Refactor actor_server.py for improved structure and logging
- Consolidated logging initialization and enhanced logging for actor processes. - Streamlined the handling of gRPC connections and process management. - Improved readability by organizing core algorithm functions and communication functions. - Added detailed comments and documentation for clarity. - Ensured proper queue management and shutdown handling for actor processes.
This commit is contained in:
AdilZouitine
parent
dcce446a66
commit
8b02e81bb5
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@ -14,11 +14,11 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import time
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from functools import lru_cache
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from queue import Empty
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from statistics import mean, quantiles
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import os
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# from lerobot.scripts.eval import eval_policy
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import grpc
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@ -57,245 +57,132 @@ from lerobot.scripts.server.utils import get_last_item_from_queue, setup_process
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ACTOR_SHUTDOWN_TIMEOUT = 30
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def receive_policy(
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cfg: TrainPipelineConfig,
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parameters_queue: Queue,
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shutdown_event: any, # Event,
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learner_client: hilserl_pb2_grpc.LearnerServiceStub | None = None,
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grpc_channel: grpc.Channel | None = None,
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):
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logging.info("[ACTOR] Start receiving parameters from the Learner")
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#################################################
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# Main entry point #
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#################################################
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@parser.wrap()
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def actor_cli(cfg: TrainPipelineConfig):
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cfg.validate()
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if not use_threads(cfg):
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# Create a process-specific log file
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log_dir = os.path.join(cfg.output_dir, "logs")
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os.makedirs(log_dir, exist_ok=True)
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log_file = os.path.join(log_dir, f"actor_receive_policy_{os.getpid()}.log")
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# Initialize logging with explicit log file
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init_logging(log_file=log_file)
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logging.info(f"Actor receive policy process logging initialized")
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# Setup process handlers to handle shutdown signal
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# But use shutdown event from the main process
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setup_process_handlers(use_threads=False)
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import torch.multiprocessing as mp
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if grpc_channel is None or learner_client is None:
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learner_client, grpc_channel = learner_service_client(
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host=cfg.policy.actor_learner_config.learner_host,
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port=cfg.policy.actor_learner_config.learner_port,
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)
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mp.set_start_method("spawn")
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try:
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iterator = learner_client.StreamParameters(hilserl_pb2.Empty())
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receive_bytes_in_chunks(
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iterator,
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parameters_queue,
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shutdown_event,
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log_prefix="[ACTOR] parameters",
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)
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# Create logs directory to ensure it exists
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log_dir = os.path.join(cfg.output_dir, "logs")
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os.makedirs(log_dir, exist_ok=True)
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log_file = os.path.join(log_dir, f"actor_{cfg.job_name}.log")
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except grpc.RpcError as e:
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logging.error(f"[ACTOR] gRPC error: {e}")
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# Initialize logging with explicit log file
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init_logging(log_file=log_file)
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logging.info(f"Actor logging initialized, writing to {log_file}")
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if not use_threads(cfg):
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grpc_channel.close()
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logging.info("[ACTOR] Received policy loop stopped")
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shutdown_event = setup_process_handlers(use_threads(cfg))
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def transitions_stream(shutdown_event: Event, transitions_queue: Queue) -> hilserl_pb2.Empty:
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while not shutdown_event.is_set():
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try:
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message = transitions_queue.get(block=True, timeout=5)
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except Empty:
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logging.debug("[ACTOR] Transition queue is empty")
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continue
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yield from send_bytes_in_chunks(
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message, hilserl_pb2.Transition, log_prefix="[ACTOR] Send transitions"
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)
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return hilserl_pb2.Empty()
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def interactions_stream(
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shutdown_event: any, # Event,
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interactions_queue: Queue,
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) -> hilserl_pb2.Empty:
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while not shutdown_event.is_set():
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try:
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message = interactions_queue.get(block=True, timeout=5)
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except Empty:
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logging.debug("[ACTOR] Interaction queue is empty")
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continue
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yield from send_bytes_in_chunks(
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message,
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hilserl_pb2.InteractionMessage,
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log_prefix="[ACTOR] Send interactions",
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)
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return hilserl_pb2.Empty()
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def send_transitions(
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cfg: TrainPipelineConfig,
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transitions_queue: Queue,
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shutdown_event: any, # Event,
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learner_client: hilserl_pb2_grpc.LearnerServiceStub | None = None,
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grpc_channel: grpc.Channel | None = None,
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) -> hilserl_pb2.Empty:
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"""
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Sends transitions to the learner.
