def save_checkpoint(self):
# create current session's checkpoint directory
if self.task_parameters.checkpoint_save_dir is None:
self.task_parameters.checkpoint_save_dir = os.path.join(
self.task_parameters.experiment_path, 'checkpoint')
if not os.path.exists(self.task_parameters.checkpoint_save_dir):
os.mkdir(self.task_parameters.checkpoint_save_dir
) # Create directory structure
if self.checkpoint_state_updater is None:
self.checkpoint_state_updater = CheckpointStateUpdater(
self.task_parameters.checkpoint_save_dir)
checkpoint_name = "{}_Step-{}.ckpt".format(
self.checkpoint_id,
self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])
checkpoint_path = os.path.join(
self.task_parameters.checkpoint_save_dir, checkpoint_name)
if not isinstance(self.task_parameters, DistributedTaskParameters):
saved_checkpoint_path = self.checkpoint_saver.save(
self.sess, checkpoint_path)
else:
saved_checkpoint_path = checkpoint_path
# this is required in order for agents to save additional information like a DND for example
[
manager.save_checkpoint(checkpoint_name)
for manager in self.level_managers
]
# the ONNX graph will be stored only if checkpoints are stored and the -onnx flag is used
if self.task_parameters.export_onnx_graph:
self.save_onnx_graph()
# write the new checkpoint name to a file to signal this checkpoint has been fully saved
self.checkpoint_state_updater.update(
SingleCheckpoint(self.checkpoint_id, checkpoint_name))
screen.log_dict(OrderedDict([
("Saving in path", saved_checkpoint_path),
]),
prefix="Checkpoint")
self.checkpoint_id += 1
self.last_checkpoint_saving_time = time.time()
if hasattr(self, 'data_store_params'):
data_store = self.get_data_store(self.data_store_params)
data_store.save_to_store()
def save_checkpoint(self):
# create current session's checkpoint directory
if self.task_parameters.checkpoint_save_dir is None:
self.task_parameters.checkpoint_save_dir = os.path.join(self.task_parameters.experiment_path, 'checkpoint')
if not os.path.exists(self.task_parameters.checkpoint_save_dir):
os.mkdir(self.task_parameters.checkpoint_save_dir) # Create directory structure
checkpoint_name = "{}_Step-{}.ckpt".format(
self.checkpoint_id, self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])
saved_checkpoint_paths = []
for agent_params in self.agents_params:
agent_checkpoint_save_dir = os.path.join(self.task_parameters.checkpoint_save_dir, agent_params.name)
if not os.path.exists(agent_checkpoint_save_dir):
os.mkdir(agent_checkpoint_save_dir)
if self.checkpoint_state_updater[agent_params.name] is None:
self.checkpoint_state_updater[agent_params.name] = CheckpointStateUpdater(agent_checkpoint_save_dir)
agent_checkpoint_path = os.path.join(agent_checkpoint_save_dir, checkpoint_name)
if not isinstance(self.task_parameters, DistributedTaskParameters):
saved_checkpoint_paths.append(self.checkpoint_saver[agent_params.name].save(self.sess[agent_params.name], agent_checkpoint_path))
else:
saved_checkpoint_paths.append(agent_checkpoint_path)
if self.num_checkpoints_to_keep < len(self.checkpoint_state_updater[agent_params.name].all_checkpoints):
checkpoint_to_delete = self.checkpoint_state_updater[agent_params.name].all_checkpoints[-self.num_checkpoints_to_keep - 1]
agent_checkpoint_to_delete = os.path.join(agent_checkpoint_save_dir, checkpoint_to_delete.name)
for file in glob.glob("{}*".format(agent_checkpoint_to_delete)):
os.remove(file)
# this is required in order for agents to save additional information like a DND for example
[manager.save_checkpoint(checkpoint_name) for manager in self.level_managers]
# Purge Redis memory after saving the checkpoint as Transitions are no longer needed at this point.
if hasattr(self, 'memory_backend'):
self.memory_backend.memory_purge()
# the ONNX graph will be stored only if checkpoints are stored and the -onnx flag is used
if self.task_parameters.export_onnx_graph:
self.save_onnx_graph()
# write the new checkpoint name to a file to signal this checkpoint has been fully saved
for agent_params in self.agents_params:
self.checkpoint_state_updater[agent_params.name].update(SingleCheckpoint(self.checkpoint_id, checkpoint_name))
screen.log_dict(
OrderedDict([
("Saving in path", saved_checkpoint_path) for saved_checkpoint_path in saved_checkpoint_paths
]),
prefix="Checkpoint"
)
self.checkpoint_id += 1
self.last_checkpoint_saving_time = time.time()
if hasattr(self, 'data_store_params'):
data_store = self.get_data_store(self.data_store_params)
data_store.save_to_store()
class MultiAgentGraphManager(object):
"""
A simple multi-agent graph manager and a single environment which is interacted with.
