AWS sagemaker documentation change

Service: sagemaker · 2026-06-28 · Documentation low

File: sagemaker/latest/dg/tabtransformer-modes.md

Summary

Added detailed code examples for SageMaker Python SDK v3 usage with ModelTrainer API and maintained legacy v2 reference

Security assessment

The change demonstrates API usage patterns without introducing security features or addressing vulnerabilities. The added code focuses on standard training workflows without security-specific configurations.

Diff

diff --git a/sagemaker/latest/dg/tabtransformer-modes.md b/sagemaker/latest/dg/tabtransformer-modes.md
index e67e169e8..22d08f62f 100644
--- a//sagemaker/latest/dg/tabtransformer-modes.md
+++ b//sagemaker/latest/dg/tabtransformer-modes.md
@@ -15 +15,85 @@ Use the TabTransformer built-in algorithm to build a TabTransformer training con
-After specifying the TabTransformer image URI, you can use the TabTransformer container to construct an estimator using the SageMaker AI Estimator API and initiate a training job. The TabTransformer built-in algorithm runs in script mode, but the training script is provided for you and there is no need to replace it. If you have extensive experience using script mode to create a SageMaker training job, then you can incorporate your own TabTransformer training scripts.
+After specifying the TabTransformer image URI, you can use the TabTransformer container to construct a ModelTrainer using the SageMaker AI ModelTrainer API and initiate a training job. The TabTransformer built-in algorithm runs in script mode, but the training script is provided for you and there is no need to replace it. If you have extensive experience using script mode to create a SageMaker training job, then you can incorporate your own TabTransformer training scripts.
+
+SageMaker Python SDK v3
+    
+    
+        from sagemaker.core import image_uris
+    from sagemaker.core import model_uris, script_uris
+    
+    train_model_id, train_model_version, train_scope = "pytorch-tabtransformerclassification-model", "*", "training"
+    training_instance_type = "ml.p3.2xlarge"
+    
+    # Retrieve the docker image
+    train_image_uri = image_uris.retrieve(
+        region=None,
+        framework=None,
+        model_id=train_model_id,
+        model_version=train_model_version,
+        image_scope=train_scope,
+        instance_type=training_instance_type
+    )
+    
+    # Retrieve the training script
+    train_source_uri = script_uris.retrieve(
+        model_id=train_model_id, model_version=train_model_version, script_scope=train_scope
+    )
+    
+    train_model_uri = model_uris.retrieve(
+        model_id=train_model_id, model_version=train_model_version, model_scope=train_scope
+    )
+    
+    # Sample training data is available in this bucket
+    training_data_bucket = f"jumpstart-cache-prod-{aws_region}"
+    training_data_prefix = "training-datasets/tabular_binary/"
+    
+    training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/train"
+    validation_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/validation"
+    
+    output_bucket = sess.default_bucket()
+    output_prefix = "jumpstart-example-tabular-training"
+    
+    s3_output_location = f"s3://{output_bucket}/{output_prefix}/output"
+    
+    from sagemaker import hyperparameters
+    
+    # Retrieve the default hyperparameters for training the model
+    hyperparameters = hyperparameters.retrieve_default(
+        model_id=train_model_id, model_version=train_model_version
+    )
+    
+    # [Optional] Override default hyperparameters with custom values
+    hyperparameters[
+        "n_epochs"
+    ] = "50"
+    print(hyperparameters)
+    
+    from sagemaker.train import ModelTrainer
+    from sagemaker.train.configs import InputData
+    from sagemaker.train.configs import SourceCode, Compute, StoppingCondition, OutputDataConfig
+    from sagemaker.utils import name_from_base
+    
+    training_job_name = name_from_base(f"built-in-algo-{train_model_id}-training")
+    
+    # Create SageMaker ModelTrainer instance
+    tabular_model_trainer = ModelTrainer(
+        role=aws_role,
+        training_image=train_image_uri,
+        source_code=SourceCode(source_dir=train_source_uri, entry_script="transfer_learning.py"),
+        # In V3, pre-trained model artifacts are passed via input_data_config
+        compute=Compute(instance_type=training_instance_type, instance_count=1),
+        stopping_condition=StoppingCondition(max_runtime_in_seconds=360000),
+        hyperparameters=hyperparameters,
+        output_data_config=OutputDataConfig(s3_output_path=s3_output_location)
+    )
+    
+    # Launch a SageMaker Training job by passing the S3 path of the training data
+    tabular_model_trainer.train(
+        input_data_config=[
+            InputData(channel_name="training", data_source=training_dataset_s3_path),
+            InputData(channel_name="validation", data_source=validation_dataset_s3_path),
+            InputData(channel_name="model", data_source=train_model_uri),
+        ]
+    )
+
+SageMaker Python SDK v2 (Legacy)
+