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lucasjia-aws merged 1 commit into from
Sep 14, 2026
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docs: Remove V2 code examples from V3 documentation #6271

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22 changes: 1 addition & 21 deletions docs/inference/index.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -14,27 +14,7 @@ Key Benefits of V3 Inference
Quick Start Example
-------------------

Here's how inference has evolved from V2 to V3:

**SageMaker Python SDK V2:**

.. code-block:: 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 V3:**
Build, deploy, and invoke a model with ``ModelBuilder``:

.. code-block:: python

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26 changes: 1 addition & 25 deletions docs/ml_ops/index.rst
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Expand Up @@ -14,31 +14,7 @@ Key Benefits of V3 ML Operations
Quick Start Example
-------------------

Here's how ML Operations workflows are simplified in V3:

**Traditional Pipeline Approach:**

.. code-block:: python

from sagemaker.workflow.pipeline import Pipeline
from sagemaker.workflow.steps import TrainingStep, ProcessingStep
from sagemaker.sklearn.processing import SKLearnProcessor

# Complex setup with multiple framework-specific classes
processor = SKLearnProcessor(
framework_version="0.23-1",
role=role,
instance_type="ml.m5.xlarge",
instance_count=1
)

processing_step = ProcessingStep(
name="PreprocessData",
processor=processor,
# ... many configuration parameters
)

**SageMaker V3 MLOps Approach:**
Define a pipeline and add a processing step:

.. code-block:: python

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187 changes: 0 additions & 187 deletions docs/ml_ops/lineage.rst
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Expand Up @@ -59,19 +59,6 @@ Use Case 1: Session Setup

Initialize a SageMaker session and set up common variables.

**V2 (Legacy):**

.. code-block:: python

import boto3
import sagemaker

region = boto3.Session().region_name
sagemaker_session = sagemaker.session.Session()
default_bucket = sagemaker_session.default_bucket()

**V3:**

.. code-block:: python

import boto3
Expand All @@ -87,25 +74,6 @@ Use Case 2: Creating a Lineage Context

Contexts provide a method to logically group other lineage entities. Each context name must be unique across all other contexts.

**V2 (Legacy):**

.. code-block:: python

from datetime import datetime
from sagemaker.lineage.context import Context

unique_id = str(int(datetime.now().replace(microsecond=0).timestamp()))
context_name = f"machine-learning-workflow-{unique_id}"

ml_workflow_context = Context.create(
context_name=context_name,
context_type="MLWorkflow",
source_uri=unique_id,
properties={"example": "true"},
)

**V3:**

.. code-block:: python

from datetime import datetime
Expand All @@ -127,18 +95,6 @@ Use Case 3: Listing Contexts

Enumerate existing contexts sorted by creation time.

**V2 (Legacy):**

.. code-block:: python

from sagemaker.lineage.context import Context

contexts = Context.list(sort_by="CreationTime", sort_order="Descending")
for ctx in contexts:
print(ctx.context_name)

**V3:**

.. code-block:: python

from sagemaker.core.lineage.context import Context
Expand All @@ -153,21 +109,6 @@ Use Case 4: Creating an Action

Actions represent computational steps such as model builds, transformations, or training jobs.

**V2 (Legacy):**

.. code-block:: python

from sagemaker.lineage.action import Action

model_build_action = Action.create(
action_name=f"model-build-step-{unique_id}",
action_type="ModelBuild",
source_uri=unique_id,
properties={"Example": "Metadata"},
)

**V3:**

.. code-block:: python

from sagemaker.core.lineage.action import Action
Expand All @@ -185,20 +126,6 @@ Use Case 5: Creating Associations

Associations are directed edges in the lineage graph. The ``association_type`` can be ``Produced``, ``DerivedFrom``, ``AssociatedWith``, or ``ContributedTo``.

**V2 (Legacy):**

.. code-block:: python

from sagemaker.lineage.association import Association

context_action_association = Association.create(
source_arn=ml_workflow_context.context_arn,
destination_arn=model_build_action.action_arn,
association_type="AssociatedWith",
)

**V3:**

.. code-block:: python

from sagemaker.core.lineage.association import Association
Expand All @@ -216,24 +143,6 @@ Use Case 6: Traversing Associations

Query incoming and outgoing associations to understand how entities are related in the lineage graph.

