Cleaned version

Author: Davide-Cocco

docs/source/user_guide/model_training/distributed_training/ray/creating.rst

@@ -179,8 +179,6 @@ Do a dry run to inspect how the yaml translates to Job and Job Runs
   ads opctl run -f train.yaml --dry-run
 
 
-**Use ads opctl to create the cluster infrastructure and run the workload:**
-
 .. include:: ../_test_and_submit.rst
 
 **Monitoring the workload logs**

docs/source/user_guide/model_training/distributed_training/ray/ray.rst

@@ -7,13 +7,9 @@ Ray is a framework for distributed computing in Python specialized in ML workloa
 The documentation shows how to create a container and ``yaml`` spec to run a ``Ray``
 code sample in distributed modality.
 
-
-.. admonition:: Ray
-  :class: note
-
-  ``Ray`` offers a core package to simply execute Python workloads in a distributed manner,
-  potentially across a cluster of machines (set up through ``Ray`` itself), but also other
-  extensions to perform more traditional ML computation, such as Hyperparameter Optimization.
+``Ray`` offers a core package to simply execute Python workloads in a distributed manner,
+potentially across a cluster of machines (set up through ``Ray`` itself), but also other
+extensions to perform more traditional ML computation, such as Hyperparameter Optimization.
 
 
 .. toctree::