#!/usr/bin/env python # coding utf-8 import asyncio import json import socket as pysocket from logging import getLogger from random import randint from time import perf_counter from typing import Literal import numpy as np from monty.json import jsanitize from monty.serialization import MontyDecoder from maggma.cli.multiprocessing import multi from maggma.cli.settings import CLISettings from maggma.core import Builder from maggma.utils import Timeout, tqdm try: import pika except ImportError: raise ImportError("Both pika and aio-pika are required to use RabbitMQ as a broker") settings = CLISettings() def find_port(): sock = pysocket.socket() sock.bind(("", 0)) return sock.getsockname()[1] def manager( url: str, builders: list[Builder], num_chunks: int, num_workers: int, queue_prefix: str, port: int = 5672, ): """ Rabbit MQ manager for distributed processing that uses a builder prechunk to modify the builder and send them out each worker to run. """ logger = getLogger("Manager") if not (num_chunks and num_workers): raise ValueError("Both num_chunks and num_workers must be non-zero") url = url.split("//")[-1] logger.info(f"Binding to Manager URL {url}:{port}") # Setup connection to RabbitMQ and ensure on all queues is one unit connection, channel, status_queue, worker_queue = setup_rabbitmq(url, queue_prefix, port, "work") workers = {} # type: ignore logger.debug("Manager started and looking for workers") for builder in builders: logger.info(f"Working on {builder.__class__.__name__}") builder_dict = builder.as_dict() try: builder.connect() chunk_dicts = [{"chunk": d, "distributed": False, "completed": False} for d in builder.prechunk(num_chunks)] pbar_distributed = tqdm( total=len(chunk_dicts), desc=f"Distributed chunks for {builder.__class__.__name__}", ) pbar_completed = tqdm( total=len(chunk_dicts), desc=f"Completed chunks for {builder.__class__.__name__}", ) logger.info(f"Distributing {len(chunk_dicts)} chunks to workers") except NotImplementedError: attempt_graceful_shutdown(connection, workers, channel, worker_queue) raise RuntimeError(f"Can't distribute process {builder.__class__.__name__} as no prechunk method exists.") completed = False while not completed: completed = all(chunk["completed"] for chunk in chunk_dicts) if num_workers <= 0: connection.close() raise RuntimeError("No workers to distribute chunks to") # If workers send messages decode and figure out what do _, _, body = channel.basic_get(queue=status_queue, auto_ack=True) if body is not None: msg = body.decode("utf-8") identity = msg.split("_")[-1] if "READY" in msg: if identity not in workers: logger.debug(f"Got connection from worker: {msg.split('_')[1]}") workers[identity] = { "working": False, "heartbeats": 1, "last_ping": perf_counter(), "work_index": -1, } elif "DONE" in msg: workers[identity]["working"] = False work_ind = workers[identity]["work_index"] if work_ind != -1: chunk_dicts[work_ind]["completed"] = True # type: ignore pbar_completed.update(1) elif "ERROR" in msg: # Remove worker and requeue work sent to it attempt_graceful_shutdown(connection, workers, channel, worker_queue) raise RuntimeError( "At least one worker has stopped with error message: {}".format(msg.split("_")[1]) ) elif "PING" in msg: # Heartbeat from worker (no pong response) workers[identity]["last_ping"] = perf_counter() workers[identity]["heartbeats"] += 1 # Decide if any workers are dead and need to be removed handle_dead_workers(connection, workers, channel, worker_queue) for work_index, chunk_dict in enumerate(chunk_dicts): if not chunk_dict["distributed"]: temp_builder_dict = dict(**builder_dict) temp_builder_dict.update(chunk_dict["chunk"]) # type: ignore temp_builder_dict = jsanitize(temp_builder_dict, recursive_msonable=True) # Send work for available workers for identity in workers: if not workers[identity]["working"]: # Send out a chunk to idle worker channel.basic_publish( exchange="", routing_key=worker_queue, body=json.dumps(temp_builder_dict).encode("utf-8"), ) workers[identity]["work_index"] = work_index workers[identity]["working"] = True chunk_dicts[work_index]["distributed"] = True pbar_distributed.update(1) # Send EXIT to any remaining workers logger.info("Sending exit messages to workers once they are