From dde0d50a461d630622f7a3765caa0886fb52f7a0 Mon Sep 17 00:00:00 2001
From: aviruthen <91846056+aviruthen@users.noreply.github.com>
Date: Tue, 25 Nov 2025 11:11:53 -0800
Subject: [PATCH 1/4] Update README for V3

---
 README.rst | 229 +++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 205 insertions(+), 24 deletions(-)

diff --git a/README.rst b/README.rst
index 9e362053b1..eb31801f1b 100644
--- a/README.rst
+++ b/README.rst
@@ -51,32 +51,185 @@ Version 3.0.0 represents a significant milestone in our product's evolution. Thi
 * Older interfaces such as Estimator, Model, Predictor and all their subclasses will not be supported in V3. 
 * Please review documentation of interfaces for parameters support (especially ModelBuilder) in our V3 examples folder.
 
-SageMaker V2 Examples
+
+
+
+Migrating to V3
+----------------
+
+**Upgrading to 3.x**
+
+To upgrade to the latest version of SageMaker Python SDK 3.x:
+
+::
+
+    pip install --upgrade sagemaker
+
+If you prefer to downgrade to the 2.x version:
+
+::
+
+    pip install sagemaker==2.*
+
+**Key Benefits of 3.x**
+
+* **Modular Architecture**: Separate PyPI packages for core, training, and serving capabilities
+
+  * `sagemaker-core <https://pypi.org/project/sagemaker-core/>`__
+  * `sagemaker-train <https://pypi.org/project/sagemaker-train/>`__
+  * `sagemaker-serve <https://pypi.org/project/sagemaker-serve/>`__
+  * `sagemaker-mlops <https://pypi.org/project/sagemaker-mlops/>`__
+
+* **Unified Training & Inference**: Single classes (ModelTrainer, ModelBuilder) replace multiple framework-specific classes
+* **Object-Oriented API**: Structured interface with auto-generated configs aligned with AWS APIs
+* **Simplified Workflows**: Reduced boilerplate and more intuitive interfaces
+
+**Training Experience**
+
+V3 introduces the unified ModelTrainer class to reduce complexity of initial setup and deployment for model training. This replaces the V2 Estimator class and framework-specific classes (PyTorchEstimator, SKLearnEstimator, etc.).
+
+This example shows how to train a model using a custom training container with training data from S3.
+
+*SageMaker Python SDK 2.x:*
+
+.. code:: python
+
+    from sagemaker.estimator import Estimator
+    estimator = Estimator(
+        image_uri="my-training-image",
+        role="arn:aws:iam::123456789012:role/SageMakerRole",
+        instance_count=1,
+        instance_type="ml.m5.xlarge",
+        output_path="s3://my-bucket/output"
+    )
+    estimator.fit({"training": "s3://my-bucket/train"})
+
+*SageMaker Python SDK 3.x:*
+
+.. code:: python
+
+    from sagemaker.train import ModelTrainer
+    from sagemaker.train.configs import InputData
+
+    trainer = ModelTrainer(
+        training_image="my-training-image",
+        role="arn:aws:iam::123456789012:role/SageMakerRole"
+    )
+
+    train_data = InputData(
+        channel_name="training",
+        data_source="s3://my-bucket/train"
+    )
+
+    trainer.train(input_data_config=[train_data])
+
+**See more examples:** `SageMaker V3 Examples <#sagemaker-v3-examples>`__
+
+**Inference Experience**
+
+V3 introduces the unified ModelBuilder class for model deployment and inference. This replaces the V2 Model class and framework-specific classes (PyTorchModel, TensorFlowModel, SKLearnModel, XGBoostModel, etc.).
+
+This example shows how to deploy a trained model for real-time inference.
+
+*SageMaker Python SDK 2.x:*
+
+.. code:: python
+
+    from sagemaker.model import Model
+    from sagemaker.predictor import Predictor
+    model = Model(
+        image_uri="my-inference-image",
+        model_data="s3://my-bucket/model.tar.gz",
+        role="arn:aws:iam::123456789012:role/SageMakerRole"
+    )
+    predictor = model.deploy(
+        initial_instance_count=1,
+        instance_type="ml.m5.xlarge"
+    )
+    result = predictor.predict(data)
+
+*SageMaker Python SDK 3.x:*
+
+.. code:: python
+
+    from sagemaker.serve import ModelBuilder
+    model_builder = ModelBuilder(
+        model="my-model",
+        model_path="s3://my-bucket/model.tar.gz"
+    )
+    endpoint = model_builder.build()
+    result = endpoint.invoke(...)
+
+**See more examples:** `SageMaker V3 Examples <#sagemaker-v3-examples>`__
+
+**SageMaker Core Experience**
+
+V3 provides an object-oriented interface for interacting with SageMaker resources, with auto-generated config classes and methods based on SageMaker API inputs and outputs. This replaces V2 config classes with SageMaker Core config objects.
+
+*SageMaker Python SDK 2.x:*
+
+.. code:: python
+
+    from sagemaker.model import Model
+    model = Model(
+        image_uri="my-image",
+        model_data="s3://my-bucket/model.tar.gz",
+        role="arn:aws:iam::123456789012:role/SageMakerRole"
+    )
+    predictor = model.deploy(
+        initial_instance_count=1,
+        instance_type="ml.m5.xlarge",
+        endpoint_name="my-endpoint"
+    )
+
+*SageMaker Python SDK 3.x:*
+
+.. code:: python
+
+    model = Model.get(model_name="my-model")
+
+    endpoint = model.deploy(
+        endpoint_name="my-endpoint",
+        instance_type="ml.m5.xlarge",
+        initial_instance_count=1
+    )
+
+SageMaker V3 Examples
 ---------------------
 
