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coach/rl_coach/training_worker.py
Balaji Subramaniam 844a5af831 Make distributed coach work end-to-end.
- With data store, memory backend and orchestrator interfaces.
2018-10-23 16:54:43 -04:00

93 lines
3.4 KiB
Python

"""
"""
import argparse
import time
import json
from threading import Thread
from rl_coach.base_parameters import TaskParameters
from rl_coach.coach import expand_preset
from rl_coach import core_types
from rl_coach.utils import short_dynamic_import
from rl_coach.memories.backend.memory_impl import construct_memory_params
from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params
def data_store_ckpt_save(data_store):
while True:
data_store.save_to_store()
time.sleep(10)
def training_worker(graph_manager, checkpoint_dir):
"""
restore a checkpoint then perform rollouts using the restored model
"""
# initialize graph
task_parameters = TaskParameters()
task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir
graph_manager.create_graph(task_parameters)
# save randomly initialized graph
graph_manager.save_checkpoint()
# training loop
while True:
graph_manager.phase = core_types.RunPhase.TRAIN
graph_manager.train(core_types.TrainingSteps(1))
graph_manager.phase = core_types.RunPhase.UNDEFINED
graph_manager.evaluate(graph_manager.evaluation_steps)
graph_manager.save_checkpoint()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--preset',
help="(string) Name of a preset to run (class name from the 'presets' directory.)",
type=str,
required=True)
parser.add_argument('--checkpoint_dir',
help='(string) Path to a folder containing a checkpoint to write the model to.',
type=str,
default='/checkpoint')
parser.add_argument('-r', '--redis_ip',
help="(string) IP or host for the redis server",
default='localhost',
type=str)
parser.add_argument('-rp', '--redis_port',
help="(int) Port of the redis server",
default=6379,
type=int)
parser.add_argument('--memory_backend_params',
help="(string) JSON string of the memory backend params",
type=str)
parser.add_argument('--data_store_params',
help="(string) JSON string of the data store params",
type=str)
args = parser.parse_args()
graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True)
if args.memory_backend_params:
args.memory_backend_params = json.loads(args.memory_backend_params)
args.memory_backend_params['run_type'] = 'trainer'
graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params))
if args.data_store_params:
data_store_params = construct_data_store_params(json.loads(args.data_store_params))
data_store_params.checkpoint_dir = args.checkpoint_dir
graph_manager.data_store_params = data_store_params
# data_store = get_data_store(data_store_params)
# thread = Thread(target = data_store_ckpt_save, args = [data_store])
# thread.start()
training_worker(
graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
)
if __name__ == '__main__':
main()