def testDontScaleBelowTarget(self): config = SMALL_CLUSTER.copy() config["min_workers"] = 0 config["max_workers"] = 2 config["target_utilization_fraction"] = 0.5 config_path = self.write_config(config) self.provider = MockProvider() lm = LoadMetrics() runner = MockProcessRunner() autoscaler = StandardAutoscaler(config_path, lm, max_failures=0, process_runner=runner, update_interval_s=0) assert len(self.provider.non_terminated_nodes({})) == 0 autoscaler.update() assert autoscaler.pending_launches.value == 0 assert len(self.provider.non_terminated_nodes({})) == 0 # Scales up as nodes are reported as used local_ip = services.get_node_ip_address() lm.update(local_ip, {"CPU": 2}, {"CPU": 0}, {}) # head # 1.0 nodes used => target nodes = 2 => target workers = 1 autoscaler.update() self.waitForNodes(1) # Make new node idle, and never used. # Should hold steady as target is still 2. lm.update("172.0.0.0", {"CPU": 0}, {"CPU": 0}, {}) lm.last_used_time_by_ip["172.0.0.0"] = 0 autoscaler.update() assert len(self.provider.non_terminated_nodes({})) == 1 # Reduce load on head => target nodes = 1 => target workers = 0 lm.update(local_ip, {"CPU": 2}, {"CPU": 1}, {}) autoscaler.update() assert len(self.provider.non_terminated_nodes({})) == 0
def testBottleneckResource(self): lm = LoadMetrics() lm.update("1.1.1.1", {"CPU": 2}, {"CPU": 0}, {}) lm.update("2.2.2.2", {"CPU": 2, "GPU": 16}, {"CPU": 2, "GPU": 2}, {}) assert lm.approx_workers_used() == 1.88
def testPruneByNodeIp(self): lm = LoadMetrics() lm.update("1.1.1.1", {"CPU": 1}, {"CPU": 0}, {}) lm.update("2.2.2.2", {"CPU": 1}, {"CPU": 0}, {}) lm.prune_active_ips({"1.1.1.1", "4.4.4.4"}) assert lm.approx_workers_used() == 1.0
def testLoadMessages(self): lm = LoadMetrics() lm.update("1.1.1.1", {"CPU": 2}, {"CPU": 1}, {}) self.assertEqual(lm.approx_workers_used(), 0.5) lm.update("1.1.1.1", {"CPU": 2}, {"CPU": 1}, {"CPU": 1}) self.assertEqual(lm.approx_workers_used(), 1.0) # Both nodes count as busy since there is a queue on one. lm.update("2.2.2.2", {"CPU": 2}, {"CPU": 2}, {}) self.assertEqual(lm.approx_workers_used(), 2.0) lm.update("2.2.2.2", {"CPU": 2}, {"CPU": 0}, {}) self.assertEqual(lm.approx_workers_used(), 2.0) lm.update("2.2.2.2", {"CPU": 2}, {"CPU": 1}, {}) self.assertEqual(lm.approx_workers_used(), 2.0) # No queue anymore, so we're back to exact accounting. lm.update("1.1.1.1", {"CPU": 2}, {"CPU": 0}, {}) self.assertEqual(lm.approx_workers_used(), 1.5) lm.update("2.2.2.2", {"CPU": 2}, {"CPU": 1}, {"GPU": 1}) self.assertEqual(lm.approx_workers_used(), 2.0) lm.update("3.3.3.3", {"CPU": 2}, {"CPU": 1}, {}) lm.update("4.3.3.3", {"CPU": 2}, {"CPU": 1}, {}) lm.update("5.3.3.3", {"CPU": 2}, {"CPU": 1}, {}) lm.update("6.3.3.3", {"CPU": 2}, {"CPU": 1}, {}) lm.update("7.3.3.3", {"CPU": 2}, {"CPU": 1}, {}) lm.update("8.3.3.3", {"CPU": 2}, {"CPU": 1}, {}) self.assertEqual(lm.approx_workers_used(), 8.0) lm.update("2.2.2.2", {"CPU": 2}, {"CPU": 1}, {}) # no queue anymore self.assertEqual(lm.approx_workers_used(), 4.5)
class Monitor: """A monitor for Ray processes. The monitor is in charge of cleaning up the tables in the global state after processes have died. The monitor is currently not responsible for detecting component failures. Attributes: redis: A connection to the Redis server. primary_subscribe_client: A pubsub client for the Redis server. This is used to receive notifications about failed components. """ def __init__(self, redis_address, autoscaling_config, redis_password=None): # Initialize the Redis clients. ray.state.state._initialize_global_state(redis_address, redis_password=redis_password) self.redis = ray.services.create_redis_client(redis_address, password=redis_password) # Setup