-#. `Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>`__
-#. `Using MXNet <https://sagemaker.readthedocs.io/en/stable/using_mxnet.html>`__
-#. `Using TensorFlow <https://sagemaker.readthedocs.io/en/stable/using_tf.html>`__
-#. `Using Chainer <https://sagemaker.readthedocs.io/en/stable/using_chainer.html>`__
-#. `Using PyTorch <https://sagemaker.readthedocs.io/en/stable/using_pytorch.html>`__
-#. `Using Scikit-learn <https://sagemaker.readthedocs.io/en/stable/using_sklearn.html>`__
-#. `Using XGBoost <https://sagemaker.readthedocs.io/en/stable/using_xgboost.html>`__
-#. `SageMaker Reinforcement Learning Estimators <https://sagemaker.readthedocs.io/en/stable/using_rl.html>`__
-#. `SageMaker SparkML Serving <#sagemaker-sparkml-serving>`__
-#. `Amazon SageMaker Built-in Algorithm Estimators <src/sagemaker/amazon/README.rst>`__
-#. `Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>`__
-#. `Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>`__
-#. `BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>`__
-#. `SageMaker Automatic Model Tuning <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-automatic-model-tuning>`__
-#. `SageMaker Batch Transform <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-batch-transform>`__
-#. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
-#. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
-#. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
-#. `Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>`__
-#. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
-#. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
-#. `SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>`__
-#. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
+**Training Examples**
+
+#. `Custom Distributed Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/custom-distributed-training-example.ipynb>`__
+#. `Distributed Local Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/distributed-local-training-example.ipynb>`__
+#. `Hyperparameter Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/hyperparameter-training-example.ipynb>`__
+#. `JumpStart Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/jumpstart-training-example.ipynb>`__
+#. `Local Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/local-training-example.ipynb>`__
+
+**Inference Examples**
+
+#. `HuggingFace Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/huggingface-example.ipynb>`__
+#. `In-Process Mode Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/in-process-mode-example.ipynb>`__
+#. `Inference Spec Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/inference-spec-example.ipynb>`__
+#. `JumpStart E2E Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/jumpstart-e2e-training-example.ipynb>`__
+#. `JumpStart Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/jumpstart-example.ipynb>`__
+#. `Local Mode Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/local-mode-example.ipynb>`__
+#. `Optimize Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/optimize-example.ipynb>`__
+#. `Train Inference E2E Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/train-inference-e2e-example.ipynb>`__
+
+**ML Ops Examples**
+
+#. `V3 Hyperparameter Tuning Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-hyperparameter-tuning-example/v3-hyperparameter-tuning-example.ipynb>`__
+#. `V3 Hyperparameter Tuning Pipeline <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-hyperparameter-tuning-example/v3-hyperparameter-tuning-pipeline.ipynb>`__
+#. `V3 Model Registry Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-model-registry-example/v3-model-registry-example.ipynb>`__
+#. `V3 PyTorch Processing Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-processing-job-pytorch/v3-pytorch-processing-example.ipynb>`__
+#. `V3 Pipeline Train Create Registry <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-pipeline-train-create-registry.ipynb>`__
+#. `V3 Processing Job Sklearn <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-processing-job-sklearn.ipynb>`__
+#. `V3 SageMaker Clarify <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-sagemaker-clarify.ipynb>`__
+#. `V3 Transform Job Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-transform-job-example.ipynb>`__
+
+**Looking for V2 Examples?** See `SageMaker V2 Examples <#sagemaker-v2-examples>`__ below.
+
+
 
