Source code for ray.rllib.env.policy_server_input

from collections import deque
from http.server import HTTPServer, SimpleHTTPRequestHandler
import logging
import queue
from socketserver import ThreadingMixIn
import threading
import time
import traceback

from typing import List
import ray.cloudpickle as pickle
from ray.rllib.env.policy_client import (
from ray.rllib.offline.input_reader import InputReader
from ray.rllib.offline.io_context import IOContext
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.evaluation.metrics import RolloutMetrics
from ray.rllib.evaluation.sampler import SamplerInput
from ray.rllib.utils.typing import SampleBatchType

logger = logging.getLogger(__name__)

[docs]@PublicAPI class PolicyServerInput(ThreadingMixIn, HTTPServer, InputReader): """REST policy server that acts as an offline data source. This launches a multi-threaded server that listens on the specified host and port to serve policy requests and forward experiences to RLlib. For high performance experience collection, it implements InputReader. For an example, run `examples/envs/external_envs/` along with `examples/envs/external_envs/ --inference-mode=local|remote`. WARNING: This class is not meant to be publicly exposed. Anyone that can communicate with this server can execute arbitary code on the machine. Use this with caution, in isolated environments, and at your own risk. .. testcode:: :skipif: True import gymnasium as gym from ray.rllib.algorithms.ppo import PPOConfig from ray.rllib.env.policy_client import PolicyClient from ray.rllib.env.policy_server_input import PolicyServerInput addr, port = ... config = ( PPOConfig() .environment("CartPole-v1") .offline_data( input_=lambda ioctx: PolicyServerInput(ioctx, addr, port) ) # Run just 1 server (in the Algorithm's EnvRunnerGroup). .env_runners(num_env_runners=0) ) algo = while True: algo.train() client = PolicyClient( "localhost:9900", inference_mode="local") eps_id = client.start_episode() env = gym.make("CartPole-v1") obs, info = env.reset() action = client.get_action(eps_id, obs) _, reward, _, _, _ = env.step(action) client.log_returns(eps_id, reward) client.log_returns(eps_id, reward) algo.stop() """ @PublicAPI def __init__( self, ioctx: IOContext, address: str, port: int, idle_timeout: float = 3.0, max_sample_queue_size: int = 20, ): """Create a PolicyServerInput. This class implements rllib.offline.InputReader, and can be used with any Algorithm by configuring [AlgorithmConfig object] .env_runners(num_env_runners=0) .offline_data(input_=lambda ioctx: PolicyServerInput(ioctx, addr, port)) Note that by setting num_env_runners: 0, the algorithm will only create one rollout worker / PolicyServerInput. Clients can connect to the launched server using rllib.env.PolicyClient. You can increase the number of available connections (ports) by setting num_env_runners to a larger number. The ports used will then be `port` + the worker's index. Args: ioctx: IOContext provided by RLlib. address: Server addr (e.g., "localhost"). port: Server port (e.g., 9900). max_queue_size: The maximum size for the sample queue. Once full, will purge (throw away) 50% of all samples, oldest first, and continue. """ self.rollout_worker = ioctx.worker # Protect ourselves from having a bottleneck on the server (learning) side. # Once the queue (deque) is full, we throw away 50% (oldest # samples first) of the samples, warn, and continue. self.samples_queue = deque(maxlen=max_sample_queue_size) self.metrics_queue = queue.Queue() self.idle_timeout = idle_timeout # Forwards client-reported metrics directly into the local rollout # worker. if self.rollout_worker.sampler is not None: # This is a bit of a hack since it is patching the get_metrics # function of the sampler. def get_metrics(): completed = [] while True: try: completed.append(self.metrics_queue.get_nowait()) except queue.Empty: break return completed self.rollout_worker.sampler.get_metrics = get_metrics else: # If there is no sampler, act like if there would be one to collect # metrics from class MetricsDummySampler(SamplerInput): """This sampler only maintains a queue to get metrics from.""" def __init__(self, metrics_queue): """Initializes a MetricsDummySampler instance. Args: metrics_queue: A queue of metrics """ self.metrics_queue = metrics_queue def get_data(self) -> SampleBatchType: raise NotImplementedError def get_extra_batches(self) -> List[SampleBatchType]: raise NotImplementedError def get_metrics(self) -> List[RolloutMetrics]: """Returns metrics computed on a policy client rollout worker.""" completed = [] while True: try: completed.append(self.metrics_queue.get_nowait()) except queue.Empty: break return completed self.rollout_worker.sampler = MetricsDummySampler(self.metrics_queue) # Create a request handler that receives commands from the clients # and sends data and metrics into the queues. handler = _make_handler( self.rollout_worker, self.samples_queue, self.metrics_queue ) try: import time time.sleep(1) HTTPServer.