class GlobalState: """A class used to interface with the Ray control state. Attributes: global_state_accessor: The client used to query gcs table from gcs server. """ def __init__(self): """Create a GlobalState object.""" # Args used for lazy init of this object. self.gcs_options = None self.global_state_accessor = None def _check_connected(self): """Ensure that the object has been initialized before it is used. This lazily initializes clients needed for state accessors. Raises: RuntimeError: An exception is raised if ray.init() has not been called yet. """ if (self.gcs_options is not None and self.global_state_accessor is None): self._really_init_global_state() # _really_init_global_state should have set self.global_state_accessor if self.global_state_accessor is None: raise ray.exceptions.RaySystemError( "Ray has not been started yet. You can start Ray with " "'ray.init()'.") def disconnect(self): """Disconnect global state from GCS.""" self.gcs_options = None if self.global_state_accessor is not None: self.global_state_accessor.disconnect() self.global_state_accessor = None def _initialize_global_state(self, gcs_options): """Set args for lazily initialization of the GlobalState object. It's possible that certain keys in gcs kv may not have been fully populated yet. In this case, we will retry this method until they have been populated or we exceed a timeout. Args: gcs_options: The client options for gcs """ # Save args for lazy init of global state. This avoids opening extra # gcs connections from each worker until needed. self.gcs_options = gcs_options def _really_init_global_state(self): self.global_state_accessor = GlobalStateAccessor(self.gcs_options) self.global_state_accessor.connect() def actor_table(self, actor_id): """Fetch and parse the actor table information for a single actor ID. Args: actor_id: A hex string of the actor ID to fetch information about. If this is None, then the actor table is fetched. Returns: Information from the actor table. """ self._check_connected() if actor_id is not None: actor_id = ray.ActorID(hex_to_binary(actor_id)) actor_info = self.global_state_accessor.get_actor_info(actor_id) if actor_info is None: return {} else: actor_table_data = gcs_utils.ActorTableData.FromString( actor_info) return self._gen_actor_info(actor_table_data) else: actor_table = self.global_state_accessor.get_actor_table() results = {} for i in range(len(actor_table)): actor_table_data = gcs_utils.ActorTableData.FromString( actor_table[i]) results[binary_to_hex(actor_table_data.actor_id)] = \ self._gen_actor_info(actor_table_data) return results def _gen_actor_info(self, actor_table_data): """Parse actor table data. Returns: Information from actor table. """ actor_info = { "ActorID": binary_to_hex(actor_table_data.actor_id), "ActorClassName": actor_table_data.class_name, "IsDetached": actor_table_data.is_detached, "Name": actor_table_data.name, "JobID": binary_to_hex(actor_table_data.job_id), "Address": { "IPAddress": actor_table_data.address.ip_address, "Port": actor_table_data.address.port, "NodeID": binary_to_hex(actor_table_data.address.raylet_id), }, "OwnerAddress": { "IPAddress": actor_table_data.owner_address.ip_address, "Port": actor_table_data.owner_address.port, "NodeID": binary_to_hex(actor_table_data.owner_address.raylet_id), }, "State": gcs_pb2.ActorTableData.ActorState.DESCRIPTOR.values_by_number[ actor_table_data.state].name, "NumRestarts": actor_table_data.num_restarts, "Timestamp": actor_table_data.timestamp, "StartTime": actor_table_data.start_time, "EndTime": actor_table_data.end_time, "DeathCause": actor_table_data.death_cause } return actor_info def node_resource_table(self, node_id=None): """Fetch and parse the node resource table info for one. Args: node_id: An node ID to fetch information about. Returns: Information from the node resource table. """ self._check_connected() node_id = ray.NodeID(hex_to_binary(node_id)) node_resource_bytes = \ self.global_state_accessor.get_node_resource_info(node_id) if node_resource_bytes is None: return {} else: node_resource_info = gcs_utils.ResourceMap.FromString( node_resource_bytes) return { key: value.resource_capacity for key, value in node_resource_info.items.items() } def node_table(self): """Fetch and parse the Gcs node info table. Returns: Information about the node in the cluster. """ self._check_connected() node_table = self.global_state_accessor.get_node_table() results = [] for node_info_item in node_table: item = gcs_utils.GcsNodeInfo.FromString(node_info_item) node_info = { "NodeID": ray._private.utils.binary_to_hex(item.node_id), "Alive": item.state == gcs_utils.GcsNodeInfo.GcsNodeState.Value( "ALIVE"), "NodeManagerAddress": item.node_manager_address, "NodeManagerHostname": item.node_manager_hostname, "NodeManagerPort": item.node_manager_port, "ObjectManagerPort": item.object_manager_port, "ObjectStoreSocketName": item.object_store_socket_name, "RayletSocketName": item.raylet_socket_name, "MetricsExportPort": item.metrics_export_port, } node_info["alive"] = node_info["Alive"] node_info["Resources"] = self.node_resource_table( node_info["NodeID"]) if node_info["Alive"] else {} results.append(node_info) return results def job_table(self): """Fetch and parse the gcs job table. Returns: Information about the Ray jobs in the cluster, namely a list of dicts with keys: - "JobID" (identifier for the job), - "DriverIPAddress" (IP address of the driver for this job), - "DriverPid" (process ID of the driver for this job), - "StartTime" (UNIX timestamp of the start time of this job), - "StopTime" (UNIX timestamp of the stop time of this job, if any) """ self._check_connected() job_table = self.global_state_accessor.get_job_table() results = [] for i in range(len(job_table)): entry = gcs_utils.JobTableData.FromString(job_table[i]) job_info = {} job_info["JobID"] = entry.job_id.hex() job_info["DriverIPAddress"] = entry.driver_ip_address job_info["DriverPid"] = entry.driver_pid job_info["Timestamp"] = entry.timestamp job_info["StartTime"] = entry.start_time job_info["EndTime"] = entry.end_time job_info["IsDead"] = entry.is_dead results.append(job_info) return results def next_job_id(self): """Get next job id from GCS. Returns: Next job id in the cluster. """ self._check_connected() return ray.JobID.from_int(self.global_state_accessor.get_next_job_id()) def