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This function continuously retrieves messages from the queue and processes:
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- **Transition Data:**
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- A batch of transitions (observation, action, reward, next observation) is collected.
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- Transitions are moved to the CPU and serialized using PyTorch.
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- The serialized data is wrapped in a `hilserl_pb2.Transition` message and sent to the learner.
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"""
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if not use_threads(cfg):
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# Create a process-specific log file
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log_dir = os.path.join(cfg.output_dir, "logs")
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os.makedirs(log_dir, exist_ok=True)
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log_file = os.path.join(log_dir, f"actor_transitions_{os.getpid()}.log")
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# Initialize logging with explicit log file
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init_logging(log_file=log_file)
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logging.info(f"Actor transitions process logging initialized")
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# Setup process handlers to handle shutdown signal
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# But use shutdown event from the main process
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setup_process_handlers(False)
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if grpc_channel is None or learner_client is None:
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learner_client, grpc_channel = learner_service_client(
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host=cfg.policy.actor_learner_config.learner_host,
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port=cfg.policy.actor_learner_config.learner_port,
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)
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try:
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learner_client.SendTransitions(transitions_stream(shutdown_event, transitions_queue))
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except grpc.RpcError as e:
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logging.error(f"[ACTOR] gRPC error: {e}")
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logging.info("[ACTOR] Finished streaming transitions")
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if not use_threads(cfg):
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grpc_channel.close()
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logging.info("[ACTOR] Transitions process stopped")
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def send_interactions(
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cfg: TrainPipelineConfig,
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interactions_queue: Queue,
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shutdown_event: any, # Event,
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learner_client: hilserl_pb2_grpc.LearnerServiceStub | None = None,
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grpc_channel: grpc.Channel | None = None,
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) -> hilserl_pb2.Empty:
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"""
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Sends interactions to the learner.
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This function continuously retrieves messages from the queue and processes:
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- **Interaction Messages:**
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- Contains useful statistics about episodic rewards and policy timings.
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- The message is serialized using `pickle` and sent to the learner.
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"""
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if not use_threads(cfg):
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# Create a process-specific log file
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log_dir = os.path.join(cfg.output_dir, "logs")
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os.makedirs(log_dir, exist_ok=True)
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log_file = os.path.join(log_dir, f"actor_interactions_{os.getpid()}.log")
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# Initialize logging with explicit log file
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init_logging(log_file=log_file)
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logging.info(f"Actor interactions process logging initialized")
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# Setup process handlers to handle shutdown signal
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# But use shutdown event from the main process
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setup_process_handlers(False)
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if grpc_channel is None or learner_client is None:
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learner_client, grpc_channel = learner_service_client(
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host=cfg.policy.actor_learner_config.learner_host,
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port=cfg.policy.actor_learner_config.learner_port,
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)
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try:
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learner_client.SendInteractions(interactions_stream(shutdown_event, interactions_queue))
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except grpc.RpcError as e:
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logging.error(f"[ACTOR] gRPC error: {e}")
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logging.info("[ACTOR] Finished streaming interactions")
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if not use_threads(cfg):
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grpc_channel.close()
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logging.info("[ACTOR] Interactions process stopped")
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@lru_cache(maxsize=1)
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def learner_service_client(
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host="127.0.0.1", port=50051
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) -> tuple[hilserl_pb2_grpc.LearnerServiceStub, grpc.Channel]:
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import json
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"""
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Returns a client for the learner service.
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GRPC uses HTTP/2, which is a binary protocol and multiplexes requests over a single connection.
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So we need to create only one client and reuse it.
