config = config
# scheduler options
self.search_strategy = config.get('search_strategy', 'random')
self.search_options = config.get('search_options', None)
if self.search_options:
self.search_options.update({'debug_log': True})
else:
self.search_options = {'debug_log': True}
self.scheduler_options = {
'resource': {'num_cpus': nthreads_per_trial, 'num_gpus': ngpus_per_trial},
'checkpoint': config.get('checkpoint', 'checkpoint/exp1.ag'),
'num_trials': config.get('num_trials', 2),
'time_out': config.get('time_limits', 60 * 60),
'resume': (len(config.get('resume', '')) > 0),
'visualizer': config.get('visualizer', 'none'),
'time_attr': 'epoch',
'reward_attr': 'map_reward',
'dist_ip_addrs': config.get('dist_ip_addrs', None),
'searcher': self.search_strategy,
'search_options': self.search_options}
if self.search_strategy == 'hyperband':
self.scheduler_options.update({
'searcher': 'random',
'max_t': config.get('epochs', 50),
'grace_period': config.get('grace_period', config.get('epochs', 50) // 4)})
elif self.search_strategy == 'bayesopt_hyperband':
self.scheduler_options.update({
'searcher': 'bayesopt',
'max_t': config.get('epochs', 50),
'grace_period': config.get('grace_period', config.get('epochs', 50) // 4)})