profile_table(self): self._check_connected() result = defaultdict(list) profile_table = self.global_state_accessor.get_profile_table() for i in range(len(profile_table)): profile = gcs_utils.ProfileTableData.FromString(profile_table[i]) component_type = profile.component_type component_id = binary_to_hex(profile.component_id) node_ip_address = profile.node_ip_address for event in profile.profile_events: try: extra_data = json.loads(event.extra_data) except ValueError: extra_data = {} profile_event = { "event_type": event.event_type, "component_id": component_id, "node_ip_address": node_ip_address, "component_type": component_type, "start_time": event.start_time, "end_time": event.end_time, "extra_data": extra_data } result[component_id].append(profile_event) return dict(result) def get_placement_group_by_name(self, placement_group_name, ray_namespace): self._check_connected() placement_group_info = ( self.global_state_accessor.get_placement_group_by_name( placement_group_name, ray_namespace)) if placement_group_info is None: return None else: placement_group_table_data = \ gcs_utils.PlacementGroupTableData.FromString( placement_group_info) return self._gen_placement_group_info(placement_group_table_data) def placement_group_table(self, placement_group_id=None): self._check_connected() if placement_group_id is not None: placement_group_id = ray.PlacementGroupID( hex_to_binary(placement_group_id.hex())) placement_group_info = ( self.global_state_accessor.get_placement_group_info( placement_group_id)) if placement_group_info is None: return {} else: placement_group_info = (gcs_utils.PlacementGroupTableData. FromString(placement_group_info)) return self._gen_placement_group_info(placement_group_info) else: placement_group_table = self.global_state_accessor.\ get_placement_group_table() results = {} for placement_group_info in placement_group_table: placement_group_table_data = gcs_utils.\ PlacementGroupTableData.FromString(placement_group_info) placement_group_id = binary_to_hex( placement_group_table_data.placement_group_id) results[placement_group_id] = \ self._gen_placement_group_info(placement_group_table_data) return results def _gen_placement_group_info(self, placement_group_info): # This should be imported here, otherwise, it will error doc build. from ray.core.generated.common_pb2 import PlacementStrategy def get_state(state): if state == gcs_utils.PlacementGroupTableData.PENDING: return "PENDING" elif state == gcs_utils.PlacementGroupTableData.CREATED: return "CREATED" else: return "REMOVED" def get_strategy(strategy): if strategy == PlacementStrategy.PACK: return "PACK" elif strategy == PlacementStrategy.STRICT_PACK: return "STRICT_PACK" elif strategy == PlacementStrategy.STRICT_SPREAD: return "STRICT_SPREAD" elif strategy == PlacementStrategy.SPREAD: return "SPREAD" else: raise ValueError( f"Invalid strategy returned: {PlacementStrategy}") stats = placement_group_info.stats assert placement_group_info is not None return { "placement_group_id": binary_to_hex(placement_group_info.placement_group_id), "name": placement_group_info.name, "bundles": { # The value here is needs to be dictionarified # otherwise, the payload becomes unserializable. bundle.bundle_id.bundle_index: MessageToDict(bundle)["unitResources"] for bundle in placement_group_info.bundles }, "strategy": get_strategy(placement_group_info.strategy), "state": get_state(placement_group_info.state), "stats": { "end_to_end_creation_latency_ms": (stats.end_to_end_creation_latency_us / 1000.0), "scheduling_latency_ms": (stats.scheduling_latency_us / 1000.0), "scheduling_attempt": stats.scheduling_attempt, "highest_retry_delay_ms": stats.highest_retry_delay_ms, "scheduling_state": gcs_pb2.PlacementGroupStats.SchedulingState.DESCRIPTOR. values_by_number[stats.scheduling_state].name } } def _seconds_to_microseconds(self, time_in_seconds): """A helper function for converting seconds to microseconds.""" time_in_microseconds = 10**6 * time_in_seconds return time_in_microseconds # Colors are specified at # https://github.com/catapult-project/catapult/blob/master/tracing/tracing/base/color_scheme.html. # noqa: E501 _default_color_mapping = defaultdict( lambda: "generic_work", { "worker_idle": "cq_build_abandoned", "task": "rail_response", "task:deserialize_arguments": "rail_load", "task:execute": "rail_animation", "task:store_outputs": "rail_idle", "wait_for_function": "detailed_memory_dump", "ray.get": "good", "ray.put": "terrible", "ray.wait": "vsync_highlight_color", "submit_task": "background_memory_dump", "fetch_and_run_function": "detailed_memory_dump", "register_remote_function": "detailed_memory_dump", }) # These colors are for use in Chrome tracing. _chrome_tracing_colors = [ "thread_state_uninterruptible", "thread_state_iowait", "thread_state_running", "thread_state_runnable", "thread_state_sleeping", "thread_state_unknown", "background_memory_dump", "light_memory_dump", "detailed_memory_dump", "vsync_highlight_color", "generic_work", "good", "bad", "terrible", # "black", # "grey", # "white", "yellow", "olive", "rail_response", "rail_animation", "rail_idle", "rail_load", "startup", "heap_dump_stack_frame", "heap_dump_object_type", "heap_dump_child_node_arrow", "cq_build_running", "cq_build_passed", "cq_build_failed", "cq_build_abandoned", "cq_build_attempt_runnig", "cq_build_attempt_passed", "cq_build_attempt_failed", ] def chrome_tracing_dump(self, filename=None): """Return a list of profiling events that can viewed as a timeline. To view this information as a timeline, simply dump it as a json file by passing in "filename" or using using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Make sure to enable "Flow events" in the "View Options" menu. Args: filename: If a filename is provided, the timeline is dumped to that file. Returns: If filename is not provided, this returns a list of profiling events. Each profile event is a dictionary. """ # TODO(rkn): Support including the task specification data in the # timeline. # TODO(rkn): This should support viewing just a window of time or a # limited number of events. self._check_connected() profile_table = self.profile_table() all_events = [] for component_id_hex, component_events in profile_table.items(): # Only consider workers and drivers. component_type = component_events[0]["component_type"] if component_type not in ["worker", "driver"]: continue for