def __init__(self,
time_attr='training_iteration',
reward_attr='episode_reward_mean',
max_t=100,
grace_period=10,
reduction_factor=3,
brackets=3):
assert max_t > 0, "Max (time_attr) not valid!"
assert max_t >= grace_period, "grace_period must be <= max_t!"
assert grace_period > 0, "grace_period must be positive!"
assert reduction_factor > 1, "Reduction Factor not valid!"
assert brackets > 0, "brackets must be positive!"
FIFOScheduler.__init__(self)
self._reduction_factor = reduction_factor
self._max_t = max_t
self._trial_info = {} # Stores Trial -> Bracket
# Tracks state for new trial add
self._brackets = [
_Bracket(grace_period, max_t, reduction_factor, s)
for s in range(brackets)
]
self._counter = 0 # for
self._num_stopped = 0
self._reward_attr = reward_attr
self._time_attr = time_attr
def __init__(
self, time_attr="time_total_s", reward_attr="episode_reward_mean",
grace_period=60.0, min_samples_required=3, hard_stop=True):
FIFOScheduler.__init__(self)
self._stopped_trials = set()
self._completed_trials = set()
self._results = collections.defaultdict(list)
self._grace_period = grace_period
self._min_samples_required = min_samples_required
self._reward_attr = reward_attr
self._time_attr = time_attr
self._hard_stop = hard_stop
def __init__(
self, time_attr='time_total_s', reward_attr='episode_reward_mean',
grace_period=60.0, min_samples_required=3, hard_stop=True):
FIFOScheduler.__init__(self)
self._stopped_trials = set()
self._completed_trials = set()
self._results = collections.defaultdict(list)
self._grace_period = grace_period
self._min_samples_required = min_samples_required
self._reward_attr = reward_attr
self._time_attr = time_attr
self._hard_stop = hard_stop
def __init__(
self, time_attr='training_iteration',
reward_attr='episode_reward_mean',
grace_period=10.0, perturbation_interval=6.0,
hyperparameter_mutations=None):
FIFOScheduler.__init__(self)
self._completed_trials = set()
self._results = collections.defaultdict(list)
self._last_perturbation_time = {}
self._grace_period = grace_period
self._reward_attr = reward_attr
self._time_attr = time_attr
self._hyperparameter_mutations = hyperparameter_mutations
self._perturbation_interval = perturbation_interval
self._checkpoint_paths = {}
def __init__(
self, time_attr="time_total_s", reward_attr="episode_reward_mean",
perturbation_interval=60.0, hyperparam_mutations={},
resample_probability=0.25, custom_explore_fn=None):
if not hyperparam_mutations and not custom_explore_fn:
raise TuneError(
"You must specify at least one of `hyperparam_mutations` or "
"`custom_explore_fn` to use PBT.")
FIFOScheduler.__init__(self)
self._reward_attr = reward_attr
self._time_attr = time_attr
self._perturbation_interval = perturbation_interval
self._hyperparam_mutations = hyperparam_mutations
self._resample_probability = resample_probability
self._trial_state = {}
self._custom_explore_fn = custom_explore_fn
# Metrics
self._num_checkpoints = 0
self._num_perturbations = 0
def __init__(self,
time_attr='training_iteration',
reward_attr='episode_reward_mean',
max_t=81):
assert max_t > 0, "Max (time_attr) not valid!"
FIFOScheduler.__init__(self)
self._eta = 3
self._s_max_1 = 5
# bracket max trials
self._get_n0 = lambda s: int(
np.ceil(self._s_max_1 / (s + 1) * self._eta**s))
# bracket initial iterations
self._get_r0 = lambda s: int((max_t * self._eta**(-s)))
self._hyperbands = [[]] # list of hyperband iterations
self._trial_info = {} # Stores Trial -> Bracket, Band Iteration
# Tracks state for new trial add
self._state = {"bracket": None, "band_idx": 0}
self._num_stopped = 0
self._reward_attr = reward_attr
self._time_attr = time_attr
def __init__(
self, time_attr='training_iteration',
reward_attr='episode_reward_mean', max_t=100,
grace_period=10, reduction_factor=3, brackets=3):
assert max_t > 0, "Max (time_attr) not valid!"
assert max_t >= grace_period, "grace_period must be <= max_t!"
assert grace_period > 0, "grace_period must be positive!"
assert reduction_factor > 1, "Reduction Factor not valid!"
assert brackets > 0, "brackets must be positive!"
FIFOScheduler.__init__(self)
self._reduction_factor = reduction_factor
self._max_t = max_t
self._trial_info = {} # Stores Trial -> Bracket
# Tracks state for new trial add
self._brackets = [_Bracket(
grace_period, max_t, reduction_factor, s) for s in range(brackets)]
self._counter = 0 # for
self._num_stopped = 0
self._reward_attr = reward_attr
self._time_attr = time_attr
def __init__(
self, time_attr='training_iteration',
reward_attr='episode_reward_mean', max_t=81):
assert max_t > 0, "Max (time_attr) not valid!"
FIFOScheduler.__init__(self)
self._eta = 3
self._s_max_1 = 5
self._max_t_attr = max_t
# bracket max trials
self._get_n0 = lambda s: int(
np.ceil(self._s_max_1/(s+1) * self._eta**s))
# bracket initial iterations
self._get_r0 = lambda s: int((max_t*self._eta**(-s)))
self._hyperbands = [[]] # list of hyperband iterations
self._trial_info = {} # Stores Trial -> Bracket, Band Iteration
# Tracks state for new trial add
self._state = {"bracket": None,
"band_idx": 0}
self._num_stopped = 0
self._reward_attr = reward_attr
self._time_attr = time_attr
def __init__(self,
time_attr="time_total_s",
reward_attr="episode_reward_mean",
perturbation_interval=60.0,
hyperparam_mutations={},
resample_probability=0.25,
custom_explore_fn=None):
if not hyperparam_mutations and not custom_explore_fn:
raise TuneError(
"You must specify at least one of `hyperparam_mutations` or "
"`custom_explore_fn` to use PBT.")
FIFOScheduler.__init__(self)
self._reward_attr = reward_attr
self._time_attr = time_attr
self._perturbation_interval = perturbation_interval
self._hyperparam_mutations = hyperparam_mutations
self._resample_probability = resample_probability
self._trial_state = {}
self._custom_explore_fn = custom_explore_fn
# Metrics
self._num_checkpoints = 0
self._num_perturbations = 0
def __init__(self, max_iter=200, eta=3):
"""
args:
max_iter (int): maximum iterations per configuration
eta (int): # defines downsampling rate (default=3)
"""
assert max_iter > 0, "Max Iterations not valid!"
assert eta > 1, "Downsampling rate (eta) not valid!"
FIFOScheduler.__init__(self)
self._eta = eta
self._s_max_1 = s_max_1 = calculate_bracket_count(max_iter, eta)
# total number of iterations per execution of Succesive Halving (n,r)
B = s_max_1 * max_iter
# bracket trial count total
self._get_n0 = lambda s: int(np.ceil(B / max_iter / (s + 1) * eta**s))
# bracket initial iterations
self._get_r0 = lambda s: int(max_iter * eta**(-s))
self._hyperbands = [[]] # list of hyperband iterations
self._trial_info = {} # Stores Trial -> Bracket, Band Iteration
# Tracks state for new trial add
self._state = {"bracket": None, "band_idx": 0}
self._num_stopped = 0