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"""
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service_config = {
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"methodConfig": [
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{
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"name": [{}], # Applies to ALL methods in ALL services
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"retryPolicy": {
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"maxAttempts": 5, # Max retries (total attempts = 5)
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"initialBackoff": "0.1s", # First retry after 0.1s
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"maxBackoff": "2s", # Max wait time between retries
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"backoffMultiplier": 2, # Exponential backoff factor
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"retryableStatusCodes": [
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"UNAVAILABLE",
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"DEADLINE_EXCEEDED",
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], # Retries on network failures
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},
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}
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]
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}
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service_config_json = json.dumps(service_config)
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channel = grpc.insecure_channel(
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f"{host}:{port}",
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options=[
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("grpc.max_receive_message_length", learner_service.MAX_MESSAGE_SIZE),
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("grpc.max_send_message_length", learner_service.MAX_MESSAGE_SIZE),
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("grpc.enable_retries", 1),
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("grpc.service_config", service_config_json),
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],
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learner_client, grpc_channel = learner_service_client(
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host=cfg.policy.actor_learner_config.learner_host,
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port=cfg.policy.actor_learner_config.learner_port,
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)
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stub = hilserl_pb2_grpc.LearnerServiceStub(channel)
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logging.info("[ACTOR] Learner service client created")
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return stub, channel
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logging.info("[ACTOR] Establishing connection with Learner")
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if not establish_learner_connection(learner_client, shutdown_event):
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logging.error("[ACTOR] Failed to establish connection with Learner")
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return
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if not use_threads(cfg):
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# If we use multithreading, we can reuse the channel
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grpc_channel.close()
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grpc_channel = None
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logging.info("[ACTOR] Connection with Learner established")
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parameters_queue = Queue()
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transitions_queue = Queue()
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interactions_queue = Queue()
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concurrency_entity = None
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if use_threads(cfg):
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from threading import Thread
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concurrency_entity = Thread
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else:
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from multiprocessing import Process
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concurrency_entity = Process
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receive_policy_process = concurrency_entity(
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target=receive_policy,
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args=(cfg, parameters_queue, shutdown_event, grpc_channel),
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daemon=True,
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)
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transitions_process = concurrency_entity(
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target=send_transitions,
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args=(cfg, transitions_queue, shutdown_event, grpc_channel),
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daemon=True,
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)
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interactions_process = concurrency_entity(
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target=send_interactions,
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args=(cfg, interactions_queue, shutdown_event, grpc_channel),
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daemon=True,
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)
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transitions_process.start()
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interactions_process.start()
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receive_policy_process.start()
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# HACK: FOR MANISKILL we do not have a reward classifier
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# TODO: Remove this once we merge into main
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reward_classifier = None
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# if (
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# cfg.env.reward_classifier["pretrained_path"] is not None
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# and cfg.env.reward_classifier["config_path"] is not None
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# ):
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# reward_classifier = get_classifier(
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# pretrained_path=cfg.env.reward_classifier["pretrained_path"],
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# config_path=cfg.env.reward_classifier["config_path"],
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# )
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act_with_policy(
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cfg=cfg,
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reward_classifier=reward_classifier,
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shutdown_event=shutdown_event,
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parameters_queue=parameters_queue,
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transitions_queue=transitions_queue,
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interactions_queue=interactions_queue,
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)
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logging.info("[ACTOR] Policy process joined")
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logging.info("[ACTOR] Closing queues")
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transitions_queue.close()
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interactions_queue.close()
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parameters_queue.close()
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transitions_process.join()
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logging.info("[ACTOR] Transitions process joined")
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interactions_process.join()
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logging.info("[ACTOR] Interactions process joined")
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receive_policy_process.join()
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logging.info("[ACTOR] Receive policy process joined")
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logging.info("[ACTOR] join queues")
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transitions_queue.cancel_join_thread()
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interactions_queue.cancel_join_thread()
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parameters_queue.cancel_join_thread()
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logging.info("[ACTOR] queues closed")
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def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device):
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if not parameters_queue.empty():
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logging.info("[ACTOR] Load new parameters from Learner.")
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bytes_state_dict = get_last_item_from_queue(parameters_queue)
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state_dict = bytes_to_state_dict(bytes_state_dict)
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state_dict = move_state_dict_to_device(state_dict, device=device)
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policy.load_state_dict(state_dict)
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#################################################
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# Core algorithm functions #
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#################################################
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def act_with_policy(
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cfg: TrainPipelineConfig,
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# robot: Robot,
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reward_classifier: nn.Module,
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shutdown_event: any, # Event,
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parameters_queue: Queue,
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@ -309,7 +196,12 @@ def act_with_policy(
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Once an episode is completed, updated network parameters received from the learner are retrieved from a queue and loaded into the network.
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Args:
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cfg (DictConfig): Configuration settings for the interaction process.
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cfg: Configuration settings for the interaction process.
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reward_classifier: Reward classifier to use for the interaction process.
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shutdown_event: Event to check if the process should shutdown.
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parameters_queue: Queue to receive updated network parameters from the learner.
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transitions_queue: Queue to send transitions to the learner.
