def _validate_config(config: PartialTrainerConfigDict,
trainer_obj_or_none: Optional["Trainer"] = None):
# Call super (Trainer) validation method first.
Trainer._validate_config(config, trainer_obj_or_none)
# Then call user defined one, if any.
if validate_config is not None:
validate_config(config)
def default_resource_request(cls, config):
cf = dict(cls._default_config, **config)
Trainer._validate_config(cf)
return Resources(
cpu=cf["num_cpus_for_driver"],
gpu=cf["num_gpus"],
extra_cpu=cf["num_cpus_per_worker"] * cf["num_workers"] +
cf["num_aggregation_workers"],
extra_gpu=cf["num_gpus_per_worker"] * cf["num_workers"])
def test_validate_config_idempotent(self):
"""
Asserts that validate_config run multiple
times on COMMON_CONFIG will be idempotent
"""
# Given:
standard_config = copy.deepcopy(COMMON_CONFIG)
# When (we validate config 2 times), ...
Trainer._validate_config(standard_config)
config_v1 = copy.deepcopy(standard_config)
Trainer._validate_config(standard_config)
config_v2 = copy.deepcopy(standard_config)
# ... then ...
self.assertEqual(config_v1, config_v2)
def test_validate_config_idempotent(self):
"""
Asserts that validate_config run multiple
times on COMMON_CONFIG will be idempotent
"""
# Given
standard_config = copy.deepcopy(COMMON_CONFIG)
standard_config["_use_trajectory_view_api"] = False
# When (we validate config 2 times)
Trainer._validate_config(standard_config)
config_v1 = copy.deepcopy(standard_config)
Trainer._validate_config(standard_config)
config_v2 = copy.deepcopy(standard_config)
# Then
self.assertEqual(config_v1, config_v2)
def default_resource_request(cls, config):
cf = dict(cls._default_config, **config)
Trainer._validate_config(cf)
eval_config = cf["evaluation_config"]
# Return PlacementGroupFactory containing all needed resources
# (already properly defined as device bundles).
return PlacementGroupFactory(
bundles=[{
# Driver + Aggregation Workers:
# Force to be on same node to maximize data bandwidth
# between aggregation workers and the learner (driver).
# Aggregation workers tree-aggregate experiences collected
# from RolloutWorkers (n rollout workers map to m
# aggregation workers, where m < n) and always use 1 CPU
# each.
"CPU":
cf["num_cpus_for_driver"] + cf["num_aggregation_workers"],
"GPU":
cf["num_gpus"]
}] + [
{
# RolloutWorkers.
"CPU": cf["num_cpus_per_worker"],
"GPU": cf["num_gpus_per_worker"],
} for _ in range(cf["num_workers"])
] + ([
{
# Evaluation (remote) workers.
# Note: The local eval worker is located on the driver CPU.
"CPU":
eval_config.get("num_cpus_per_worker",
cf["num_cpus_per_worker"]),
"GPU":
eval_config.get("num_gpus_per_worker",
cf["num_gpus_per_worker"]),
} for _ in range(cf["evaluation_num_workers"])
] if cf["evaluation_interval"] else []),
strategy=config.get("placement_strategy", "PACK"))
def default_resource_request(cls, config):
cf = dict(cls._default_config, **config)
Trainer._validate_config(cf)
eval_config = cf["evaluation_config"]
# Return PlacementGroupFactory containing all needed resources
# (already properly defined as device bundles).
return PlacementGroupFactory(
bundles=[{
# Local worker + replay buffer actors.
# Force replay buffers to be on same node to maximize
# data bandwidth between buffers and the learner (driver).
# Replay buffer actors each contain one shard of the total
# replay buffer and use 1 CPU each.
"CPU":
cf["num_cpus_for_driver"] +
cf["optimizer"]["num_replay_buffer_shards"],
"GPU":
cf["num_gpus"]
}] + [
{
# RolloutWorkers.
"CPU": cf["num_cpus_per_worker"],
"GPU": cf["num_gpus_per_worker"],
} for _ in range(cf["num_workers"])
] + ([
{
# Evaluation workers.
# Note: The local eval worker is located on the driver CPU.
"CPU":
eval_config.get("num_cpus_per_worker",
cf["num_cpus_per_worker"]),
"GPU":
eval_config.get("num_gpus_per_worker",
cf["num_gpus_per_worker"]),
} for _ in range(cf["evaluation_num_workers"])
] if cf["evaluation_interval"] else []),
strategy=config.get("placement_strategy", "PACK"))