AWS sagemaker documentation change

Service: sagemaker · 2026-07-01 · Documentation medium

File: sagemaker/latest/dg/IC-TF-how-to-use.md

Summary

Removed SageMaker Python SDK v3 header and v2 (Legacy) code example

Security assessment

Removal of outdated SDK examples without security context. No evidence this change addresses specific vulnerabilities, though it may indirectly promote using current secure versions.

Diff

diff --git a/sagemaker/latest/dg/IC-TF-how-to-use.md b/sagemaker/latest/dg/IC-TF-how-to-use.md
index 9fbc376ba..8aeee9614 100644
--- a//sagemaker/latest/dg/IC-TF-how-to-use.md
+++ b//sagemaker/latest/dg/IC-TF-how-to-use.md
@@ -21,3 +20,0 @@ This example uses the [`tf_flowers`](https://www.tensorflow.org/datasets/catalog
-SageMaker Python SDK v3
-    
-    
@@ -79,52 +75,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker import image_uris, model_uris, script_uris, hyperparameters
-    from sagemaker.estimator import Estimator
-    
-    model_id, model_version = "tensorflow-ic-imagenet-mobilenet-v2-100-224-classification-4", "*"
-    training_instance_type = "ml.p3.2xlarge"
-    
-    # Retrieve the Docker image
-    train_image_uri = image_uris.retrieve(model_id=model_id,model_version=model_version,image_scope="training",instance_type=training_instance_type,region=None,framework=None)
-    
-    # Retrieve the training script
-    train_source_uri = script_uris.retrieve(model_id=model_id, model_version=model_version, script_scope="training")
-    
-    # Retrieve the pretrained model tarball for transfer learning
-    train_model_uri = model_uris.retrieve(model_id=model_id, model_version=model_version, model_scope="training")
-    
-    # Retrieve the default hyper-parameters for fine-tuning the model
-    hyperparameters = hyperparameters.retrieve_default(model_id=model_id, model_version=model_version)
-    
-    # [Optional] Override default hyperparameters with custom values
-    hyperparameters["epochs"] = "5"
-    
-    # The sample training data is available in the following S3 bucket
-    training_data_bucket = f"jumpstart-cache-prod-{aws_region}"
-    training_data_prefix = "training-datasets/tf_flowers/"
-    
-    training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}"
-    
-    output_bucket = sess.default_bucket()
-    output_prefix = "jumpstart-example-ic-training"
-    s3_output_location = f"s3://{output_bucket}/{output_prefix}/output"
-    
-    # Create SageMaker Estimator instance
-    tf_ic_estimator = Estimator(
-        role=aws_role,
-        image_uri=train_image_uri,
-        source_dir=train_source_uri,
-        model_uri=train_model_uri,
-        entry_point="transfer_learning.py",
-        instance_count=1,
-        instance_type=training_instance_type,
-        max_run=360000,
-        hyperparameters=hyperparameters,
-        output_path=s3_output_location,
-    )
-    
-    # Use S3 path of the training data to launch SageMaker TrainingJob
-    tf_ic_estimator.fit({"training": training_dataset_s3_path}, logs=True)
-