event in component_events: new_event = { # The category of the event. "cat": event["event_type"], # The string displayed on the event. "name": event["event_type"], # The identifier for the group of rows that the event # appears in. "pid": event["node_ip_address"], # The identifier for the row that the event appears in. "tid": event["component_type"] + ":" + event["component_id"], # The start time in microseconds. "ts": self._seconds_to_microseconds(event["start_time"]), # The duration in microseconds. "dur": self._seconds_to_microseconds(event["end_time"] - event["start_time"]), # What is this? "ph": "X", # This is the name of the color to display the box in. "cname": self._default_color_mapping[event["event_type"]], # The extra user-defined data. "args": event["extra_data"], } # Modify the json with the additional user-defined extra data. # This can be used to add fields or override existing fields. if "cname" in event["extra_data"]: new_event["cname"] = event["extra_data"]["cname"] if "name" in event["extra_data"]: new_event["name"] = event["extra_data"]["name"] all_events.append(new_event) if not all_events: logger.warning( "No profiling events found. Ray profiling must be enabled " "by setting RAY_PROFILING=1.") if filename is not None: with open(filename, "w") as outfile: json.dump(all_events, outfile) else: return all_events def chrome_tracing_object_transfer_dump(self, filename=None): """Return a list of transfer events that can viewed as a timeline. To view this information as a timeline, simply dump it as a json file by passing in "filename" or using using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Make sure to enable "Flow events" in the "View Options" menu. Args: filename: If a filename is provided, the timeline is dumped to that file. Returns: If filename is not provided, this returns a list of profiling events. Each profile event is a dictionary. """ self._check_connected() node_id_to_address = {} for node_info in self.node_table(): node_id_to_address[node_info["NodeID"]] = "{}:{}".format( node_info["NodeManagerAddress"], node_info["ObjectManagerPort"]) all_events = [] for key, items in self.profile_table().items(): # Only consider object manager events. if items[0]["component_type"] != "object_manager": continue for event in items: if event["event_type"] == "transfer_send": object_ref, remote_node_id, _, _ = event["extra_data"] elif event["event_type"] == "transfer_receive": object_ref, remote_node_id, _ = event["extra_data"] elif event["event_type"] == "receive_pull_request": object_ref, remote_node_id = event["extra_data"] else: assert False, "This should be unreachable." # Choose a color by reading the first couple of hex digits of # the object ref as an integer and turning that into a color. object_ref_int = int(object_ref[:2], 16) color = self._chrome_tracing_colors[object_ref_int % len( self._chrome_tracing_colors)] new_event = { # The category of the event. "cat": event["event_type"], # The string displayed on the event. "name": event["event_type"], # The identifier for the group of rows that the event # appears in. "pid": node_id_to_address[key], # The identifier for the row that the event appears in. "tid": node_id_to_address[remote_node_id], # The start time in microseconds. "ts": self._seconds_to_microseconds(event["start_time"]), # The duration in microseconds. "dur": self._seconds_to_microseconds(event["end_time"] - event["start_time"]), # What is this? "ph": "X", # This is the name of the color to display the box in. "cname": color, # The extra user-defined data. "args": event["extra_data"], } all_events.append(new_event) # Add another box with a color indicating whether it was a send # or a receive event. if event["event_type"] == "transfer_send": additional_event = new_event.copy() additional_event["cname"] = "black" all_events.append(additional_event) elif event["event_type"] == "transfer_receive": additional_event = new_event.copy() additional_event["cname"] = "grey" all_events.append(additional_event) else: pass if filename is not None: with open(filename, "w") as outfile: json.dump(all_events, outfile) else: return all_events def workers(self): """Get a dictionary mapping worker ID to worker information.""" self._check_connected() # Get all data in worker table worker_table = self.global_state_accessor.get_worker_table() workers_data = {} for i in range(len(worker_table)): worker_table_data = gcs_utils.WorkerTableData.FromString( worker_table[i]) if worker_table_data.is_alive and \ worker_table_data.worker_type == gcs_utils.WORKER: worker_id = binary_to_hex( worker_table_data.worker_address.worker_id) worker_info = worker_table_data.worker_info workers_data[worker_id] = { "node_ip_address": decode(worker_info[b"node_ip_address"]), "plasma_store_socket": decode(worker_info[b"plasma_store_socket"]) } if b"stderr_file" in worker_info: workers_data[worker_id]["stderr_file"] = decode( worker_info[b"stderr_file"]) if b"stdout_file" in worker_info: workers_data[worker_id]["stdout_file"] = decode( worker_info[b"stdout_file"]) return workers_data def add_worker(self, worker_id, worker_type, worker_info): """ Add a worker to the cluster. Args: worker_id: ID of this worker. Type is bytes. worker_type: Type of this worker. Value is gcs_utils.DRIVER or gcs_utils.WORKER. worker_info: Info of this worker. Type is dict{str: str}. Returns: Is operation success """ worker_data = gcs_utils.WorkerTableData() worker_data.is_alive = True worker_data.worker_address.worker_id = worker_id worker_data.worker_type = worker_type for k, v in worker_info.items(): worker_data.worker_info[k] = bytes(v, encoding="utf-8") return self.global_state_accessor.add_worker_info( worker_data.SerializeToString()) def cluster_resources(self): """Get the current total cluster resources. Note that this information can grow stale as nodes are added to or removed from the cluster. Returns: A dictionary mapping resource name to the total quantity of that resource in the cluster. """ self._check_connected() resources = defaultdict(int) nodes = self.node_table() for node in nodes: # Only count resources from latest entries of live nodes. if node["Alive"]: for key, value in node["Resources"].items(): resources[key] += value return dict(resources) def _live_node_ids(self): """Returns a set of node IDs corresponding to nodes still alive.""" return { node["NodeID"] for