mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
Refactor launcher to be object-oriented (#63)
* Import of annoy library uses failed_import mechanism.
This commit is contained in:
Leo Dirac
@@ -51,200 +51,6 @@ if len(set(failed_imports)) > 0:
|
||||
screen.warning("Warning: failed to import the following packages - {}".format(', '.join(set(failed_imports))))
|
||||
|
||||
|
||||
def get_graph_manager_from_args(args: argparse.Namespace) -> 'GraphManager':
|
||||
"""
|
||||
Return the graph manager according to the command line arguments given by the user
|
||||
:param args: the arguments given by the user
|
||||
:return: the updated graph manager
|
||||
"""
|
||||
|
||||
graph_manager = None
|
||||
|
||||
# if a preset was given we will load the graph manager for the preset
|
||||
if args.preset is not None:
|
||||
graph_manager = short_dynamic_import(args.preset, ignore_module_case=True)
|
||||
|
||||
# for human play we need to create a custom graph manager
|
||||
if args.play:
|
||||
env_params = short_dynamic_import(args.environment_type, ignore_module_case=True)()
|
||||
env_params.human_control = True
|
||||
schedule_params = HumanPlayScheduleParameters()
|
||||
graph_manager = BasicRLGraphManager(HumanAgentParameters(), env_params, schedule_params, VisualizationParameters())
|
||||
|
||||
# Set framework
|
||||
# Note: Some graph managers (e.g. HAC preset) create multiple agents and the attribute is called agents_params
|
||||
if hasattr(graph_manager, 'agent_params'):
|
||||
for network_parameters in graph_manager.agent_params.network_wrappers.values():
|
||||
network_parameters.framework = args.framework
|
||||
elif hasattr(graph_manager, 'agents_params'):
|
||||
for ap in graph_manager.agents_params:
|
||||
for network_parameters in ap.network_wrappers.values():
|
||||
network_parameters.framework = args.framework
|
||||
|
||||
if args.level:
|
||||
if isinstance(graph_manager.env_params.level, SingleLevelSelection):
|
||||
graph_manager.env_params.level.select(args.level)
|
||||
else:
|
||||
graph_manager.env_params.level = args.level
|
||||
|
||||
# set the seed for the environment
|
||||
if args.seed is not None:
|
||||
graph_manager.env_params.seed = args.seed
|
||||
|
||||
# visualization
|
||||
graph_manager.visualization_parameters.dump_gifs = graph_manager.visualization_parameters.dump_gifs or args.dump_gifs
|
||||
graph_manager.visualization_parameters.dump_mp4 = graph_manager.visualization_parameters.dump_mp4 or args.dump_mp4
|
||||
graph_manager.visualization_parameters.render = args.render
|
||||
graph_manager.visualization_parameters.tensorboard = args.tensorboard
|
||||
graph_manager.visualization_parameters.print_networks_summary = args.print_networks_summary
|
||||
|
||||
# update the custom parameters
|
||||
if args.custom_parameter is not None:
|
||||
unstripped_key_value_pairs = [pair.split('=') for pair in args.custom_parameter.split(';')]
|
||||
stripped_key_value_pairs = [tuple([pair[0].strip(), pair[1].strip()]) for pair in
|
||||
unstripped_key_value_pairs if len(pair) == 2]
|
||||
|
||||
# load custom parameters into run_dict
|
||||
for key, value in stripped_key_value_pairs:
|
||||
exec("graph_manager.{}={}".format(key, value))
|
||||
|
||||
return graph_manager
|
||||
|
||||
|
||||
def display_all_presets_and_exit():
|
||||
# list available presets
|
||||
screen.log_title("Available Presets:")
|
||||
for preset in sorted(list_all_presets()):
|
||||
print(preset)
|
||||
sys.exit(0)
|
||||
|
||||
|
||||
def expand_preset(preset):
|
||||
if preset.lower() in [p.lower() for p in list_all_presets()]:
|
||||
preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset))
|
||||
else:
|
||||
preset = "{}".format(preset)
|
||||
# if a graph manager variable was not specified, try the default of :graph_manager
|
||||
if len(preset.split(":")) == 1:
|
||||
preset += ":graph_manager"
|
||||
|
||||
# verify that the preset exists
|
||||
preset_path = preset.split(":")[0]
|
||||
if not os.path.exists(preset_path):
|
||||
screen.error("The given preset ({}) cannot be found.".format(preset))
|
||||
|
||||
# verify that the preset can be instantiated
|
||||
try:
|
||||
short_dynamic_import(preset, ignore_module_case=True)
|
||||
except TypeError as e:
|
||||
traceback.print_exc()
|
||||
screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated."