"""
def __init__(
self,
agents_params: List[AgentParameters],
env_params: EnvironmentParameters,
schedule_params: ScheduleParameters,
vis_params: VisualizationParameters = VisualizationParameters(),
preset_validation_params:
PresetValidationParameters = PresetValidationParameters(),
done_condition=any):
self.done_condition = done_condition
self.sess = {agent_params.name: None for agent_params in agents_params}
self.level_managers = [] # type: List[MultiAgentLevelManager]
self.top_level_manager = None
self.environments = []
self.set_schedule_params(schedule_params)
self.visualization_parameters = vis_params
self.name = 'multi_agent_graph'
self.task_parameters = None
self._phase = self.phase = RunPhase.UNDEFINED
self.preset_validation_params = preset_validation_params
self.reset_required = False
self.num_checkpoints_to_keep = 4 # TODO: make this a parameter
# timers
self.graph_creation_time = None
self.last_checkpoint_saving_time = time.time()
# counters
self.total_steps_counters = {
RunPhase.HEATUP: TotalStepsCounter(),
RunPhase.TRAIN: TotalStepsCounter(),
RunPhase.TEST: TotalStepsCounter()
}
self.checkpoint_id = 0
self.checkpoint_saver = {
agent_params.name: None
for agent_params in agents_params
}
self.checkpoint_state_updater = None
self.graph_logger = Logger()
self.data_store = None
self.is_batch_rl = False
self.time_metric = TimeTypes.EpisodeNumber
self.env_params = env_params
self.agents_params = agents_params
self.agent_params = agents_params[0] # ...(find a better way)...
for agent_index, agent_params in enumerate(agents_params):
if len(agents_params) == 1:
agent_params.name = "agent"
else:
agent_params.name = "agent_{}".format(agent_index)
agent_params.visualization = copy.copy(vis_params)
if agent_params.input_filter is None:
agent_params.input_filter = copy.copy(
env_params.default_input_filter())
if agent_params.output_filter is None:
agent_params.output_filter = copy.copy(
env_params.default_output_filter())
def create_graph(self,
task_parameters=TaskParameters(),
stop_physics=None,
start_physics=None,
empty_service_call=None):
self.graph_creation_time = time.time()
self.task_parameters = task_parameters
if isinstance(task_parameters, DistributedTaskParameters):
screen.log_title(
"Creating graph - name: {} task id: {} type: {}".format(
self.__class__.__name__, task_parameters.task_index,
task_parameters.job_type))
else:
screen.log_title("Creating graph - name: {}".format(
self.__class__.__name__))
# "hide" the gpu if necessary
if task_parameters.use_cpu:
set_cpu()
# create a target server for the worker and a device
if isinstance(task_parameters, DistributedTaskParameters):
task_parameters.worker_target, task_parameters.device = \
self.create_worker_or_parameters_server(task_parameters=task_parameters)
# If necessary start the physics and then stop it after agent creation
screen.log_title("Start physics before creating graph")
if start_physics and empty_service_call:
start_physics(empty_service_call())
# create the graph modules
screen.log_title("Create graph")
self.level_managers, self.environments = self._create_graph(
task_parameters)
screen.log_title("Stop physics after creating graph")
if stop_physics and empty_service_call:
stop_physics(empty_service_call())
# set self as the parent of all the level managers
self.top_level_manager = self.level_managers[0]
for level_manager in self.level_managers:
level_manager.parent_graph_manager = self
# create a session (it needs to be created after all the graph ops were created)
self.sess = {
agent_params.name: None
for agent_params in self.agents_params
}
screen.log_title("Creating session")
self.create_session(task_parameters=task_parameters)
self._phase = self.phase = RunPhase.UNDEFINED
self.setup_logger()
return self
def _create_graph(
self, task_parameters: TaskParameters
) -> Tuple[List[MultiAgentLevelManager], List[Environment]]:
# environment loading
self.env_params.seed = task_parameters.seed
self.env_params.experiment_path = task_parameters.experiment_path
env = short_dynamic_import(self.env_params.path)(
