def start_tick(self):
"""Start the next tick."""
config = get_config()
lid_rank = config.lid_rank
pid_prog_rank = config.pid_prog_rank
visit_schema = config.visit_schema
state_schema = config.state_schema
current_state_df = self.behavior_model.next_state_df
visit_df = self.behavior_model.next_visit_df
LOG.debug("BehaviorActor: Sending out visit batches to LocationActor")
df_scatter(
visit_df,
"lid",
lid_rank,
asys.ranks(),
visit_schema,
LOC_AID,
"visit",
)
LOG.debug(
"BehaviorActor: Sending out current state batches to ProgressionActor"
)
df_scatter(
current_state_df,
"pid",
pid_prog_rank,
asys.ranks(),
state_schema,
PROG_AID,
"current_state",
)
def try_run_behavior_model(self):
"""Try running the behavior model."""
if len(self.visit_output_batches) < len(asys.ranks()):
return
if len(self.new_state_batches) < len(asys.ranks()):
return
LOG.debug("BehaviorActor: Running behavior model")
self.run_behavior_model()
def pong(self):
self.workers_done += 1
if self.workers_done == len(xa.ranks()):
self.end = time()
print("n_ranks: %d" % len(xa.ranks()))
print("n_nodes: %d" % len(xa.nodes()))
runtime = (self.end - self.start)
print("runtime: %e" % runtime)
xa.stop()
def maybe_stop(self):
if self.num_consumer_done == len(xa.ranks()):
self.end = time()
print("n_sent: %d" % self.objects_sent)
print("n_received: %d" % self.objects_received)
print("n_ranks: %d" % len(xa.ranks()))
print("n_nodes: %d" % len(xa.nodes()))
runtime = (self.end - self.start)
print("runtime: %e" % runtime)
xa.stop()
def __init__(self, store_proxies, coordinator_aid, runner_aid):
"""Initialize the runner.
Parameters
----------
store_proxies : dict [str -> ActorProxy]
Actor proxy objects to stores
coordinator_aid : str
ID of the coordinator actor
runner_aid : str
ID of the runner actors
"""
self.local_agents = {}
self.store_proxies = store_proxies
self.coordinator_proxy = asys.ActorProxy(asys.MASTER_RANK,
coordinator_aid)
self.every_runner_proxy = asys.ActorProxy(asys.EVERY_RANK, runner_aid)
self.runner_proxies = [
asys.ActorProxy(rank, runner_aid) for rank in asys.ranks()
]
# Step variables
self.timestep = None
self.flag_create_agent_done = None
self.flag_move_agents_done = None
self.num_receive_agent_done = None
self._prepare_for_next_step()
def try_compute_prgression_output(self):
"""Try to run compute_progression_output."""
if len(self.current_state_batches) < len(self.behav_ranks):
return
if len(self.visit_output_batches) < len(asys.ranks()):
return
self.compute_progression_output()
def __init__(self, per_node_behavior, java_behavior):
"""Initialize."""
nodes = list(asys.nodes())
current_rank = asys.current_rank()
current_node = [
node for node in nodes if current_rank in asys.node_ranks(node)
]
current_node = current_node[0]
current_node_index = nodes.index(current_node)
os.environ["CURRENT_NODE"] = str(current_node_index)
self.per_node_behavior = per_node_behavior
self.java_behavior = java_behavior
self.seed = int(os.environ["SEED"])
self.tick_time = int(os.environ["TICK_TIME"])
self.attr_names = os.environ["VISUAL_ATTRIBUTES"].strip().split(",")
self.disease_model = DiseaseModel(os.environ["DISEASE_MODEL_FILE"])
self.visit_schema = make_visit_schema(self.attr_names)
self.visit_output_schema = make_visit_output_schema(self.attr_names)
self.state_schema = make_state_schema()
self.empty_visit_df = self.visit_schema.empty_table().to_pandas()
self.empty_visit_output_df = self.visit_output_schema.empty_table(
).to_pandas()
self.empty_state_df = self.state_schema.empty_table().to_pandas()
lid_part_file = os.environ["LID_PARTITION"]
pid_part_file = os.environ["PID_PARTITION"]
lid_part_df = pd.read_csv(lid_part_file)
pid_part_df = pd.read_csv(pid_part_file)
self.lid_rank = {}
for lid, node, cpu in lid_part_df.itertuples(index=False, name=None):
self.lid_rank[lid] = node_rank(node, cpu)
self.pid_prog_rank = {}
self.pid_behav_rank = {}
for pid, node, cpu in pid_part_df.itertuples(index=False, name=None):
self.pid_prog_rank[pid] = node_rank(node, cpu)
if per_node_behavior:
self.pid_behav_rank[pid] = node_rank(node, 0)
else:
self.pid_behav_rank[pid] = node_rank(node, cpu)
if per_node_behavior:
self.behav_ranks = [
asys.node_ranks(node)[0] for node in asys.nodes()
]
else:
self.behav_ranks = asys.ranks()
def main(self):
# Create a greeter on very rank.
greeter_id = "greeter"
for rank in xa.ranks():
xa.create_actor(rank, greeter_id, Greeter)
# Send the greeters the greet message.
every_greeter = xa.ActorProxy(xa.EVERY_RANK, greeter_id)
every_greeter.greet("world", send_immediate=True)
# We are done now.
xa.stop()
def main(self):
source_rank = 0
sink_rank = 1
shape = (100, 100)
source = xa.ActorProxy(source_rank, "source")
sink = xa.ActorProxy(sink_rank, "sink")
workers = {r: xa.ActorProxy(r, "worker") for r in xa.ranks() if r > 1}
n = len(workers)
source.create_actor_(Source, n, shape, workers)
sink.create_actor_(Sink, n)
for worker in workers.values():
worker.create_actor_(Worker, source, sink)
def produce(self):
objects_sent = 0
n_messages = random.randint(MIN_NUM, MAX_NUM)
for _ in range(n_messages):
rank = random.choice(xa.ranks())
msg_size = random.randint(MIN_SIZE, MAX_SIZE)
objects_sent += msg_size
msg = list(range(msg_size))
self.consumer[rank].consume(msg)
#print("Sent %d objects to %d" % (len(msg), rank))
self.every_consumer.producer_done()
self.main.producer_done(objects_sent)
def main(self):
"""Run the simulation."""