|
||||
.format(preset))
|
||||
|
||||
return preset
|
||||
|
||||
|
||||
def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace:
|
||||
"""
|
||||
Parse the arguments that the user entered
|
||||
:param parser: the argparse command line parser
|
||||
:return: the parsed arguments
|
||||
"""
|
||||
args = parser.parse_args()
|
||||
|
||||
# if no arg is given
|
||||
if len(sys.argv) == 1:
|
||||
parser.print_help()
|
||||
sys.exit(0)
|
||||
|
||||
# list available presets
|
||||
if args.list:
|
||||
display_all_presets_and_exit()
|
||||
|
||||
# Read args from config file for distributed Coach.
|
||||
if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
|
||||
coach_config = ConfigParser({
|
||||
'image': '',
|
||||
'memory_backend': 'redispubsub',
|
||||
'data_store': 's3',
|
||||
's3_end_point': 's3.amazonaws.com',
|
||||
's3_bucket_name': '',
|
||||
's3_creds_file': ''
|
||||
})
|
||||
try:
|
||||
coach_config.read(args.distributed_coach_config_path)
|
||||
args.image = coach_config.get('coach', 'image')
|
||||
args.memory_backend = coach_config.get('coach', 'memory_backend')
|
||||
args.data_store = coach_config.get('coach', 'data_store')
|
||||
args.s3_end_point = coach_config.get('coach', 's3_end_point')
|
||||
args.s3_bucket_name = coach_config.get('coach', 's3_bucket_name')
|
||||
args.s3_creds_file = coach_config.get('coach', 's3_creds_file')
|
||||
except Error as e:
|
||||
screen.error("Error when reading distributed Coach config file: {}".format(e))
|
||||
|
||||
if args.image == '':
|
||||
screen.error("Image cannot be empty.")
|
||||
|
||||
data_store_choices = ['s3']
|
||||
if args.data_store not in data_store_choices:
|
||||
screen.warning("{} data store is unsupported.".format(args.data_store))
|
||||
screen.error("Supported data stores are {}.".format(data_store_choices))
|
||||
|
||||
memory_backend_choices = ['redispubsub']
|
||||
if args.memory_backend not in memory_backend_choices:
|
||||
screen.warning("{} memory backend is not supported.".format(args.memory_backend))
|
||||
screen.error("Supported memory backends are {}.".format(memory_backend_choices))
|
||||
|
||||
if args.s3_bucket_name == '':
|
||||
screen.error("S3 bucket name cannot be empty.")
|
||||
|
||||
if args.s3_creds_file == '':
|
||||
args.s3_creds_file = None
|
||||
|
||||
if args.play and args.distributed_coach:
|
||||
screen.error("Playing is not supported in distributed Coach.")
|
||||
|
||||
# replace a short preset name with the full path
|
||||
if args.preset is not None:
|
||||
args.preset = expand_preset(args.preset)
|
||||
|
||||
# validate the checkpoints args
|
||||
if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir):
|
||||
screen.error("The requested checkpoint folder to load from does not exist.")
|
||||
|
||||
# no preset was given. check if the user requested to play some environment on its own
|
||||
if args.preset is None and args.play and not args.environment_type:
|
||||
screen.error('When no preset is given for Coach to run, and the user requests human control over '
|
||||
'the environment, the user is expected to input the desired environment_type and level.'
|
||||
'\nAt least one of these parameters was not given.')
|
||||
elif args.preset and args.play:
|
||||
screen.error("Both the --preset and the --play flags were set. These flags can not be used together. "
|
||||
"For human control, please use the --play flag together with the environment type flag (-et)")
|
||||
elif args.preset is None and not args.play:
|
||||
screen.error("Please choose a preset using the -p flag or use the --play flag together with choosing an "
|
||||
"environment type (-et) in order to play the game.")