**self.env_params.__dict__,
visualization_parameters=self.visualization_parameters)
# agent loading
agents = OrderedDict()
for agent_params in self.agents_params:
agent_params.task_parameters = copy.copy(task_parameters)
agent = short_dynamic_import(agent_params.path)(agent_params)
agents[agent_params.name] = agent
screen.log_title("Created agent: {}".format(agent_params.name))
if hasattr(self, 'memory_backend_params') and \
self.memory_backend_params.run_type == str(RunType.ROLLOUT_WORKER):
agent.memory.memory_backend = deepracer_memory.DeepRacerRolloutBackEnd(
self.memory_backend_params,
agent_params.algorithm.num_consecutive_playing_steps,
agent_params.name)
# set level manager
level_manager = MultiAgentLevelManager(
agents=agents,
environment=env,
name="main_level",
done_condition=self.done_condition)
return [level_manager], [env]
@staticmethod
def _create_worker_or_parameters_server_tf(
task_parameters: DistributedTaskParameters):
import tensorflow as tf
config = tf.ConfigProto()
config.allow_soft_placement = True # allow placing ops on cpu if they are not fit for gpu
config.gpu_options.allow_growth = True # allow the gpu memory allocated for the worker to grow if needed
config.gpu_options.per_process_gpu_memory_fraction = 0.2
config.intra_op_parallelism_threads = 1
config.inter_op_parallelism_threads = 1
from rl_coach.architectures.tensorflow_components.distributed_tf_utils import \
create_and_start_parameters_server, \
create_cluster_spec, create_worker_server_and_device
# create cluster spec
cluster_spec = create_cluster_spec(
parameters_server=task_parameters.parameters_server_hosts,
workers=task_parameters.worker_hosts)
# create and start parameters server (non-returning function) or create a worker and a device setter
if task_parameters.job_type == "ps":
create_and_start_parameters_server(cluster_spec=cluster_spec,
config=config)
elif task_parameters.job_type == "worker":
return create_worker_server_and_device(
cluster_spec=cluster_spec,
task_index=task_parameters.task_index,
use_cpu=task_parameters.use_cpu,
config=config)
else:
raise ValueError(
"The job type should be either ps or worker and not {}".format(
task_parameters.job_type))
@staticmethod
def create_worker_or_parameters_server(
task_parameters: DistributedTaskParameters):
if task_parameters.framework_type == Frameworks.tensorflow:
return GraphManager._create_worker_or_parameters_server_tf(
task_parameters)
elif task_parameters.framework_type == Frameworks.mxnet:
raise NotImplementedError(
'Distributed training not implemented for MXNet')
else:
raise ValueError('Invalid framework {}'.format(
task_parameters.framework_type))
def _create_session_tf(self, task_parameters: TaskParameters):
import tensorflow as tf
config = tf.ConfigProto()
config.allow_soft_placement = True # allow placing ops on cpu if they are not fit for gpu
config.gpu_options.allow_growth = True # allow the gpu memory allocated for the worker to grow if needed
# config.gpu_options.per_process_gpu_memory_fraction = 0.2
config.intra_op_parallelism_threads = 1
config.inter_op_parallelism_threads = 1
if isinstance(task_parameters, DistributedTaskParameters):
# the distributed tensorflow setting
from rl_coach.architectures.tensorflow_components.distributed_tf_utils import create_monitored_session
if hasattr(self.task_parameters, 'checkpoint_restore_path'
) and self.task_parameters.checkpoint_restore_path:
checkpoint_dir = os.path.join(task_parameters.experiment_path,
'checkpoint')
if os.path.exists(checkpoint_dir):
remove_tree(checkpoint_dir)
# in the locally distributed case, checkpoints are always restored from a directory (and not from a
# file)
copy_tree(task_parameters.checkpoint_restore_path,
checkpoint_dir)
else:
checkpoint_dir = task_parameters.checkpoint_save_dir
self.sess = create_monitored_session(
target=task_parameters.worker_target,
task_index=task_parameters.task_index,
checkpoint_dir=checkpoint_dir,
checkpoint_save_secs=task_parameters.checkpoint_save_secs,
config=config)
# set the session for all the modules
self.set_session(self.sess)