**V2 (Legacy):**

.. code-block:: python

from sagemaker.lineage.association import Association

# List incoming associations to an action
incoming = Association.list(destination_arn=model_build_action.action_arn)
for association in incoming:
print(f"{model_build_action.action_name} has incoming association from {association.source_name}")

# List outgoing associations from a context
outgoing = Association.list(source_arn=ml_workflow_context.context_arn)
for association in outgoing:
print(f"{ml_workflow_context.context_name} has outgoing association to {association.destination_name}")

**V3:**

.. code-block:: python

from sagemaker.core.lineage.association import Association
Expand All @@ -254,35 +163,6 @@ Use Case 7: Creating Artifacts

Artifacts represent URI-addressable objects or data, such as datasets, labels, or trained models.

**V2 (Legacy):**

.. code-block:: python

from sagemaker.lineage.artifact import Artifact

input_test_images = Artifact.create(
artifact_name="mnist-test-images",
artifact_type="TestData",
source_types=[{"SourceIdType": "Custom", "Value": unique_id}],
source_uri=f"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/t10k-images-idx3-ubyte.gz",
)

input_test_labels = Artifact.create(
artifact_name="mnist-test-labels",
artifact_type="TestLabels",
source_types=[{"SourceIdType": "Custom", "Value": unique_id}],
source_uri=f"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/t10k-labels-idx1-ubyte.gz",
)

output_model = Artifact.create(
artifact_name="mnist-model",
artifact_type="Model",
source_types=[{"SourceIdType": "Custom", "Value": unique_id}],
source_uri=f"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/model/tensorflow-training-2020-11-20-23-57-13-077/model.tar.gz",
)

**V3:**

.. code-block:: python

from sagemaker.core.lineage.artifact import Artifact
Expand Down Expand Up @@ -314,30 +194,6 @@ Use Case 8: Linking Artifacts to Actions

Associate data artifacts as inputs to an action, and the action's output to a model artifact, forming a complete lineage chain.

**V2 (Legacy):**

.. code-block:: python

from sagemaker.lineage.association import Association

# Link input data to the model build action
Association.create(
source_arn=input_test_images.artifact_arn,
destination_arn=model_build_action.action_arn,
)
Association.create(
source_arn=input_test_labels.artifact_arn,
destination_arn=model_build_action.action_arn,
)

# Link the action output to the model artifact
Association.create(
source_arn=model_build_action.action_arn,
destination_arn=output_model.artifact_arn,
)

**V3:**

.. code-block:: python

from sagemaker.core.lineage.association import Association
Expand All @@ -364,49 +220,6 @@ Use Case 9: Cleaning Up Lineage Data

Delete associations first, then delete the entities themselves. Associations must be removed before their source or destination entities can be deleted.

**V2 (Legacy):**

.. code-block:: python

import sagemaker
from sagemaker.lineage.association import Association
from sagemaker.lineage.context import Context
from sagemaker.lineage.action import Action
from sagemaker.lineage.artifact import Artifact

sagemaker_session = sagemaker.session.Session()

def delete_associations(arn):
for summary in Association.list(destination_arn=arn):
assct = Association(
source_arn=summary.source_arn,
destination_arn=summary.destination_arn,
sagemaker_session=sagemaker_session,
)
assct.delete()
for summary in Association.list(source_arn=arn):
assct = Association(
source_arn=summary.source_arn,
destination_arn=summary.destination_arn,
sagemaker_session=sagemaker_session,
)
assct.delete()

# Delete context
delete_associations(ml_workflow_context.context_arn)
Context(context_name=ml_workflow_context.context_name, sagemaker_session=sagemaker_session).delete()

# Delete action
delete_associations(model_build_action.action_arn)
Action(action_name=model_build_action.action_name, sagemaker_session=sagemaker_session).delete()

# Delete artifacts
for artifact in [input_test_images, input_test_labels, output_model]:
delete_associations(artifact.artifact_arn)
Artifact(artifact_arn=artifact.artifact_arn, sagemaker_session=sagemaker_session).delete()

**V3:**

.. code-block:: python

from sagemaker.core.helper.session_helper import Session
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17 changes: 0 additions & 17 deletions docs/training/index.rst
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Expand Up @@ -13,23 +13,6 @@ Key Benefits of V3 Training
Quick Start Example
-------------------

**SageMaker Python SDK V2:**

.. code-block:: 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 V3:**

.. code-block:: python

from sagemaker.train import ModelTrainer
Expand Down
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