def __init__( self, _callable: Callable, deployment_name: str, replica_tag: ReplicaTag, deployment_config: DeploymentConfig, user_config: Any, version: DeploymentVersion, is_function: bool, controller_handle: ActorHandle, ) -> None: self.deployment_config = deployment_config self.deployment_name = deployment_name self.replica_tag = replica_tag self.callable = _callable self.is_function = is_function self.user_config = user_config self.version = version self.rwlock = aiorwlock.RWLock() user_health_check = getattr(_callable, HEALTH_CHECK_METHOD, None) if not callable(user_health_check): def user_health_check(): pass self.user_health_check = sync_to_async(user_health_check) self.num_ongoing_requests = 0 self.request_counter = metrics.Counter( "serve_deployment_request_counter", description= ("The number of queries that have been processed in this replica." ), tag_keys=("deployment", "replica"), ) self.request_counter.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.error_counter = metrics.Counter( "serve_deployment_error_counter", description=( "The number of exceptions that have occurred in this replica." ), tag_keys=("deployment", "replica"), ) self.error_counter.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.restart_counter = metrics.Counter( "serve_deployment_replica_starts", description= ("The number of times this replica has been restarted due to failure." ), tag_keys=("deployment", "replica"), ) self.restart_counter.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.processing_latency_tracker = metrics.Histogram( "serve_deployment_processing_latency_ms", description="The latency for queries to be processed.", boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("deployment", "replica"), ) self.processing_latency_tracker.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.num_processing_items = metrics.Gauge( "serve_replica_processing_queries", description="The current number of queries being processed.", tag_keys=("deployment", "replica"), ) self.num_processing_items.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.restart_counter.inc() self._shutdown_wait_loop_s = deployment_config.graceful_shutdown_wait_loop_s if deployment_config.autoscaling_config: process_remote_func = controller_handle.record_autoscaling_metrics.remote config = deployment_config.autoscaling_config start_metrics_pusher( interval_s=config.metrics_interval_s, collection_callback=self._collect_autoscaling_metrics, metrics_process_func=process_remote_func, ) # NOTE(edoakes): we used to recommend that users use the "ray" logger # and tagged the logs with metadata as below. We now recommend using # the "ray.serve" 'component logger' (as of Ray 1.13). This is left to # maintain backwards compatibility with users who were using the # existing logger. We can consider removing it in Ray 2.0. ray_logger = logging.getLogger("ray") for handler in ray_logger.handlers: handler.setFormatter( logging.Formatter( handler.formatter._fmt + f" component=serve deployment={self.deployment_name} " f"replica={self.replica_tag}"))
def __init__(self, _callable: Callable, backend_config: BackendConfig, is_function: bool, controller_handle: ActorHandle) -> None: self.backend_tag = ray.serve.api.get_replica_context().backend_tag self.replica_tag = ray.serve.api.get_replica_context().replica_tag self.callable = _callable self.is_function = is_function self.config = backend_config self.batch_queue = _BatchQueue(self.config.max_batch_size or 1, self.config.batch_wait_timeout) self.reconfigure(self.config.user_config) self.num_ongoing_requests = 0 self.request_counter = metrics.Counter( "serve_backend_request_counter", description=("The number of queries that have been " "processed in this replica."), tag_keys=("backend", )) self.request_counter.set_default_tags({"backend": self.backend_tag}) self.long_poll_client = LongPollAsyncClient(controller_handle, { LongPollKey.BACKEND_CONFIGS: self._update_backend_configs, }) self.error_counter = metrics.Counter( "serve_backend_error_counter", description=("The number of exceptions that have " "occurred in the backend."), tag_keys=("backend", )) self.error_counter.set_default_tags({"backend": self.backend_tag}) self.restart_counter = metrics.Counter( "serve_backend_replica_starts", description=("The number of times this replica " "has been restarted due to failure."), tag_keys=("backend", "replica")) self.restart_counter.set_default_tags({ "backend": self.backend_tag, "replica": self.replica_tag }) self.queuing_latency_tracker = metrics.Histogram( "serve_backend_queuing_latency_ms", description=("The