else:
# regular session
print("Creating regular session")
self.sess = {
agent_params.name: tf.Session(config=config)
for agent_params in self.agents_params
}
# set the session for all the modules
self.set_session(self.sess)
# the TF graph is static, and therefore is saved once - in the beginning of the experiment
if hasattr(self.task_parameters, 'checkpoint_save_dir'
) and self.task_parameters.checkpoint_save_dir:
self.save_graph()
def _create_session_mx(self):
"""
Call set_session to initialize parameters and construct checkpoint_saver
"""
self.set_session(sess=None) # Initialize all modules
def create_session(self, task_parameters: TaskParameters):
if task_parameters.framework_type == Frameworks.tensorflow:
self._create_session_tf(task_parameters)
elif task_parameters.framework_type == Frameworks.mxnet:
self._create_session_mx()
else:
raise ValueError('Invalid framework {}'.format(
task_parameters.framework_type))
# Create parameter saver
self.checkpoint_saver = {
agent_params.name: SaverCollection()
for agent_params in self.agents_params
}
for level in self.level_managers:
for agent_params in self.agents_params:
self.checkpoint_saver[agent_params.name].update(
level.collect_savers(agent_params.name))
# restore from checkpoint if given
self.restore_checkpoint()
def save_graph(self) -> None:
"""
Save the TF graph to a protobuf description file in the experiment directory
:return: None
"""
import tensorflow as tf
# write graph
tf.train.write_graph(tf.get_default_graph(),
logdir=self.task_parameters.checkpoint_save_dir,
name='graphdef.pb',
as_text=False)
def _save_onnx_graph_tf(self) -> None:
"""
Save the tensorflow graph as an ONNX graph.
This requires the graph and the weights checkpoint to be stored in the experiment directory.
It then freezes the graph (merging the graph and weights checkpoint), and converts it to ONNX.
:return: None
"""
# collect input and output nodes
input_nodes = []
output_nodes = []
for level in self.level_managers:
for agent in level.agents.values():
for network in agent.networks.values():
for input_key, input in network.online_network.inputs.items(
):
if not input_key.startswith("output_"):
input_nodes.append(input.name)
for output in network.online_network.outputs:
output_nodes.append(output.name)
from rl_coach.architectures.tensorflow_components.architecture import save_onnx_graph
save_onnx_graph(input_nodes, output_nodes,
self.task_parameters.checkpoint_save_dir)
def save_onnx_graph(self) -> None:
"""
Save the graph as an ONNX graph.
This requires the graph and the weights checkpoint to be stored in the experiment directory.
It then freezes the graph (merging the graph and weights checkpoint), and converts it to ONNX.
:return: None
"""
if self.task_parameters.framework_type == Frameworks.tensorflow:
self._save_onnx_graph_tf()
def setup_logger(self) -> None:
# dump documentation
logger_prefix = "{graph_name}".format(graph_name=self.name)
self.graph_logger.set_logger_filenames(
self.task_parameters.experiment_path,
logger_prefix=logger_prefix,
add_timestamp=True,
task_id=self.task_parameters.task_index)
if self.visualization_parameters.dump_parameters_documentation:
self.graph_logger.dump_documentation(str(self))
[manager.setup_logger() for manager in self.level_managers]
@property
def phase(self) -> RunPhase:
"""
Get the phase of the graph
:return: the current phase
"""
return self._phase
@phase.setter
def phase(self, val: RunPhase):
"""
Change the phase of the graph and all the hierarchy levels below it
:param val: the new phase
:return: None
"""
self._phase = val
for level_manager in self.level_managers:
level_manager.phase = val
for environment in self.environments:
environment.phase = val
environment._notify_phase(val)
@property
def current_step_counter(self) -> TotalStepsCounter:
return self.total_steps_counters[self.phase]
@contextlib.contextmanager
def phase_context(self, phase):
"""
Create a context which temporarily sets the phase to the provided phase.
The previous phase is restored afterwards.