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interactions_queue: Queue to send interactions to the learner.
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"""
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# Initialize logging for multiprocessing
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if not use_threads(cfg):
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@ -317,7 +209,7 @@ def act_with_policy(
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os.makedirs(log_dir, exist_ok=True)
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log_file = os.path.join(log_dir, f"actor_policy_{os.getpid()}.log")
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init_logging(log_file=log_file)
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logging.info(f"Actor policy process logging initialized")
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logging.info("Actor policy process logging initialized")
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logging.info("make_env online")
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@ -462,6 +354,278 @@ def act_with_policy(
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busy_wait(1 / cfg.env.fps - dt_time)
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#################################################
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# Communication Functions - Group all gRPC/messaging functions #
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#################################################
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def establish_learner_connection(
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stub,
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shutdown_event: any, # Event,
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attempts=30,
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):
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for _ in range(attempts):
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if shutdown_event.is_set():
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logging.info("[ACTOR] Shutting down establish_learner_connection")
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return False
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# Force a connection attempt and check state
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try:
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logging.info("[ACTOR] Send ready message to Learner")
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if stub.Ready(hilserl_pb2.Empty()) == hilserl_pb2.Empty():
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return True
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except grpc.RpcError as e:
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logging.error(f"[ACTOR] Waiting for Learner to be ready... {e}")
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time.sleep(2)
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return False
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@lru_cache(maxsize=1)
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def learner_service_client(
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host="127.0.0.1", port=50051
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) -> tuple[hilserl_pb2_grpc.LearnerServiceStub, grpc.Channel]:
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import json
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"""
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Returns a client for the learner service.
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GRPC uses HTTP/2, which is a binary protocol and multiplexes requests over a single connection.
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So we need to create only one client and reuse it.
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"""
|
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|
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service_config = {
|
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"methodConfig": [
|
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{
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"name": [{}], # Applies to ALL methods in ALL services
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"retryPolicy": {
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"maxAttempts": 5, # Max retries (total attempts = 5)
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"initialBackoff": "0.1s", # First retry after 0.1s
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"maxBackoff": "2s", # Max wait time between retries
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"backoffMultiplier": 2, # Exponential backoff factor
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"retryableStatusCodes": [
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"UNAVAILABLE",
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"DEADLINE_EXCEEDED",
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], # Retries on network failures
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},
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}
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]
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}
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service_config_json = json.dumps(service_config)
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channel = grpc.insecure_channel(
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f"{host}:{port}",
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options=[
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("grpc.max_receive_message_length", learner_service.MAX_MESSAGE_SIZE),
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("grpc.max_send_message_length", learner_service.MAX_MESSAGE_SIZE),
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("grpc.enable_retries", 1),
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("grpc.service_config", service_config_json),
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],
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)
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stub = hilserl_pb2_grpc.LearnerServiceStub(channel)
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logging.info("[ACTOR] Learner service client created")
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return stub, channel
|
||||
|
||||
|
||||
def receive_policy(
|
||||
cfg: TrainPipelineConfig,
|
||||
parameters_queue: Queue,
|
||||
shutdown_event: any, # Event,
|
||||
learner_client: hilserl_pb2_grpc.LearnerServiceStub | None = None,
|
||||
grpc_channel: grpc.Channel | None = None,
|
||||
):
|
||||
logging.info("[ACTOR] Start receiving parameters from the Learner")
|
||||
|
||||
if not use_threads(cfg):
|
||||
# Create a process-specific log file
|
||||
log_dir = os.path.join(cfg.output_dir, "logs")
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_file = os.path.join(log_dir, f"actor_receive_policy_{os.getpid()}.log")
|
||||
|
||||
# Initialize logging with explicit log file
|
||||
init_logging(log_file=log_file)
|
||||
logging.info(f"Actor receive policy process logging initialized")
|
||||
|
||||
# Setup process handlers to handle shutdown signal
|
||||
# But use shutdown event from the main process
|
||||
setup_process_handlers(use_threads=False)
|
||||
|
||||
if grpc_channel is None or learner_client is None:
|
||||
learner_client, grpc_channel = learner_service_client(
|
||||
host=cfg.policy.actor_learner_config.learner_host,
|
||||
port=cfg.policy.actor_learner_config.learner_port,
|
||||
)
|
||||
|
||||
try:
|
||||
iterator = learner_client.StreamParameters(hilserl_pb2.Empty())
|
||||
receive_bytes_in_chunks(
|
||||
iterator,
|
||||
parameters_queue,
|
||||
shutdown_event,
|
||||
log_prefix="[ACTOR] parameters",
|
||||
)
|
||||
|
||||
except grpc.RpcError as e:
|
||||
logging.error(f"[ACTOR] gRPC error: {e}")
|
||||
|
||||
if not use_threads(cfg):
|
||||
grpc_channel.close()
|
||||
logging.info("[ACTOR] Received policy loop stopped")
|
||||
|
||||
|
||||
def send_transitions(
|
||||
cfg: TrainPipelineConfig,
|
||||
transitions_queue: Queue,
|
||||
shutdown_event: any, # Event,
|
||||
learner_client: hilserl_pb2_grpc.LearnerServiceStub | None = None,
|
||||
grpc_channel: grpc.Channel | None = None,
|
||||
) -> hilserl_pb2.Empty:
|
||||
"""
|
||||
Sends transitions to the learner.
