[Doc] Run pre-commit on core docs

doc/source/ray-core/accelerator-types.rst

@@ -8,4 +8,4 @@ Accelerator Types
 Ray supports the following accelerator types:
 
 .. literalinclude:: ../../../python/ray/util/accelerators/accelerators.py
-    :language: python
+    :language: python

doc/source/ray-core/actors.rst

@@ -104,7 +104,7 @@ Use `ray list actors` from :ref:`State API <state-api-overview-ref>` to see acto
   Stats:
   ------------------------------
   Total: 1
-  
+
   Table:
   ------------------------------
       ACTOR_ID                          CLASS_NAME    STATE      JOB_ID  NAME    NODE_ID                                                     PID  RAY_NAMESPACE
@@ -403,17 +403,17 @@ Cancel Actor Tasks by calling :func:`ray.cancel() <ray.cancel>` on the returned
 In Ray, Task cancellation behavior is contingent on the Task's current state:
 
 **Unscheduled Tasks**:
-If the Actor Task hasn't been scheduled yet, Ray attempts to cancel the scheduling. 
-When successfully cancelled at this stage, invoking ``ray.get(actor_task_ref)`` 
+If the Actor Task hasn't been scheduled yet, Ray attempts to cancel the scheduling.
+When successfully cancelled at this stage, invoking ``ray.get(actor_task_ref)``
 produce a :class:`TaskCancelledError <ray.exceptions.TaskCancelledError>`.
 
 **Running Actor Tasks (Regular Actor, Threaded Actor)**:
 For tasks classified as a single-threaded Actor or a multi-threaded Actor,
 Ray offers no mechanism for interruption.
 
 **Running Async Actor Tasks**:
-For Tasks classified as `async Actors <_async-actors>`, Ray seeks to cancel the associated `asyncio.Task`. 
-This cancellation approach aligns with the standards presented in 
+For Tasks classified as `async Actors <_async-actors>`, Ray seeks to cancel the associated `asyncio.Task`.
+This cancellation approach aligns with the standards presented in
 `asyncio task cancellation <https://docs.python.org/3/library/asyncio-task.html#task-cancellation>`__.
 Note that `asyncio.Task` won't be interrupted in the middle of execution if you don't `await` within the async function.
 
@@ -424,7 +424,7 @@ the Task might not be cancelled.
 You can check if a Task was successfully cancelled using ``ray.get(actor_task_ref)``.
 
 **Recursive Cancellation**:
-Ray tracks all child and Actor Tasks. When the``recursive=True`` argument is given,
+Ray tracks all child and Actor Tasks. When the ``recursive=True`` argument is given,
 it cancels all child and Actor Tasks.
 
 Scheduling
@@ -466,7 +466,7 @@ define can run. This also implies that tasks are scheduled more flexibly,
 and that if you don't need the stateful part of an actor, you're mostly
 better off using tasks.
 
-Task Events 
+Task Events
 -----------
 
 By default, Ray traces the execution of actor tasks, reporting task status events and profiling events

doc/source/ray-core/actors/terminating-actors.rst

@@ -87,7 +87,7 @@ Use `ray list actors --detail` from :ref:`State API <state-api-overview-ref>` to
       serialized_runtime_env: '{}'
       required_resources: {}
       death_cause:
-          actor_died_error_context: # <---- You could see the error message w.r.t why the actor exits. 
+          actor_died_error_context: # <---- You could see the error message w.r.t why the actor exits.
               error_message: The actor is dead because `ray.kill` killed it.
               owner_id: 01000000ffffffffffffffffffffffffffffffffffffffffffffffff
               owner_ip_address: 127.0.0.1
@@ -153,7 +153,7 @@ Note that this method of termination waits until any previously submitted
 tasks finish executing and then exits the process gracefully with sys.exit.
 
