AWS sagemaker documentation change

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

File: sagemaker/latest/dg/train-warm-pools-how-to-use.md

Summary

Removed SageMaker Python SDK v2 and v3 code examples from warm pools documentation

Security assessment

Changes eliminate legacy code samples without altering security configurations or introducing security features. No security implications identified.

Diff

diff --git a/sagemaker/latest/dg/train-warm-pools-how-to-use.md b/sagemaker/latest/dg/train-warm-pools-how-to-use.md
index ac2940d39..3849b8a22 100644
--- a//sagemaker/latest/dg/train-warm-pools-how-to-use.md
+++ b//sagemaker/latest/dg/train-warm-pools-how-to-use.md
@@ -49,3 +48,0 @@ To create a warm pool, use the SageMaker Python SDK to create a training job wit
-SageMaker Python SDK v3
-    
-
@@ -96,40 +92,0 @@ Use the SageMaker Python SDK to create a `ModelTrainer` with a `keep_alive_perio
-SageMaker Python SDK v2 (Legacy)
-    
-
-Use the SageMaker Python SDK to create an estimator with a `keep_alive_period_in_seconds` value greater than 0 and call `fit()`. For more information on training scripts and estimators, see [Train a Model with the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/overview.html#train-a-model-with-the-sagemaker-python-sdk).
-    
-    
-    import sagemaker
-    from sagemaker import get_execution_role
-    from sagemaker.tensorflow import TensorFlow
-    
-    # Creates a SageMaker AI session and gets execution role
-    session = sagemaker.Session()
-    role = get_execution_role()
-    
-    # Creates an example estimator
-    estimator = TensorFlow(
-        ...
-        entry_point='my-training-script.py',
-        source_dir='code',
-        role=role,
-        model_dir='model_dir',
-        framework_version='2.2',
-        py_version='py37',
-        job_name='my-training-job-1',
-        instance_type='ml.g4dn.xlarge',
-        instance_count=1,
-        volume_size=250,
-        hyperparameters={
-            "batch-size": 512,
-            "epochs": 1,
-            "learning-rate": 1e-3,
-            "beta_1": 0.9,
-            "beta_2": 0.999,
-        },
-        keep_alive_period_in_seconds=1800,
-    )
-    
-    # Starts a SageMaker training job and waits until completion
-    estimator.fit('s3://my_bucket/my_training_data/')             
-
@@ -138,3 +94,0 @@ Next, create a second matching training job. In this example, we create `my-trai
-SageMaker Python SDK v3
-    
-    
@@ -166,30 +119,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    # Creates an example estimator
-    estimator = TensorFlow(
-        ...
-        entry_point='my-training-script.py',
-        source_dir='code',
-        role=role,
-        model_dir='model_dir',
-        framework_version='py37',
-        py_version='pyxy',
-        job_name='my-training-job-2',
-        instance_type='ml.g4dn.xlarge',
-        instance_count=1,
-        volume_size=250,
-        hyperparameters={
-            "batch-size": 512,
-            "epochs": 2,
-            "learning-rate": 1e-3,
-            "beta_1": 0.9,
-            "beta_2": 0.999,
-        },
-        keep_alive_period_in_seconds=1800,
-    )
-    
-    # Starts a SageMaker training job and waits until completion
-    estimator.fit('s3://my_bucket/my_training_data/')           
-