AWS sagemaker documentation change

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

File: sagemaker/latest/dg/multiple-algorithm-hpo-create-tuning-jobs.md

Summary

Removed SageMaker Python SDK v3 placeholders and v2 legacy code examples throughout multiple algorithm HPO documentation

Security assessment

Change deletes legacy SDK implementation examples without referencing security fixes, vulnerabilities, or security features. Pure documentation cleanup without security implications.

Diff

diff --git a/sagemaker/latest/dg/multiple-algorithm-hpo-create-tuning-jobs.md b/sagemaker/latest/dg/multiple-algorithm-hpo-create-tuning-jobs.md
index f2409f0c6..2e3b89fcb 100644
--- a//sagemaker/latest/dg/multiple-algorithm-hpo-create-tuning-jobs.md
+++ b//sagemaker/latest/dg/multiple-algorithm-hpo-create-tuning-jobs.md
@@ -191,3 +190,0 @@ The following code example shows how to retrieve two SageMaker AI containers con
-SageMaker Python SDK v3
-    
-    
@@ -243,48 +239,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import sagemaker
-    from sagemaker import image_uris
-    
-    from sagemaker.estimator import Estimator
-    
-    sess = sagemaker.Session()
-    region = sess.boto_region_name
-    role = sagemaker.get_execution_role()
-    
-    bucket = sess.default_bucket()
-    prefix = "sagemaker/multi-algo-hpo"
-    
-    # Define the training containers and intialize the estimators
-    xgb_container = image_uris.retrieve("xgboost", region, "latest")
-    ll_container = image_uris.retrieve("linear-learner", region, "latest")
-    
-    xgb_estimator = Estimator(
-        xgb_container,
-        role=role,
-        instance_count=1,
-        instance_type="ml.m4.xlarge",
-        output_path='s3://{}/{}/xgb_output".format(bucket, prefix)',
-        sagemaker_session=sess,
-    )
-    
-    ll_estimator = Estimator(
-        ll_container,
-        role,
-        instance_count=1,
-        instance_type="ml.c4.xlarge",
-        output_path="s3://{}/{}/ll_output".format(bucket, prefix),
-        sagemaker_session=sess,
-    )
-    
-    # Set static hyperparameters
-    ll_estimator.set_hyperparameters(predictor_type="binary_classifier")
-    xgb_estimator.set_hyperparameters(
-        eval_metric="auc",
-        objective="binary:logistic",
-        num_round=100,
-        rate_drop=0.3,
-        tweedie_variance_power=1.4,
-    )
-
@@ -293,3 +241,0 @@ Next, define your input data by specifying the training, validation, and testing
-SageMaker Python SDK v3
-    
-    
@@ -322,27 +267,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    training_data = sagemaker.inputs.TrainingInput(
-        s3_data="s3://{}/{}/train".format(bucket, prefix), content_type="csv"
-    )
-    validation_data = sagemaker.inputs.TrainingInput(
-        s3_data="s3://{}/{}/validate".format(bucket, prefix), content_type="csv"
-    )
-    test_data = sagemaker.inputs.TrainingInput(
-        s3_data="s3://{}/{}/test".format(bucket, prefix), content_type="csv"
-    )
-    
-    train_inputs = {
-        "estimator-1": {
-            "train": training_data,
-            "validation": validation_data,
-            "test": test_data,
-        },
-        "estimator-2": {
-            "train": training_data,
-            "validation": validation_data,
-            "test": test_data,
-        },
-    }
-
@@ -361,3 +279,0 @@ The following code example shows how to initialize a tuner and set hyperparamete
-SageMaker Python SDK v3
-    
-    
@@ -384,23 +299,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.tuner import HyperparameterTuner
-    from sagemaker.parameter import ContinuousParameter, IntegerParameter
-    
-    hyperparameter_ranges = {
-        "max_depth": IntegerParameter(1, 10),
-        "eta": ContinuousParameter(0.1, 0.3),
-    }
-    
-    objective_metric_name = "validation:accuracy"
-    
-    tuner = HyperparameterTuner(
-        xgb_estimator,
-        objective_metric_name,
-        hyperparameter_ranges,
-        objective_type="Maximize",
-        max_jobs=5,
-        max_parallel_jobs=2,
-    )       
-
@@ -411,3 +303,0 @@ Each training job requires different configurations, and these are specified usi
-SageMaker Python SDK v3
-    
-    
@@ -448,37 +337,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.tuner import HyperparameterTuner
-    from sagemaker.parameter import ContinuousParameter, IntegerParameter
-    
-    # Initialize your tuner
-    tuner = HyperparameterTuner.create(
-        estimator_dict={
-            "estimator-1": xgb_estimator,
-            "estimator-2": ll_estimator,
-        },
-        objective_metric_name_dict={
-            "estimator-1": "validation:auc",
-            "estimator-2": "test:binary_classification_accuracy",
-        },
-        hyperparameter_ranges_dict={
-            "estimator-1": {"eta": ContinuousParameter(0.1, 0.3)},
-            "estimator-2": {"learning_rate": ContinuousParameter(0.1, 0.3)},
-        },
-        metric_definitions_dict={
-            "estimator-1": [
-                {"Name": "validation:auc", "Regex": "Overall test accuracy: (.*?);"}
-            ],
-            "estimator-2": [
-                {
-                    "Name": "test:binary_classification_accuracy",
-                    "Regex": "Overall test accuracy: (.*?);",
-                }
-            ],
-        },
-        strategy="Bayesian",
-        max_jobs=10,
-        max_parallel_jobs=3,
-    )          
-
@@ -489,3 +341,0 @@ Now you can run your tuning job by passing your training inputs to the tuner. Th
-SageMaker Python SDK v3
-    
-    
@@ -495,6 +344,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    tuner.fit(inputs=train_inputs, include_cls_metadata ={}, estimator_kwargs ={})   
-