"""
old_phase = self.phase
self.phase = phase
yield
self.phase = old_phase
def set_session(self, sess) -> None:
"""
Set the deep learning framework session for all the modules in the graph
:return: None
"""
[manager.set_session(sess) for manager in self.level_managers]
def heatup(self, steps: PlayingStepsType) -> None:
"""
Perform heatup for several steps, which means taking random actions and storing the results in memory
:param steps: the number of steps as a tuple of steps time and steps count
:return: None
"""
self.verify_graph_was_created()
if steps.num_steps > 0:
with self.phase_context(RunPhase.HEATUP):
screen.log_title("{}: Starting heatup".format(self.name))
# reset all the levels before starting to heatup
self.reset_internal_state(force_environment_reset=True)
# act for at least steps, though don't interrupt an episode
count_end = self.current_step_counter + steps
while self.current_step_counter < count_end:
self.act(EnvironmentEpisodes(1))
def handle_episode_ended(self) -> None:
"""
End an episode and reset all the episodic parameters
:return: None
"""
self.current_step_counter[EnvironmentEpisodes] += 1
[
environment.handle_episode_ended()
for environment in self.environments
]
def train(self) -> None:
"""
Perform several training iterations for all the levels in the hierarchy
:param steps: number of training iterations to perform
:return: None
"""
self.verify_graph_was_created()
with self.phase_context(RunPhase.TRAIN):
self.current_step_counter[TrainingSteps] += 1
[manager.train() for manager in self.level_managers]
def reset_internal_state(self, force_environment_reset=False) -> None:
"""
Reset an episode for all the levels
:param force_environment_reset: force the environment to reset the episode even if it has some conditions that
tell it not to. for example, if ale life is lost, gym will tell the agent that
the episode is finished but won't actually reset the episode if there are more
lives available
:return: None
"""
self.verify_graph_was_created()
self.reset_required = False
[
environment.reset_internal_state(force_environment_reset)
for environment in self.environments
]
[manager.reset_internal_state() for manager in self.level_managers]
def act(self,
steps: PlayingStepsType,
wait_for_full_episodes=False) -> None:
"""
Do several steps of acting on the environment
:param wait_for_full_episodes: if set, act for at least `steps`, but make sure that the last episode is complete
:param steps: the number of steps as a tuple of steps time and steps count
"""
self.verify_graph_was_created()
# perform several steps of playing
count_end = self.current_step_counter + steps
done = False
while self.current_step_counter < count_end or (wait_for_full_episodes
and not done):
# reset the environment if the previous episode was terminated
if self.reset_required:
self.reset_internal_state()
steps_begin = self.environments[0].total_steps_counter
done = self.top_level_manager.step(None)
steps_end = self.environments[0].total_steps_counter
if done:
self.handle_episode_ended()
self.reset_required = True
self.current_step_counter[EnvironmentSteps] += (steps_end -
steps_begin)
# if no steps were made (can happen when no actions are taken while in the TRAIN phase, either in batch RL
# or in imitation learning), we force end the loop, so that it will not continue forever.
if (steps_end - steps_begin) == 0:
break
def train_and_act(self, steps: StepMethod) -> None:
"""
Train the agent by doing several acting steps followed by several training steps continually
:param steps: the number of steps as a tuple of steps time and steps count
:return: None
"""
self.verify_graph_was_created()
# perform several steps of training interleaved with acting
if steps.num_steps > 0:
with self.phase_context(RunPhase.TRAIN):
self.reset_internal_state(force_environment_reset=True)
count_end = self.current_step_counter + steps
while self.current_step_counter < count_end:
# The actual number of steps being done on the environment
# is decided by the agent, though this inner loop always
# takes at least one step in the environment (at the GraphManager level).
# The agent might also decide to skip acting altogether.
# Depending on internal counters and parameters, it doesn't always train or save checkpoints.
self.act(EnvironmentSteps(1))
self.train()
self.occasionally_save_checkpoint()
def sync(self) -> None:
"""
Sync the global network parameters to the graph
:return:
"""
[manager.sync() for manager in self.level_managers]
def evaluate(self, steps: PlayingStepsType) -> bool:
"""
Perform evaluation for several steps
:param steps: the number of steps as a tuple of steps time and steps count
:return: bool, True if the target reward and target success has been reached
"""
self.verify_graph_was_created()
if steps.num_steps > 0:
with self.phase_context(RunPhase.TEST):
# reset all the levels before starting to evaluate
self.reset_internal_state(force_environment_reset=True)
self.sync()
# act for at least `steps`, though don't interrupt an episode
count_end = self.current_step_counter + steps
while self.current_step_counter < count_end:
self.act(EnvironmentEpisodes(1))
self.sync()
if self.should_stop():
self.flush_finished()
screen.success("Reached required success rate. Exiting.")
return True
return False
def improve(self):
"""
The main loop of the run.
