def _setup_autoscaler(self):
self.runner = MockProcessRunner()
self.config = yaml.safe_load(open(self.config_path).read())
self.provider.create_node(
{},
{
TAG_RAY_NODE_KIND: NODE_KIND_HEAD,
TAG_RAY_USER_NODE_TYPE: self.config["head_node_type"],
},
1,
)
self.head_ip = self.provider.non_terminated_node_ips({})[0]
self.load_metrics = LoadMetrics(local_ip=self.head_ip)
self.autoscaler = StandardAutoscaler(
self.config_path,
self.load_metrics,
# Don't let the autoscaler start any node launchers. Instead, we
# will launch nodes ourself after every update call.
max_concurrent_launches=0,
max_failures=0,
process_runner=self.runner,
update_interval_s=0,
)
# Manually create a node launcher. Note that we won't start it as a
# separate thread.
self.node_launcher = NodeLauncher(
provider=self.autoscaler.provider,
queue=self.autoscaler.launch_queue,
index=0,
pending=self.autoscaler.pending_launches,
node_types=self.autoscaler.available_node_types,
)
def __init__(self,
config_path,
load_metrics,
max_launch_batch=AUTOSCALER_MAX_LAUNCH_BATCH,
max_concurrent_launches=AUTOSCALER_MAX_CONCURRENT_LAUNCHES,
max_failures=AUTOSCALER_MAX_NUM_FAILURES,
process_runner=subprocess,
update_interval_s=AUTOSCALER_UPDATE_INTERVAL_S):
self.config_path = config_path
# Keep this before self.reset (self.provider needs to be created
# exactly once).
self.provider = None
self.resource_demand_scheduler = None
self.reset(errors_fatal=True)
self.load_metrics = load_metrics
self.max_failures = max_failures
self.max_launch_batch = max_launch_batch
self.max_concurrent_launches = max_concurrent_launches
self.process_runner = process_runner
# Map from node_id to NodeUpdater processes
self.updaters = {}
self.num_failed_updates = defaultdict(int)
self.num_successful_updates = defaultdict(int)
self.num_failures = 0
self.last_update_time = 0.0
self.update_interval_s = update_interval_s
# Node launchers
self.launch_queue = queue.Queue()
self.pending_launches = ConcurrentCounter()
max_batches = math.ceil(max_concurrent_launches /
float(max_launch_batch))
for i in range(int(max_batches)):
node_launcher = NodeLauncher(
provider=self.provider,
queue=self.launch_queue,
index=i,
pending=self.pending_launches,
node_types=self.available_node_types,
)
node_launcher.daemon = True
node_launcher.start()
# Expand local file_mounts to allow ~ in the paths. This can't be done
# earlier when the config is written since we might be on different
# platform and the expansion would result in wrong path.
self.config["file_mounts"] = {
remote: os.path.expanduser(local)
for remote, local in self.config["file_mounts"].items()
}
for local_path in self.config["file_mounts"].values():
assert os.path.exists(local_path)
# List of resource bundles the user is requesting of the cluster.
self.resource_demand_vector = []
logger.info("StandardAutoscaler: {}".format(self.config))
def __init__(
self,
config_path: str,
load_metrics: LoadMetrics,
max_launch_batch: int = AUTOSCALER_MAX_LAUNCH_BATCH,
max_concurrent_launches: int = AUTOSCALER_MAX_CONCURRENT_LAUNCHES,
max_failures: int = AUTOSCALER_MAX_NUM_FAILURES,
process_runner: Any = subprocess,
update_interval_s: int = AUTOSCALER_UPDATE_INTERVAL_S,
prefix_cluster_info: bool = False,
event_summarizer: Optional[EventSummarizer] = None,
prom_metrics: Optional[AutoscalerPrometheusMetrics] = None):
"""Create a StandardAutoscaler.