node in self.node_table() if (node["Alive"]) } def _available_resources_per_node(self): """Returns a dictionary mapping node id to avaiable resources.""" self._check_connected() available_resources_by_id = {} all_available_resources = \ self.global_state_accessor.get_all_available_resources() for available_resource in all_available_resources: message = gcs_utils.AvailableResources.FromString( available_resource) # Calculate available resources for this node. dynamic_resources = {} for resource_id, capacity in \ message.resources_available.items(): dynamic_resources[resource_id] = capacity # Update available resources for this node. node_id = ray._private.utils.binary_to_hex(message.node_id) available_resources_by_id[node_id] = dynamic_resources # Update nodes in cluster. node_ids = self._live_node_ids() # Remove disconnected nodes. for node_id in list(available_resources_by_id.keys()): if node_id not in node_ids: del available_resources_by_id[node_id] return available_resources_by_id def available_resources(self): """Get the current available cluster resources. This is different from `cluster_resources` in that this will return idle (available) resources rather than total resources. Note that this information can grow stale as tasks start and finish. Returns: A dictionary mapping resource name to the total quantity of that resource in the cluster. """ self._check_connected() available_resources_by_id = self._available_resources_per_node() # Calculate total available resources. total_available_resources = defaultdict(int) for available_resources in available_resources_by_id.values(): for resource_id, num_available in available_resources.items(): total_available_resources[resource_id] += num_available return dict(total_available_resources) def get_system_config(self): """Get the system config of the cluster. """ self._check_connected() return json.loads(self.global_state_accessor.get_system_config()) def get_node_to_connect_for_driver(self, node_ip_address): """Get the node to connect for a Ray driver.""" self._check_connected() node_info_str = (self.global_state_accessor. get_node_to_connect_for_driver(node_ip_address)) return gcs_utils.GcsNodeInfo.FromString(node_info_str)
class GlobalState: """A class used to interface with the Ray control state. # TODO(zongheng): In the future move this to use Ray's redis module in the # backend to cut down on # of request RPCs. Attributes: redis_client: The Redis client used to query the primary redis server. redis_clients: Redis clients for each of the Redis shards. global_state_accessor: The client used to query gcs table from gcs server. """ def __init__(self): """Create a GlobalState object.""" # The redis server storing metadata, such as function table, client # table, log files, event logs, workers/actions info. self.redis_client = None # Clients for the redis shards, storing the object table & task table. self.redis_clients = None self.global_state_accessor = None def _check_connected(self): """Check that the object has been initialized before it is used. Raises: RuntimeError: An exception is raised if ray.init() has not been called yet. """ if (self.redis_client is None or self.redis_clients is None or self.global_state_accessor is None): raise ray.exceptions.RaySystemError( "Ray has not been started yet. You can start Ray with " "'ray.init()'.") def disconnect(self): """Disconnect global state from GCS.""" self.redis_client = None self.redis_clients = None if self.global_state_accessor is not None: self.global_state_accessor.disconnect() self.global_state_accessor = None def _initialize_global_state(self, redis_address, redis_password=None, timeout=20): """Initialize the GlobalState object by connecting to Redis. It's possible that certain keys in Redis may not have been fully populated yet. In this case, we will retry this method until they have been populated or we exceed a timeout. Args: redis_address: The Redis address to connect. redis_password: The password of the redis server. """ self.redis_client = services.create_redis_client( redis_address, redis_password) self.global_state_accessor = GlobalStateAccessor( redis_address, redis_password, False) self.global_state_accessor.connect() start_time = time.time() num_redis_shards = None redis_shard_addresses = [] while time.time() - start_time < timeout: # Attempt to get the number of Redis shards. num_redis_shards = self.redis_client.get("NumRedisShards") if num_redis_shards is None: print("Waiting longer for NumRedisShards to be populated.") time.sleep(1) continue num_redis_shards = int(num_redis_shards) assert num_redis_shards >= 1, ( f"Expected at least one Redis shard, found {num_redis_shards}." ) # Attempt to get all of the Redis shards. redis_shard_addresses = self.redis_client.lrange("RedisShards", start=0, end=-1) if len(redis_shard_addresses) != num_redis_shards: print("Waiting longer for RedisShards to be populated.") time.sleep(1) continue # If we got here then we successfully got all of the information. break # Check to see if we timed out. if time.time() - start_time >= timeout: raise TimeoutError("Timed out while attempting to initialize the " "global state. " f"num_redis_shards = {num_redis_shards}, " "redis_shard_addresses = " f"{redis_shard_addresses}") # Get the rest of the information. self.redis_clients = [] for shard_address in redis_shard_addresses: self.redis_clients.append( services.create_redis_client(shard_address.decode(), redis_password)) def _execute_command(self, key, *args): """Execute a Redis command on the appropriate Redis shard based on key. Args: key: The object ref or the task ID that the query is about. args: The command to run. Returns: The value returned by the Redis command. """ client = self.redis_clients[key.redis_shard_hash() % len(self.redis_clients)] return client.execute_command(*args) def _keys(self, pattern): """Execute the KEYS command on all Redis shards. Args: pattern: The KEYS pattern to query. Returns: The concatenated list of results from all shards. """ result = [] for client in self.redis_clients: result.extend(list(client.scan_iter(match=pattern))) return result def object_table(self, object_ref=None): """Fetch and parse the object table info for one or more object refs. Args: object_ref: An object ref to fetch information about. If this is None, then the entire object table is