subscriptions to the primary Redis server and the Redis shards. self.primary_subscribe_client = self.redis.pubsub( ignore_subscribe_messages=True) # Keep a mapping from raylet client ID to IP address to use # for updating the load metrics. self.raylet_id_to_ip_map = {} self.load_metrics = LoadMetrics() if autoscaling_config: self.autoscaler = StandardAutoscaler(autoscaling_config, self.load_metrics) self.autoscaling_config = autoscaling_config else: self.autoscaler = None self.autoscaling_config = None def __del__(self): """Destruct the monitor object.""" # We close the pubsub client to avoid leaking file descriptors. try: primary_subscribe_client = self.primary_subscribe_client except AttributeError: primary_subscribe_client = None if primary_subscribe_client is not None: primary_subscribe_client.close() def subscribe(self, channel): """Subscribe to the given channel on the primary Redis shard. Args: channel (str): The channel to subscribe to. Raises: Exception: An exception is raised if the subscription fails. """ self.primary_subscribe_client.subscribe(channel) def psubscribe(self, pattern): """Subscribe to the given pattern on the primary Redis shard. Args: pattern (str): The pattern to subscribe to. Raises: Exception: An exception is raised if the subscription fails. """ self.primary_subscribe_client.psubscribe(pattern) def xray_heartbeat_batch_handler(self, unused_channel, data): """Handle an xray heartbeat batch message from Redis.""" pub_message = ray.gcs_utils.PubSubMessage.FromString(data) heartbeat_data = pub_message.data message = ray.gcs_utils.HeartbeatBatchTableData.FromString( heartbeat_data) for heartbeat_message in message.batch: resource_load = dict( zip(heartbeat_message.resource_load_label, heartbeat_message.resource_load_capacity)) total_resources = dict( zip(heartbeat_message.resources_total_label, heartbeat_message.resources_total_capacity)) available_resources = dict( zip(heartbeat_message.resources_available_label, heartbeat_message.resources_available_capacity)) for resource in total_resources: available_resources.setdefault(resource, 0.0) # Update the load metrics for this raylet. client_id = ray.utils.binary_to_hex(heartbeat_message.client_id) ip = self.raylet_id_to_ip_map.get(client_id) if ip: self.load_metrics.update(ip, total_resources, available_resources, resource_load) else: logger.warning( "Monitor: " "could not find ip for client {}".format(client_id)) def xray_job_notification_handler(self, unused_channel, data): """Handle a notification that a job has been added or removed. Args: unused_channel: The message channel. data: The message data. """ pub_message = ray.gcs_utils.PubSubMessage.FromString(data) job_data = pub_message.data message = ray.gcs_utils.JobTableData.FromString(job_data) job_id = message.job_id if message.is_dead: logger.info("Monitor: " "XRay Driver {} has been removed.".format( binary_to_hex(job_id))) def autoscaler_resource_request_handler(self, _, data): """Handle a notification of a resource request for the autoscaler. Args: channel: unused data: a resource request as JSON, e.g. {"CPU": 1} """ if not self.autoscaler: return try: self.autoscaler.request_resources(json.loads(data)) except Exception: # We don't want this to kill the monitor. traceback.print_exc() def process_messages(self, max_messages=10000): """Process all messages ready in the subscription channels. This reads messages from the subscription channels and calls the appropriate handlers until there are no messages left. Args: max_messages: The maximum number of messages to process before returning. """ subscribe_clients = [self.primary_subscribe_client] for subscribe_client in subscribe_clients: for _ in range(max_messages): message = subscribe_client.get_message() if message is None: # Continue on to the next subscribe client. break # Parse the message. pattern = message["pattern"] channel = message["channel"] data = message["data"] # Determine the appropriate message handler. if pattern == ray.gcs_utils.XRAY_HEARTBEAT_BATCH_PATTERN: # Similar functionality as raylet info channel message_handler = self.xray_heartbeat_batch_handler elif pattern == ray.gcs_utils.XRAY_JOB_PATTERN: # Handles driver death. message_handler = self.xray_job_notification_handler elif (channel == ray.ray_constants.AUTOSCALER_RESOURCE_REQUEST_CHANNEL): message_handler = self.autoscaler_resource_request_handler else: assert False, "This code should be unreachable." # Call the handler. message_handler(channel, data) def update_raylet_map(self, _append_port=False): """Updates internal raylet map. Args: _append_port (bool): Defaults to False. Appending the port is useful in testing, as mock clusters have many nodes with the same IP and cannot be uniquely identified. """ all_raylet_nodes = ray.nodes() self.raylet_id_to_ip_map = {} for raylet_info in all_raylet_nodes: node_id = (raylet_info.get("DBClientID") or raylet_info["NodeID"]) ip_address = (raylet_info.get("AuxAddress") or raylet_info["NodeManagerAddress"]).split(":")[0] if _append_port: ip_address += ":" + str(raylet_info["NodeManagerPort"]) self.raylet_id_to_ip_map[node_id] = ip_address def _run(self): """Run the monitor. This function loops forever, checking for messages about dead database clients and cleaning up state accordingly. """ # Initialize the subscription channel. self.psubscribe(ray.gcs_utils.XRAY_HEARTBEAT_BATCH_PATTERN) self.psubscribe(ray.gcs_utils.XRAY_JOB_PATTERN) if self.autoscaler: self.subscribe( ray.ray_constants.AUTOSCALER_RESOURCE_REQUEST_CHANNEL) # TODO(rkn): If there were any dead clients at startup, we should clean # up the associated state in the state tables. # Handle messages from the subscription channels. while True: # Update the mapping from raylet client ID to IP address. # This is only used to update the load metrics for the autoscaler. self.update_raylet_map() # Process autoscaling actions if self.autoscaler: self.autoscaler.update() # Process a round of messages. self.process_messages() # Wait for a heartbeat interval before processing the next round of # messages. time.sleep(ray._config.raylet_heartbeat_timeout_milliseconds() * 1e-3) def destroy_autoscaler_workers(self): """Cleanup the autoscaler, in case of an exception in the run() method. We kill the worker nodes, but retain the head node in order to keep logs around, keeping costs minimal. This monitor process runs on the head node anyway, so this is more reliable.""" if self.autoscaler is None: return # Nothing to clean up. if self.autoscaling_config is None: # This is a logic error in the program. Can't do anything. logger.error( "Monitor: Cleanup failed due to lack of autoscaler config.") return logger.info("Monitor: Exception caught. Taking down workers...") clean = False while not clean: try: teardown_cluster( config_file=self.autoscaling_config, yes=True, # Non-interactive. workers_only=True, # Retain head node for logs. override_cluster_name=None, keep_min_workers=True, # Retain minimal amount of workers. ) clean = True logger.info("Monitor: Workers taken down.") except Exception: logger.error("Monitor: Cleanup exception. Trying again...") time.sleep(2) def run(self): try: self._run() except Exception: logger.exception("Error in monitor loop") if self.autoscaler: self.autoscaler.kill_workers() raise