Args:
config_path: Path to a Ray Autoscaler YAML.
load_metrics: Provides metrics for the Ray cluster.
max_launch_batch: Max number of nodes to launch in one request.
max_concurrent_launches: Max number of nodes that can be concurrently
launched. This value and `max_launch_batch` determine the number
of batches that are used to launch nodes.
max_failures: Number of failures that the autoscaler will tolerate
before exiting.
process_runner: Subprocess-like interface used by the CommandRunner.
update_interval_s: Seconds between running the autoscaling loop.
prefix_cluster_info: Whether to add the cluster name to info strings.
event_summarizer: Utility to consolidate duplicated messages.
prom_metrics: Prometheus metrics for autoscaler-related operations.
"""
self.config_path = config_path
# Prefix each line of info string with cluster name if True
self.prefix_cluster_info = prefix_cluster_info
# Keep this before self.reset (self.provider needs to be created
# exactly once).
self.provider = None
# Keep this before self.reset (if an exception occurs in reset
# then prom_metrics must be instantitiated to increment the
# exception counter)
self.prom_metrics = prom_metrics or \
AutoscalerPrometheusMetrics()
self.resource_demand_scheduler = None
self.reset(errors_fatal=True)
self.head_node_ip = load_metrics.local_ip
self.load_metrics = load_metrics
self.max_failures = max_failures
self.max_launch_batch = max_launch_batch
self.max_concurrent_launches = max_concurrent_launches
self.process_runner = process_runner
self.event_summarizer = event_summarizer or EventSummarizer()
# Map from node_id to NodeUpdater threads
self.updaters = {}
self.num_failed_updates = defaultdict(int)
self.num_successful_updates = defaultdict(int)
self.num_failures = 0
self.last_update_time = 0.0
self.update_interval_s = update_interval_s
# Tracks active worker nodes
self.workers = []
# Tracks nodes scheduled for termination
self.nodes_to_terminate = []
# Disable NodeUpdater threads if true.
# Should be set to true in situations where another component, such as
# a Kubernetes operator, is responsible for Ray setup on nodes.
self.disable_node_updaters = self.config["provider"].get(
"disable_node_updaters", False)
# Node launchers
self.launch_queue = queue.Queue()
self.pending_launches = ConcurrentCounter()
max_batches = math.ceil(max_concurrent_launches /
float(max_launch_batch))
for i in range(int(max_batches)):
node_launcher = NodeLauncher(provider=self.provider,
queue=self.launch_queue,
index=i,
pending=self.pending_launches,
node_types=self.available_node_types,
prom_metrics=self.prom_metrics)
node_launcher.daemon = True
node_launcher.start()
# NodeTracker maintains soft state to track the number of recently
# failed nodes. It is best effort only.
self.node_tracker = NodeTracker()
# Expand local file_mounts to allow ~ in the paths. This can't be done
# earlier when the config is written since we might be on different
# platform and the expansion would result in wrong path.
self.config["file_mounts"] = {
remote: os.path.expanduser(local)
for remote, local in self.config["file_mounts"].items()
}
for local_path in self.config["file_mounts"].values():
assert os.path.exists(local_path)
logger.info("StandardAutoscaler: {}".format(self.config))
class Simulator:
"""This autoscaler simulator consists of a few components.
State is stored in 3 main data structures:
* Resource management state is stored in self.ip_to_nodes
* The scheduler's work queue is stored in self.work_queue
* An event queue which acts as the simulation's "timeline" in
self.event_queue
The logic is organized into 3 functions (and their helpers):
* self.run_autoscaler plays the role of `monitor.py` and translates
resource management state for load_metrics to consume.
* self.schedule is the only consumer of the work queue. It dispatches
work to the appropriate schedulers, which mutate cluster state and
produce events for the event queue.
* self.process_event is the sole consumer of the event queue. It
dispatches work to the appropriate event handlers.