|
||||
|
||||
This function continuously retrieves messages from the queue and processes:
|
||||
|
||||
- **Transition Data:**
|
||||
- A batch of transitions (observation, action, reward, next observation) is collected.
|
||||
- Transitions are moved to the CPU and serialized using PyTorch.
|
||||
- The serialized data is wrapped in a `hilserl_pb2.Transition` message and sent to the learner.
|
||||
"""
|
||||
|
||||
if not use_threads(cfg):
|
||||
# Create a process-specific log file
|
||||
log_dir = os.path.join(cfg.output_dir, "logs")
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_file = os.path.join(log_dir, f"actor_transitions_{os.getpid()}.log")
|
||||
|
||||
# Initialize logging with explicit log file
|
||||
init_logging(log_file=log_file)
|
||||
logging.info("Actor transitions process logging initialized")
|
||||
|
||||
# Setup process handlers to handle shutdown signal
|
||||
# But use shutdown event from the main process
|
||||
setup_process_handlers(False)
|
||||
|
||||
if grpc_channel is None or learner_client is None:
|
||||
learner_client, grpc_channel = learner_service_client(
|
||||
host=cfg.policy.actor_learner_config.learner_host,
|
||||
port=cfg.policy.actor_learner_config.learner_port,
|
||||
)
|
||||
|
||||
try:
|
||||
learner_client.SendTransitions(transitions_stream(shutdown_event, transitions_queue))
|
||||
except grpc.RpcError as e:
|
||||
logging.error(f"[ACTOR] gRPC error: {e}")
|
||||
|
||||
logging.info("[ACTOR] Finished streaming transitions")
|
||||
|
||||
if not use_threads(cfg):
|
||||
grpc_channel.close()
|
||||
logging.info("[ACTOR] Transitions process stopped")
|
||||
|
||||
|
||||
def send_interactions(
|
||||
cfg: TrainPipelineConfig,
|
||||
interactions_queue: Queue,
|
||||
shutdown_event: any, # Event,
|
||||
learner_client: hilserl_pb2_grpc.LearnerServiceStub | None = None,
|
||||
grpc_channel: grpc.Channel | None = None,
|
||||
) -> hilserl_pb2.Empty:
|
||||
"""
|
||||
Sends interactions to the learner.
|
||||
|
||||
This function continuously retrieves messages from the queue and processes:
|
||||
|
||||
- **Interaction Messages:**
|
||||
- Contains useful statistics about episodic rewards and policy timings.
|
||||
- The message is serialized using `pickle` and sent to the learner.
