Remove legacy Ray code. (#3121)

* Remove legacy Ray code.

* Fix cmake and simplify monitor.

* Fix linting

* Updates

* Fix

* Implement some methods.

* Remove more plasma manager references.

* Fix

* Linting

* Fix

* Fix

* Make sure class IDs are strings.

* Some path fixes

* Fix

* Path fixes and update arrow

* Fixes.

* linting

* Fixes

* Java fixes

* Some java fixes

* TaskLanguage -> Language

* Minor

* Fix python test and remove unused method signature.

* Fix java tests

* Fix jenkins tests

* Remove commented out code.

python/ray/tune/ray_trial_executor.py

@@ -213,20 +213,9 @@ class RayTrialExecutor(TrialExecutor):
         assert self._committed_resources.gpu >= 0
 
     def _update_avail_resources(self):
-        if ray.worker.global_worker.use_raylet:
-            # TODO(rliaw): Remove once raylet flag is swapped
-            resources = ray.global_state.cluster_resources()
-            num_cpus = resources["CPU"]
-            num_gpus = resources["GPU"]
-        else:
-            clients = ray.global_state.client_table()
-            local_schedulers = [
-                entry for client in clients.values() for entry in client
-                if (entry['ClientType'] == 'local_scheduler'
-                    and not entry['Deleted'])
-            ]
-            num_cpus = sum(ls['CPU'] for ls in local_schedulers)
-            num_gpus = sum(ls.get('GPU', 0) for ls in local_schedulers)
+        resources = ray.global_state.cluster_resources()
+        num_cpus = resources["CPU"]
+        num_gpus = resources["GPU"]
         self._avail_resources = Resources(int(num_cpus), int(num_gpus))
         self._resources_initialized = True
 

python/ray/tune/test/trial_runner_test.py

@@ -107,7 +107,7 @@ class TrainableFunctionApiTest(unittest.TestCase):
                 return Resources(cpu=config["cpu"], gpu=config["gpu"])
 
             def _train(self):
-                return dict(timesteps_this_iter=1, done=True)
+                return {"timesteps_this_iter": 1, "done": True}
 
         register_trainable("B", B)
 
@@ -440,7 +440,7 @@ class TrainableFunctionApiTest(unittest.TestCase):
                 self.state = {"hi": 1}
 
             def _train(self):
-                return dict(timesteps_this_iter=1, done=True)
+                return {"timesteps_this_iter": 1, "done": True}
 
             def _save(self, path):
                 return self.state
@@ -471,7 +471,7 @@ class TrainableFunctionApiTest(unittest.TestCase):
 
             def _train(self):
                 self.state["iter"] += 1
-                return dict(timesteps_this_iter=1, done=True)
+                return {"timesteps_this_iter": 1, "done": True}
 
             def _save(self, path):
                 return self.state
@@ -604,7 +604,7 @@ class RunExperimentTest(unittest.TestCase):
 
         class B(Trainable):
             def _train(self):
-                return dict(timesteps_this_iter=1, done=True)
+                return {"timesteps_this_iter": 1, "done": True}
 
         register_trainable("f1", train)
         trials = run_experiments({
@@ -624,7 +624,7 @@ class RunExperimentTest(unittest.TestCase):
     def testCheckpointAtEnd(self):
         class train(Trainable):
             def _train(self):
-                return dict(timesteps_this_iter=1, done=True)
+                return {"timesteps_this_iter": 1, "done": True}
 
             def _save(self, path):
                 return path
@@ -887,7 +887,7 @@ class TrialRunnerTest(unittest.TestCase):
         self.assertEqual(trials[1].status, Trial.PENDING)
 
     def testFractionalGpus(self):
-        ray.init(num_cpus=4, num_gpus=1, use_raylet=True)
+        ray.init(num_cpus=4, num_gpus=1)
         runner = TrialRunner(BasicVariantGenerator())
         kwargs = {
             "resources": Resources(cpu=1, gpu=0.5),

python/ray/tune/util.py

@@ -28,7 +28,7 @@ def pin_in_object_store(obj):
 def get_pinned_object(pinned_id):
     """Retrieve a pinned object from the object store."""
 
-    from ray.local_scheduler import ObjectID
+    from ray.raylet import ObjectID
 
     return _from_pinnable(
         ray.get(