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chore: Update Vertex AI naming references in README (#904)
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* Docs: Updated docstrings with exception error classes

* chore: Update Vertex AI naming references in README

Co-authored-by: sasha-gitg <44654632+sasha-gitg@users.noreply.github.com>
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22 changes: 11 additions & 11 deletions README.rst
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@@ -1,4 +1,4 @@
Vertex SDK for Python
Vertex AI SDK for Python
=================================================

|GA| |pypi| |versions| |unit-tests| |system-tests| |sample-tests|
Expand Down Expand Up @@ -78,7 +78,7 @@ Windows
Overview
~~~~~~~~
This section provides a brief overview of the Vertex SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.
This section provides a brief overview of the Vertex AI SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.

.. _vertex-ai-samples: https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/community/sdk

Expand Down Expand Up @@ -106,7 +106,7 @@ Initialize the SDK to store common configurations that you use with the SDK.
# defaults to us-central1
location='us-central1',
# Googlge Cloud Stoage bucket in same region as location
# Google Cloud Storage bucket in same region as location
# used to stage artifacts
staging_bucket='gs://my_staging_bucket',
Expand Down Expand Up @@ -166,7 +166,7 @@ Vertex AI supports a variety of dataset schemas. References to these schemas are

Training
^^^^^^^^
The Vertex SDK for Python allows you train Custom and AutoML Models.
The Vertex AI SDK for Python allows you train Custom and AutoML Models.

You can train custom models using a custom Python script, custom Python package, or container.

Expand Down Expand Up @@ -217,7 +217,7 @@ In the code block above `my_dataset` is managed dataset created in the `Dataset`

AutoMLs
-------
The Vertex SDK for Python supports AutoML tabular, image, text, video, and forecasting.
The Vertex AI SDK for Python supports AutoML tabular, image, text, video, and forecasting.

To train an AutoML tabular model:

Expand Down Expand Up @@ -358,7 +358,7 @@ To delete an endpoint:
Pipelines
---------

To create a Vertex Pipeline run and monitor until completion:
To create a Vertex AI Pipeline run and monitor until completion:

.. code-block:: Python
Expand All @@ -382,7 +382,7 @@ To create a Vertex Pipeline run and monitor until completion:
pipeline_root=pipeline_root,
)
# Execute pipeline in Vertex and monitor until completion
# Execute pipeline in Vertex AI and monitor until completion
pl.run(
# Email address of service account to use for the pipeline run
# You must have iam.serviceAccounts.actAs permission on the service account to use it
Expand All @@ -393,7 +393,7 @@ To create a Vertex Pipeline run and monitor until completion:
sync=True
)
To create a Vertex Pipeline without monitoring until completion, use `submit` instead of `run`:
To create a Vertex AI Pipeline without monitoring until completion, use `submit` instead of `run`:

.. code-block:: Python
Expand All @@ -417,7 +417,7 @@ To create a Vertex Pipeline without monitoring until completion, use `submit` in
pipeline_root=pipeline_root,
)
# Submit the Pipeline to Vertex
# Submit the Pipeline to Vertex AI
pl.submit(
# Email address of service account to use for the pipeline run
# You must have iam.serviceAccounts.actAs permission on the service account to use it
Expand Down Expand Up @@ -467,7 +467,7 @@ To use Explanation Metadata in endpoint deployment and model upload:
Cloud Profiler
----------------------------

Cloud Profiler allows you to profile your remote Vertex AI Training jobs on demand and visualize the results in Vertex Tensorboard.
Cloud Profiler allows you to profile your remote Vertex AI Training jobs on demand and visualize the results in Vertex AI Tensorboard.

To start using the profiler with TensorFlow, update your training script to include the following:

Expand All @@ -477,7 +477,7 @@ To start using the profiler with TensorFlow, update your training script to incl
...
cloud_profiler.init()
Next, run the job with with a Vertex TensorBoard instance. For full details on how to do this, visit https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview
Next, run the job with with a Vertex AI TensorBoard instance. For full details on how to do this, visit https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview

Finally, visit your TensorBoard in your Google Cloud Console, navigate to the "Profile" tab, and click the `Capture Profile` button. This will allow users to capture profiling statistics for the running jobs.

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18 changes: 9 additions & 9 deletions docs/README.rst
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@@ -1,4 +1,4 @@
Vertex SDK for Python
Vertex AI SDK for Python
=================================================

|GA| |pypi| |versions|
Expand Down Expand Up @@ -71,7 +71,7 @@ Windows
Overview
~~~~~~~~
This section provides a brief overview of the Vertex SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.
This section provides a brief overview of the Vertex AI SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.

.. _vertex-ai-samples: https://github.com/GoogleCloudPlatform/ai-platform-samples/tree/master/ai-platform-unified/notebooks/unofficial/sdk

Expand Down Expand Up @@ -99,7 +99,7 @@ Initialize the SDK to store common configurations that you use with the SDK.
# defaults to us-central1
location='us-central1',
# Googlge Cloud Stoage bucket in same region as location
# Google Cloud Storage bucket in same region as location
# used to stage artifacts
staging_bucket='gs://my_staging_bucket',
Expand Down Expand Up @@ -159,7 +159,7 @@ Vertex AI supports a variety of dataset schemas. References to these schemas are

Training
^^^^^^^^
The Vertex SDK for Python allows you train Custom and AutoML Models.
The Vertex AI SDK for Python allows you train Custom and AutoML Models.

You can train custom models using a custom Python script, custom Python package, or container.

Expand Down Expand Up @@ -210,7 +210,7 @@ In the code block above `my_dataset` is managed dataset created in the `Dataset`

AutoMLs
-------
The Vertex SDK for Python supports AutoML tabular, image, text, video, and forecasting.
The Vertex AI SDK for Python supports AutoML tabular, image, text, video, and forecasting.

To train an AutoML tabular model:

Expand Down Expand Up @@ -351,7 +351,7 @@ To delete an endpoint:
Pipelines
---------

To create a Vertex Pipeline run and monitor until completion:
To create a Vertex AI Pipeline run and monitor until completion:

.. code-block:: Python
Expand All @@ -375,7 +375,7 @@ To create a Vertex Pipeline run and monitor until completion:
pipeline_root=pipeline_root,
)
# Execute pipeline in Vertex and monitor until completion
# Execute pipeline in Vertex AI and monitor until completion
pl.run(
# Email address of service account to use for the pipeline run
# You must have iam.serviceAccounts.actAs permission on the service account to use it
Expand All @@ -386,7 +386,7 @@ To create a Vertex Pipeline run and monitor until completion:
sync=True
)
To create a Vertex Pipeline without monitoring until completion, use `submit` instead of `run`:
To create a Vertex AI Pipeline without monitoring until completion, use `submit` instead of `run`:

.. code-block:: Python
Expand All @@ -410,7 +410,7 @@ To create a Vertex Pipeline without monitoring until completion, use `submit` in
pipeline_root=pipeline_root,
)
# Submit the Pipeline to Vertex
# Submit the Pipeline to Vertex AI
pl.submit(
# Email address of service account to use for the pipeline run
# You must have iam.serviceAccounts.actAs permission on the service account to use it
Expand Down

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