Defined in the following steps:
1. Heatup
2. Repeat:
2.1. Repeat:
2.1.1. Act
2.1.2. Train
2.1.3. Possibly save checkpoint
2.2. Evaluate
:return: None
"""
self.verify_graph_was_created()
# initialize the network parameters from the global network
self.sync()
# heatup
self.heatup(self.heatup_steps)
# improve
if self.task_parameters.task_index is not None:
screen.log_title("Starting to improve {} task index {}".format(
self.name, self.task_parameters.task_index))
else:
screen.log_title("Starting to improve {}".format(self.name))
count_end = self.total_steps_counters[
RunPhase.TRAIN] + self.improve_steps
while self.total_steps_counters[RunPhase.TRAIN] < count_end:
self.train_and_act(self.steps_between_evaluation_periods)
if self.evaluate(self.evaluation_steps):
break
def restore_checkpoint(self):
self.verify_graph_was_created()
# TODO: find better way to load checkpoints that were saved with a global network into the online network
if self.task_parameters.checkpoint_restore_path:
restored_checkpoint_paths = []
for agent_params in self.agents_params:
if len(self.agents_params) == 1:
agent_checkpoint_restore_path = self.task_parameters.checkpoint_restore_path
else:
agent_checkpoint_restore_path = os.path.join(
self.task_parameters.checkpoint_restore_path,
agent_params.name)
if os.path.isdir(agent_checkpoint_restore_path):
# a checkpoint dir
if self.task_parameters.framework_type == Frameworks.tensorflow and\
'checkpoint' in os.listdir(agent_checkpoint_restore_path):
# TODO-fixme checkpointing
# MonitoredTrainingSession manages save/restore checkpoints autonomously. Doing so,
# it creates it own names for the saved checkpoints, which do not match the "{}_Step-{}.ckpt"
# filename pattern. The names used are maintained in a CheckpointState protobuf file named
# 'checkpoint'. Using Coach's '.coach_checkpoint' protobuf file, results in an error when trying to
# restore the model, as the checkpoint names defined do not match the actual checkpoint names.
raise NotImplementedError(
'Checkpointing not implemented for TF monitored training session'
)
else:
checkpoint = get_checkpoint_state(
agent_checkpoint_restore_path,
all_checkpoints=True)
if checkpoint is None:
raise ValueError(
"No checkpoint to restore in: {}".format(
agent_checkpoint_restore_path))
model_checkpoint_path = checkpoint.model_checkpoint_path
checkpoint_restore_dir = self.task_parameters.checkpoint_restore_path
restored_checkpoint_paths.append(model_checkpoint_path)
# Set the last checkpoint ID - only in the case of the path being a dir
chkpt_state_reader = CheckpointStateReader(
agent_checkpoint_restore_path,
checkpoint_state_optional=False)
self.checkpoint_id = chkpt_state_reader.get_latest(
).num + 1
else:
# a checkpoint file
if self.task_parameters.framework_type == Frameworks.tensorflow:
model_checkpoint_path = agent_checkpoint_restore_path
checkpoint_restore_dir = os.path.dirname(
model_checkpoint_path)
restored_checkpoint_paths.append(model_checkpoint_path)
else:
raise ValueError(
"Currently restoring a checkpoint using the --checkpoint_restore_file argument is"
" only supported when with tensorflow.")