|
||||
"""
|
||||
|
||||
if not use_threads(cfg):
|
||||
# Create a process-specific log file
|
||||
log_dir = os.path.join(cfg.output_dir, "logs")
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_file = os.path.join(log_dir, f"actor_interactions_{os.getpid()}.log")
|
||||
|
||||
# Initialize logging with explicit log file
|
||||
init_logging(log_file=log_file)
|
||||
logging.info("Actor interactions process logging initialized")
|
||||
|
||||
# Setup process handlers to handle shutdown signal
|
||||
# But use shutdown event from the main process
|
||||
setup_process_handlers(False)
|
||||
|
||||
if grpc_channel is None or learner_client is None:
|
||||
learner_client, grpc_channel = learner_service_client(
|
||||
host=cfg.policy.actor_learner_config.learner_host,
|
||||
port=cfg.policy.actor_learner_config.learner_port,
|
||||
)
|
||||
|
||||
try:
|
||||
learner_client.SendInteractions(interactions_stream(shutdown_event, interactions_queue))
|
||||
except grpc.RpcError as e:
|
||||
logging.error(f"[ACTOR] gRPC error: {e}")
|
||||
|
||||
logging.info("[ACTOR] Finished streaming interactions")
|
||||
|
||||
if not use_threads(cfg):
|
||||
grpc_channel.close()
|
||||
logging.info("[ACTOR] Interactions process stopped")
|
||||
|
||||
|
||||
def transitions_stream(shutdown_event: Event, transitions_queue: Queue) -> hilserl_pb2.Empty:
|
||||
while not shutdown_event.is_set():
|
||||
try:
|
||||
message = transitions_queue.get(block=True, timeout=5)
|
||||
except Empty:
|
||||
logging.debug("[ACTOR] Transition queue is empty")
|
||||
continue
|
||||
|
||||
yield from send_bytes_in_chunks(
|
||||
message, hilserl_pb2.Transition, log_prefix="[ACTOR] Send transitions"
|
||||
)
|
||||
|
||||
return hilserl_pb2.Empty()
|
||||
|
||||
|
||||
def interactions_stream(
|
||||
shutdown_event: any, # Event,
|
||||
interactions_queue: Queue,
|
||||
) -> hilserl_pb2.Empty:
|
||||
while not shutdown_event.is_set():
|
||||
try:
|
||||
message = interactions_queue.get(block=True, timeout=5)
|
||||
except Empty:
|
||||
logging.debug("[ACTOR] Interaction queue is empty")
|
||||
continue
|
||||
|
||||
yield from send_bytes_in_chunks(
|
||||
message,
|
||||
hilserl_pb2.InteractionMessage,
|
||||
log_prefix="[ACTOR] Send interactions",
|
||||
)
|
||||
|
||||
return hilserl_pb2.Empty()
|
||||
|
||||
|
||||
#################################################
|
||||
# Policy functions #
|
||||
#################################################
|
||||
|
||||
|
||||
def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device):
|
||||
if not parameters_queue.empty():
|
||||
logging.info("[ACTOR] Load new parameters from Learner.")
|
||||
bytes_state_dict = get_last_item_from_queue(parameters_queue)
|
||||
state_dict = bytes_to_state_dict(bytes_state_dict)
|
||||
state_dict = move_state_dict_to_device(state_dict, device=device)
|
||||
policy.load_state_dict(state_dict)
|
||||
|
||||
|
||||
#################################################
|
||||
# Utilities functions #
|
||||
#################################################
|
||||
|
||||
|
||||
def push_transitions_to_transport_queue(transitions: list, transitions_queue):
|
||||
"""Send transitions to learner in smaller chunks to avoid network issues.
|
||||
|
||||
|
|
@ -504,144 +668,9 @@ def log_policy_frequency_issue(policy_fps: float, cfg: TrainPipelineConfig, inte
|
|||
)
|
||||
|
||||
|
||||
def establish_learner_connection(
|
||||
stub,
|
||||
shutdown_event: any, # Event,
|
||||
attempts=30,
|
||||
):
|
||||
for _ in range(attempts):
|
||||
if shutdown_event.is_set():
|
||||
logging.info("[ACTOR] Shutting down establish_learner_connection")
|
||||
return False
|
||||
|
||||
# Force a connection attempt and check state
|
||||
try:
|
||||
logging.info("[ACTOR] Send ready message to Learner")
|
||||
if stub.Ready(hilserl_pb2.Empty()) == hilserl_pb2.Empty():
|
||||
return True
|
||||
except grpc.RpcError as e:
|
||||
logging.error(f"[ACTOR] Waiting for Learner to be ready... {e}")
|
||||
time.sleep(2)
|
||||
return False
|
||||
|
||||
|
||||
def use_threads(cfg: TrainPipelineConfig) -> bool:
