import logging import os import numpy as np import ray.ray_constants as ray_constants logger = logging.getLogger(__name__) class RayParams: """A class used to store the parameters used by Ray. Attributes: redis_address (str): The address of the Redis server to connect to. If this address is not provided, then this command will start Redis, a raylet, a plasma store, a plasma manager, and some workers. It will also kill these processes when Python exits. redis_port (int): The port that the primary Redis shard should listen to. If None, then a random port will be chosen. redis_shard_ports: A list of the ports to use for the non-primary Redis shards. num_cpus (int): Number of CPUs to configure the raylet with. num_gpus (int): Number of GPUs to configure the raylet with. resources: A dictionary mapping the name of a resource to the quantity of that resource available. memory: Total available memory for workers requesting memory. object_store_memory: The amount of memory (in bytes) to start the object store with. redis_max_memory: The max amount of memory (in bytes) to allow redis to use, or None for no limit. Once the limit is exceeded, redis will start LRU eviction of entries. This only applies to the sharded redis tables (task and object tables). object_manager_port int: The port to use for the object manager. node_manager_port: The port to use for the node manager. gcs_server_port: The port to use for the GCS server. node_ip_address (str): The IP address of the node that we are on. raylet_ip_address (str): The IP address of the raylet that this node connects to. min_worker_port (int): The lowest port number that workers will bind on. If not set or set to 0, random ports will be chosen. max_worker_port (int): The highest port number that workers will bind on. If set, min_worker_port must also be set. object_ref_seed (int): Used to seed the deterministic generation of object refs. The same value can be used across multiple runs of the same job in order to generate the object refs in a consistent manner. However, the same ID should not be used for different jobs. redirect_worker_output: True if the stdout and stderr of worker processes should be redirected to files. redirect_output (bool): True if stdout and stderr for non-worker processes should be redirected to files and false otherwise. num_redis_shards: The number of Redis shards to start in addition to the primary Redis shard. redis_max_clients: If provided, attempt to configure Redis with this maxclients number. redis_password (str): Prevents external clients without the password from connecting to Redis if provided. plasma_directory: A directory where the Plasma memory mapped files will be created. worker_path (str): The path of the source code that will be run by the worker. huge_pages: Boolean flag indicating whether to start the Object Store with hugetlbfs support. Requires plasma_directory. include_dashboard: Boolean flag indicating whether to start the web UI, which displays the status of the Ray cluster. If this value is None, then the UI will be started if the relevant dependencies are present. dashboard_host: The host to bind the web UI server to. Can either be localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces). By default, this is set to localhost to prevent access from external machines. dashboard_port: The port to bind the dashboard server to. Defaults to 8265. logging_level: Logging level, default will be logging.INFO. logging_format: Logging format, default contains a timestamp, filename, line number, and message. See ray_constants.py. plasma_store_socket_name (str): If provided, it will specify the socket name used by the plasma store. raylet_socket_name (str): If provided, it will specify the socket path used by the raylet process. temp_dir (str): If provided, it will specify the root temporary directory for the Ray process. include_log_monitor (bool): If True, then start a log monitor to monitor the log files for all processes on this node and push their contents to Redis. autoscaling_config: path to autoscaling config file. include_java (bool): If True, the raylet backend can also support Java worker. java_worker_options (list): The command options for Java worker. load_code_from_local: Whether load code from local file or from GCS. metrics_agent_port(int): The port to bind metrics agent. _internal_config (str): JSON configuration for overriding RayConfig defaults. For testing purposes ONLY. lru_evict (bool): Enable LRU eviction if space is needed. """ def __init__(self, redis_address=None, num_cpus=None, num_gpus=None, resources=None, memory=None, object_store_memory=None, redis_max_memory=None, redis_port=None, redis_shard_ports=None, object_manager_port=None, node_manager_port=None, gcs_server_port=None, node_ip_address=None, raylet_ip_address=None, min_worker_port=None, max_worker_port=None, object_ref_seed=None, driver_mode=None, redirect_worker_output=None, redirect_output=None, num_redis_shards=None, redis_max_clients=None, redis_password=ray_constants.REDIS_DEFAULT_PASSWORD, plasma_directory=None, worker_path=None, huge_pages=False, include_dashboard=None, dashboard_host="localhost", dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT, logging_level=logging.INFO, logging_format=ray_constants.LOGGER_FORMAT, plasma_store_socket_name=None, raylet_socket_name=None, temp_dir=None, include_log_monitor=None, autoscaling_config=None, include_java=False, java_worker_options=None, load_code_from_local=False, _internal_config=None, metrics_agent_port=None, lru_evict=False): self.object_ref_seed = object_ref_seed self.redis_address = redis_address self.num_cpus = num_cpus self.num_gpus = num_gpus self.memory = memory self.object_store_memory = object_store_memory self.resources = resources self.redis_max_memory = redis_max_memory self.redis_port = redis_port self.redis_shard_ports = redis_shard_ports self.object_manager_port = object_manager_port self.node_manager_port = node_manager_port self.gcs_server_port = gcs_server_port self.node_ip_address = node_ip_address self.raylet_ip_address = raylet_ip_address self.min_worker_port = min_worker_port self.max_worker_port = max_worker_port self.driver_mode = driver_mode self.redirect_worker_output = redirect_worker_output self.redirect_output = redirect_output self.num_redis_shards = num_redis_shards self.redis_max_clients = redis_max_clients self.redis_password = redis_password self.plasma_directory = plasma_directory self.worker_path = worker_path self.huge_pages = huge_pages self.include_dashboard = include_dashboard self.dashboard_host = dashboard_host self.dashboard_port = dashboard_port self.plasma_store_socket_name = plasma_store_socket_name self.raylet_socket_name = raylet_socket_name self.temp_dir = temp_dir self.include_log_monitor = include_log_monitor self.autoscaling_config = autoscaling_config self.include_java = include_java self.java_worker_options = java_worker_options self.load_code_from_local = load_code_from_local self.metrics_agent_port = metrics_agent_port self._internal_config = _internal_config self._lru_evict = lru_evict self._check_usage() # Set the internal config options for LRU eviction. if lru_evict: # Turn off object pinning. if self._internal_config is None: self._internal_config = dict() if self._internal_config.get("object_pinning_enabled", False): raise Exception( "Object pinning cannot be enabled if using LRU eviction.") self._internal_config["object_pinning_enabled"] = False self._internal_config["object_store_full_max_retries"] = -1 self._internal_config["free_objects_period_milliseconds"] = 1000 def update(self, **kwargs): """Update the settings according to the keyword arguments. Args: kwargs: The keyword arguments to set corresponding fields. """ for arg in kwargs: if hasattr(self, arg): setattr(self, arg, kwargs[arg]) else: raise ValueError("Invalid RayParams parameter in" " update: %s" % arg) self._check_usage() def update_if_absent(self, **kwargs): """Update the settings when the target fields are None. Args: kwargs: The keyword arguments to set corresponding fields. """ for arg in kwargs: if hasattr(self, arg): if getattr(self, arg) is None: setattr(self, arg, kwargs[arg]) else: raise ValueError("Invalid RayParams parameter in" " update_if_absent: %s" % arg) self._check_usage() def _check_usage(self): # Used primarily for testing. if os.environ.get("RAY_USE_RANDOM_PORTS", False): if self.min_worker_port is None and self.min_worker_port is None: self.min_worker_port = 0 self.max_worker_port = 0 if self.min_worker_port is not None: if self.min_worker_port != 0 and (self.min_worker_port < 1024 or self.min_worker_port > 65535): raise ValueError("min_worker_port must be 0 or an integer " "between 1024 and 65535.") if self.max_worker_port is not None: if self.min_worker_port is None: raise ValueError("If max_worker_port is set, min_worker_port " "must also be set.") elif self.max_worker_port != 0: if self.max_worker_port < 1024 or self.max_worker_port > 65535: raise ValueError( "max_worker_port must be 0 or an integer between " "1024 and 65535.") elif self.max_worker_port <= self.min_worker_port: raise ValueError("max_worker_port must be higher than " "min_worker_port.") if self.resources is not None: assert "CPU" not in self.resources, ( "'CPU' should not be included in the resource dictionary. Use " "num_cpus instead.") assert "GPU" not in self.resources, ( "'GPU' should not be included in the resource dictionary. Use " "num_gpus instead.") if self.redirect_worker_output is not None: raise DeprecationWarning( "The redirect_worker_output argument is deprecated. To " "control logging to the driver, use the 'log_to_driver' " "argument to 'ray.init()'") if self.redirect_output is not None: raise DeprecationWarning( "The redirect_output argument is deprecated.") # Parse the numpy version. numpy_version = np.__version__.split(".") numpy_major, numpy_minor = int(numpy_version[0]), int(numpy_version[1]) if numpy_major <= 1 and numpy_minor < 16: logger.warning("Using ray with numpy < 1.16.0 will result in slow " "serialization. Upgrade numpy if using with ray.")