try:
self.checkpoint_saver[agent_params.name].restore(
self.sess[agent_params.name], model_checkpoint_path)
except Exception as ex:
raise ValueError(
"Failed to restore {}'s checkpoint: {}".format(
agent_params.name, ex))
all_checkpoints = sorted(
list(set([c.name for c in checkpoint.all_checkpoints
]))) # remove duplicates :-(
if self.num_checkpoints_to_keep < len(all_checkpoints):
checkpoint_to_delete = all_checkpoints[
-self.num_checkpoints_to_keep - 1]
agent_checkpoint_to_delete = os.path.join(
agent_checkpoint_restore_path, checkpoint_to_delete)
for file in glob.glob(
"{}*".format(agent_checkpoint_to_delete)):
os.remove(file)
[
manager.restore_checkpoint(checkpoint_restore_dir)
for manager in self.level_managers
]
[
manager.post_training_commands()
for manager in self.level_managers
]
screen.log_dict(OrderedDict([
("Restoring from path", restored_checkpoint_path)
for restored_checkpoint_path in restored_checkpoint_paths
]),
prefix="Checkpoint")
def _get_checkpoint_state_tf(self, checkpoint_restore_dir):
import tensorflow as tf
return tf.train.get_checkpoint_state(checkpoint_restore_dir)
def occasionally_save_checkpoint(self):
# only the chief process saves checkpoints
if self.task_parameters.checkpoint_save_secs \
and time.time() - self.last_checkpoint_saving_time >= self.task_parameters.checkpoint_save_secs \
and (self.task_parameters.task_index == 0 # distributed
or self.task_parameters.task_index is None # single-worker
):
self.save_checkpoint()
def save_checkpoint(self):
# create current session's checkpoint directory
if self.task_parameters.checkpoint_save_dir is None:
self.task_parameters.checkpoint_save_dir = os.path.join(
self.task_parameters.experiment_path, 'checkpoint')
if not os.path.exists(self.task_parameters.checkpoint_save_dir):
os.mkdir(self.task_parameters.checkpoint_save_dir
) # Create directory structure
if self.checkpoint_state_updater is None:
self.checkpoint_state_updater = CheckpointStateUpdater(
self.task_parameters.checkpoint_save_dir)
checkpoint_name = "{}_Step-{}.ckpt".format(
self.checkpoint_id,
self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])
saved_checkpoint_paths = []
for agent_params in self.agents_params:
if len(self.agents_params) == 1:
agent_checkpoint_save_dir = self.task_parameters.checkpoint_save_dir
else:
agent_checkpoint_save_dir = os.path.join(
self.task_parameters.checkpoint_save_dir,
agent_params.name)
if not os.path.exists(agent_checkpoint_save_dir):
os.mkdir(agent_checkpoint_save_dir)
agent_checkpoint_path = os.path.join(agent_checkpoint_save_dir,
checkpoint_name)
if not isinstance(self.task_parameters, DistributedTaskParameters):
saved_checkpoint_paths.append(
self.checkpoint_saver[agent_params.name].save(
self.sess[agent_params.name], agent_checkpoint_path))
else:
saved_checkpoint_paths.append(agent_checkpoint_path)
if self.num_checkpoints_to_keep < len(
self.checkpoint_state_updater.all_checkpoints):
checkpoint_to_delete = self.checkpoint_state_updater.all_checkpoints[
-self.num_checkpoints_to_keep - 1]
agent_checkpoint_to_delete = os.path.join(
agent_checkpoint_save_dir, checkpoint_to_delete.name)
for file in glob.glob(
"{}*".format(agent_checkpoint_to_delete)):
os.remove(file)
# this is required in order for agents to save additional information like a DND for example
[
manager.save_checkpoint(checkpoint_name)
for manager in self.level_managers
]