__init__(self, (address, port), handler) except OSError: print(f"Creating a PolicyServer on {address}:{port} failed!") import time time.sleep(1) raise "Starting connector server at " f"{self.server_name}:{self.server_port}" ) # Start the serving thread, listening on socket and handling commands. serving_thread = threading.Thread(name="server", target=self.serve_forever) serving_thread.daemon = True serving_thread.start() # Start a dummy thread that puts empty SampleBatches on the queue, just # in case we don't receive anything from clients (or there aren't # any). The latter would block sample collection entirely otherwise, # even if other workers' PolicyServerInput receive incoming data from # actual clients. heart_beat_thread = threading.Thread( name="heart-beat", target=self._put_empty_sample_batch_every_n_sec ) heart_beat_thread.daemon = True heart_beat_thread.start()
[docs] @override(InputReader) def next(self): # Blocking wait until there is something in the deque. while len(self.samples_queue) == 0: time.sleep(0.1) # Utilize last items first in order to remain as closely as possible # to operating on-policy. return self.samples_queue.pop()
def _put_empty_sample_batch_every_n_sec(self): # Places an empty SampleBatch every `idle_timeout` seconds onto the # `samples_queue`. This avoids hanging of all RolloutWorkers parallel # to this one in case this PolicyServerInput does not have incoming # data (e.g. no client connected) and the driver algorithm uses parallel # synchronous sampling (e.g. PPO). while True: time.sleep(self.idle_timeout) self.samples_queue.append(SampleBatch())
def _make_handler(rollout_worker, samples_queue, metrics_queue): # Only used in remote inference mode. We must create a new rollout worker # then since the original worker doesn't have the env properly wrapped in # an ExternalEnv interface. child_rollout_worker = None inference_thread = None lock = threading.Lock() def setup_child_rollout_worker(): nonlocal lock with lock: nonlocal child_rollout_worker nonlocal inference_thread if child_rollout_worker is None: ( child_rollout_worker, inference_thread, ) = _create_embedded_rollout_worker( rollout_worker.creation_args(), report_data ) child_rollout_worker.set_weights(rollout_worker.get_weights()) def report_data(data): nonlocal child_rollout_worker batch = data["samples"] batch.decompress_if_needed() samples_queue.append(batch) # Deque is full -> purge 50% (oldest samples) if len(samples_queue) == samples_queue.maxlen: logger.warning( "PolicyServerInput queue is full! Purging half of the samples (oldest)." ) for _ in range(samples_queue.maxlen // 2): samples_queue.popleft() for rollout_metric in data["metrics"]: metrics_queue.put(rollout_metric) if child_rollout_worker is not None: child_rollout_worker.set_weights( rollout_worker.get_weights(), rollout_worker.get_global_vars() ) class Handler(SimpleHTTPRequestHandler): def __init__(self, *a, **kw): super().__init__(*a, **kw) def do_POST(self): content_len = int(self.headers.get("Content-Length"), 0) raw_body = parsed_input = pickle.loads(raw_body) try: response = self.execute_command(parsed_input) self.send_response(200) self.end_headers() self.wfile.write(pickle.dumps(response)) except Exception: self.send_error(500, traceback.format_exc()) def execute_command(self, args): command = args["command"] response = {} # Local inference commands: if command == Commands.GET_WORKER_ARGS:"Sending worker creation args to client.") response["worker_args"] = rollout_worker.creation_args() elif command == Commands.GET_WEIGHTS:"Sending worker weights to client.") response["weights"] = rollout_worker.get_weights() response["global_vars"] = rollout_worker.get_global_vars() elif command == Commands.REPORT_SAMPLES: "Got sample batch of size {} from client.".format( args["samples"].count ) ) report_data(args) # Remote inference commands: elif command == Commands.START_EPISODE: setup_child_rollout_worker() assert inference_thread.is_alive() response["episode_id"] = child_rollout_worker.env.start_episode( args["episode_id"], args["training_enabled"] ) elif command == Commands.GET_ACTION: assert inference_thread.is_alive() response["action"] = child_rollout_worker.env.get_action( args["episode_id"], args["observation"] ) elif command == Commands.LOG_ACTION: assert inference_thread.is_alive() child_rollout_worker.env.log_action( args["episode_id"], args["observation"], args["action"] ) elif command == Commands.LOG_RETURNS: assert inference_thread.is_alive() if args["done"]: child_rollout_worker.env.log_returns( args["episode_id"], args["reward"], args["info"], args["done"] ) else: child_rollout_worker.env.log_returns( args["episode_id"], args["reward"], args["info"] ) elif command == Commands.END_EPISODE: assert inference_thread.is_alive() child_rollout_worker.env.end_episode( args["episode_id"], args["observation"] ) else: raise ValueError("Unknown command: {}".format(command)) return response return Handler