def __init__(self, config: Dict[str, any], result_dir: str, cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(self._observe_expired_incomplete_experience)
self.experimental_reward = config.get('experimental_reward', False)
agent_config = config['agent_config']
self.converter = CachingStrategyRLConverter()
# action space: should cache: true or false
# state space: [capacity (1), query key(1), query result set(num_indexes)]
fields_in_state = len(CachingAgentSystemState.__slots__)
self.agent = Agent.from_spec(agent_config,
state_space=FloatBox(shape=(fields_in_state,)),
action_space=IntBox(2))
self.logger = logging.getLogger(__name__)
name = 'rl_caching_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger', result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger', result_dir=self.result_dir)
self.observation_logger = create_file_logger(name=f'{name}_observation_logger', result_dir=self.result_dir)
self.entry_hits_logger = create_file_logger(name=f'{name}_entry_hits_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def __init__(self, observers: List[Observer], results_dir: str,
cache_stats: CacheInformation):
self.observers = observers
self.episode_num = 0
self.cache_stats = cache_stats
self.evaluation_logger = create_file_logger(result_dir=results_dir,
name='evaluation_logger')
self.end_of_episode_logger = create_file_logger(
result_dir=results_dir, name='end_of_episode_logger')
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
name = 'read_write_caching_strategy'
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.entry_hits_logger = create_file_logger(
name=f'{name}_entry_hits_logger', result_dir=self.result_dir)
self.observed_entries = {}
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.supported_observations = {
ObservationType.Hit, ObservationType.Miss,
ObservationType.Invalidate
}
# evaluation specific variables
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
fields_in_state = len(MultiTaskAgentSystemState.__slots__)
action_space = RLDict({
'ttl': IntBox(low=0, high=self.maximum_ttl),
'eviction': IntBox(low=0, high=2)
})
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=action_space)
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_multi_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.ttl = self.config['ttl']
name = 'fixed_strategy'
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observed_keys = {}
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.lru = OrderedDict()
self.logger = logging.getLogger(__name__)
name = 'lru_eviction_strategy'
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=result_dir)
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expired_incomplete_experience)
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
# evaluation specific variables
self.observation_seen = 0
self.episode_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expired_incomplete_experience)
self.view_of_the_cache = {} # type: Dict[str, Dict[str, any]]
self._end_episode_observation = {
ObservationType.Invalidate, ObservationType.Miss,
ObservationType.Expiration
}
# TODO refactor into common RL interface for all strategies
# Agent configuration (can be shared with others)
agent_config = config['agent_config']
fields_in_state = len(EvictionAgentSystemState.__slots__)
self.converter = EvictionStrategyRLConverter(self.result_dir)
# State: fields to observe in question
# Action: to evict or not that key
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=IntBox(low=0, high=2))
self.logger = logging.getLogger(__name__)
name = 'rl_eviction_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
def __init__(self, config: Dict[str, any], result_dir: str,
cache_stats: CacheInformation):
super().__init__(config, result_dir, cache_stats)
self.observation_seen = 0
self.cum_reward = 0
self.checkpoint_steps = config['checkpoint_steps']
self._incomplete_experiences = TTLCache(InMemoryStorage())
self._incomplete_experiences.expired_entry_callback(
self._observe_expiry_eviction)
self.non_terminal_observations = {
ObservationType.EvictionPolicy, ObservationType.Expiration
}
agent_config = config['agent_config']
self.maximum_ttl = config['max_ttl']
self.experimental_reward = config.get('experimental_reward', False)
fields_in_state = len(TTLAgentSystemState.__slots__)
self.agent = Agent.from_spec(
agent_config,
state_space=FloatBox(shape=(fields_in_state, )),
action_space=FloatBox(low=0, high=self.maximum_ttl, shape=(1, )))
# TODO refactor into common RL interface for all strategies
self.logger = logging.getLogger(__name__)
name = 'rl_ttl_strategy'
self.reward_logger = create_file_logger(name=f'{name}_reward_logger',
result_dir=self.result_dir)
self.loss_logger = create_file_logger(name=f'{name}_loss_logger',
result_dir=self.result_dir)
self.ttl_logger = create_file_logger(name=f'{name}_ttl_logger',
result_dir=self.result_dir)
self.observation_logger = create_file_logger(
name=f'{name}_observation_logger', result_dir=self.result_dir)
self.key_vocab = Vocabulary()
self.errors = create_file_logger(name=f'{name}_error_logger',
result_dir=self.result_dir)
def __init__(self, result_dir: str):
self.vocabulary = Vocabulary(add_pad=True, add_unk=False)
self.logger = logging.getLogger(__name__)
name = 'rl_eviction_strategy'
self.performance_logger = create_file_logger(
name=f'{name}_performance_logger', result_dir=result_dir)