 
 Installing the SageMaker Python SDK
@@ -265,3 +418,31 @@ For more information about the different ``content-type`` and ``Accept`` formats
 ``schema`` that SageMaker SparkML Serving recognizes, please see `SageMaker SparkML Serving Container`_.
 
 .. _SageMaker SparkML Serving Container: https://github.com/aws/sagemaker-sparkml-serving-container
+
+
+SageMaker V2 Examples
+---------------------
+
+#. `Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>`__
+#. `Using MXNet <https://sagemaker.readthedocs.io/en/stable/using_mxnet.html>`__
+#. `Using TensorFlow <https://sagemaker.readthedocs.io/en/stable/using_tf.html>`__
+#. `Using Chainer <https://sagemaker.readthedocs.io/en/stable/using_chainer.html>`__
+#. `Using PyTorch <https://sagemaker.readthedocs.io/en/stable/using_pytorch.html>`__
+#. `Using Scikit-learn <https://sagemaker.readthedocs.io/en/stable/using_sklearn.html>`__
+#. `Using XGBoost <https://sagemaker.readthedocs.io/en/stable/using_xgboost.html>`__
+#. `SageMaker Reinforcement Learning Estimators <https://sagemaker.readthedocs.io/en/stable/using_rl.html>`__
+#. `SageMaker SparkML Serving <#sagemaker-sparkml-serving>`__
+#. `Amazon SageMaker Built-in Algorithm Estimators <src/sagemaker/amazon/README.rst>`__
+#. `Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>`__
+#. `Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>`__
+#. `BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>`__
+#. `SageMaker Automatic Model Tuning <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-automatic-model-tuning>`__
+#. `SageMaker Batch Transform <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-batch-transform>`__
+#. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
+#. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
+#. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
+#. `Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>`__
+#. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
+#. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
+#. `SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>`__
+#. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
\ No newline at end of file

From 6aa577e24323708e2983c1263bba4003224f3b06 Mon Sep 17 00:00:00 2001
From: aviruthen <91846056+aviruthen@users.noreply.github.com>
Date: Tue, 25 Nov 2025 11:25:52 -0800
Subject: [PATCH 2/4] Small fixes to README

---
 README.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index eb31801f1b..5658212405 100644
--- a/README.rst
+++ b/README.rst
@@ -32,7 +32,7 @@ SageMaker Python SDK
 
 SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
 
-With the SDK, you can train and deploy models using popular deep learning frameworks **Apache MXNet** and **TensorFlow**.
+With the SDK, you can train and deploy models using popular deep learning frameworks **Apache MXNet** and **PyTorch**.
 You can also train and deploy models with **Amazon algorithms**,
 which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training.
 If you have **your own algorithms** built into SageMaker compatible Docker containers, you can train and host models using these as well.
@@ -49,7 +49,7 @@ Version 3.0.0 represents a significant milestone in our product's evolution. Thi
 **Important: Please review these breaking changes before upgrading.**
 
 * Older interfaces such as Estimator, Model, Predictor and all their subclasses will not be supported in V3. 
-* Please review documentation of interfaces for parameters support (especially ModelBuilder) in our V3 examples folder.
+* Please review documentation of interfaces for parameter support in our `V3 examples folder <https://github.com/aws/sagemaker-python-sdk/tree/master/v3-examples>`__.
 