There are 3 main ways of interacting with the simulator:
* simulator.submit: To submit tasks
* simulator.step: To go to the next "event"
* task/actor/placement group start/done callbacks
"""
def __init__(
self,
config_path,
provider,
autoscaler_update_interval_s=AUTOSCALER_UPDATE_INTERVAL_S,
node_startup_delay_s=120,
):
self.config_path = config_path
self.provider = provider
self.autoscaler_update_interval_s = autoscaler_update_interval_s
self.node_startup_delay_s = node_startup_delay_s
self._setup_autoscaler()
self._setup_simulator()
def _setup_autoscaler(self):
self.runner = MockProcessRunner()
self.config = yaml.safe_load(open(self.config_path).read())
self.provider.create_node(
{},
{
TAG_RAY_NODE_KIND: NODE_KIND_HEAD,
TAG_RAY_USER_NODE_TYPE: self.config["head_node_type"],
},
1,
)
self.head_ip = self.provider.non_terminated_node_ips({})[0]
self.load_metrics = LoadMetrics(local_ip=self.head_ip)
self.autoscaler = StandardAutoscaler(
self.config_path,
self.load_metrics,
# Don't let the autoscaler start any node launchers. Instead, we
# will launch nodes ourself after every update call.
max_concurrent_launches=0,
max_failures=0,
process_runner=self.runner,
update_interval_s=0,
)
# Manually create a node launcher. Note that we won't start it as a
# separate thread.
self.node_launcher = NodeLauncher(
provider=self.autoscaler.provider,
queue=self.autoscaler.launch_queue,
index=0,
pending=self.autoscaler.pending_launches,
node_types=self.autoscaler.available_node_types,
)
def _setup_simulator(self):
self.virtual_time = 0
self.ip_to_nodes = {}
self._update_cluster_state(join_immediately=True)
self.work_queue = []
self.event_queue = PriorityQueue()
self.event_queue.put(Event(0, SIMULATOR_EVENT_AUTOSCALER_UPDATE))
def _update_cluster_state(self, join_immediately=False):
nodes = self.provider.non_terminated_nodes(tag_filters={})
for node_id in nodes:
ip = self.provider.internal_ip(node_id)
if ip in self.ip_to_nodes:
continue
node_tags = self.provider.node_tags(node_id)
if TAG_RAY_USER_NODE_TYPE in node_tags:
node_type = node_tags[TAG_RAY_USER_NODE_TYPE]
resources = self.config["available_node_types"][node_type].get(
"resources", {})
node = Node(resources, join_immediately, node_type,
self.virtual_time)
self.ip_to_nodes[ip] = node
if not join_immediately:
join_time = self.virtual_time + self.node_startup_delay_s
self.event_queue.put(
Event(join_time, SIMULATOR_EVENT_NODE_JOINED, node))
def submit(self, work):
if isinstance(work, list):
self.work_queue.extend(work)
else:
self.work_queue.append(work)
def _get_node_to_run(self, bundle, nodes):
for ip, node in nodes.items():
if node.bundle_fits(bundle):
return ip, node
return None, None
def _schedule_placement_group(self, pg, nodes):
# This scheduling algorithm is bad, but it is approximately as bad as
# the real placement group scheduler.
to_allocate = []
if (pg.strategy == PlacementStrategy.STRICT_PACK
or pg.strategy == PlacementStrategy.PACK):
combined = collections.defaultdict(float)
for bundle in pg.bundles:
for k, v in bundle.items():
combined[k] += v
ip, node_to_run = self._get_node_to_run(combined, nodes)
if node_to_run is None:
return False
to_allocate.append((combined, ip))
elif (pg.strategy == PlacementStrategy.STRICT_SPREAD
or pg.strategy == PlacementStrategy.SPREAD):
# TODO (Alex): More accurate handling of non-STRICT_PACK groups.
remaining_nodes = nodes.copy()
for bundle in pg.bundles:
ip, node_to_run = self._get_node_to_run(
bundle, remaining_nodes)
if node_to_run is None:
return False
del remaining_nodes[ip]
to_allocate.append((bundle, ip))
for bundle, ip in to_allocate:
node = self.ip_to_nodes[ip]
node.allocate(bundle)
pg.start_time = self.virtual_time
end_time = self.virtual_time + pg.duration
self.event_queue.put(
Event(end_time, SIMULATOR_EVENT_PG_DONE, (pg, to_allocate)))
if pg.start_callback:
pg.start_callback()
return True
def _schedule_task(self, task, nodes):
ip, node = self._get_node_to_run(task.resources, nodes)
if node is None:
return False
node.allocate(task.resources)
task.node = node
task.start_time = self.virtual_time
end_time = self.virtual_time + task.duration
self.event_queue.put(Event(end_time, SIMULATOR_EVENT_TASK_DONE, task))
if task.start_callback:
task.start_callback()
return True
def schedule(self):
# TODO (Alex): Implement a more realistic scheduling algorithm.
new_work_queue = []
for work in self.work_queue:
if isinstance(work, Task):
scheduled = self._schedule_task(work, self.ip_to_nodes)
elif isinstance(work, PlacementGroup):
scheduled = self._schedule_placement_group(
work, self.ip_to_nodes)
else:
assert False, "Unknown work object!"