|
||||
|
||||
# get experiment name and path
|
||||
args.experiment_name = logger.get_experiment_name(args.experiment_name)
|
||||
args.experiment_path = logger.get_experiment_path(args.experiment_name)
|
||||
|
||||
if args.play and args.num_workers > 1:
|
||||
screen.warning("Playing the game as a human is only available with a single worker. "
|
||||
"The number of workers will be reduced to 1")
|
||||
args.num_workers = 1
|
||||
|
||||
args.framework = Frameworks[args.framework.lower()]
|
||||
|
||||
# checkpoints
|
||||
args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None
|
||||
|
||||
if args.export_onnx_graph and not args.checkpoint_save_secs:
|
||||
screen.warning("Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. "
|
||||
"The --export_onnx_graph will have no effect.")
|
||||
|
||||
return args
|
||||
|
||||
|
||||
def add_items_to_dict(target_dict, source_dict):
|
||||
updated_task_parameters = copy.copy(source_dict)
|
||||
updated_task_parameters.update(target_dict)
|
||||
@@ -263,6 +69,10 @@ def open_dashboard(experiment_path):
|
||||
|
||||
|
||||
def start_graph(graph_manager: 'GraphManager', task_parameters: 'TaskParameters'):
|
||||
"""
|
||||
Runs the graph_manager using the configured task_parameters.
|
||||
This stand-alone method is a convenience for multiprocessing.
|
||||
"""
|
||||
graph_manager.create_graph(task_parameters)
|
||||
|
||||
# let the adventure begin
|
||||
@@ -360,7 +170,243 @@ def handle_distributed_coach_orchestrator(graph_manager, args):
|
||||
orchestrator.undeploy()
|
||||
|
||||
|
||||
def main():
|
||||
class CoachLauncher(object):
|
||||
"""
|
||||
This class is responsible for gathering all user-specified configuration options, parsing them,
|
||||
instantiating a GraphManager and then starting that GraphManager with either improve() or evaluate().
|
||||
This class is also responsible for launching multiple processes.
|
||||
It is structured so that it can be sub-classed to provide alternate mechanisms to configure and launch
|
||||
Coach jobs.
|
||||
|
||||
The key entry-point for this class is the .launch() method which is expected to be called from __main__
|
||||
and handle absolutely everything for a job.
|
||||
"""
|
||||
|
||||
def launch(self):
|
||||
"""
|
||||
Main entry point for the class, and the standard way to run coach from the command line.
|
||||
Parses command-line arguments through argparse, instantiates a GraphManager and then runs it.
|
||||
"""
|
||||
parser = self.get_argument_parser()
|
||||
args = self.get_config_args(parser)
|
||||
graph_manager = self.get_graph_manager_from_args(args)
|
||||
self.run_graph_manager(graph_manager, args)
|
||||
|
||||
|
||||
def get_graph_manager_from_args(self, args: argparse.Namespace) -> 'GraphManager':
|
||||
"""
|
||||
Return the graph manager according to the command line arguments given by the user.
|
||||
:param args: the arguments given by the user
|
||||
:return: the graph manager, not bound to task_parameters yet.
