mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
update of api docstrings across coach and tutorials [WIP] (#91)
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
This commit is contained in:
Itai Caspi
@@ -83,91 +83,91 @@ def start_graph(graph_manager: 'GraphManager', task_parameters: 'TaskParameters'
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def handle_distributed_coach_tasks(graph_manager, args):
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ckpt_inside_container = "/checkpoint"
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ckpt_inside_container = "/checkpoint"
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memory_backend_params = None
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if args.memory_backend_params:
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memory_backend_params = json.loads(args.memory_backend_params)
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memory_backend_params['run_type'] = str(args.distributed_coach_run_type)
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graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(memory_backend_params))
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memory_backend_params = None
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if args.memory_backend_params:
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memory_backend_params = json.loads(args.memory_backend_params)
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memory_backend_params['run_type'] = str(args.distributed_coach_run_type)
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graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(memory_backend_params))
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data_store_params = None
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data_store_params = None
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if args.data_store_params:
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data_store_params = construct_data_store_params(json.loads(args.data_store_params))
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data_store_params.checkpoint_dir = ckpt_inside_container
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graph_manager.data_store_params = data_store_params
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if args.distributed_coach_run_type == RunType.TRAINER:
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training_worker(
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graph_manager=graph_manager,
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checkpoint_dir=ckpt_inside_container
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)
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if args.distributed_coach_run_type == RunType.ROLLOUT_WORKER:
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data_store = None
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if args.data_store_params:
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data_store_params = construct_data_store_params(json.loads(args.data_store_params))
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data_store_params.checkpoint_dir = ckpt_inside_container
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graph_manager.data_store_params = data_store_params
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data_store = get_data_store(data_store_params)
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wait_for_checkpoint(checkpoint_dir=ckpt_inside_container, data_store=data_store)
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if args.distributed_coach_run_type == RunType.TRAINER:
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training_worker(
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graph_manager=graph_manager,
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checkpoint_dir=ckpt_inside_container
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)
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if args.distributed_coach_run_type == RunType.ROLLOUT_WORKER:
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data_store = None
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if args.data_store_params:
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data_store = get_data_store(data_store_params)
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wait_for_checkpoint(checkpoint_dir=ckpt_inside_container, data_store=data_store)
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rollout_worker(
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graph_manager=graph_manager,
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checkpoint_dir=ckpt_inside_container,
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data_store=data_store,
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num_workers=args.num_workers
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)
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rollout_worker(
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graph_manager=graph_manager,
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checkpoint_dir=ckpt_inside_container,
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data_store=data_store,
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num_workers=args.num_workers
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)
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def handle_distributed_coach_orchestrator(graph_manager, args):
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ckpt_inside_container = "/checkpoint"
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rollout_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.ROLLOUT_WORKER)] + sys.argv[1:]
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trainer_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.TRAINER)] + sys.argv[1:]
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ckpt_inside_container = "/checkpoint"
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rollout_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.ROLLOUT_WORKER)] + sys.argv[1:]
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trainer_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.TRAINER)] + sys.argv[1:]
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if '--experiment_name' not in rollout_command:
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rollout_command = rollout_command + ['--experiment_name', args.experiment_name]
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if '--experiment_name' not in rollout_command:
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rollout_command = rollout_command + ['--experiment_name', args.experiment_name]
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if '--experiment_name' not in trainer_command:
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trainer_command = trainer_command + ['--experiment_name', args.experiment_name]
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if '--experiment_name' not in trainer_command:
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trainer_command = trainer_command + ['--experiment_name', args.experiment_name]
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memory_backend_params = None
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if args.memory_backend == "redispubsub":
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memory_backend_params = RedisPubSubMemoryBackendParameters()
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memory_backend_params = None
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if args.memory_backend == "redispubsub":
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memory_backend_params = RedisPubSubMemoryBackendParameters()
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ds_params_instance = None
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if args.data_store == "s3":
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ds_params = DataStoreParameters("s3", "", "")
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ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=args.s3_end_point, bucket_name=args.s3_bucket_name,
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creds_file=args.s3_creds_file, checkpoint_dir=ckpt_inside_container)
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ds_params_instance = None
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if args.data_store == "s3":
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ds_params = DataStoreParameters("s3", "", "")
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ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=args.s3_end_point, bucket_name=args.s3_bucket_name,
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creds_file=args.s3_creds_file, checkpoint_dir=ckpt_inside_container)
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worker_run_type_params = RunTypeParameters(args.image, rollout_command, run_type=str(RunType.ROLLOUT_WORKER), num_replicas=args.num_workers)
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trainer_run_type_params = RunTypeParameters(args.image, trainer_command, run_type=str(RunType.TRAINER))
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worker_run_type_params = RunTypeParameters(args.image, rollout_command, run_type=str(RunType.ROLLOUT_WORKER), num_replicas=args.num_workers)
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trainer_run_type_params = RunTypeParameters(args.image, trainer_command, run_type=str(RunType.TRAINER))
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orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params],
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kubeconfig='~/.kube/config',
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memory_backend_parameters=memory_backend_params,
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data_store_params=ds_params_instance)
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orchestrator = Kubernetes(orchestration_params)
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if not orchestrator.setup():
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print("Could not setup.")
