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))
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))