fetched. Returns: Information from the object table. """ self._check_connected() if object_ref is not None: object_ref = ray.ObjectRef(hex_to_binary(object_ref)) object_info = self.global_state_accessor.get_object_info( object_ref) if object_info is None: return {} else: object_location_info = gcs_utils.ObjectLocationInfo.FromString( object_info) return self._gen_object_info(object_location_info) else: object_table = self.global_state_accessor.get_object_table() results = {} for i in range(len(object_table)): object_location_info = gcs_utils.ObjectLocationInfo.FromString( object_table[i]) results[binary_to_hex(object_location_info.object_id)] = \ self._gen_object_info(object_location_info) return results def _gen_object_info(self, object_location_info): """Parse object location info. Returns: Information from object. """ locations = [] for location in object_location_info.locations: locations.append(ray.utils.binary_to_hex(location.manager)) object_info = { "ObjectRef": ray.utils.binary_to_hex(object_location_info.object_id), "Locations": locations, } return object_info def actor_table(self, actor_id): """Fetch and parse the actor table information for a single actor ID. Args: actor_id: A hex string of the actor ID to fetch information about. If this is None, then the actor table is fetched. Returns: Information from the actor table. """ self._check_connected() if actor_id is not None: actor_id = ray.ActorID(hex_to_binary(actor_id)) actor_info = self.global_state_accessor.get_actor_info(actor_id) if actor_info is None: return {} else: actor_table_data = gcs_utils.ActorTableData.FromString( actor_info) return self._gen_actor_info(actor_table_data) else: actor_table = self.global_state_accessor.get_actor_table() results = {} for i in range(len(actor_table)): actor_table_data = gcs_utils.ActorTableData.FromString( actor_table[i]) results[binary_to_hex(actor_table_data.actor_id)] = \ self._gen_actor_info(actor_table_data) return results def _gen_actor_info(self, actor_table_data): """Parse actor table data. Returns: Information from actor table. """ actor_info = { "ActorID": binary_to_hex(actor_table_data.actor_id), "JobID": binary_to_hex(actor_table_data.job_id), "Address": { "IPAddress": actor_table_data.address.ip_address, "Port": actor_table_data.address.port, "NodeID": binary_to_hex(actor_table_data.address.raylet_id), }, "OwnerAddress": { "IPAddress": actor_table_data.owner_address.ip_address, "Port": actor_table_data.owner_address.port, "NodeID": binary_to_hex(actor_table_data.owner_address.raylet_id), }, "State": actor_table_data.state, "NumRestarts": actor_table_data.num_restarts, "Timestamp": actor_table_data.timestamp, } return actor_info def node_resource_table(self, node_id=None): """Fetch and parse the node resource table info for one. Args: node_id: An node ID to fetch information about. Returns: Information from the node resource table. """ self._check_connected() node_id = ray.NodeID(hex_to_binary(node_id)) node_resource_bytes = \ self.global_state_accessor.get_node_resource_info(node_id) if node_resource_bytes is None: return {} else: node_resource_info = gcs_utils.ResourceMap.FromString( node_resource_bytes) return { key: value.resource_capacity for key, value in node_resource_info.items.items() } def node_table(self): """Fetch and parse the Gcs node info table. Returns: Information about the node in the cluster. """ self._check_connected() node_table = self.global_state_accessor.get_node_table() results = [] for node_info_item in node_table: item = gcs_utils.GcsNodeInfo.FromString(node_info_item) node_info = { "NodeID": ray.utils.binary_to_hex(item.node_id), "Alive": item.state == gcs_utils.GcsNodeInfo.GcsNodeState.Value( "ALIVE"), "NodeManagerAddress": item.node_manager_address, "NodeManagerHostname": item.node_manager_hostname, "NodeManagerPort": item.node_manager_port, "ObjectManagerPort": item.object_manager_port, "ObjectStoreSocketName": item.object_store_socket_name, "RayletSocketName": item.raylet_socket_name, "MetricsExportPort": item.metrics_export_port, } node_info["alive"] = node_info["Alive"] node_info["Resources"] = self.node_resource_table( node_info["NodeID"]) if node_info["Alive"] else {} results.append(node_info) return results def job_table(self): """Fetch and parse the Redis job table. Returns: Information about the Ray jobs in the cluster, namely a list of dicts with keys: - "JobID" (identifier for the job), - "DriverIPAddress" (IP address of the driver for this job), - "DriverPid" (process ID of the driver for this job), - "StartTime" (UNIX timestamp of the start time of this job), - "StopTime" (UNIX timestamp of the stop time of this job, if any) """ self._check_connected() job_table = self.global_state_accessor.get_job_table() results = [] for i in range(len(job_table)): entry = gcs_utils.JobTableData.FromString(job_table[i]) job_info = {} job_info["JobID"] = entry.job_id.hex() job_info["DriverIPAddress"] = entry.driver_ip_address job_info["DriverPid"] = entry.driver_pid if entry.is_dead: job_info["StopTime"] = entry.timestamp else: job_info["StartTime"] = entry.timestamp results.append(job_info) return results def profile_table(self): self._check_connected() result = defaultdict(list) profile_table = self.global_state_accessor.get_profile_table() for i in range(len(profile_table)): profile = gcs_utils.ProfileTableData.FromString(profile_table[i]) component_type = profile.component_type component_id = binary_to_hex(profile.component_id) node_ip_address = profile.node_ip_address for event in profile.profile_events: try: extra_data = json.loads(event.extra_data) except ValueError: extra_data = {} profile_event = { "event_type": event.event_type, "component_id": component_id, "node_ip_address": node_ip_address, "component_type": component_type, "start_time": event.start_time, "end_time": event.end_time, "extra_data": extra_data } result[component_id].append(profile_event) return dict(result) def placement_group_table(self, placement_group_id=None): self._check_connected() if placement_group_id is not None: placement_group_id = ray.PlacementGroupID( hex_to_binary(placement_group_id.hex())) placement_group_info = ( self.global_state_accessor.get_placement_group_info( placement_group_id)) if placement_group_info is None: return {} else: placement_group_info = (gcs_utils.PlacementGroupTableData. FromString(placement_group_info)) return self._gen_placement_group_info(placement_group_info) else: placement_group_table = self.global_state_accessor.