Revert [rllib] Reserve CPUs for replay actors in apex (#4404)

* Revert "[rllib] Reserve CPUs for replay actors in apex (#4217)"

This reverts commit 2781d74680.

* comment
This commit is contained in:
Eric Liang
2019-03-19 09:58:45 -07:00
committed by GitHub
parent c93eb126ec
commit c6f15a0057
3 changed files with 4 additions and 19 deletions
-16
View File
@@ -12,7 +12,6 @@ from ray.rllib.agents.dqn.dqn_policy_graph import DQNPolicyGraph
from ray.rllib.evaluation.metrics import collect_metrics
from ray.rllib.utils.annotations import override
from ray.rllib.utils.schedules import ConstantSchedule, LinearSchedule
from ray.tune.trial import Resources
logger = logging.getLogger(__name__)
@@ -142,21 +141,6 @@ class DQNAgent(Agent):
_policy_graph = DQNPolicyGraph
_optimizer_shared_configs = OPTIMIZER_SHARED_CONFIGS
@classmethod
@override(Agent)
def default_resource_request(cls, config):
cf = dict(cls._default_config, **config)
Agent._validate_config(cf)
if cf["optimizer_class"] == "AsyncReplayOptimizer":
extra = cf["optimizer"]["num_replay_buffer_shards"]
else:
extra = 0
return Resources(
cpu=cf["num_cpus_for_driver"],
gpu=cf["num_gpus"],
extra_cpu=cf["num_cpus_per_worker"] * cf["num_workers"] + extra,
extra_gpu=cf["num_gpus_per_worker"] * cf["num_workers"])
@override(Agent)
def _init(self):
self._validate_config()
@@ -230,8 +230,7 @@ class AsyncReplayOptimizer(PolicyOptimizer):
return sample_timesteps, train_timesteps
# reserve 1 CPU so that our method calls don't get stalled
@ray.remote(num_cpus=1)
@ray.remote(num_cpus=0)
class ReplayActor(object):
"""A replay buffer shard.
@@ -317,6 +316,8 @@ class ReplayActor(object):
return stat
# note: we set num_cpus=0 to avoid failing to create replay actors when
# resources are fragmented. This isn't ideal.
@ray.remote(num_cpus=0)
class BatchReplayActor(object):
"""The batch replay version of the replay actor.
@@ -105,7 +105,7 @@ def check_support_multiagent(alg, config):
class ModelSupportedSpaces(unittest.TestCase):
def setUp(self):
ray.init(num_cpus=10)
ray.init(num_cpus=4)
def tearDown(self):
ray.shutdown()