Update docs based on comments.

Author: qiuosier

docs/source/user_guide/cli/opctl/_template/jobs.rst

@@ -1,6 +1,6 @@
-++++++++++++++++
-Working with CLI
-++++++++++++++++
+++++++++++++++++++++
+Working with the CLI
+++++++++++++++++++++
 
 Prerequisite
 ------------

docs/source/user_guide/jobs/infrastructure.rst

@@ -16,7 +16,7 @@ For example:
         .with_shape_name("VM.Standard.E3.Flex")
         # Shape config details are applicable only for the flexible shapes.
         .with_shape_config_details(memory_in_gbs=16, ocpus=1)
-        # Minimum block storage size is 50
+        # Minimum block storage size is 50 (GB)
         .with_block_storage_size(50)
         .with_log_group_id("<log_group_ocid>")
         .with_log_id("<log_ocid>")
@@ -65,7 +65,7 @@ The :py:class:`~ads.jobs.DataScienceJob` class provides two static methods to ob
 
 * You can get a list of currently supported compute shapes by calling
   :py:meth:`~ads.jobs.DataScienceJob.instance_shapes`.
-* can get a list of shapes are available for fast launch by calling
+* can get a list of shapes that are available for fast launch by calling
   :py:meth:`~ads.jobs.DataScienceJob.fast_launch_shapes`.
   Specifying a fast launch shape will allow your job to start as fast as possible.
 
@@ -86,7 +86,7 @@ Logging
 =======
 
 Logging is not required to create the job.
-However, it is highly recommended to enable logging for debugging and monitoring purpose.
+However, it is highly recommended to enable logging for debugging and monitoring.
 
 In the preceding example, both the log OCID and corresponding log group OCID are specified
 with the :py:class:`~ads.jobs.DataScienceJob` instance.

docs/source/user_guide/jobs/policies.rst

@@ -1,5 +1,5 @@
-I AM Policies
-*************
+IAM Policies
+************
 
 Oracle Cloud Infrastructure Identity and Access Management (IAM)
 lets you specify policies to control the access to your cloud resources.

docs/source/user_guide/jobs/tabs/name_substitution.rst

@@ -18,7 +18,7 @@
       )
       .with_runtime(
         PythonRuntime()
-        .with_service_conda("pytorch19_p37_cpu_v1")
+        .with_service_conda("pytorch110_p38_gpu_v1")
         .with_environment_variable(DATASET_NAME="MyData")
         .with_source("local/path/to/training_script.py")
         .with_output("output", "oci://bucket_name@namespace/prefix/${JOB_RUN_OCID}")