import random
import logging
import numpy as np
import math
from baseline.utils import exporter
__all__ = []
export = exporter(__all__)
logger = logging.getLogger('baseline')
@export
class DataFeed:
"""Data collection that, when iterated, produces an epoch of data
This class manages producing a dataset to the trainer, by iterating an epoch and producing
a single step at a time. The data can be shuffled per epoch, if requested, otherwise it is
returned in the order of the dateset
"""
def __init__(self):
self.steps = 0
self.shuffle = False
def _batch(self, i):
pass
def __getitem__(self, i):
return self._batch(i)
def __iter__(self):
shuffle = np.random.permutation(np.arange(self.steps)) if self.shuffle else np.arange(self.steps)
import math
import time
import platform
from enum import Enum
from pprint import pformat
from collections import defaultdict
from multiprocessing.managers import BaseManager
import numpy as np
from baseline.utils import export as exporter
from baseline.utils import import_user_module, optional_params, register
from mead.utils import hash_config
from hpctl.utils import Label
__all__ = []
export = exporter(__all__)
RESULTS = {}
@export
@optional_params
def register_results(cls, name=None):
return register(cls, RESULTS, name, 'results')
@six.python_2_unicode_compatible
class States(Enum):
DONE = '\u2714'
KILLED = '\u2717'
RUNNING = '\u21bb'
WAITING = '\u231b'
