Cleanup ray docs to reflect current functionality

Signed-off-by: Jason Parraga <[email protected]>

examples/ray_plugin/README.md

@@ -29,30 +29,6 @@ To enable the plugin in the backend, refer to the instructions provided in the {
 
 ## Implementation details
 
-### Submit a Ray job to existing cluster
-
-```{eval-rst}
-.. testcode:: ray-quickstart-1
-    import ray
-    from flytekit import task
-    from flytekitplugins.ray import RayJobConfig
-
-    @ray.remote
-    def f(x):
-        return x * x
-
-    @task(
-        task_config=RayJobConfig(
-            address=<RAY_CLUSTER_ADDRESS>
-            runtime_env={"pip": ["numpy", "pandas"]}
-        )
-    )
-    def ray_task() -> typing.List[int]:
-        futures = [f.remote(i) for i in range(5)]
-        return ray.get(futures)
-
-```
-
 ### Create a Ray cluster managed by Flyte and run a Ray Job on the cluster
 
 ```{eval-rst}

examples/ray_plugin/ray_plugin/ray_example.py

@@ -1,8 +1,7 @@
 # %% [markdown]
 # # Running Ray Tasks
 #
-# The Ray task offers the capability to execute a Ray job either on a pre-existing Ray cluster
-# or by creating a new Ray cluster using the Ray operator.
+# The Ray task offers the capability to execute a Ray job on a dynamically created Ray cluster using the Ray operator.
 #
 # :::{Warning}
 # **Version Compatibility**
@@ -51,7 +50,7 @@ def f(x):
 # Here's a breakdown of the parameters:
 #
 # - `ray_start_params`: These are the [parameters](https://docs.ray.io/en/latest/ray-core/api/doc/ray.init.html)
-#   used in the Ray `init` method, encompassing the address and object-store-memory settings.
+#   used in the Ray `init` method, encompassing the object-store-memory settings.
 # - `replicas`: Specifies the desired number of replicas for the worker group. The default is 1.
 # - `group_name`: A RayCluster can host multiple worker groups, each differentiated by its name.
 # - `runtime_env`: The [runtime environment](https://docs.ray.io/en/latest/ray-core/handling-dependencies.html#runtime-environments)