if scheduled is False:
new_work_queue.append(work)
self.work_queue = new_work_queue
def _launch_nodes(self):
"""Launch all queued nodes. Since this will be run serially after
`autoscaler.update` there are no race conditions in checking if the
queue is empty.
"""
while not self.node_launcher.queue.empty():
config, count, node_type = self.node_launcher.queue.get()
try:
self.node_launcher._launch_node(config, count, node_type)
except Exception:
pass
finally:
self.node_launcher.pending.dec(node_type, count)
def _infeasible(self, bundle):
for node in self.ip_to_nodes.values():
if node.feasible(bundle):
return False
return True
def run_autoscaler(self):
waiting_bundles = []
infeasible_bundles = []
placement_groups = []
for work in self.work_queue:
if isinstance(work, Task):
shape = work.resources
if self._infeasible(shape):
infeasible_bundles.append(shape)
else:
waiting_bundles.append(shape)
if isinstance(work, PlacementGroup):
placement_groups.append(
PlacementGroupTableData(
state=PlacementGroupTableData.PENDING,
strategy=work.strategy,
bundles=[
Bundle(unit_resources=bundle)
for bundle in work.bundles
],
))
for ip, node in self.ip_to_nodes.items():
if not node.in_cluster:
continue
self.load_metrics.update(
ip=ip,
static_resources=node.total_resources,
dynamic_resources=node.available_resources,
resource_load={},
waiting_bundles=waiting_bundles,
infeasible_bundles=infeasible_bundles,
pending_placement_groups=placement_groups,
)
self.autoscaler.update()
self._launch_nodes()
self._update_cluster_state()
def process_event(self, event):
if event.event_type == SIMULATOR_EVENT_AUTOSCALER_UPDATE:
self.run_autoscaler()
next_update = self.virtual_time + self.autoscaler_update_interval_s
self.event_queue.put(
Event(next_update, SIMULATOR_EVENT_AUTOSCALER_UPDATE))
elif event.event_type == SIMULATOR_EVENT_TASK_DONE:
task = event.data
task.node.free(task.resources)
if task.done_callback:
task.done_callback()
elif event.event_type == SIMULATOR_EVENT_NODE_JOINED:
node = event.data
node.in_cluster = True
elif event.event_type == SIMULATOR_EVENT_PG_DONE:
pg, allocated = event.data
for bundle, ip in allocated:
self.ip_to_nodes[ip].free(bundle)
if pg.done_callback:
pg.done_callback()
else:
assert False, "Unknown event!"
def step(self):
self.virtual_time = self.event_queue.queue[0].time
while self.event_queue.queue[0].time == self.virtual_time:
event = self.event_queue.get()
self.process_event(event)
self.schedule()
print(self.info_string())
return self.virtual_time
def node_costs(self):
"""Returns the cost of nodes. Cost is measured in terms of cumulative hours of
runtime per node type.
"""
costs = collections.defaultdict(float)
for node in self.ip_to_nodes.values():
if not node.in_cluster:
continue
runtime = self.virtual_time - node.start_time
costs[node.node_type] += runtime
return costs
def info_string(self):
num_connected_nodes = len(
[node for node in self.ip_to_nodes.values() if node.in_cluster])
num_pending_nodes = len(self.ip_to_nodes) - num_connected_nodes
return (f"[t={self.virtual_time}] "
f"Connected: {num_connected_nodes}, "
f"Pending: {num_pending_nodes}, "
f"Remaining: {len(self.work_queue)}")
def __init__(self,
config_path,
load_metrics,
max_launch_batch=AUTOSCALER_MAX_LAUNCH_BATCH,
max_concurrent_launches=AUTOSCALER_MAX_CONCURRENT_LAUNCHES,
max_failures=AUTOSCALER_MAX_NUM_FAILURES,
process_runner=subprocess,
update_interval_s=AUTOSCALER_UPDATE_INTERVAL_S,
prefix_cluster_info=False,
event_summarizer=None,
prom_metrics=None):
