Spelling fixes

docs/source/release_notes.rst

@@ -4,7 +4,7 @@ Release Notes
 
 2.8.4
 -----
-Release date: May 4, 2023
+Release date: May 5, 2023
 
 * Added support for creating ADSDataset from pandas dataframe.
 * Added support for multi-model deployment using Triton.
@@ -272,7 +272,7 @@ Release date: March 3, 2022
 
 Release date: February 4, 2022
 
-* Fixed bug in DataFlow ``Job`` creation.
+* Fixed bug in Data Flow ``Job`` creation.
 * Fixed bug in ADSDataset ``get_recommendations`` raising ``HTML is not defined`` exception.
 * Fixed bug in jobs ``ScriptRuntime`` causing the parent artifact folder to be zipped and uploaded instead of the specified folder.
 * Fixed bug in ``ModelDeployment`` raising ``TypeError`` exception when updating an existing model deployment.

docs/source/user_guide/apachespark/quickstart.rst

@@ -4,13 +4,13 @@ Quick Start
 
 Data Flow is a hosted Apache Spark server. It is quick to start, and can scale to handle large datasets in parallel. ADS provides a convenient API for creating and maintaining workloads on Data Flow.
 
-Submit a Toy Python Script to DataFlow
-======================================
+Submit a Toy Python Script to Data Flow
+=======================================
 
 From a Python Environment
 -------------------------
 
-Submit a python script to DataFlow entirely from your python environment. 
+Submit a python script to Data Flow entirely from your python environment. 
 The following snippet uses a toy python script that prints "Hello World" 
 followed by the spark version, 3.2.1.
 
@@ -111,7 +111,7 @@ Assuming you have the following two files written in your current directory as `
 Real Data Flow Example with Conda Environment
 =============================================
 
-From PySpark v3.0.0 and onwards, Data Flow allows a published conda environment as the `Spark runtime environment <https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html#using-conda>`_ when built with `ADS`. Data Flow supports published conda environments only. Conda packs are tar'd conda environments. When you publish your own conda packs to object storage, ensure that the DataFlow Resource has access to read the object or bucket.
+From PySpark v3.0.0 and onwards, Data Flow allows a published conda environment as the `Spark runtime environment <https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html#using-conda>`_ when built with `ADS`. Data Flow supports published conda environments only. Conda packs are tar'd conda environments. When you publish your own conda packs to object storage, ensure that the Data Flow Resource has access to read the object or bucket.
 Below is a more built-out example using conda packs:
 
 From a Python Environment

docs/source/user_guide/jobs/data_science_job.rst

@@ -8,8 +8,6 @@ Quick Start
 
   See :doc:`policies` and  `About Data Science Policies <https://docs.oracle.com/en-us/iaas/data-science/using/policies.htm>`_.
 
-.. include:: ../jobs/toc_local.rst
-
 Define a Job
 ============
 

docs/source/user_guide/jobs/overview.rst

@@ -28,4 +28,4 @@ Each model can write its results to the Logging service or Object Storage.
 Then you can run a final sequential job that uses the best model class, and trains the final model on the entire dataset.
 
 The following sections provides details on running workloads with OCI Data Science Jobs using ADS Jobs APIs.
-You can use similar APIs to :doc:`Run a OCI DataFlow Application <../apachespark/quickstart>`.
+You can use similar APIs to :doc:`Run a OCI Data Flow Application <../apachespark/quickstart>`.

docs/source/user_guide/jobs/yaml_schema.rst

@@ -31,8 +31,8 @@ Following is the YAML schema for validating the YAML using `Cerberus <https://do
   :linenos:
 
 
-DataFlow
-========
+Data Flow
+=========
 
 .. raw:: html
   :file: ../../yaml_schema/jobs/dataFlow.html
@@ -126,8 +126,8 @@ Following is the YAML schema for validating the YAML using `Cerberus <https://do
   :linenos:
 
 
-DataFlow Runtime
---------------
+Data Flow Runtime
+----------------
 
 .. raw:: html
   :file: ../../yaml_schema/jobs/dataFlowRuntime.html

docs/source/yaml_schema/jobs/job.html

@@ -89,7 +89,7 @@ <h4 id="job.spec"><code>job.spec</code> schema</h4>
                 <code>dict</code>
             </td>
             <td>
-                See Data Science Job or DataFlow schema. 
+                See Data Science Job or Data Flow schema. 
             </td>
         </tr>
 

docs/source/yaml_schema/jobs/job.yaml

@@ -15,7 +15,7 @@ spec:
       type: string
     infrastructure:
       type: dict
-      meta: See Data Science Job or DataFlow schema.
+      meta: See Data Science Job or Data Flow schema.
     name:
       required: false
       type: string