Fix cmd line arguments handling (#68)

* refactoring the merging of the task parameters and the command line parameters
* removing some unused command line arguments
* fix for saving checkpoints when not passing through coach.py

rl_coach/architectures/tensorflow_components/distributed_tf_utils.py

@@ -94,7 +94,7 @@ def create_monitored_session(target: tf.train.Server, task_index: int,
         is_chief=is_chief,
         hooks=[],
         checkpoint_dir=checkpoint_dir,
-        checkpoint_save_secs=checkpoint_save_secs,
+        save_checkpoint_secs=checkpoint_save_secs,
         config=config
     )
 

rl_coach/base_parameters.py

@@ -432,14 +432,17 @@ class AgentParameters(Parameters):
 
 class TaskParameters(Parameters):
     def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False,
-                 experiment_path='/tmp', seed=None, checkpoint_save_secs=None):
+                 experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
+                 checkpoint_save_dir=None):
         """
         :param framework_type: deep learning framework type. currently only tensorflow is supported
         :param evaluate_only: the task will be used only for evaluating the model
         :param use_cpu: use the cpu for this task
         :param experiment_path: the path to the directory which will store all the experiment outputs
-        :param checkpoint_save_secs: the number of seconds between each checkpoint saving
         :param seed: a seed to use for the random numbers generator
+        :param checkpoint_save_secs: the number of seconds between each checkpoint saving
+        :param checkpoint_restore_dir: the directory to restore the checkpoints from
+        :param checkpoint_save_dir: the directory to store the checkpoints in
         """
         self.framework_type = framework_type
         self.task_index = 0  # TODO: not really needed
@@ -447,6 +450,8 @@ class TaskParameters(Parameters):
         self.use_cpu = use_cpu
         self.experiment_path = experiment_path
         self.checkpoint_save_secs = checkpoint_save_secs
+        self.checkpoint_restore_dir = checkpoint_restore_dir
+        self.checkpoint_save_dir = checkpoint_save_dir
         self.seed = seed
 
 
@@ -454,7 +459,8 @@ class DistributedTaskParameters(TaskParameters):
     def __init__(self, framework_type: Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str,
                  task_index: int, evaluate_only: bool=False, num_tasks: int=None,
                  num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None,
-                 shared_memory_scratchpad=None, seed=None):
+                 shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
+                 checkpoint_save_dir=None):
         """
         :param framework_type: deep learning framework type. currently only tensorflow is supported
         :param evaluate_only: the task will be used only for evaluating the model
@@ -469,9 +475,13 @@ class DistributedTaskParameters(TaskParameters):
         :param experiment_path: the path to the directory which will store all the experiment outputs
         :param dnd: an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad.
         :param seed: a seed to use for the random numbers generator
+        :param checkpoint_save_secs: the number of seconds between each checkpoint saving
+        :param checkpoint_restore_dir: the directory to restore the checkpoints from
+        :param checkpoint_save_dir: the directory to store the checkpoints in
         """
         super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu,
-                         experiment_path=experiment_path, seed=seed)
+                         experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs,
+                         checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir)
         self.parameters_server_hosts = parameters_server_hosts
         self.worker_hosts = worker_hosts
         self.job_type = job_type

rl_coach/coach.py

@@ -213,10 +213,7 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace:
         screen.error("The requested checkpoint folder to load from does not exist.")
 
