Move global state API out of global_state object. (ray-project#4857)

ci/stress_tests/test_many_tasks_and_transfers.py

@@ -24,10 +24,10 @@
 
 # Wait until the expected number of nodes have joined the cluster.
 while True:
-    if len(ray.global_state.client_table()) >= num_remote_nodes + 1:
+    if len(ray.nodes()) >= num_remote_nodes + 1:
         break
 logger.info("Nodes have all joined. There are %s resources.",
-            ray.global_state.cluster_resources())
+            ray.cluster_resources())
 
 
 # Require 1 GPU to force the tasks to be on remote machines.

doc/source/api.rst

@@ -27,6 +27,26 @@ The Ray API
 
 .. autofunction:: ray.method
 
+Inspect the Cluster State
+-------------------------
+
+.. autofunction:: ray.nodes()
+
+.. autofunction:: ray.tasks()
+
+.. autofunction:: ray.objects()
+
+.. autofunction:: ray.timeline()
+
+.. autofunction:: ray.object_transfer_timeline()
+
+.. autofunction:: ray.cluster_resources()
+
+.. autofunction:: ray.available_resources()
+
+.. autofunction:: ray.errors()
+
+
 The Ray Command Line API
 ------------------------
 

doc/source/development.rst

@@ -60,7 +60,7 @@ Python script with the following:
 .. code-block:: bash
 
  RAY_RAYLET_GDB=1 RAY_RAYLET_TMUX=1 python
- 
+
 You can then list the ``tmux`` sessions with ``tmux ls`` and attach to the
 appropriate one.
 
@@ -71,14 +71,14 @@ allow core dump files to be written.
 
 Inspecting Redis shards
 ~~~~~~~~~~~~~~~~~~~~~~~
-To inspect Redis, you can use the ``ray.experimental.state.GlobalState`` Python
-API.  The easiest way to do this is to start or connect to a Ray cluster with
-``ray.init()``, then query the API like so:
+To inspect Redis, you can use the global state API. The easiest way to do this
+is to start or connect to a Ray cluster with ``ray.init()``, then query the API
+like so:
 
 .. code-block:: python
 
  ray.init()
- ray.worker.global_state.client_table()
+ ray.nodes()
  # Returns current information about the nodes in the cluster, such as:
  # [{'ClientID': '2a9d2b34ad24a37ed54e4fcd32bf19f915742f5b',
  #   'EntryType': 0,

doc/source/user-profiling.rst

@@ -18,7 +18,7 @@ following command.
 
 .. code-block:: python
 
-  ray.global_state.chrome_tracing_dump(filename="/tmp/timeline.json")
+  ray.timeline(filename="/tmp/timeline.json")
 
 Then open `chrome://tracing`_ in the Chrome web browser, and load
 ``timeline.json``.

kubernetes/example.py

@@ -14,7 +14,7 @@
 
     # Wait for all 4 nodes to join the cluster.
     while True:
-        num_nodes = len(ray.global_state.client_table())
+        num_nodes = len(ray.nodes())
         if num_nodes < 4:
             print("{} nodes have joined so far. Waiting for more."
                   .format(num_nodes))

python/ray/__init__.py

@@ -66,19 +66,20 @@
 _config = _Config()
 
 from ray.profiling import profile  # noqa: E402
+from ray.state import (global_state, nodes, tasks, objects, timeline,
+                       object_transfer_timeline, cluster_resources,
+                       available_resources, errors)  # noqa: E402
 from ray.worker import (
     LOCAL_MODE,
     PYTHON_MODE,
     SCRIPT_MODE,
     WORKER_MODE,
     connect,
     disconnect,
-    error_info,
     get,
     get_gpu_ids,
     get_resource_ids,
     get_webui_url,
-    global_state,
     init,
     is_initialized,
     put,
@@ -98,6 +99,15 @@
 __version__ = "0.8.0.dev0"
 
 __all__ = [
+    "global_state",
+    "nodes",
+    "tasks",
+    "objects",
+    "timeline",
+    "object_transfer_timeline",
+    "cluster_resources",
+    "available_resources",
+    "errors",
     "LOCAL_MODE",
     "PYTHON_MODE",
     "SCRIPT_MODE",
@@ -108,12 +118,10 @@
     "actor",
     "connect",
     "disconnect",
-    "error_info",
     "get",
     "get_gpu_ids",
     "get_resource_ids",
     "get_webui_url",
-    "global_state",
     "init",
     "internal",
     "is_initialized",

python/ray/actor.py

@@ -811,7 +811,7 @@ def exit_actor():
         worker.raylet_client.disconnect()
         ray.disconnect()
         # Disconnect global state from GCS.
-        ray.global_state.disconnect()
+        ray.state.state.disconnect()
         sys.exit(0)
         assert False, "This process should have terminated."
     else:
@@ -931,7 +931,7 @@ def get_checkpoints_for_actor(actor_id):
     """Get the available checkpoints for the given actor ID, return a list
     sorted by checkpoint timestamp in descending order.
     """
-    checkpoint_info = ray.worker.global_state.actor_checkpoint_info(actor_id)
+    checkpoint_info = ray.state.state.actor_checkpoint_info(actor_id)
     if checkpoint_info is None:
         return []
     checkpoints = [

python/ray/experimental/__init__.py

@@ -2,10 +2,6 @@
 from __future__ import division
 from __future__ import print_function
 
-from .features import (
-    flush_redis_unsafe, flush_task_and_object_metadata_unsafe,
-    flush_finished_tasks_unsafe, flush_evicted_objects_unsafe,
-    _flush_finished_tasks_unsafe_shard, _flush_evicted_objects_unsafe_shard)
 from .gcs_flush_policy import (set_flushing_policy, GcsFlushPolicy,
                                SimpleGcsFlushPolicy)
 from .named_actors import get_actor, register_actor
@@ -20,10 +16,7 @@ def TensorFlowVariables(*args, **kwargs):
 
 
 __all__ = [
-    "TensorFlowVariables", "flush_redis_unsafe",
-    "flush_task_and_object_metadata_unsafe", "flush_finished_tasks_unsafe",
-    "flush_evicted_objects_unsafe", "_flush_finished_tasks_unsafe_shard",
-    "_flush_evicted_objects_unsafe_shard", "get_actor", "register_actor",
-    "get", "wait", "set_flushing_policy", "GcsFlushPolicy",
-    "SimpleGcsFlushPolicy", "set_resource"
+    "TensorFlowVariables", "get_actor", "register_actor", "get", "wait",
+    "set_flushing_policy", "GcsFlushPolicy", "SimpleGcsFlushPolicy",
+    "set_resource"
 ]