# Purge Redis memory after saving the checkpoint as Transitions are no longer needed at this point.
if hasattr(self, 'memory_backend'):
self.memory_backend.memory_purge()
# the ONNX graph will be stored only if checkpoints are stored and the -onnx flag is used
if self.task_parameters.export_onnx_graph:
self.save_onnx_graph()
# write the new checkpoint name to a file to signal this checkpoint has been fully saved
self.checkpoint_state_updater.update(
SingleCheckpoint(self.checkpoint_id, checkpoint_name))
screen.log_dict(OrderedDict([
("Saving in path", saved_checkpoint_path)
for saved_checkpoint_path in saved_checkpoint_paths
]),
prefix="Checkpoint")
self.checkpoint_id += 1
self.last_checkpoint_saving_time = time.time()
if hasattr(self, 'data_store_params'):
data_store = self.get_data_store(self.data_store_params)
data_store.save_to_store()
def verify_graph_was_created(self):
"""
Verifies that the graph was already created, and if not, it creates it with the default task parameters
:return: None
"""
if self.graph_creation_time is None:
self.create_graph()
def __str__(self):
result = ""
for key, val in self.__dict__.items():
params = ""
if isinstance(val, list) or isinstance(val, dict) or isinstance(
val, OrderedDict):
items = iterable_to_items(val)
for k, v in items:
params += "{}: {}\n".format(k, v)
else:
params = val
result += "{}: \n{}\n".format(key, params)
return result
def should_train(self) -> bool:
return any([manager.should_train() for manager in self.level_managers])
# TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create
# an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()]
def emulate_act_on_trainer(self, steps: PlayingStepsType,
transitions: Dict[str, Transition]) -> None:
"""
This emulates the act using the transition obtained from the rollout worker on the training worker
in case of distributed training.
Do several steps of acting on the environment
:param steps: the number of steps as a tuple of steps time and steps count
"""
self.verify_graph_was_created()
# perform several steps of playing
count_end = self.current_step_counter + steps
while self.current_step_counter < count_end:
# reset the environment if the previous episode was terminated
if self.reset_required:
self.reset_internal_state()
steps_begin = self.environments[0].total_steps_counter
done = self.top_level_manager.emulate_step_on_trainer(transitions)
steps_end = self.environments[0].total_steps_counter
# add the diff between the total steps before and after stepping, such that environment initialization steps
# (like in Atari) will not be counted.
# We add at least one step so that even if no steps were made (in case no actions are taken in the training
# phase), the loop will end eventually.
self.current_step_counter[EnvironmentSteps] += max(
1, steps_end - steps_begin)
if done:
self.handle_episode_ended()
self.reset_required = True
def fetch_from_worker(self, num_consecutive_playing_steps=None):
if hasattr(self, 'memory_backend'):
for transitions in self.memory_backend.fetch(
num_consecutive_playing_steps):
self.emulate_act_on_trainer(EnvironmentSteps(1), transitions)
if hasattr(self, 'sample_collector'):
self.sample_collector.sample(transitions)
def setup_memory_backend(self) -> None:
if hasattr(self, 'memory_backend_params'):
self.memory_backend = deepracer_memory.DeepRacerTrainerBackEnd(
self.memory_backend_params, self.agents_params)
def should_stop(self) -> bool:
return self.task_parameters.apply_stop_condition and all(
[manager.should_stop() for manager in self.level_managers])
def get_data_store(self, param):
if self.data_store:
return self.data_store
return data_store_creator(param)
def signal_ready(self):
if self.task_parameters.checkpoint_save_dir and os.path.exists(
self.task_parameters.checkpoint_save_dir):
open(
os.path.join(self.task_parameters.checkpoint_save_dir,
SyncFiles.TRAINER_READY.value), 'w').close()
if hasattr(self, 'data_store_params'):
data_store = self.get_data_store(self.data_store_params)
data_store.save_to_store()
def close(self) -> None:
"""
Clean up to close environments.
:return: None
"""
for env in self.environments:
env.close()
def get_current_episodes_count(self):
"""
Returns the current EnvironmentEpisodes counter
"""
return self.current_step_counter[EnvironmentEpisodes]
def flush_finished(self):
"""
To indicate the training has finished, writes a `.finished` file to the checkpoint directory and calls
the data store to updload that file.
"""
if self.task_parameters.checkpoint_save_dir and os.path.exists(
self.task_parameters.checkpoint_save_dir):
open(
os.path.join(self.task_parameters.checkpoint_save_dir,
SyncFiles.FINISHED.value), 'w').close()
if hasattr(self, 'data_store_params'):
data_store = self.get_data_store(self.data_store_params)
data_store.save_to_store()
def set_schedule_params(self, schedule_params: ScheduleParameters):
"""
Set schedule parameters for the graph.
:param schedule_params: the schedule params to set.
"""
self.heatup_steps = schedule_params.heatup_steps
self.evaluation_steps = schedule_params.evaluation_steps
self.steps_between_evaluation_periods = schedule_params.steps_between_evaluation_periods
self.improve_steps = schedule_params.improve_steps