def __init__(self, trainable_name, config=None, local_dir=DEFAULT_RESULTS_DIR, experiment_tag=None, resources=Resources(cpu=1, gpu=0), stopping_criterion=None, checkpoint_freq=0, restore_path=None, upload_dir=None, max_failures=0): """Initialize a new trial. The args here take the same meaning as the command line flags defined in ray.tune.config_parser. """ if not _default_registry.contains(TRAINABLE_CLASS, trainable_name): raise TuneError("Unknown trainable: " + trainable_name) if stopping_criterion: for k in stopping_criterion: if k not in TrainingResult._fields: raise TuneError( "Stopping condition key `{}` must be one of {}".format( k, TrainingResult._fields)) # Trial config self.trainable_name = trainable_name self.config = config or {} self.local_dir = local_dir self.experiment_tag = experiment_tag self.resources = resources self.stopping_criterion = stopping_criterion or {} self.checkpoint_freq = checkpoint_freq self.upload_dir = upload_dir self.verbose = True self.max_failures = max_failures # Local trial state that is updated during the run self.last_result = None self._checkpoint_path = restore_path self._checkpoint_obj = None self.runner = None self.status = Trial.PENDING self.location = None self.logdir = None self.result_logger = None self.last_debug = 0 self.trial_id = binary_to_hex(random_string())[:8] self.error_file = None self.num_failures = 0
def __init__( self, trainable_name, config=None, local_dir=DEFAULT_RESULTS_DIR, experiment_tag=None, resources=Resources(cpu=1, gpu=0), stopping_criterion=None, checkpoint_freq=0, restore_path=None, upload_dir=None, max_failures=0): """Initialize a new trial. The args here take the same meaning as the command line flags defined in ray.tune.config_parser. """ if not _default_registry.contains( TRAINABLE_CLASS, trainable_name): raise TuneError("Unknown trainable: " + trainable_name) if stopping_criterion: for k in stopping_criterion: if k not in TrainingResult._fields: raise TuneError( "Stopping condition key `{}` must be one of {}".format( k, TrainingResult._fields)) # Trial config self.trainable_name = trainable_name self.config = config or {} self.local_dir = local_dir self.experiment_tag = experiment_tag self.resources = resources self.stopping_criterion = stopping_criterion or {} self.checkpoint_freq = checkpoint_freq self.upload_dir = upload_dir self.verbose = True self.max_failures = max_failures # Local trial state that is updated during the run self.last_result = None self._checkpoint_path = restore_path self._checkpoint_obj = None self.runner = None self.status = Trial.PENDING self.location = None self.logdir = None self.result_logger = None self.last_debug = 0 self.trial_id = binary_to_hex(random_string())[:8] self.error_file = None self.num_failures = 0
def __init__(self, trainable_name, config={}, local_dir='/tmp/ray', experiment_tag=None, resources=Resources(cpu=1, gpu=0), stopping_criterion={}, checkpoint_freq=0, restore_path=None, upload_dir=None): """Initialize a new trial. The args here take the same meaning as the command line flags defined in ray.tune.config_parser. """ if not _default_registry.contains(TRAINABLE_CLASS, trainable_name): raise TuneError("Unknown trainable: " + trainable_name) for k in stopping_criterion: if k not in TrainingResult._fields: raise TuneError( "Stopping condition key `{}` must be one of {}".format( k, TrainingResult._fields)) # Immutable config self.trainable_name = trainable_name self.config = config self.local_dir = local_dir self.experiment_tag = experiment_tag self.resources = resources self.stopping_criterion = stopping_criterion self.checkpoint_freq = checkpoint_freq self.upload_dir = upload_dir # Local trial state that is updated during the run self.last_result = None self._checkpoint_path = restore_path self._checkpoint_obj = None self.runner = None self.status = Trial.PENDING self.location = None self.logdir = None self.result_logger = None