\ get_placement_group_table() results = {} for placement_group_info in placement_group_table: placement_group_table_data = gcs_utils.\ PlacementGroupTableData.FromString(placement_group_info) placement_group_id = binary_to_hex( placement_group_table_data.placement_group_id) results[placement_group_id] = \ self._gen_placement_group_info(placement_group_table_data) return results def _gen_placement_group_info(self, placement_group_info): # This should be imported here, otherwise, it will error doc build. from ray.core.generated.common_pb2 import PlacementStrategy def get_state(state): if state == ray.gcs_utils.PlacementGroupTableData.PENDING: return "PENDING" elif state == ray.gcs_utils.PlacementGroupTableData.CREATED: return "CREATED" else: return "REMOVED" def get_strategy(strategy): if strategy == PlacementStrategy.PACK: return "PACK" elif strategy == PlacementStrategy.STRICT_PACK: return "STRICT_PACK" elif strategy == PlacementStrategy.STRICT_SPREAD: return "STRICT_SPREAD" elif strategy == PlacementStrategy.SPREAD: return "SPREAD" else: raise ValueError( f"Invalid strategy returned: {PlacementStrategy}") assert placement_group_info is not None return { "placement_group_id": binary_to_hex(placement_group_info.placement_group_id), "name": placement_group_info.name, "bundles": { # The value here is needs to be dictionarified # otherwise, the payload becomes unserializable. bundle.bundle_id.bundle_index: MessageToDict(bundle)["unitResources"] for bundle in placement_group_info.bundles }, "strategy": get_strategy(placement_group_info.strategy), "state": get_state(placement_group_info.state), } def _seconds_to_microseconds(self, time_in_seconds): """A helper function for converting seconds to microseconds.""" time_in_microseconds = 10**6 * time_in_seconds return time_in_microseconds # Colors are specified at # https://github.com/catapult-project/catapult/blob/master/tracing/tracing/base/color_scheme.html. # noqa: E501 _default_color_mapping = defaultdict( lambda: "generic_work", { "worker_idle": "cq_build_abandoned", "task": "rail_response", "task:deserialize_arguments": "rail_load", "task:execute": "rail_animation", "task:store_outputs": "rail_idle", "wait_for_function": "detailed_memory_dump", "ray.get": "good", "ray.put": "terrible", "ray.wait": "vsync_highlight_color", "submit_task": "background_memory_dump", "fetch_and_run_function": "detailed_memory_dump", "register_remote_function": "detailed_memory_dump", }) # These colors are for use in Chrome tracing. _chrome_tracing_colors = [ "thread_state_uninterruptible", "thread_state_iowait", "thread_state_running", "thread_state_runnable", "thread_state_sleeping", "thread_state_unknown", "background_memory_dump", "light_memory_dump", "detailed_memory_dump", "vsync_highlight_color", "generic_work", "good", "bad", "terrible", # "black", # "grey", # "white", "yellow", "olive", "rail_response", "rail_animation", "rail_idle", "rail_load", "startup", "heap_dump_stack_frame", "heap_dump_object_type", "heap_dump_child_node_arrow", "cq_build_running", "cq_build_passed", "cq_build_failed", "cq_build_abandoned", "cq_build_attempt_runnig", "cq_build_attempt_passed", "cq_build_attempt_failed", ] def chrome_tracing_dump(self, filename=None): """Return a list of profiling events that can viewed as a timeline. To view this information as a timeline, simply dump it as a json file by passing in "filename" or using using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Make sure to enable "Flow events" in the "View Options" menu. Args: filename: If a filename is provided, the timeline is dumped to that file. Returns: If filename is not provided, this returns a list of profiling events. Each profile event is a dictionary. """ # TODO(rkn): Support including the task specification data in the # timeline. # TODO(rkn): This should support viewing just a window of time or a # limited number of events. self._check_connected() profile_table = self.profile_table() all_events = [] for component_id_hex, component_events in profile_table.items(): # Only consider workers and drivers. component_type = component_events[0]["component_type"] if component_type not in ["worker", "driver"]: continue for event in component_events: new_event = { # The category of the event. "cat": event["event_type"], # The string displayed on the event. "name": event["event_type"], # The identifier for the group of rows that the event # appears in. "pid": event["node_ip_address"], # The identifier for the row that the event appears in. "tid": event["component_type"] + ":" + event["component_id"], # The start time in microseconds. "ts": self._seconds_to_microseconds(event["start_time"]), # The duration in microseconds. "dur": self._seconds_to_microseconds(event["end_time"] - event["start_time"]), # What is this? "ph": "X", # This is the name of the color to display the box in. "cname": self._default_color_mapping[event["event_type"]], # The extra user-defined data. "args": event["extra_data"], } # Modify the json with the additional user-defined extra data. # This can be used to add fields or override existing fields. if "cname" in event["extra_data"]: new_event["cname"] = event["extra_data"]["cname"] if "name" in event["extra_data"]: new_event["name"] = event["extra_data"]["name"] all_events.append(new_event) if filename is not None: with open(filename, "w") as outfile: json.dump(all_events, outfile) else: return all_events def chrome_tracing_object_transfer_dump(self, filename=None): """Return a list of transfer events that can viewed as a timeline. To view this information as a timeline, simply dump it as a json file by passing in "filename" or using using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Make sure to enable "Flow events" in the "View Options" menu. Args: filename: If a filename is provided, the timeline is dumped to that file. Returns: If filename is not provided, this returns a list of profiling events. Each profile event is a dictionary. """ self._check_connected() node_id_to_address = {} for node_info in self.node_table(): node_id_to_address[node_info["NodeID"]] = "{}:{}".format( node_info["NodeManagerAddress"], node_info["ObjectManagerPort"]) all_events = [] for key, items in self.profile_table().items(): # Only