update

Author: Ziqun Ye

docs/source/ads.model.extractor.rst

@@ -4,14 +4,6 @@ ads.model.extractor package
 Submodules
 ----------
 
-ads.model.extractor.automl\_extractor module
---------------------------------------------
-
-.. automodule:: ads.model.extractor.automl_extractor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 ads.model.extractor.keras\_extractor module
 -------------------------------------------
 

docs/source/user_guide/model_registration/frameworks/automlmodel.rst

@@ -8,7 +8,7 @@ AutoMLModel
 Working with AutoML has moved from within ADS to working directly with the automlx library.
 To deploy an AutoMlx model, use `GenericModel <../../../ads.model.html#ads.model.generic_model.GenericModel>`__ class.
 
-The following example take your trained ``AutoML`` model using ``GenericModel`` and deploy it into production.
+The following example takes your trained ``AutoML`` model using ``GenericModel`` and deploys it into production.
 
 
 Example
@@ -244,7 +244,7 @@ Verify score.py changes by running inference locally.
 
     automl_model.verify(X_test.iloc[:2], auto_serialize_data=True)
 
-Save model and Deploy the model. After it is successfully deployed, invoke the endpoint by calling .predict() function.
+Save the model, and Deploy the model. After it is successfully deployed, invoke the endpoint by calling .predict() function.
 
 
  .. code-block:: python3

docs/source/user_guide/model_registration/frameworks/genericmodel.rst

@@ -11,7 +11,7 @@ Overview
 
 The ``ads.model.generic_model.GenericModel`` class in ADS provides an efficient way to serialize almost any model class. This section demonstrates how to use the ``GenericModel`` class to prepare model artifacts, verify models, save models to the model catalog, deploy models, and perform predictions on model deployment endpoints.
 
-The ``GenericModel`` class works with any unsupported model framework that has a ``.predict()`` method. For the most common model classes such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, and AutoML, we recommend that you use the ADS provided, framework-specific serializations models. For example, for a scikit-learn model, use SKLearnmodel. For other models, use the ``GenericModel`` class.
+The ``GenericModel`` class works with any unsupported model framework that has a ``.predict()`` method. For the most common model classes such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, we recommend that you use the ADS provided, framework-specific serializations models. For example, for a scikit-learn model, use SKLearnmodel. For other models, use the ``GenericModel`` class.
 
 .. include:: ../_template/overview.rst
 
@@ -177,7 +177,7 @@ Example -- Save Your Own Model
 ==============================
 
 By default, the ``serialize`` in ``GenericModel`` class is True, and it will serialize the model using cloudpickle. However, you can set ``serialize=False`` to disable it. And serialize the model on your own. You just need to copy the serialized model into the ``.artifact_dir``. This example shows step by step how you can do that.
-The example is illustrated using an AutoMLx model.
+The example is illustrated using a Sklearn model.
 
 .. code-block:: python3
 
@@ -369,8 +369,8 @@ After verify run successfully, you can save the model to model catalog, deploy a
 
 .. code-block:: python3
 
-    model_id = model.save(display_name='Demo AutoMLModel model')
-    deploy = model.deploy(display_name='Demo AutoMLModel deployment')
+    model_id = model.save(display_name='Demo Sklearn model')
+    deploy = model.deploy(display_name='Demo Sklearn deployment')
     model.predict(X_test[:2].tolist())
 
 You can also use the shortcut ``.prepare_save_deploy()`` instead of calling ``.prepare()``, ``.save()`` and ``.deploy()`` seperately.
@@ -393,4 +393,4 @@ You can also use the shortcut ``.prepare_save_deploy()`` instead of calling ``.p
     model.verify(2)
     model.predict(2)
     model.delete_deployment(wait_for_completion=True)
-    ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model.model_id)
+    model.delete()

docs/source/user_guide/model_serialization/_template/summary_status.rst

@@ -1,4 +1,4 @@
-You can call the ``.summary_status()`` method after a model serialization instance such as ``AutoMLModel``, ``GenericModel``, ``SklearnModel``, ``TensorFlowModel``, or ``PyTorchModel`` is created. The ``.summary_status()`` method returns a Pandas dataframe that guides you through the entire workflow. It shows which methods are available to call and which ones aren't. Plus it outlines what each method does. If extra actions are required, it also shows those actions.
+You can call the ``.summary_status()`` method after a model serialization instance such as ``GenericModel``, ``SklearnModel``, ``TensorFlowModel``, or ``PyTorchModel`` is created. The ``.summary_status()`` method returns a Pandas dataframe that guides you through the entire workflow. It shows which methods are available to call and which ones aren't. Plus it outlines what each method does. If extra actions are required, it also shows those actions.
 