latency for queries in the replica's queue " "waiting to be processed or batched."), boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("backend", "replica")) self.queuing_latency_tracker.set_default_tags({ "backend": self.backend_tag, "replica": self.replica_tag }) self.processing_latency_tracker = metrics.Histogram( "serve_backend_processing_latency_ms", description="The latency for queries to be processed.", boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("backend", "replica", "batch_size")) self.processing_latency_tracker.set_default_tags({ "backend": self.backend_tag, "replica": self.replica_tag }) self.num_queued_items = metrics.Gauge( "serve_replica_queued_queries", description=("The current number of queries queued in " "the backend replicas."), tag_keys=("backend", "replica")) self.num_queued_items.set_default_tags({ "backend": self.backend_tag, "replica": self.replica_tag }) self.num_processing_items = metrics.Gauge( "serve_replica_processing_queries", description="The current number of queries being processed.", tag_keys=("backend", "replica")) self.num_processing_items.set_default_tags({ "backend": self.backend_tag, "replica": self.replica_tag }) self.restart_counter.inc() ray_logger = logging.getLogger("ray") for handler in ray_logger.handlers: handler.setFormatter( logging.Formatter( handler.formatter._fmt + f" component=serve backend={self.backend_tag} " f"replica={self.replica_tag}")) asyncio.get_event_loop().create_task(self.main_loop())
def __init__(self, _callable: Callable, backend_config: BackendConfig, is_function: bool, controller_handle: ActorHandle) -> None: self.backend_tag = ray.serve.api.get_replica_context().deployment self.replica_tag = ray.serve.api.get_replica_context().replica_tag self.callable = _callable self.is_function = is_function self.config = backend_config self.num_ongoing_requests = 0 self.request_counter = metrics.Counter( "serve_deployment_request_counter", description=("The number of queries that have been " "processed in this replica."), tag_keys=("deployment", "replica")) self.request_counter.set_default_tags({ "deployment": self.backend_tag, "replica": self.replica_tag }) self.loop = asyncio.get_event_loop() self.long_poll_client = LongPollClient( controller_handle, { (LongPollNamespace.BACKEND_CONFIGS, self.backend_tag): self._update_backend_configs, }, call_in_event_loop=self.loop, ) self.error_counter = metrics.Counter( "serve_deployment_error_counter", description=("The number of exceptions that have " "occurred in this replica."), tag_keys=("deployment", "replica")) self.error_counter.set_default_tags({ "deployment": self.backend_tag, "replica": self.replica_tag }) self.restart_counter = metrics.Counter( "serve_deployment_replica_starts", description=("The number of times this replica " "has been restarted due to failure."), tag_keys=("deployment", "replica")) self.restart_counter.set_default_tags({ "deployment": self.backend_tag, "replica": self.replica_tag }) self.processing_latency_tracker = metrics.Histogram( "serve_deployment_processing_latency_ms", description="The latency for queries to be processed.", boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("deployment", "replica")) self.processing_latency_tracker.set_default_tags({ "deployment": self.backend_tag, "replica": self.replica_tag }) self.num_processing_items = metrics.Gauge( "serve_replica_processing_queries", description="The current number of queries being processed.", tag_keys=("deployment", "replica")) self.num_processing_items.set_default_tags({ "deployment": self.backend_tag, "replica": self.replica_tag }) self.restart_counter.inc() ray_logger = logging.getLogger("ray") for handler in ray_logger.handlers: handler.setFormatter( logging.Formatter( handler.formatter._fmt + f" component=serve deployment={self.backend_tag} " f"replica={self.replica_tag}"))