for rank in asys.ranks():
asys.create_actor(
rank,
CONFIG_AID,
ConfigActor,
per_node_behavior=self.per_node_behavior,
java_behavior=self.java_behavior,
)
asys.create_actor(rank, LOC_AID, LocationActor)
asys.create_actor(rank, PROG_AID, ProgressionActor)
for rank in self.behav_ranks:
asys.create_actor(rank, BEHAV_AID, BehaviorActor)
LOG.info("MainActor: Starting tick %d", self.cur_tick)
for rank in self.behav_ranks:
asys.ActorProxy(rank, BEHAV_AID).start_tick()
def __init__(self):
"""Initialize."""
self.num_ticks = int(os.environ["NUM_TICKS"])
self.output_file = os.environ["OUTPUT_FILE"]
self.per_node_behavior = bool(
int(os.environ.get("PER_NODE_BEHAVIOR", "0")))
self.java_behavior = int(os.environ.get("JAVA_BEHAVIOR", "0"))
if self.java_behavior:
self.per_node_behavior = True
self.epicurve_parts = []
self.cur_tick = 0
self.tick_epicurve = []
if self.per_node_behavior:
self.behav_ranks = [
asys.node_ranks(node)[0] for node in asys.nodes()
]
else:
self.behav_ranks = asys.ranks()
def compute_visit_output(self):
"""Compute the visit outputs."""
config = get_config()
disease_model = config.disease_model
attr_names = config.attr_names
pid_prog_rank = config.pid_prog_rank
visit_output_schema = config.visit_output_schema
visit_df = [
unserialize_df(batch) for batch in self.visit_batches
if batch is not None
]
if visit_df:
visit_df = pd.concat(visit_df, axis=0)
else:
visit_df = get_config().empty_visit_df
visit_outputs = defaultdict(list)
for lid, group in visit_df.groupby("lid"):
visit_output = disease_model.compute_visit_output(
group, attr_names, lid)
for k, v in visit_output.items():
visit_outputs[k].append(v)
visit_outputs.default_factory = None
visit_outputs = {k: np.hstack(vs) for k, vs in visit_outputs.items()}
visit_output_df = pd.DataFrame(visit_outputs)
LOG.debug("LocationActor: Sending visit output to ProgressionActor")
df_scatter(
visit_output_df,
"pid",
pid_prog_rank,
asys.ranks(),
visit_output_schema,
PROG_AID,
"visit_output",
)
self.visit_batches = []
def __init__(self, balancer, simulator_aid, runner_aid, summary_writer_aid=None):
"""Initialize.
Parameters
----------
balancer: LoadBalancer
The agent load balancer
simulator_aid : str
The ID of the simulator actor
runner_aid : str
The ID of the runner actors
summary_writer_aid : str
The ID of the local summary writer actor
"""
self.balancer = balancer
self.simulator_proxy = asys.ActorProxy(asys.MASTER_RANK, simulator_aid)
self.runner_proxies = [
asys.ActorProxy(rank, runner_aid) for rank in asys.ranks()
]
self.every_runner_proxy = asys.ActorProxy(asys.EVERY_RANK, runner_aid)
self.summary_writer_aid = summary_writer_aid
self.num_agents_created = 0
self.num_agents_died = 0
# Step variables
self.timestep = None
self.agent_constructor = None
self.flag_create_agent_done = None
self.num_agent_step_profile_done = None
self.rank_step_time = None
self.rank_memory_usage = None
self.rank_n_updates = None
self.agent_step_time = None
self.agent_memory_usage = None
self.agent_n_updates = None
self.balancing_time = None
self._prepare_for_next_step()
def main():
if len(xa.ranks()) != 2:
print("This program must be run with exactly 2 ranks.")
sys.exit(1)
xa.start("main", Main)
def __init__(self):
self.main = xa.ActorProxy(xa.MASTER_RANK, "main")
self.consumer = [
xa.ActorProxy(rank, "consumer") for rank in xa.ranks()
]
self.every_consumer = xa.ActorProxy(xa.EVERY_RANK, "consumer")
def main():
if len(xa.ranks()) < 3:
print("This program must be run with 3 or more ranks.")
sys.exit(1)
xa.start("main", Main)
"""The Matrix ABM.""" import logging import xactor as asys INFO_FINE = logging.INFO - 1 WORLD_SIZE = len(asys.ranks()) from .datatypes import Timestep, Constructor, StateUpdate from .agent import Agent, AgentPopulation from .simulator import Simulator from .coordinator import Coordinator from .runner import Runner from .timestep_generator import TimestepGenerator, RangeTimestepGenerator from .state_store import StateStore, SQLite3Store from .load_balancer import RandomLoadBalancer, GreedyLoadBalancer from .resource_manager import SQLite3Manager, TensorboardWriter