self.config_path = config_path
# Prefix each line of info string with cluster name if True
self.prefix_cluster_info = prefix_cluster_info
# Keep this before self.reset (self.provider needs to be created
# exactly once).
self.provider = None
# Keep this before self.reset (if an exception occurs in reset
# then prom_metrics must be instantitiated to increment the
# exception counter)
self.prom_metrics = prom_metrics or \
AutoscalerPrometheusMetrics()
self.resource_demand_scheduler = None
self.reset(errors_fatal=True)
self.head_node_ip = load_metrics.local_ip
self.load_metrics = load_metrics
self.max_failures = max_failures
self.max_launch_batch = max_launch_batch
self.max_concurrent_launches = max_concurrent_launches
self.process_runner = process_runner
self.event_summarizer = event_summarizer or EventSummarizer()
# Map from node_id to NodeUpdater processes
self.updaters = {}
self.num_failed_updates = defaultdict(int)
self.num_successful_updates = defaultdict(int)
self.num_failures = 0
self.last_update_time = 0.0
self.update_interval_s = update_interval_s
# Node launchers
self.launch_queue = queue.Queue()
self.pending_launches = ConcurrentCounter()
max_batches = math.ceil(max_concurrent_launches /
float(max_launch_batch))
for i in range(int(max_batches)):
node_launcher = NodeLauncher(provider=self.provider,
queue=self.launch_queue,
index=i,
pending=self.pending_launches,
node_types=self.available_node_types,
prom_metrics=self.prom_metrics)
node_launcher.daemon = True
node_launcher.start()
# NodeTracker maintains soft state to track the number of recently
# failed nodes. It is best effort only.
self.node_tracker = NodeTracker()
# Expand local file_mounts to allow ~ in the paths. This can't be done
# earlier when the config is written since we might be on different
# platform and the expansion would result in wrong path.
self.config["file_mounts"] = {
remote: os.path.expanduser(local)
for remote, local in self.config["file_mounts"].items()
}
for local_path in self.config["file_mounts"].values():
assert os.path.exists(local_path)
logger.info("StandardAutoscaler: {}".format(self.config))
class Simulator:
def __init__(
self,
config_path,
provider,
autoscaler_update_interval_s=AUTOSCALER_UPDATE_INTERVAL_S,
node_startup_delay_s=120,
):
self.config_path = config_path
self.provider = provider
self.autoscaler_update_interval_s = autoscaler_update_interval_s
self.node_startup_delay_s = node_startup_delay_s
self._setup_autoscaler()
self._setup_simulator()
def _setup_autoscaler(self):
self.runner = MockProcessRunner()
self.config = yaml.safe_load(open(self.config_path).read())
self.provider.create_node(
{},
{
TAG_RAY_NODE_KIND: "head",
TAG_RAY_USER_NODE_TYPE: self.config["head_node_type"],
},
1,
)
self.head_ip = self.provider.non_terminated_node_ips({})[0]
self.load_metrics = LoadMetrics(local_ip=self.head_ip)
self.autoscaler = StandardAutoscaler(
self.config_path,
self.load_metrics,
# Don't let the autoscaler start any node launchers. Instead, we
# will launch nodes ourself after every update call.
max_concurrent_launches=0,
max_failures=0,
process_runner=self.runner,
update_interval_s=0,
)
