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Checkpoint and evaluation optimizations

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This commit is contained in:
Ajay Deshpande
2018-10-08 17:49:40 -07:00
committed by zach dwiel
parent b285a02023
commit fb1039fcb5
4 changed files with 61 additions and 26 deletions
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1
rl_coach/orchestrators/kubernetes_orchestrator.py
View File

@@ -179,6 +179,7 @@ class Kubernetes(Deploy):
worker_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)]
worker_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)]
worker_params.command += ['--num-workers', worker_params.num_replicas]
name = "{}-{}".format(worker_params.run_type, uuid.uuid4())

13
rl_coach/orchestrators/start_training.py
View File

@@ -8,9 +8,10 @@ from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters
def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str=None, nfs_path: str=None,
memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None):
rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset]
training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset]
memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None,
policy_type: str="OFF"):
rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset, '--policy-type', policy_type]
training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset, '--policy-type', policy_type]
memory_backend_params = None
if memory_backend == "redispubsub":
@@ -95,6 +96,10 @@ if __name__ == '__main__':
type=int,
required=False,
default=1)
parser.add_argument('--policy-type',
help="(string) The type of policy: OFF/ON",
type=str,
default='OFF')
# parser.add_argument('--checkpoint_dir',
# help='(string) Path to a folder containing a checkpoint to write the model to.',
@@ -104,4 +109,4 @@ if __name__ == '__main__':
main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path,
memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point,
s3_bucket_name=args.s3_bucket_name, num_workers=args.num_workers)
s3_bucket_name=args.s3_bucket_name, num_workers=args.num_workers, policy_type=args.policy_type)

45
rl_coach/rollout_worker.py
View File

@@ -11,6 +11,7 @@ import argparse
import time
import os
import json
import math
from threading import Thread
@@ -69,20 +70,16 @@ def data_store_ckpt_load(data_store):
time.sleep(10)
def check_for_new_checkpoint(checkpoint_dir, last_checkpoint):
def get_latest_checkpoint(checkpoint_dir):
if os.path.exists(os.path.join(checkpoint_dir, 'checkpoint')):
ckpt = CheckpointState()
contents = open(os.path.join(checkpoint_dir, 'checkpoint'), 'r').read()
text_format.Merge(contents, ckpt)
rel_path = os.path.relpath(ckpt.model_checkpoint_path, checkpoint_dir)
current_checkpoint = int(rel_path.split('_Step')[0])
if current_checkpoint > last_checkpoint:
last_checkpoint = current_checkpoint
return last_checkpoint
return int(rel_path.split('_Step')[0])
def rollout_worker(graph_manager, checkpoint_dir, data_store):
def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers, policy_type):
"""
wait for first checkpoint then perform rollouts using the model
"""
@@ -98,22 +95,28 @@ def rollout_worker(graph_manager, checkpoint_dir, data_store):
last_checkpoint = 0
act_steps = graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation
print(act_steps, graph_manager.improve_steps.num_steps)
act_steps = math.ceil((graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation)/num_workers)
for i in range(int(graph_manager.improve_steps.num_steps/act_steps)):
graph_manager.act(EnvironmentSteps(num_steps=act_steps))
new_checkpoint = last_checkpoint + 1
while last_checkpoint < new_checkpoint:
new_checkpoint = get_latest_checkpoint(checkpoint_dir)
if policy_type == 'ON':
while new_checkpoint < last_checkpoint + 1:
if data_store:
data_store.load_from_store()
last_checkpoint = check_for_new_checkpoint(checkpoint_dir, last_checkpoint)
new_checkpoint = get_latest_checkpoint(checkpoint_dir)
graph_manager.restore_checkpoint()
if policy_type == "OFF":
if new_checkpoint > last_checkpoint:
graph_manager.restore_checkpoint()
last_checkpoint = new_checkpoint
graph_manager.restore_checkpoint()
graph_manager.phase = RunPhase.UNDEFINED
@@ -134,6 +137,14 @@ def main():
parser.add_argument('--data-store-params',
help="(string) JSON string of the data store params",
type=str)
parser.add_argument('--num-workers',
help="(int) The number of workers started in this pool",
type=int,
default=1)
parser.add_argument('--policy-type',
help="(string) The type of policy: OFF/ON",
type=str,
default='OFF')
args = parser.parse_args()
@@ -142,9 +153,7 @@ def main():
data_store = None
if args.memory_backend_params:
args.memory_backend_params = json.loads(args.memory_backend_params)
print(args.memory_backend_params)
args.memory_backend_params['run_type'] = 'worker'
print(construct_memory_params(args.memory_backend_params))
graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params))
if args.data_store_params:
@@ -159,7 +168,9 @@ def main():
rollout_worker(
graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
data_store=data_store
data_store=data_store,
num_workers=args.num_workers,
policy_type=args.policy_type
)
if __name__ == '__main__':

20
rl_coach/training_worker.py
View File

@@ -18,13 +18,14 @@ def data_store_ckpt_save(data_store):
data_store.save_to_store()
time.sleep(10)
def training_worker(graph_manager, checkpoint_dir):
def training_worker(graph_manager, checkpoint_dir, policy_type):
"""
restore a checkpoint then perform rollouts using the restored model
"""
# initialize graph
task_parameters = TaskParameters()
task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir
task_parameters.__dict__['save_checkpoint_secs'] = 60
graph_manager.create_graph(task_parameters)
# save randomly initialized graph
@@ -32,14 +33,26 @@ def training_worker(graph_manager, checkpoint_dir):
# training loop
steps = 0
# evaluation offset
eval_offset = 1
while(steps < graph_manager.improve_steps.num_steps):
if graph_manager.should_train():
steps += 1
graph_manager.phase = core_types.RunPhase.TRAIN
graph_manager.train(core_types.TrainingSteps(1))
graph_manager.phase = core_types.RunPhase.UNDEFINED
if steps * graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps > graph_manager.steps_between_evaluation_periods.num_steps * eval_offset:
graph_manager.evaluate(graph_manager.evaluation_steps)
eval_offset += 1
if policy_type == 'ON':
graph_manager.save_checkpoint()
else:
graph_manager.occasionally_save_checkpoint()
def main():
@@ -58,6 +71,10 @@ def main():
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)
@@ -78,6 +95,7 @@ def main():
training_worker(
graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
policy_type=args.policy_type
)