[tune] [rllib] Automatically determine RLlib resources and add queueing mechanism for autoscaling (#1848)

python/ray/tune/trial.py

@@ -82,7 +82,7 @@ class Trial(object):
                  config=None,
                  local_dir=DEFAULT_RESULTS_DIR,
                  experiment_tag="",
-                 resources=Resources(cpu=1, gpu=0),
+                 resources=None,
                  stopping_criterion=None,
                  checkpoint_freq=0,
                  restore_path=None,
@@ -112,7 +112,9 @@ class Trial(object):
         self.config = config or {}
         self.local_dir = local_dir
         self.experiment_tag = experiment_tag
-        self.resources = resources
+        self.resources = (
+            resources
+            or self._get_trainable_cls().default_resource_request(self.config))
         self.stopping_criterion = stopping_criterion or {}
         self.checkpoint_freq = checkpoint_freq
         self.upload_dir = upload_dir
@@ -350,11 +352,9 @@ class Trial(object):
 
     def _setup_runner(self):
         self.status = Trial.RUNNING
-        trainable_cls = ray.tune.registry.get_registry().get(
-            ray.tune.registry.TRAINABLE_CLASS, self.trainable_name)
         cls = ray.remote(
             num_cpus=self.resources.cpu,
-            num_gpus=self.resources.gpu)(trainable_cls)
+            num_gpus=self.resources.gpu)(self._get_trainable_cls())
         if not self.result_logger:
             if not os.path.exists(self.local_dir):
                 os.makedirs(self.local_dir)
@@ -380,6 +380,10 @@ class Trial(object):
             registry=ray.tune.registry.get_registry(),
             logger_creator=logger_creator)
 
+    def _get_trainable_cls(self):
+        return ray.tune.registry.get_registry().get(
+            ray.tune.registry.TRAINABLE_CLASS, self.trainable_name)
+
     def set_verbose(self, verbose):
         self.verbose = verbose