AWS sagemaker documentation change

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

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

Summary

Replaced Estimator API references with ModelTrainer API and added comprehensive SDK v3/v2 code examples for LightGBM training

Security assessment

Documentation update for new SDK patterns without security-specific content. Added code samples show standard training workflows without security configurations.

Diff

diff --git a/sagemaker/latest/dg/lightgbm-modes.md b/sagemaker/latest/dg/lightgbm-modes.md
index 33fc51450..882d90917 100644
--- a//sagemaker/latest/dg/lightgbm-modes.md
+++ b//sagemaker/latest/dg/lightgbm-modes.md
@@ -15 +15,85 @@ Use the LightGBM built-in algorithm to build a LightGBM training container as sh
-After specifying the LightGBM image URI, you can use the LightGBM container to construct an estimator using the SageMaker AI Estimator API and initiate a training job. The LightGBM 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 LightGBM training scripts.
+After specifying the LightGBM image URI, you can use the LightGBM container to construct a ModelTrainer using the SageMaker AI ModelTrainer API and initiate a training job. The LightGBM 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 LightGBM 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 = "lightgbm-classification-model", "*", "training"
+    training_instance_type = "ml.m5.xlarge"
+    
+    # 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_multiclass/"
+    
+    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[
+        "num_boost_round"
+    ] = "500"
+    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), # for distributed training, specify an instance_count greater than 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)
+