consider object manager events. if items[0]["component_type"] != "object_manager": continue for event in items: if event["event_type"] == "transfer_send": object_ref, remote_node_id, _, _ = event["extra_data"] elif event["event_type"] == "transfer_receive": object_ref, remote_node_id, _, _ = event["extra_data"] elif event["event_type"] == "receive_pull_request": object_ref, remote_node_id = event["extra_data"] else: assert False, "This should be unreachable." # Choose a color by reading the first couple of hex digits of # the object ref as an integer and turning that into a color. object_ref_int = int(object_ref[:2], 16) color = self._chrome_tracing_colors[object_ref_int % len( self._chrome_tracing_colors)] new_event = { # The category of the event. "cat": event["event_type"], # The string displayed on the event. "name": event["event_type"], # The identifier for the group of rows that the event # appears in. "pid": node_id_to_address[key], # The identifier for the row that the event appears in. "tid": node_id_to_address[remote_node_id], # The start time in microseconds. "ts": self._seconds_to_microseconds(event["start_time"]), # The duration in microseconds. "dur": self._seconds_to_microseconds(event["end_time"] - event["start_time"]), # What is this? "ph": "X", # This is the name of the color to display the box in. "cname": color, # The extra user-defined data. "args": event["extra_data"], } all_events.append(new_event) # Add another box with a color indicating whether it was a send # or a receive event. if event["event_type"] == "transfer_send": additional_event = new_event.copy() additional_event["cname"] = "black" all_events.append(additional_event) elif event["event_type"] == "transfer_receive": additional_event = new_event.copy() additional_event["cname"] = "grey" all_events.append(additional_event) else: pass if filename is not None: with open(filename, "w") as outfile: json.dump(all_events, outfile) else: return all_events def workers(self): """Get a dictionary mapping worker ID to worker information.""" self._check_connected() # Get all data in worker table worker_table = self.global_state_accessor.get_worker_table() workers_data = {} for i in range(len(worker_table)): worker_table_data = gcs_utils.WorkerTableData.FromString( worker_table[i]) if worker_table_data.is_alive and \ worker_table_data.worker_type == gcs_utils.WORKER: worker_id = binary_to_hex( worker_table_data.worker_address.worker_id) worker_info = worker_table_data.worker_info workers_data[worker_id] = { "node_ip_address": decode(worker_info[b"node_ip_address"]), "plasma_store_socket": decode(worker_info[b"plasma_store_socket"]) } if b"stderr_file" in worker_info: workers_data[worker_id]["stderr_file"] = decode( worker_info[b"stderr_file"]) if b"stdout_file" in worker_info: workers_data[worker_id]["stdout_file"] = decode( worker_info[b"stdout_file"]) return workers_data def add_worker(self, worker_id, worker_type, worker_info): """ Add a worker to the cluster. Args: worker_id: ID of this worker. Type is bytes. worker_type: Type of this worker. Value is ray.gcs_utils.DRIVER or ray.gcs_utils.WORKER. worker_info: Info of this worker. Type is dict{str: str}. Returns: Is operation success """ worker_data = ray.gcs_utils.WorkerTableData() worker_data.is_alive = True worker_data.worker_address.worker_id = worker_id worker_data.worker_type = worker_type for k, v in worker_info.items(): worker_data.worker_info[k] = bytes(v, encoding="utf-8") return self.global_state_accessor.add_worker_info( worker_data.SerializeToString()) def _job_length(self): event_log_sets = self.redis_client.keys("event_log*") overall_smallest = sys.maxsize overall_largest = 0 num_tasks = 0 for event_log_set in event_log_sets: fwd_range = self.redis_client.zrange(event_log_set, start=0, end=0, withscores=True) overall_smallest = min(overall_smallest, fwd_range[0][1]) rev_range = self.redis_client.zrevrange(event_log_set, start=0, end=0, withscores=True) overall_largest = max(overall_largest, rev_range[0][1]) num_tasks += self.redis_client.zcount(event_log_set, min=0, max=time.time()) if num_tasks == 0: return 0, 0, 0 return overall_smallest, overall_largest, num_tasks def cluster_resources(self): """Get the current total cluster resources. Note that this information can grow stale as nodes are added to or removed from the cluster. Returns: A dictionary mapping resource name to the total quantity of that resource in the cluster. """ self._check_connected() resources = defaultdict(int) clients = self.node_table() for client in clients: # Only count resources from latest entries of live clients. if client["Alive"]: for key, value in client["Resources"].items(): resources[key] += value return dict(resources) def _live_client_ids(self): """Returns a set of client IDs corresponding to clients still alive.""" return { client["NodeID"] for client in self.node_table() if (client["Alive"]) } def _available_resources_per_node(self): """Returns a dictionary mapping node id to avaiable resources.""" available_resources_by_id = {} all_available_resources = \ self.global_state_accessor.get_all_available_resources() for available_resource in all_available_resources: message = ray.gcs_utils.AvailableResources.FromString( available_resource) # Calculate available resources for this node. dynamic_resources = {} for resource_id, capacity in \ message.resources_available.items(): dynamic_resources[resource_id] = capacity # Update available resources for this node. node_id = ray.utils.binary_to_hex(message.node_id) available_resources_by_id[node_id] = dynamic_resources # Update nodes in cluster. node_ids = self._live_client_ids() # Remove disconnected nodes. for node_id in available_resources_by_id.keys(): if node_id not in node_ids: del available_resources_by_id[node_id] return available_resources_by_id def available_resources(self): """Get the current available cluster resources. This is different from `cluster_resources` in that this will return idle (available) resources rather than total resources. Note that this information can grow stale as tasks start and finish. Returns: A dictionary mapping resource name to the total quantity of that resource in the cluster. """ self._check_connected() available_resources_by_id = self._available_resources_per_node() # Calculate total available resources. total_available_resources = defaultdict(int) for available_resources in available_resources_by_id.values(): for resource_id, num_available in available_resources.items(): total_available_resources[resource_id] += num_available return dict(total_available_resources) def actor_checkpoint_info(self, actor_id): """Get checkpoint info for the given actor id. Args: actor_id: Actor's ID. Returns: A dictionary with information about the actor's checkpoint IDs and their timestamps. """ self._check_connected() message = self._execute_command( actor_id, "RAY.TABLE_LOOKUP", gcs_utils.TablePrefix.Value("ACTOR_CHECKPOINT_ID"), "", actor_id.binary(), ) if message is None: return None gcs_entry = gcs_utils.GcsEntry.FromString(message) entry = gcs_utils.ActorCheckpointIdData.FromString( gcs_entry.entries[0]) checkpoint_ids = [ ray.ActorCheckpointID(checkpoint_id) for checkpoint_id in entry.checkpoint_ids ] return { "ActorID": ray.utils.binary_to_hex(entry.actor_id), "CheckpointIds": checkpoint_ids, "Timestamps": list(entry.timestamps), }
def initialize(num_cpus: int, num_gpus: int, log_root_path: str, log_name: Optional[str] = None, logger_cls: type = TBXLogger, launch_tensorboard: bool = True, debug: bool = False, verbose: bool = True) -> Callable[[Dict[str, Any]], Logger]: """Initialize Ray and Tensorboard daemons. It will be used later for almost everything from dashboard, remote/client management, to multithreaded environment. .. note: The default Tensorboard port will be used, namely 6006 if available, using 0.0.0.0 (binding to all IPv4 addresses on local machine). Similarly, Ray dashboard port is 8265 if available. In both cases, the port will be increased interatively until to find one available. :param num_cpus: Maximum number of CPU threads that can be executed in parallel. Note that it does not actually reserve part of the CPU, so that several processes can reserve the number of threads available on the system at the same time. :param num_gpu: Maximum number of GPU unit that can be used, which can be fractional to only allocate part of the resource. Note that contrary to CPU resource, the memory is likely to actually be reserve and allocated by the process, in particular using Tensorflow backend. :param log_root_path: Fullpath of root log directory. :param log_name: Name of the subdirectory where to save data. `None` to use default name, empty string '' to set it interactively in command prompt. It must be a valid Python identifier. Optional: full date _ hostname by default. :param logger_cls: Custom logger class type deriving from `TBXLogger`. Optional: `TBXLogger` by default. :param launch_tensorboard: Whether or not to launch tensorboard automatically. Optional: Enabled by default. :param debug: Whether or not to display debugging trace. Optional: Disabled by default. :param verbose: Whether or not to print information about what is going on. Optional: True by default. :returns: lambda function to pass a `ray.Trainer` to monitor learning progress in Tensorboard. """ # Make sure provided logger class derives from ray.tune.logger.Logger assert issubclass(logger_cls, Logger), ( "Logger class must derive from `ray.tune.logger.Logger`") # Check if cluster servers are already running, and if requested resources # are available. is_cluster_running = False redis_addresses = services.find_redis_address() if redis_addresses: for redis_address in redis_addresses: # Connect to redis global state accessor global_state_accessor = GlobalStateAccessor( redis_address, ray_constants.REDIS_DEFAULT_PASSWORD) global_state_accessor.connect() # Get available resources resources: Dict[str, int] = defaultdict(int) for info in global_state_accessor.get_all_available_resources(): # pylint: disable=no-member message = ray.gcs_utils.AvailableResources.FromString(info) for field, capacity in message.resources_available.items(): resources[field] += capacity # Disconnect global state accessor time.sleep(0.1) global_state_accessor.disconnect() # Check if enough computation resources are available is_cluster_running = (resources["CPU"] >= num_cpus and resources["GPU"] >= num_gpus) # Stop looking as soon as a cluster with enough resources is found if is_cluster_running: break # Connect to Ray server if necessary, starting one if not already running if not ray.is_initialized(): if not is_cluster_running: # Start new Ray server, if not already running ray.init( # Address of Ray cluster to connect to, if any address=None, # Number of CPUs assigned to each raylet num_cpus=num_cpus, # Number of GPUs assigned to each raylet num_gpus=num_gpus, # Enable object eviction in LRU order under memory pressure _lru_evict=False, # Whether or not to execute the code serially (for debugging) local_mode=debug, # Logging level logging_level=logging.DEBUG if debug else logging.ERROR, # Whether to redirect outputs from every worker to the driver log_to_driver=debug, # Whether to start Ray dashboard, to monitor cluster's status include_dashboard=True, # The host to bind the dashboard server to dashboard_host="0.0.0.0") else: # Connect to existing Ray cluster ray.init( address="auto", _lru_evict=False, local_mode=debug, logging_level=logging.DEBUG if debug else logging.ERROR, log_to_driver=debug, include_dashboard=False) # Configure Tensorboard if launch_tensorboard: tb = TensorBoard() tb.configure(host="0.0.0.0", logdir=os.path.abspath(log_root_path)) url = tb.launch() if verbose: print(f"Started Tensorboard {url}.", f"Root directory: {log_root_path}") # Define log filename interactively if requested if log_name == "": while True: log_name = input( "Enter desired log subdirectory name (empty for default)...") if not log_name or re.match(r'^[A-Za-z0-9_]+$', log_name): break print("Unvalid name. Only Python identifiers are supported.") # Handling of default log name and sanity checks if not log_name: log_name = "_".join(( datetime.now().strftime("%Y_%m_%d_%H_%M_%S"), re.sub(r'[^A-Za-z0-9_]', "_", socket.gethostname()))) else: assert re.match(r'^[A-Za-z0-9_]+$', log_name), ( "Log name must be a valid Python identifier.") # Create log directory log_path = os.path.join(log_root_path, log_name) pathlib.Path(log_path).mkdir(parents=True, exist_ok=True) if verbose: print(f"Tensorboard logfiles directory: {log_path}") # Define Ray logger def logger_creator(config: Dict[str, Any]) -> Logger: return logger_cls(config, log_path) return logger_creator