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return
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orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params],
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kubeconfig='~/.kube/config',
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memory_backend_parameters=memory_backend_params,
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data_store_params=ds_params_instance)
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orchestrator = Kubernetes(orchestration_params)
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if not orchestrator.setup():
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print("Could not setup.")
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return
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if orchestrator.deploy_trainer():
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print("Successfully deployed trainer.")
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else:
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print("Could not deploy trainer.")
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return
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if orchestrator.deploy_trainer():
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print("Successfully deployed trainer.")
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else:
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print("Could not deploy trainer.")
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return
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if orchestrator.deploy_worker():
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print("Successfully deployed rollout worker(s).")
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else:
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print("Could not deploy rollout worker(s).")
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return
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if orchestrator.deploy_worker():
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print("Successfully deployed rollout worker(s).")
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else:
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print("Could not deploy rollout worker(s).")
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return
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try:
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orchestrator.trainer_logs()
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except KeyboardInterrupt:
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pass
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try:
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orchestrator.trainer_logs()
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except KeyboardInterrupt:
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pass
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orchestrator.undeploy()
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orchestrator.undeploy()
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class CoachLauncher(object):
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@@ -192,7 +192,6 @@ class CoachLauncher(object):
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graph_manager = self.get_graph_manager_from_args(args)
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self.run_graph_manager(graph_manager, args)
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def get_graph_manager_from_args(self, args: argparse.Namespace) -> 'GraphManager':
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"""
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Return the graph manager according to the command line arguments given by the user.
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@@ -251,7 +250,6 @@ class CoachLauncher(object):
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return graph_manager
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def display_all_presets_and_exit(self):
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# list available presets
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screen.log_title("Available Presets:")
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@@ -259,7 +257,6 @@ class CoachLauncher(object):
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print(preset)
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sys.exit(0)
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def expand_preset(self, preset):
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"""
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Replace a short preset name with the full python path, and verify that it can be imported.
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@@ -287,7 +284,6 @@ class CoachLauncher(object):
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return preset
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def get_config_args(self, parser: argparse.ArgumentParser) -> argparse.Namespace:
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"""
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Returns a Namespace object with all the user-specified configuration options needed to launch.
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@@ -317,7 +313,6 @@ class CoachLauncher(object):
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if args.list:
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self.display_all_presets_and_exit()
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# Read args from config file for distributed Coach.
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if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
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coach_config = ConfigParser({
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@@ -401,7 +396,6 @@ class CoachLauncher(object):
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return args
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def get_argument_parser(self) -> argparse.ArgumentParser:
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"""
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This returns an ArgumentParser object which defines the set of options that customers are expected to supply in order
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@@ -545,7 +539,6 @@ class CoachLauncher(object):
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return parser
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def run_graph_manager(self, graph_manager: 'GraphManager', args: argparse.Namespace):
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if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type:
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screen.error("{} algorithm is not supported using distributed Coach.".format(graph_manager.agent_params.algorithm))
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@@ -581,7 +574,6 @@ class CoachLauncher(object):
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else:
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self.start_multi_threaded(graph_manager, args)
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def start_single_threaded(self, graph_manager: 'GraphManager', args: argparse.Namespace):
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# Start the training or evaluation
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task_parameters = TaskParameters(
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@@ -598,7 +590,6 @@ class CoachLauncher(object):
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start_graph(graph_manager=graph_manager, task_parameters=task_parameters)
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def start_multi_threaded(self, graph_manager: 'GraphManager', args: argparse.Namespace):
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total_tasks = args.num_workers
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if args.evaluation_worker:
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