 The following image displays an example summary status table created after a user initiates a model instance. The table's Step column displays a Status of Done for the initiate step. And the ``Details`` column explains what the initiate step did such as generating a ``score.py`` file. The Step column also displays  the ``prepare()``, ``verify()``, ``save()``, ``deploy()``, and ``predict()`` methods for the model. The Status column displays which method is available next. After the initiate step,  the ``prepare()`` method is available. The next step is to call the ``prepare()`` method. 
 

docs/source/user_guide/model_serialization/automlmodel.rst

@@ -9,7 +9,7 @@ The ``ads.model.framework.automl_model.AutoMLModel`` class is deprecated. See th
 
 To deploy an AutoMlx model, use `GenericModel <../../../ads.model.html#ads.model.generic_model.GenericModel>`__ class.
 
-The following example take your trained ``AutoML`` model using ``GenericModel`` and deploy it into production with a few lines of code.
+The following example takes your trained ``AutoML`` model using ``GenericModel`` and deploys it into production with a few lines of code.
 
 
 Example
@@ -245,7 +245,7 @@ Verify score.py changes by running inference locally
 
     automl_model.verify(X_test.iloc[:2])
 
-Save model and Deploy the model. After it is successfully deployed, invoke the endpoint by calling .predict() function.
+Save model, and Deploy the model. After it is successfully deployed, invoke the endpoint by calling .predict() function.
  .. code-block:: python3
 
     model_id = automl_model.save(display_name='Demo AutoMLModel model')

docs/source/user_guide/model_serialization/genericmodel.rst

@@ -8,7 +8,7 @@ Overview
 
 The ``GenericModel`` class in ADS provides an efficient way to serialize almost any model class. This section demonstrates how to use the ``GenericModel`` class to prepare model artifacts, verify models, save models to the model catalog, deploy models, and perform predictions on model deployment endpoints.
 
-The ``GenericModel`` class works with any unsupported model framework that has a ``.predict()`` method. For the most common model classes such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, and AutoML, we recommend that you use the ADS provided, framework-specific serializations models. For example, for a scikit-learn model, use SKLearnmodel. For other models, use the ``GenericModel`` class.
+The ``GenericModel`` class works with any unsupported model framework that has a ``.predict()`` method. For the most common model classes such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, we recommend that you use the ADS provided, framework-specific serializations models. For example, for a scikit-learn model, use SKLearnmodel. For other models, use the ``GenericModel`` class.
 
 .. include:: _template/overview.rst
 
@@ -190,83 +190,46 @@ By default, the ``GenericModel`` serializes to a pickle file. The following exam
     catboost_model.delete_deployment(wait_for_completion=True)
     catboost_model.delete() # delete the model
 
-You can also use the shortcut ``.prepare_save_deploy()`` instead of calling ``.prepare()``, ``.save()`` and ``.deploy()`` seperately.
-
-.. code-block:: python3
-
-    import tempfile
-    from ads.catalog.model import ModelCatalog
-    from ads.model.generic_model import GenericModel
-
-    class Toy:
-        def predict(self, x):
-            return x ** 2
-    estimator = Toy()
-
-    model = GenericModel(estimator=estimator)
-    model.summary_status()
-    # If you are running the code inside a notebook session and using a service pack, `inference_conda_env` can be omitted.
-    model.prepare_save_deploy(inference_conda_env="dataexpl_p37_cpu_v3")
-    model.verify(2)
-    model.predict(2)
-    model.delete_deployment(wait_for_completion=True)
-    ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model.model_id)
-
 
 Example -- Save Your Own Model
 ==============================
 
 By default, the ``serialize`` in ``GenericModel`` class is True, and it will serialize the model using cloudpickle. However, you can set ``serialize=False`` to disable it. And serialize the model on your own. You just need to copy the serialized model into the ``.artifact_dir``. This example shows step by step how you can do that.
-The example is illustrated using an AutoMLx model.
+The example is illustrated using a Sklearn model.
 
 .. code-block:: python3
 
-    import automl
-    import ads
-    from automl import init
-    from sklearn.datasets import fetch_openml
-    from sklearn.model_selection import train_test_split
+    import tempfile
+    from ads import set_auth
     from ads.model import GenericModel
+    from sklearn.datasets import load_iris
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split
 
-    dataset = fetch_openml(name='adult', as_frame=True)
-    df, y = dataset.data, dataset.target
-
-    # Several of the columns are incorrectly labeled as category type in the original dataset
-    numeric_columns = ['age', 'capitalgain', 'capitalloss', 'hoursperweek']
-    for col in df.columns:
-        if col in numeric_columns:
-            df[col] = df[col].astype(int)
-        
-
-    X_train, X_test, y_train, y_test = train_test_split(df,
-                                                        y.map({'>50K': 1, '<=50K': 0}).astype(int),
-                                                        train_size=0.7,
-                                                        random_state=0)
-
-    X_train.shape, X_test.shape
-
-    # create a AutoMLx model
-    init(engine='local')
+    set_auth(auth="resource_principal")
 