 
 
@@ -71,6 +71,8 @@ If you prefer to downgrade to the 2.x version:
 
     pip install sagemaker==2.*
 
+See `SageMaker V2 Examples <#sagemaker-v2-examples>`__ for V2 documentation and examples.
+
 **Key Benefits of 3.x**
 
 * **Modular Architecture**: Separate PyPI packages for core, training, and serving capabilities

From c351583a855c6e686db9d5d53df3b6f67f35f63c Mon Sep 17 00:00:00 2001
From: aviruthen <91846056+aviruthen@users.noreply.github.com>
Date: Tue, 25 Nov 2025 11:36:19 -0800
Subject: [PATCH 3/4] minor changes to readme

---
 README.rst | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/README.rst b/README.rst
index 5658212405..ff1c4d51b4 100644
--- a/README.rst
+++ b/README.rst
@@ -10,10 +10,6 @@ SageMaker Python SDK
    :target: https://pypi.python.org/pypi/sagemaker
    :alt: Latest Version
 
-.. image:: https://img.shields.io/conda/vn/conda-forge/sagemaker-python-sdk.svg
-   :target: https://anaconda.org/conda-forge/sagemaker-python-sdk
-   :alt: Conda-Forge Version
-
 .. image:: https://img.shields.io/pypi/pyversions/sagemaker.svg
    :target: https://pypi.python.org/pypi/sagemaker
    :alt: Supported Python Versions
@@ -49,9 +45,7 @@ Version 3.0.0 represents a significant milestone in our product's evolution. Thi
 **Important: Please review these breaking changes before upgrading.**
 
 * Older interfaces such as Estimator, Model, Predictor and all their subclasses will not be supported in V3. 
-* Please review documentation of interfaces for parameter support in our `V3 examples folder <https://github.com/aws/sagemaker-python-sdk/tree/master/v3-examples>`__.
-
-
+* Please see our `V3 examples folder <https://github.com/aws/sagemaker-python-sdk/tree/master/v3-examples>`__ for example notebooks and usage patterns.
 
 
 Migrating to V3

From f9cf7bf621c9d0ee205565c93d663b193fecbe9f Mon Sep 17 00:00:00 2001
From: aviruthen <91846056+aviruthen@users.noreply.github.com>
Date: Tue, 25 Nov 2025 11:43:33 -0800
Subject: [PATCH 4/4] small update

---
 README.rst | 32 --------------------------------
 1 file changed, 32 deletions(-)

diff --git a/README.rst b/README.rst
index ff1c4d51b4..7482dd5667 100644
--- a/README.rst
+++ b/README.rst
@@ -158,38 +158,6 @@ This example shows how to deploy a trained model for real-time inference.
 
 **See more examples:** `SageMaker V3 Examples <#sagemaker-v3-examples>`__
 
-**SageMaker Core Experience**
-
-V3 provides an object-oriented interface for interacting with SageMaker resources, with auto-generated config classes and methods based on SageMaker API inputs and outputs. This replaces V2 config classes with SageMaker Core config objects.
-
-*SageMaker Python SDK 2.x:*
-
-.. code:: python
-
-    from sagemaker.model import Model
-    model = Model(
-        image_uri="my-image",
-        model_data="s3://my-bucket/model.tar.gz",
-        role="arn:aws:iam::123456789012:role/SageMakerRole"
-    )
-    predictor = model.deploy(
-        initial_instance_count=1,
-        instance_type="ml.m5.xlarge",
-        endpoint_name="my-endpoint"
-    )
-
-*SageMaker Python SDK 3.x:*
-
-.. code:: python
-
-    model = Model.get(model_name="my-model")
-
-    endpoint = model.deploy(
-        endpoint_name="my-endpoint",
-        instance_type="ml.m5.xlarge",
-        initial_instance_count=1
-    )
-
 SageMaker V3 Examples
 ---------------------