AWS sagemaker documentation change

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

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

Summary

Replaced Estimator API references with ModelTrainer API and added SDK v3 implementation examples

Security assessment

Changes involve API updates and code sample modernization. No security configurations, vulnerabilities, or security features are introduced or discussed.

Diff

diff --git a/sagemaker/latest/dg/catboost-modes.md b/sagemaker/latest/dg/catboost-modes.md
index 34734b377..d5b4cab51 100644
--- a//sagemaker/latest/dg/catboost-modes.md
+++ b//sagemaker/latest/dg/catboost-modes.md
@@ -15 +15,85 @@ Use the CatBoost built-in algorithm to build a CatBoost training container as sh
-After specifying the CatBoost image URI, you can use the CatBoost container to construct an estimator using the SageMaker AI Estimator API and initiate a training job. The CatBoost 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 CatBoost training scripts.
+After specifying the CatBoost image URI, you can use the CatBoost container to construct a ModelTrainer using the SageMaker AI ModelTrainer API and initiate a training job. The CatBoost 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 CatBoost 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 = "catboost-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[
+        "iterations"
+    ] = "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),
+        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)
+