# Manually create a node launcher. Note that we won't start it as a
# separate thread.
self.node_launcher = NodeLauncher(
provider=self.autoscaler.provider,
queue=self.autoscaler.launch_queue,
index=0,
pending=self.autoscaler.pending_launches,
node_types=self.autoscaler.available_node_types,
)
def _setup_simulator(self):
self.virtual_time = 0
self.ip_to_nodes = {}
self._update_cluster_state(join_immediately=True)
self.work_queue = []
self.event_queue = PriorityQueue()
self.event_queue.put(Event(0, SIMULATOR_EVENT_AUTOSCALER_UPDATE))
def _update_cluster_state(self, join_immediately=False):
nodes = self.provider.non_terminated_nodes(tag_filters={})
for node_id in nodes:
ip = self.provider.internal_ip(node_id)
if ip in self.ip_to_nodes:
continue
node_tags = self.provider.node_tags(node_id)
if TAG_RAY_USER_NODE_TYPE in node_tags:
node_type = node_tags[TAG_RAY_USER_NODE_TYPE]
resources = self.config["available_node_types"][node_type].get(
"resources", {})
node = Node(resources, join_immediately)
self.ip_to_nodes[ip] = node
if not join_immediately:
join_time = self.virtual_time + self.node_startup_delay_s
self.event_queue.put(
Event(join_time, SIMULATOR_EVEN_NODE_JOINED, node))
def submit(self, work):
if isinstance(work, list):
self.work_queue.extend(work)
else:
self.work_queue.append(work)
def _schedule_task(self, task):
for ip, node in self.ip_to_nodes.items():
if node.bundle_fits(task.resources):
node.allocate(task.resources)
task.node = node
task.start_time = self.virtual_time
end_time = self.virtual_time + task.duration
self.event_queue.put(
Event(end_time, SIMULATOR_EVENT_TASK_DONE, task))
if task.start_callback:
task.start_callback()
return True
return False
def schedule(self):
# TODO (Alex): Implement a more realistic scheduling algorithm.
new_work_queue = []
for work in self.work_queue:
if isinstance(work, Task):
scheduled = self._schedule_task(work)
if scheduled is False:
new_work_queue.append(work)
self.work_queue = new_work_queue
def _launch_nodes(self):
"""Launch all queued nodes. Since this will be run serially after
`autoscaler.update` there are no race conditions in checking if the
queue is empty.
"""
while not self.node_launcher.queue.empty():
config, count, node_type = self.node_launcher.queue.get()
try:
self.node_launcher._launch_node(config, count, node_type)
except Exception:
pass
finally:
self.node_launcher.pending.dec(node_type, count)
def _infeasible(self, bundle):
for node in self.ip_to_nodes.values():
if node.feasible(bundle):
return False
return True
def run_autoscaler(self):
waiting_bundles = []
infeasible_bundles = []
for work in self.work_queue:
if isinstance(work, Task):
shape = work.resources
if self._infeasible(shape):
infeasible_bundles.append(shape)
else:
waiting_bundles.append(shape)
for ip, node in self.ip_to_nodes.items():
if not node.in_cluster:
continue
self.load_metrics.update(
ip=ip,
static_resources=node.total_resources,
update_dynamic_resources=True,
dynamic_resources=node.available_resources,
update_resource_load=False,
resource_load={},
waiting_bundles=waiting_bundles,
infeasible_bundles=infeasible_bundles,
pending_placement_groups=[],
)
self.autoscaler.update()
self._launch_nodes()
self._update_cluster_state()
def process_event(self, event):
if event.event_type == SIMULATOR_EVENT_AUTOSCALER_UPDATE:
self.run_autoscaler()
next_update = self.virtual_time + self.autoscaler_update_interval_s
self.event_queue.put(
Event(next_update, SIMULATOR_EVENT_AUTOSCALER_UPDATE))
elif event.event_type == SIMULATOR_EVENT_TASK_DONE:
task = event.data
task.node.free(task.resources)
if task.done_callback:
task.done_callback()
elif event.event_type == SIMULATOR_EVEN_NODE_JOINED:
node = event.data
node.in_cluster = True
def step(self):
self.virtual_time = self.event_queue.queue[0].time
while self.event_queue.queue[0].time == self.virtual_time:
event = self.event_queue.get()
self.process_event(event)
self.schedule()
print(self.info_string())
return self.virtual_time
def info_string(self):
return (f"[t={self.virtual_time}] Nodes: {len(self.ip_to_nodes)} " +
f"Remaining requests: {len(self.work_queue)} ")