     # no preset was given. check if the user requested to play some environment on its own
-    if args.preset is None and args.play:
+    if args.preset is None and args.play and not args.environment_type:
-        if args.environment_type:
-            args.agent_type = 'Human'
-        else:
         screen.error('When no preset is given for Coach to run, and the user requests human control over '
                      'the environment, the user is expected to input the desired environment_type and level.'
                      '\nAt least one of these parameters was not given.')
@@ -428,24 +425,8 @@ def main():
     parser.add_argument('-dm', '--dump_mp4',
                         help="(flag) Enable the mp4 saving functionality.",
                         action='store_true')
-    parser.add_argument('-at', '--agent_type',
-                        help="(string) Choose an agent type class to override on top of the selected preset. "
-                             "If no preset is defined, a preset can be set from the command-line by combining settings "
-                             "which are set by using --agent_type, --experiment_type, --environemnt_type",
-                        default=None,
-                        type=str)
     parser.add_argument('-et', '--environment_type',
-                        help="(string) Choose an environment type class to override on top of the selected preset."
+                        help="(string) Choose an environment type class to override on top of the selected preset.",
-                             "If no preset is defined, a preset can be set from the command-line by combining settings "
-                             "which are set by using --agent_type, --experiment_type, --environemnt_type",
-                        default=None,
-                        type=str)
-    parser.add_argument('-ept', '--exploration_policy_type',
-                        help="(string) Choose an exploration policy type class to override on top of the selected "
-                             "preset."
-                             "If no preset is defined, a preset can be set from the command-line by combining settings "
-                             "which are set by using --agent_type, --experiment_type, --environemnt_type"
-                        ,
                         default=None,
                         type=str)
     parser.add_argument('-lvl', '--level',
@@ -546,13 +527,16 @@ def main():
     # Single-threaded runs
     if args.num_workers == 1:
         # Start the training or evaluation
-        task_parameters = TaskParameters(framework_type=args.framework,
+        task_parameters = TaskParameters(
+            framework_type=args.framework,
             evaluate_only=args.evaluate,
             experiment_path=args.experiment_path,
             seed=args.seed,
             use_cpu=args.use_cpu,
-                                         checkpoint_save_secs=args.checkpoint_save_secs)
+            checkpoint_save_secs=args.checkpoint_save_secs,
-        task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__)
+            checkpoint_restore_dir=args.checkpoint_restore_dir,
+            checkpoint_save_dir=args.checkpoint_save_dir
+        )
 
         start_graph(graph_manager=graph_manager, task_parameters=task_parameters)
 
@@ -575,7 +559,8 @@ def main():
 
         def start_distributed_task(job_type, task_index, evaluation_worker=False,
                                    shared_memory_scratchpad=shared_memory_scratchpad):
-            task_parameters = DistributedTaskParameters(framework_type=args.framework,
+            task_parameters = DistributedTaskParameters(
+                framework_type=args.framework,
                 parameters_server_hosts=ps_hosts,
                 worker_hosts=worker_hosts,
                 job_type=job_type,
@@ -586,8 +571,12 @@ def main():
                 num_training_tasks=args.num_workers,
                 experiment_path=args.experiment_path,
                 shared_memory_scratchpad=shared_memory_scratchpad,
-                                                        seed=args.seed+task_index if args.seed is not None else None)  # each worker gets a different seed
+                seed=args.seed+task_index if args.seed is not None else None,  # each worker gets a different seed
-            task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__)
+                checkpoint_save_secs=args.checkpoint_save_secs,
+                checkpoint_restore_dir=args.checkpoint_restore_dir,
+                checkpoint_save_dir=args.checkpoint_save_dir
+            )
+
             # we assume that only the evaluation workers are rendering
             graph_manager.visualization_parameters.render = args.render and evaluation_worker
             p = Process(target=start_graph, args=(graph_manager, task_parameters))
@@ -607,7 +596,7 @@ def main():
             workers.append(start_distributed_task("worker", task_index))
 
         # evaluation worker
-        if args.evaluation_worker:
+        if args.evaluation_worker or args.render:
             evaluation_worker = start_distributed_task("worker", args.num_workers, evaluation_worker=True)
 
         # wait for all workers

rl_coach/graph_managers/graph_manager.py

@@ -100,6 +100,8 @@ class GraphManager(object):
         self.preset_validation_params = PresetValidationParameters()
         self.reset_required = False
 
+        # timers
+        self.graph_creation_time = None
         self.last_checkpoint_saving_time = time.time()
 
         # counters
@@ -520,6 +522,8 @@ class GraphManager(object):
             self.save_checkpoint()
 
     def save_checkpoint(self):
+        if self.task_parameters.checkpoint_save_dir is None:
+            self.task_parameters.checkpoint_save_dir = os.path.join(self.task_parameters.experiment_path, 'checkpoint')
         checkpoint_path = os.path.join(self.task_parameters.checkpoint_save_dir,
                                        "{}_Step-{}.ckpt".format(
                                            self.checkpoint_id,