def __init__( self, _callable: Callable, deployment_name: str, replica_tag: ReplicaTag, deployment_config: DeploymentConfig, user_config: Any, version: DeploymentVersion, is_function: bool, controller_handle: ActorHandle, ) -> None: self.deployment_config = deployment_config self.deployment_name = deployment_name self.replica_tag = replica_tag self.callable = _callable self.is_function = is_function self.user_config = user_config self.version = version self.rwlock = aiorwlock.RWLock() user_health_check = getattr(_callable, HEALTH_CHECK_METHOD, None) if not callable(user_health_check): def user_health_check(): pass self.user_health_check = sync_to_async(user_health_check) self.num_ongoing_requests = 0 self.request_counter = metrics.Counter( "serve_deployment_request_counter", description=("The number of queries that have been " "processed in this replica."), tag_keys=("deployment", "replica"), ) self.request_counter.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.error_counter = metrics.Counter( "serve_deployment_error_counter", description=("The number of exceptions that have " "occurred in this replica."), tag_keys=("deployment", "replica"), ) self.error_counter.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.restart_counter = metrics.Counter( "serve_deployment_replica_starts", description=("The number of times this replica " "has been restarted due to failure."), tag_keys=("deployment", "replica"), ) self.restart_counter.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.processing_latency_tracker = metrics.Histogram( "serve_deployment_processing_latency_ms", description="The latency for queries to be processed.", boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("deployment", "replica"), ) self.processing_latency_tracker.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.num_processing_items = metrics.Gauge( "serve_replica_processing_queries", description="The current number of queries being processed.", tag_keys=("deployment", "replica"), ) self.num_processing_items.set_default_tags({ "deployment": self.deployment_name, "replica": self.replica_tag }) self.restart_counter.inc() self._shutdown_wait_loop_s = deployment_config.graceful_shutdown_wait_loop_s if deployment_config.autoscaling_config: config = deployment_config.autoscaling_config start_metrics_pusher( interval_s=config.metrics_interval_s, collection_callback=self._collect_autoscaling_metrics, controller_handle=controller_handle, ) ray_logger = logging.getLogger("ray") for handler in ray_logger.handlers: handler.setFormatter( logging.Formatter( handler.formatter._fmt + f" component=serve deployment={self.deployment_name} " f"replica={self.replica_tag}"))
def __init__(self, backend_tag: str, replica_tag: str, _callable: Callable, backend_config: BackendConfig, is_function: bool) -> None: self.backend_tag = backend_tag self.replica_tag = replica_tag self.callable = _callable self.is_function = is_function self.config = backend_config self.batch_queue = BatchQueue(self.config.max_batch_size or 1, self.config.batch_wait_timeout) self.num_ongoing_requests = 0 self.request_counter = metrics.Count( "backend_request_counter", description=("Number of queries that have been " "processed in this replica"), tag_keys=("backend", )) self.request_counter.set_default_tags({"backend": self.backend_tag}) self.error_counter = metrics.Count( "backend_error_counter", description=("Number of exceptions that have " "occurred in the backend"), tag_keys=("backend", )) self.error_counter.set_default_tags({"backend": self.backend_tag}) self.restart_counter = metrics.Count( "backend_worker_starts", description=("The number of time this replica workers " "has been restarted due to failure."), tag_keys=("backend", "replica_tag")) self.restart_counter.set_default_tags({ "backend": self.backend_tag, "replica_tag": self.replica_tag }) self.queuing_latency_tracker = metrics.Histogram( "backend_queuing_latency_ms", description=( "The latency for queries waiting in the replica's queue " "waiting to be processed or batched."), boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("backend", "replica_tag")) self.queuing_latency_tracker.set_default_tags({ "backend": self.backend_tag, "replica_tag": self.replica_tag }) self.processing_latency_tracker = metrics.Histogram( "backend_processing_latency_ms", description="The latency for queries to be processed", boundaries=DEFAULT_LATENCY_BUCKET_MS, tag_keys=("backend", "replica_tag", "batch_size")) self.processing_latency_tracker.set_default_tags({ "backend": self.backend_tag, "replica_tag": self.replica_tag }) self.num_queued_items = metrics.Gauge( "replica_queued_queries", description=("Current number of queries queued in the " "the backend replicas"), tag_keys=("backend", "replica_tag")) self.num_queued_items.set_default_tags({ "backend": self.backend_tag, "replica_tag": self.replica_tag }) self.num_processing_items = metrics.Gauge( "replica_processing_queries", description="Current number of queries being processed", tag_keys=("backend", "replica_tag")) self.num_processing_items.set_default_tags({ "backend": self.backend_tag, "replica_tag": self.replica_tag }) self.restart_counter.record(1) asyncio.get_event_loop().create_task(self.main_loop())