-    est = automl.Pipeline(task='classification')
-    est.fit(X_train, y_train)
+    # Load dataset and Prepare train and test split
+    iris = load_iris()
+    X, y = iris.data, iris.target
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
 
-    # Authentication
-    ads.set_auth(auth="resource_principal")
+    # Train a LogisticRegression model
+    sklearn_estimator = LogisticRegression()
+    sklearn_estimator.fit(X_train, y_train)
 
     # Serialize your model. You can choose your own way to serialize your model.
     import cloudpickle
     with open("./model.pkl", "wb") as f:
-        cloudpickle.dump(est, f)
+        cloudpickle.dump(sklearn_estimator, f)
 
-    model = GenericModel(est, artifact_dir = "model_artifact_folder", serialize=False)
-    model.prepare(inference_conda_env="automlx_p38_cpu_v1",force_overwrite=True, model_file_name="model.pkl", X_sample=X_test)
+    model = GenericModel(sklearn_estimator, artifact_dir = "model_artifact_folder", serialize=False)
+    model.prepare(inference_conda_env="generalml_p38_cpu_v1",force_overwrite=True, model_file_name="model.pkl", X_sample=X_test)
 
-Now copy the model.pkl file and paste into the ``model_artifact_folder`` folder. And open the score.py in the ``model_artifact_folder`` folder and add implement the ``load_model`` function. You can also edit ``pre_inference`` and ``post_inference`` function. Below is an example implementation of the score.py.
+Now copy the model.pkl file and paste into the ``model_artifact_folder`` folder. And open the score.py in the ``model_artifact_folder`` folder to add implementation of the ``load_model`` function. You can also add your preprocessing steps in ``pre_inference`` function and postprocessing steps in ``post_inference`` function. Below is an example implementation of the score.py.
 Replace your score.py with the code below.
 
 .. code-block:: python3
-    :emphasize-lines: 28, 29, 30, 31, 122
+    :emphasize-lines: 28, 29, 30, 31, 123
 
     # score.py 1.0 generated by ADS 2.8.2 on 20230301_065458
     import os
@@ -414,16 +377,38 @@ Replace your score.py with the code below.
         )
         return {'prediction': yhat}
 
-Save the score.py and now call verify to check if it works locally.
+Save the score.py and now call ``.verify()`` to check if it works locally.
 
 .. code-block:: python3
 
-    model.verify(X_test.iloc[:2], auto_serialize_data=True)
+    model.verify(X_test[:2], auto_serialize_data=True)
 
 After verify run successfully, you can save the model to model catalog, deploy and call predict to invoke the endpoint.
 
 .. code-block:: python3
 
-    model_id = model.save(display_name='Demo AutoMLModel model')
-    deploy = model.deploy(display_name='Demo AutoMLModel deployment')
-    model.predict(X_test.iloc[:2].to_json())
+    model_id = model.save(display_name='Demo Sklearn model')
+    deploy = model.deploy(display_name='Demo Sklearn deployment')
+    model.predict(X_test[:2].tolist())
+
+You can also use the shortcut ``.prepare_save_deploy()`` instead of calling ``.prepare()``, ``.save()`` and ``.deploy()`` seperately.
+
+.. code-block:: python3
+
+    import tempfile
+    from ads.catalog.model import ModelCatalog
+    from ads.model.generic_model import GenericModel
+
+    class Toy:
+        def predict(self, x):
+            return x ** 2
+    estimator = Toy()
+
+    model = GenericModel(estimator=estimator)
+    model.summary_status()
+    # If you are running the code inside a notebook session and using a service pack, `inference_conda_env` can be omitted.
+    model.prepare_save_deploy(inference_conda_env="dataexpl_p37_cpu_v3")
+    model.verify(2)
+    model.predict(2)
+    model.delete_deployment(wait_for_completion=True)
+    model.delete()

docs/source/user_guide/model_training/index.rst

@@ -3,7 +3,7 @@ Train Models
 ============
 
 In this section you will learn about model training on the Data Science cloud service using a variety of popular frameworks. This section
-covers the popular ``sklearn``  framework, along with gradient boosted tree estimators like LightGBM and XGBoost, Oracle AutoML  and
+covers the popular ``sklearn``  framework, along with gradient boosted tree estimators like LightGBM and XGBoost, and
 deep learning packages likes TensorFlow and PyTorch.
 
 The section covers how to serialize models and make use of the OCI Model Catalog to store model artifacts and meta data all using