AWS sagemaker documentation change

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

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

Summary

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

Security assessment

Deletion of outdated SDK examples without security context. No security features added or vulnerabilities addressed.

Diff

diff --git a/sagemaker/latest/dg/train-warm-pools.md b/sagemaker/latest/dg/train-warm-pools.md
index 4ef243596..7e7083a85 100644
--- a//sagemaker/latest/dg/train-warm-pools.md
+++ b//sagemaker/latest/dg/train-warm-pools.md
@@ -149,3 +148,0 @@ The following code example shows you how to set up a warm pool and use persisten
-SageMaker Python SDK v3
-    
-    
@@ -190,32 +186,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import sagemakerfrom sagemaker import get_execution_rolefrom sagemaker.tensorflow import TensorFlow
-    # Creates a SageMaker 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,
-        environment={"PIP_CACHE_DIR": "/opt/ml/sagemaker/warmpoolcache/pip"}
-    )
-