|
||||
"""
|
||||
graph_manager = None
|
||||
|
||||
# if a preset was given we will load the graph manager for the preset
|
||||
if args.preset is not None:
|
||||
graph_manager = short_dynamic_import(args.preset, ignore_module_case=True)
|
||||
|
||||
# for human play we need to create a custom graph manager
|
||||
if args.play:
|
||||
env_params = short_dynamic_import(args.environment_type, ignore_module_case=True)()
|
||||
env_params.human_control = True
|
||||
schedule_params = HumanPlayScheduleParameters()
|
||||
graph_manager = BasicRLGraphManager(HumanAgentParameters(), env_params, schedule_params, VisualizationParameters())
|
||||
|
||||
# Set framework
|
||||
# Note: Some graph managers (e.g. HAC preset) create multiple agents and the attribute is called agents_params
|
||||
if hasattr(graph_manager, 'agent_params'):
|
||||
for network_parameters in graph_manager.agent_params.network_wrappers.values():
|
||||
network_parameters.framework = args.framework
|
||||
elif hasattr(graph_manager, 'agents_params'):
|
||||
for ap in graph_manager.agents_params:
|
||||
for network_parameters in ap.network_wrappers.values():
|
||||
network_parameters.framework = args.framework
|
||||
|
||||
if args.level:
|
||||
if isinstance(graph_manager.env_params.level, SingleLevelSelection):
|
||||
graph_manager.env_params.level.select(args.level)
|
||||
else:
|
||||
graph_manager.env_params.level = args.level
|
||||
|
||||
# set the seed for the environment
|
||||
if args.seed is not None:
|
||||
graph_manager.env_params.seed = args.seed
|
||||
|
||||
# visualization
|
||||
graph_manager.visualization_parameters.dump_gifs = graph_manager.visualization_parameters.dump_gifs or args.dump_gifs
|
||||
graph_manager.visualization_parameters.dump_mp4 = graph_manager.visualization_parameters.dump_mp4 or args.dump_mp4
|
||||
graph_manager.visualization_parameters.render = args.render
|
||||
graph_manager.visualization_parameters.tensorboard = args.tensorboard
|
||||
graph_manager.visualization_parameters.print_networks_summary = args.print_networks_summary
|
||||
|
||||
# update the custom parameters
|
||||
if args.custom_parameter is not None:
|
||||
unstripped_key_value_pairs = [pair.split('=') for pair in args.custom_parameter.split(';')]
|
||||
stripped_key_value_pairs = [tuple([pair[0].strip(), pair[1].strip()]) for pair in
|
||||
unstripped_key_value_pairs if len(pair) == 2]
|
||||
|
||||
# load custom parameters into run_dict
|
||||
for key, value in stripped_key_value_pairs:
|
||||
exec("graph_manager.{}={}".format(key, value))
|
||||
|
||||
return graph_manager
|
||||
|
||||
|
||||
def display_all_presets_and_exit(self):
|
||||
# list available presets
|
||||
screen.log_title("Available Presets:")
|
||||
for preset in sorted(list_all_presets()):
|
||||
print(preset)
|
||||
sys.exit(0)
|
||||
|
||||
|
||||
def expand_preset(self, preset):
|
||||
"""
|
||||
Replace a short preset name with the full python path, and verify that it can be imported.
|
||||
"""
|
||||
if preset.lower() in [p.lower() for p in list_all_presets()]:
|
||||
preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset))
|
||||
else:
|
||||
preset = "{}".format(preset)
|
||||
# if a graph manager variable was not specified, try the default of :graph_manager
|
||||
if len(preset.split(":")) == 1:
|
||||
preset += ":graph_manager"
|
||||
|
||||
# verify that the preset exists
|
||||
preset_path = preset.split(":")[0]
|
||||
if not os.path.exists(preset_path):
|
||||
screen.error("The given preset ({}) cannot be found.".format(preset))
|
||||
|
||||
# verify that the preset can be instantiated
|
||||
try:
|
||||
short_dynamic_import(preset, ignore_module_case=True)
|
||||
except TypeError as e:
|
||||
traceback.print_exc()
|
||||
screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated."
|
||||
.format(preset))
|
||||
|
||||
return preset
|
||||
|
||||
|
||||
def get_config_args(self, parser: argparse.ArgumentParser) -> argparse.Namespace:
|
||||
"""
|
||||
Returns a Namespace object with all the user-specified configuration options needed to launch.
|
||||
This implementation uses argparse to take arguments from the CLI, but this can be over-ridden by
|
||||
another method that gets its configuration from elsewhere. An equivalent method however must
|
||||
return an identically structured Namespace object, which conforms to the structure defined by
|
||||
get_argument_parser.
|
||||
|
||||
This method parses the arguments that the user entered, does some basic validation, and
|
||||
modification of user-specified values in short form to be more explicit.
|
||||
|
||||
:param parser: a parser object which implicitly defines the format of the Namespace that
|
||||
is expected to be returned.
