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Integrate coach.py params with distributed Coach. (#42)

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* Integrate coach.py params with distributed Coach.
* Minor improvements
- Use enums instead of constants.
- Reduce code duplication.
- Ask experiment name with timeout.
This commit is contained in:
Balaji Subramaniam
2018-11-05 09:33:30 -08:00
committed by GitHub
parent 95b4fc6888
commit 7e7006305a
13 changed files with 263 additions and 285 deletions
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64
rl_coach/training_worker.py
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@@ -1,24 +1,18 @@
"""
"""
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.base_parameters import TaskParameters, DistributedCoachSynchronizationType
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, policy_type):
def training_worker(graph_manager, checkpoint_dir):
"""
restore a checkpoint then perform rollouts using the restored model
"""
@@ -49,55 +43,7 @@ def training_worker(graph_manager, checkpoint_dir, policy_type):
graph_manager.evaluate(graph_manager.evaluation_steps)
eval_offset += 1
if policy_type == 'ON':
if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC:
graph_manager.save_checkpoint()
else:
graph_manager.occasionally_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('--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)
parser.add_argument('--policy-type',
help="(string) The type of policy: OFF/ON",
type=str,
default='OFF')
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,
policy_type=args.policy_type
)
if __name__ == '__main__':
main()