 
-    
+
 You could see the actor is dead as a result of the user's `exit_actor()` call:
 
 .. code-block:: bash
@@ -190,4 +190,4 @@ You could see the actor is dead as a result of the user's `exit_actor()` call:
               never_started: false
       is_detached: false
       placement_group_id: null
-      repr_name: ''
+      repr_name: ''

doc/source/ray-core/api/cli.rst

@@ -50,4 +50,4 @@ This section contains commands to enable/disable :ref:`Ray usage stats <ref-usag
 
 .. click:: ray.scripts.scripts:enable_usage_stats
    :prog: ray enable-usage-stats
-   :show-nested:
+   :show-nested:

doc/source/ray-core/doc_code/ray-dag.py

@@ -169,10 +169,10 @@ def __init__(self):
     def forward(self, input_data: int):
         self.forwarded += 1
         return input_data + 1
-    
+
     def num_forwarded(self):
         return self.forwarded
-    
+
 # Create an actor via ``remote`` API not ``bind`` API to avoid
 # killing actors when a DAG is finished.
 worker = Worker.remote()

doc/source/ray-core/doc_code/streaming_generator.py

@@ -163,7 +163,7 @@ def task():
 assert len(unready) == 0
 next(gen)
 
-# Because the second yield hasn't happened yet, 
+# Because the second yield hasn't happened yet,
 ready, unready = ray.wait([gen], timeout=0)
 print("Wait for 0 seconds. The next item is not ready.")
 print(ready, unready)

doc/source/ray-core/examples/BUILD

@@ -2,4 +2,4 @@ filegroup(
     name = "core_examples",
     srcs = glob(["*.ipynb"]),
     visibility = ["//doc:__subpackages__"]
-)
+)

doc/source/ray-core/examples/lm/preprocess.sh

@@ -30,4 +30,4 @@ fairseq-preprocess \
     --validpref wikitext-103-raw/wiki.valid.bpe \
     --testpref wikitext-103-raw/wiki.test.bpe \
     --destdir data-bin/wikitext-103 \
-    --workers 60
+    --workers 60

doc/source/ray-core/examples/overview.rst

@@ -22,7 +22,7 @@ Beginner
   * - :doc:`A Gentle Introduction to Ray Core by Example <gentle_walkthrough>`
   * - :doc:`Using Ray for Highly Parallelizable Tasks <highly_parallel>`
   * - :doc:`Monte Carlo Estimation of π <monte_carlo_pi>`
-  
+
 
 Intermediate
 ------------
@@ -37,7 +37,7 @@ Advanced
 --------
 
 .. list-table::
-    
+
   * - :doc:`Build Simple AutoML for Time Series Using Ray <automl_for_time_series>`
   * - :doc:`Build Batch Prediction Using Ray <batch_prediction>`
   * - :doc:`Build a Simple Parameter Server Using Ray <plot_parameter_server>`

doc/source/ray-core/examples/plot_hyperparameter.ipynb

@@ -314,4 +314,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

doc/source/ray-core/fault_tolerance/actors.rst

@@ -183,4 +183,3 @@ Retry behavior depends on the value you set ``retry_exceptions`` to:
 - The default value,`0`.
 
 For example, if a method sets `max_task_retries=5` and `retry_exceptions=True`, and the actor sets `max_restarts=2`, Ray executes the method up to 6 times: once for the initial invocation, and 5 additional retries. The 6 invocations may include 2 actor crashes. After the 6th invocation, a `ray.get` call to the result Ray ObjectRef raises the exception raised in the last invocation, or `ray.exceptions.RayActorError` if the actor crashed in the last invocation.
-

doc/source/ray-core/fault_tolerance/tasks.rst

@@ -53,7 +53,7 @@ Use `ray list tasks` from :ref:`State API CLI <state-api-overview-ref>` to query
 .. code-block:: bash
 
   # This API is only available when you download Ray via `pip install "ray[default]"`
-  ray list tasks 
+  ray list tasks
 
 .. code-block:: bash