|
||||
:return: the parsed arguments as a Namespace
|
||||
"""
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.nocolor:
|
||||
screen.set_use_colors(False)
|
||||
|
||||
# if no arg is given
|
||||
if len(sys.argv) == 1:
|
||||
parser.print_help()
|
||||
sys.exit(0)
|
||||
|
||||
# list available presets
|
||||
if args.list:
|
||||
self.display_all_presets_and_exit()
|
||||
|
||||
|
||||
# Read args from config file for distributed Coach.
|
||||
if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
|
||||
coach_config = ConfigParser({
|
||||
'image': '',
|
||||
'memory_backend': 'redispubsub',
|
||||
'data_store': 's3',
|
||||
's3_end_point': 's3.amazonaws.com',
|
||||
's3_bucket_name': '',
|
||||
's3_creds_file': ''
|
||||
})
|
||||
try:
|
||||
coach_config.read(args.distributed_coach_config_path)
|
||||
args.image = coach_config.get('coach', 'image')
|
||||
args.memory_backend = coach_config.get('coach', 'memory_backend')
|
||||
args.data_store = coach_config.get('coach', 'data_store')
|
||||
args.s3_end_point = coach_config.get('coach', 's3_end_point')
|
||||
args.s3_bucket_name = coach_config.get('coach', 's3_bucket_name')
|
||||
args.s3_creds_file = coach_config.get('coach', 's3_creds_file')
|
||||
except Error as e:
|
||||
screen.error("Error when reading distributed Coach config file: {}".format(e))
|
||||
|
||||
if args.image == '':
|
||||
screen.error("Image cannot be empty.")
|
||||
|
||||
data_store_choices = ['s3']
|
||||
if args.data_store not in data_store_choices:
|
||||
screen.warning("{} data store is unsupported.".format(args.data_store))
|
||||
screen.error("Supported data stores are {}.".format(data_store_choices))
|
||||
|
||||
memory_backend_choices = ['redispubsub']
|
||||
if args.memory_backend not in memory_backend_choices:
|
||||
screen.warning("{} memory backend is not supported.".format(args.memory_backend))
|
||||
screen.error("Supported memory backends are {}.".format(memory_backend_choices))
|
||||
|
||||
if args.s3_bucket_name == '':
|
||||
screen.error("S3 bucket name cannot be empty.")
|
||||
|
||||
if args.s3_creds_file == '':
|
||||
args.s3_creds_file = None
|
||||
|
||||
if args.play and args.distributed_coach:
|
||||
screen.error("Playing is not supported in distributed Coach.")
|
||||
|
||||
# replace a short preset name with the full path
|
||||
if args.preset is not None:
|
||||
args.preset = self.expand_preset(args.preset)
|
||||
|
||||
# validate the checkpoints args
|
||||
if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir):
|
||||
screen.error("The requested checkpoint folder to load from does not exist.")
|
||||
|
||||
# no preset was given. check if the user requested to play some environment on its own
|
||||
if args.preset is None and args.play and not args.environment_type:
|
||||
screen.error('When no preset is given for Coach to run, and the user requests human control over '
|
||||
'the environment, the user is expected to input the desired environment_type and level.'
|
||||
'\nAt least one of these parameters was not given.')
|
||||
elif args.preset and args.play:
|
||||
screen.error("Both the --preset and the --play flags were set. These flags can not be used together. "
|
||||
"For human control, please use the --play flag together with the environment type flag (-et)")
|
||||
elif args.preset is None and not args.play:
|
||||
screen.error("Please choose a preset using the -p flag or use the --play flag together with choosing an "
|
||||
"environment type (-et) in order to play the game.")
|
||||
|
||||
# get experiment name and path
|
||||
args.experiment_name = logger.get_experiment_name(args.experiment_name)
|
||||
args.experiment_path = logger.get_experiment_path(args.experiment_name)
|
||||
|
||||
if args.play and args.num_workers > 1:
|
||||
screen.warning("Playing the game as a human is only available with a single worker. "
|
||||
"The number of workers will be reduced to 1")
|
||||
args.num_workers = 1
|
||||
|
||||
args.framework = Frameworks[args.framework.lower()]
|
||||
|
||||
# checkpoints
|
||||
args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None
|
||||
|
||||
if args.export_onnx_graph and not args.checkpoint_save_secs:
|
||||
screen.warning("Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. "
|
||||
"The --export_onnx_graph will have no effect.")