done") attempt_graceful_shutdown(connection, workers, channel, worker_queue) def setup_rabbitmq(url: str, queue_prefix: str, port: int, outbound_queue: Literal["status", "work"]): connection = pika.BlockingConnection(pika.ConnectionParameters(url, port)) channel = connection.channel() channel.basic_qos(prefetch_count=1, global_qos=True) # Ensure both worker status and work distribution queues exist status_queue = queue_prefix + "_status" worker_queue = queue_prefix + "_work" channel.queue_declare(queue=status_queue, auto_delete=True) channel.queue_declare(queue=worker_queue, auto_delete=True) # Clear out outbound queue if outbound_queue == "work": channel.queue_purge(queue=worker_queue) else: channel.queue_purge(queue=status_queue) return connection, channel, status_queue, worker_queue def attempt_graceful_shutdown(connection, workers, channel, worker_queue): for _ in workers: channel.basic_publish( exchange="", routing_key=worker_queue, body=b"EXIT", ) connection.close() def handle_dead_workers(connection, workers, channel, worker_queue): if len(workers) == 1: # Use global timeout identity = next(iter(workers.keys())) if (perf_counter() - workers[identity]["last_ping"]) >= settings.WORKER_TIMEOUT: attempt_graceful_shutdown(connection, workers, channel, worker_queue) raise RuntimeError("Worker has timed out. Stopping distributed build.") elif len(workers) == 2: # Use 10% ratio between workers workers_sorted = sorted(workers.items(), key=lambda x: x[1]["heartbeats"]) ratio = workers_sorted[1][1]["heartbeats"] / workers_sorted[0][1]["heartbeats"] if ratio <= 0.1: attempt_graceful_shutdown(connection, workers, channel, worker_queue) raise RuntimeError("One worker has timed out. Stopping distributed build.") elif len(workers) > 2: # Calculate modified z-score of heartbeat counts and see if any are <= -3.5 hearbeat_vals = [w["heartbeats"] for w in workers.values()] median = np.median(hearbeat_vals) mad = np.median([abs(i - median) for i in hearbeat_vals]) if mad > 0: for identity in list(workers.keys()): z_score = 0.6745 * (workers[identity]["heartbeats"] - median) / mad if z_score <= -3.5: attempt_graceful_shutdown(connection, workers, channel, worker_queue) raise RuntimeError("At least one worker has timed out. Stopping distributed build.") def worker(url: str, port: int, num_processes: int, no_bars: bool, queue_prefix: str): """ Simple distributed worker that connects to a manager asks for work and deploys using multiprocessing. """ identity = f"{randint(0, 0x10000):04X}-{randint(0, 0x10000):04X}" logger = getLogger(f"Worker {identity}") url = url.split("//")[-1] logger.info(f"Connecting to Manager at {url}:{port}") # Setup connection to RabbitMQ and ensure on all queues is one unit connection, channel, status_queue, worker_queue = setup_rabbitmq(url, queue_prefix, port, "status") # Send ready signal to status queue channel.basic_publish( exchange="", routing_key=status_queue, body=f"READY_{identity}".encode(), ) try: running = True while running: # Wait for work from manager with Timeout(seconds=settings.MANAGER_TIMEOUT): _, _, body = channel.basic_get(queue=worker_queue, auto_ack=True) if body is not None: message = body.decode("utf-8") if "@class" in message and "@module" in message: # We have a valid builder work = json.loads(message) builder = MontyDecoder().process_decoded(work) logger.info(f"Working on builder {builder.__class__}") channel.basic_publish( exchange="", routing_key=status_queue, body=f"WORKING_{identity}".encode(), ) work = json.loads(message) builder = MontyDecoder().process_decoded(work) asyncio.run( multi( builder, num_processes, no_bars=no_bars, heartbeat_func=ping_manager, heartbeat_func_kwargs={ "channel": channel, "identity": identity, "status_queue": status_queue, }, ) ) channel.basic_publish( exchange="", routing_key=status_queue, body=f"DONE_{identity}".encode(), ) elif message == "EXIT": # End the worker running = False except Exception as e: logger.error(f"A worker failed with error: {e!r}") channel.basic_publish( exchange="", routing_key=status_queue, body=f"ERROR_{identity}".encode(), ) connection.close() connection.close() def ping_manager(channel, identity, status_queue): channel.basic_publish( exchange="", routing_key=status_queue, body=f"PING_{identity}".encode(), )