def gather_experiences_tree_aggregation(workers, config):
"""Tree aggregation version of gather_experiences_directly()."""
rollouts = ParallelRollouts(workers, mode="raw")
# Divide up the workers between aggregators.
worker_assignments = [[] for _ in range(config["num_aggregation_workers"])]
i = 0
for w in range(len(workers.remote_workers())):
worker_assignments[i].append(w)
i += 1
i %= len(worker_assignments)
logger.info("Worker assignments: {}".format(worker_assignments))
# Create parallel iterators that represent each aggregation group.
rollout_groups: List["ParallelIterator[SampleBatchType]"] = [
rollouts.select_shards(assigned) for assigned in worker_assignments
]
# This spawns |num_aggregation_workers| intermediate actors that aggregate
# experiences in parallel. We force colocation on the same node to maximize
# data bandwidth between them and the driver.
train_batches = from_actors([
create_colocated(Aggregator, [config, g], 1)[0] for g in rollout_groups
])
# TODO(ekl) properly account for replay.
def record_steps_sampled(batch):
metrics = _get_shared_metrics()
metrics.counters[STEPS_SAMPLED_COUNTER] += batch.count
return batch
return train_batches.gather_async().for_each(record_steps_sampled)
def gather_experiences_tree_aggregation(workers: WorkerSet,
config: Dict) -> "LocalIterator[Any]":
"""Tree aggregation version of gather_experiences_directly()."""
rollouts = ParallelRollouts(workers, mode="raw")
# Divide up the workers between aggregators.
worker_assignments = [[] for _ in range(config["num_aggregation_workers"])]
i = 0
for worker_idx in range(len(workers.remote_workers())):
worker_assignments[i].append(worker_idx)
i += 1
i %= len(worker_assignments)
logger.info("Worker assignments: {}".format(worker_assignments))
# Create parallel iterators that represent each aggregation group.
rollout_groups: List["ParallelIterator[SampleBatchType]"] = [
rollouts.select_shards(assigned) for assigned in worker_assignments
]
# This spawns |num_aggregation_workers| intermediate actors that aggregate
# experiences in parallel. We force colocation on the same node (localhost)
# to maximize data bandwidth between them and the driver.
localhost = platform.node()
assert localhost != "", ("ERROR: Cannot determine local node name! "
"`platform.node()` returned empty string.")
all_co_located = create_colocated_actors(
actor_specs=[
# (class, args, kwargs={}, count=1)
(Aggregator, [config, g], {}, 1) for g in rollout_groups
],
node=localhost,
)
# Use the first ([0]) of each created group (each group only has one
# actor: count=1).
train_batches = from_actors([group[0] for group in all_co_located])
# TODO(ekl) properly account for replay.
def record_steps_sampled(batch):
metrics = _get_shared_metrics()
metrics.counters[STEPS_SAMPLED_COUNTER] += batch.count
if isinstance(batch, MultiAgentBatch):
metrics.counters[AGENT_STEPS_SAMPLED_COUNTER] += batch.agent_steps(
)
else:
metrics.counters[AGENT_STEPS_SAMPLED_COUNTER] += batch.count
return batch
return train_batches.gather_async().for_each(record_steps_sampled)