|
||||
return cfg.policy.concurrency.actor == "threads"
|
||||
|
||||
|
||||
@parser.wrap()
|
||||
def actor_cli(cfg: TrainPipelineConfig):
|
||||
cfg.validate()
|
||||
if not use_threads(cfg):
|
||||
import torch.multiprocessing as mp
|
||||
|
||||
mp.set_start_method("spawn")
|
||||
|
||||
# Create logs directory to ensure it exists
|
||||
log_dir = os.path.join(cfg.output_dir, "logs")
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_file = os.path.join(log_dir, f"actor_{cfg.job_name}.log")
|
||||
|
||||
# Initialize logging with explicit log file
|
||||
init_logging(log_file=log_file)
|
||||
logging.info(f"Actor logging initialized, writing to {log_file}")
|
||||
|
||||
shutdown_event = setup_process_handlers(use_threads(cfg))
|
||||
|
||||
learner_client, grpc_channel = learner_service_client(
|
||||
host=cfg.policy.actor_learner_config.learner_host,
|
||||
port=cfg.policy.actor_learner_config.learner_port,
|
||||
)
|
||||
|
||||
logging.info("[ACTOR] Establishing connection with Learner")
|
||||
if not establish_learner_connection(learner_client, shutdown_event):
|
||||
logging.error("[ACTOR] Failed to establish connection with Learner")
|
||||
return
|
||||
|
||||
if not use_threads(cfg):
|
||||
# If we use multithreading, we can reuse the channel
|
||||
grpc_channel.close()
|
||||
grpc_channel = None
|
||||
|
||||
logging.info("[ACTOR] Connection with Learner established")
|
||||
|
||||
parameters_queue = Queue()
|
||||
transitions_queue = Queue()
|
||||
interactions_queue = Queue()
|
||||
|
||||
concurrency_entity = None
|
||||
if use_threads(cfg):
|
||||
from threading import Thread
|
||||
|
||||
concurrency_entity = Thread
|
||||
else:
|
||||
from multiprocessing import Process
|
||||
|
||||
concurrency_entity = Process
|
||||
|
||||
receive_policy_process = concurrency_entity(
|
||||
target=receive_policy,
|
||||
args=(cfg, parameters_queue, shutdown_event, grpc_channel),
|
||||
daemon=True,
|
||||
)
|
||||
|
||||
transitions_process = concurrency_entity(
|
||||
target=send_transitions,
|
||||
args=(cfg, transitions_queue, shutdown_event, grpc_channel),
|
||||
daemon=True,
|
||||
)
|
||||
|
||||
interactions_process = concurrency_entity(
|
||||
target=send_interactions,
|
||||
args=(cfg, interactions_queue, shutdown_event, grpc_channel),
|
||||
daemon=True,
|
||||
)
|
||||
|
||||
transitions_process.start()
|
||||
interactions_process.start()
|
||||
receive_policy_process.start()
|
||||
|
||||
# HACK: FOR MANISKILL we do not have a reward classifier
|
||||
# TODO: Remove this once we merge into main
|
||||
reward_classifier = None
|
||||
# if (
|
||||
# cfg.env.reward_classifier["pretrained_path"] is not None
|
||||
# and cfg.env.reward_classifier["config_path"] is not None
|
||||
# ):
|
||||
# reward_classifier = get_classifier(
|
||||
# pretrained_path=cfg.env.reward_classifier["pretrained_path"],
|
||||
# config_path=cfg.env.reward_classifier["config_path"],
|
||||
# )
|
||||
|
||||
act_with_policy(
|
||||
cfg=cfg,
|
||||
reward_classifier=reward_classifier,
|
||||
shutdown_event=shutdown_event,
|
||||
parameters_queue=parameters_queue,
|
||||
transitions_queue=transitions_queue,
|
||||
interactions_queue=interactions_queue,
|
||||
)
|
||||
logging.info("[ACTOR] Policy process joined")
|
||||
|
||||
logging.info("[ACTOR] Closing queues")
|
||||
transitions_queue.close()
|
||||
interactions_queue.close()
|
||||
parameters_queue.close()
|
||||
|
||||
transitions_process.join()
|
||||
logging.info("[ACTOR] Transitions process joined")
|
||||
interactions_process.join()
|
||||
logging.info("[ACTOR] Interactions process joined")
|
||||
receive_policy_process.join()
|
||||
logging.info("[ACTOR] Receive policy process joined")
|
||||
|
||||
logging.info("[ACTOR] join queues")
|
||||
transitions_queue.cancel_join_thread()
|
||||
interactions_queue.cancel_join_thread()
|
||||
parameters_queue.cancel_join_thread()
|
||||
|
||||
logging.info("[ACTOR] queues closed")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
actor_cli()
|
||||
|
|
|
|||
Loading…
Reference in New Issue