|
||||
|
||||
return args
|
||||
|
||||
|
||||
def get_argument_parser(self) -> argparse.ArgumentParser:
|
||||
"""
|
||||
This returns an ArgumentParser object which defines the set of options that customers are expected to supply in order
|
||||
to launch a coach job.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-p', '--preset',
|
||||
help="(string) Name of a preset to run (class name from the 'presets' directory.)",
|
||||
@@ -429,6 +475,14 @@ def main():
|
||||
help="(string) Choose an environment type class to override on top of the selected preset.",
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-ept', '--exploration_policy_type',
|
||||
help="(string) Choose an exploration policy type class to override on top of the selected "
|
||||
"preset."
|
||||
"If no preset is defined, a preset can be set from the command-line by combining settings "
|
||||
"which are set by using --agent_type, --experiment_type, --environemnt_type"
|
||||
,
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-lvl', '--level',
|
||||
help="(string) Choose the level that will be played in the environment that was selected."
|
||||
"This value will override the level parameter in the environment class."
|
||||
@@ -489,15 +543,12 @@ def main():
|
||||
default=RunType.ORCHESTRATOR,
|
||||
choices=list(RunType))
|
||||
|
||||
args = parse_arguments(parser)
|
||||
return parser
|
||||
|
||||
if args.nocolor:
|
||||
screen.set_use_colors(False)
|
||||
|
||||
graph_manager = get_graph_manager_from_args(args)
|
||||
|
||||
def run_graph_manager(self, graph_manager: 'GraphManager', args: argparse.Namespace):
|
||||
if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type:
|
||||
screen.error("{} preset is not supported using distributed Coach.".format(args.preset))
|
||||
screen.error("{} algorithm is not supported using distributed Coach.".format(graph_manager.agent_params.algorithm))
|
||||
|
||||
# Intel optimized TF seems to run significantly faster when limiting to a single OMP thread.
|
||||
# This will not affect GPU runs.
|
||||
@@ -526,6 +577,12 @@ def main():
|
||||
|
||||
# Single-threaded runs
|
||||
if args.num_workers == 1:
|
||||
self.start_single_threaded(graph_manager, args)
|
||||
else:
|
||||
self.start_multi_threaded(graph_manager, args)
|
||||
|
||||
|
||||
def start_single_threaded(self, graph_manager: 'GraphManager', args: argparse.Namespace):
|
||||
# Start the training or evaluation
|
||||
task_parameters = TaskParameters(
|
||||
framework_type=args.framework,
|
||||
@@ -541,8 +598,8 @@ def main():
|
||||
|
||||
start_graph(graph_manager=graph_manager, task_parameters=task_parameters)
|
||||
|
||||
# Multi-threaded runs
|
||||
else:
|
||||
|
||||
def start_multi_threaded(self, graph_manager: 'GraphManager', args: argparse.Namespace):
|
||||
total_tasks = args.num_workers
|
||||
if args.evaluation_worker:
|
||||
total_tasks += 1
|
||||
@@ -578,7 +635,6 @@ def main():
|
||||
checkpoint_save_dir=args.checkpoint_save_dir,
|
||||
export_onnx_graph=args.export_onnx_graph
|
||||
)
|
||||
|
||||
# we assume that only the evaluation workers are rendering
|
||||
graph_manager.visualization_parameters.render = args.render and evaluation_worker
|
||||
p = Process(target=start_graph, args=(graph_manager, task_parameters))
|
||||
@@ -608,4 +664,5 @@ def main():
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
launcher = CoachLauncher()
|
||||
launcher.launch()
|
||||
|
||||
@@ -18,7 +18,12 @@ import os
|
||||
import pickle
|
||||
|
||||
import numpy as np
|
||||
try:
|
||||
import annoy
|
||||
from annoy import AnnoyIndex
|
||||
except ImportError:
|
||||
from rl_coach.logger import failed_imports
|
||||
failed_imports.append("annoy")
|
||||
|
||||
|
||||
class AnnoyDictionary(object):
|
||||
|
||||
Reference in New Issue
Block a user