class_docstr: TrainLoop contains the core training loop logic.
class_docstr_tok: ["TrainLoop","contains","the","core","training","loop","logic","."]
docstr: Perform a validation run, checking whether we should stop training.
  
  :return: boolean indicating whether training should stop
  :rtype: bool
docstr_tok: ["Perform","a","validation","run",",","checking","whether","we","should","stop","training",".",":","return",":","boolean","indicating","whether","training","should","stop",":","rtype",":","bool"]
code: def validate(self):
      """
      Perform a validation run, checking whether we should stop training.
  
      :return: boolean indicating whether training should stop
      :rtype: bool
      """
      opt = self.opt
  
      if self.valid_worlds is None:
          # we need to load the world now
          self.valid_worlds = load_eval_worlds(self.agent, opt, "valid")
  
      # run evaluation on valid set
      valid_report = self._run_eval(
          self.valid_worlds, opt, "valid", opt["validation_max_exs"]
      )
      v = dict_report(valid_report)
      v["train_time"] = self.train_time.time()
      v["parleys"] = self.parleys
      v["train_steps"] = self._train_steps
      v["total_exs"] = self._total_exs
      v["total_epochs"] = self._total_epochs
      self.valid_reports.append(v)
      # logging
      if opt["tensorboard_log"] and is_primary_worker():
          valid_report["total_exs"] = self._total_exs
          self.tb_logger.log_metrics(
              "valid", self.parleys, valid_report
          )
          # flush on a validation
          self.tb_logger.flush()
      if opt["wandb_log"] and is_primary_worker():
          valid_report["total_exs"] = self._total_exs
          self.wb_logger.log_metrics(
              "valid", self.parleys, valid_report
          )
  
      if opt["clearml_log"] and is_primary_worker():
          valid_report["total_exs"] = self._total_exs
          self.clearml_logger.log_metrics(
              "valid", self.parleys, valid_report
          )
          self.clearml_logger.flush()
  
      # send valid metrics to agent if the agent wants them
      if hasattr(self.agent, "receive_metrics"):
          self.agent.receive_metrics(valid_report)
  
      # check which metric to look at
      new_valid = valid_report[opt["validation_metric"]]
  
      if isinstance(new_valid, Metric):
          new_valid = new_valid.value()
  
      # check if this is the best validation so far
      if (
          self.best_valid is None
          or self.valid_optim * new_valid
          > self.valid_optim * self.best_valid
      ):
          logging.success(
              "new best {}: {:.4g}{}".format(
                  opt["validation_metric"],
                  new_valid,
                  " (previous best was {:.4g})".format(self.best_valid)
                  if self.best_valid is not None
                  else "",
              )
          )
          self.best_valid = new_valid
          self.impatience = 0
          if opt.get("model_file"):
              logging.info(
                  f"saving best valid model: {opt['model_file']}"
              )
              self.save_model()
              self.saved = True
          if (
              opt["validation_metric_mode"] == "max"
              and self.best_valid >= opt["validation_cutoff"]
          ) or (
              opt["validation_metric_mode"] == "min"
              and self.best_valid <= opt["validation_cutoff"]
          ):
              logging.info("task solved! stopping.")
              return True
      else:
          self.impatience += 1
          logging.report(
              "did not beat best {}: {} impatience: {}".format(
                  opt["validation_metric"],
                  round(self.best_valid, 4),
                  self.impatience,
              )
          )
      self.validate_time.reset()
  
      # saving
      if opt.get("model_file") and opt.get("save_after_valid"):
          logging.info(
              f"saving model checkpoint: {opt['model_file']}.checkpoint"
          )
          self.save_model(".checkpoint")
  
      # check if we are out of patience
      if (
          opt["validation_patience"] > 0
          and self.impatience >= opt["validation_patience"]
      ):
          logging.info("ran out of patience! stopping training.")
          return True
      return False
  
imports: import copy
  import random
  import torch
  import json
  import os
  import signal
  from typing import Tuple
  import numpy
  import parlai.utils.logging
  from parlai.core.agents import create_agent, create_agent_from_shared
  from parlai.core.exceptions import StopTrainException
  from parlai.core.logs import TensorboardLogger, WandbLogger, ClearMLLogger
  from parlai.core.metrics import Metric
  from parlai.core.metrics import aggregate_named_reports, aggregate_unnamed_reports, dict_report
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser, print_announcements
  from parlai.core.script import ParlaiScript, register_script
  from parlai.core.worlds import create_task, World
  from parlai.scripts.build_dict import build_dict, setup_args
  from parlai.scripts.eval_model import get_task_world_logs
  from parlai.utils.distributed import sync_object, is_primary_worker, all_gather_list, is_distributed, get_rank, num_workers
  from parlai.utils.io import PathManager
  from parlai.utils.misc import Timer, nice_report
  from parlai.utils.world_logging import WorldLogger
label_desc: Use image node_id 4 for calling the TrainLoop obj's underlying member method code with example usage: obj.validate() and returns: False, True, True