AWS sagemaker documentation change

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

File: sagemaker/latest/dg/deploy-models-frameworks-torchserve.md

Summary

Removed legacy SageMaker Python SDK v2 deployment examples and empty v3 sections from TorchServe deployment guide

Security assessment

Changes involve deleting deprecated code samples for model deployment without modifying security configurations or mentioning vulnerabilities. No evidence of security fixes or security feature additions.

Diff

diff --git a/sagemaker/latest/dg/deploy-models-frameworks-torchserve.md b/sagemaker/latest/dg/deploy-models-frameworks-torchserve.md
index d238404f1..08e9d754b 100644
--- a//sagemaker/latest/dg/deploy-models-frameworks-torchserve.md
+++ b//sagemaker/latest/dg/deploy-models-frameworks-torchserve.md
@@ -181,3 +180,0 @@ The following example shows you how to create a [single model real-time inferenc
-SageMaker Python SDK v3
-    
-    
@@ -217,29 +213,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.model import Model
-    from sagemaker.predictor import Predictor
-    
-    # create the single model endpoint and deploy it on SageMaker AI
-    model = Model(model_data = f'{output_path}/mnist.tar.gz', 
-                  image_uri = baseimage,
-                  role = role,
-                  predictor_cls = Predictor,
-                  name = "mnist",
-                  sagemaker_session = smsess)
-                  
-    endpoint_name = 'torchserve-endpoint-' + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
-    predictor = model.deploy(instance_type='ml.g4dn.xlarge',
-                             initial_instance_count=1,
-                             endpoint_name = endpoint_name,
-                             serializer=JSONSerializer(),
-                             deserializer=JSONDeserializer())  
-                             
-    # test the endpoint
-    import random
-    import numpy as np
-    dummy_data = {"inputs": np.random.rand(16, 1, 28, 28).tolist()}
-    
-    res = predictor.predict(dummy_data)  
-
@@ -254,3 +221,0 @@ The following example shows you how to create a multi-model endpoint, deploy the
-SageMaker Python SDK v3
-    
-    
@@ -290,48 +254,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.multidatamodel import MultiDataModel
-    from sagemaker.model import Model
-    from sagemaker.predictor import Predictor
-    
-    # create the single model endpoint and deploy it on SageMaker AI
-    model = Model(model_data = f'{output_path}/mnist.tar.gz', 
-                  image_uri = baseimage,
-                  role = role,
-                  sagemaker_session = smsess)
-                  
-    endpoint_name = 'torchserve-endpoint-' + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
-    mme = MultiDataModel(
-        name = endpoint_name,
-        model_data_prefix = output_path,
-        model = model,
-        sagemaker_session = smsess)
-    
-    mme.deploy(
-        initial_instance_count = 1,
-        instance_type = "ml.g4dn.xlarge",
-        serializer=sagemaker.serializers.JSONSerializer(),
-        deserializer=sagemaker.deserializers.JSONDeserializer())
-    
-    # list models
-    list(mme.list_models())
-    
-    # create mnist v2 model artifacts
-    cp mnist.tar.gz mnistv2.tar.gz
-    
-    # add mnistv2
-    mme.add_model(mnistv2.tar.gz)
-    
-    # list models
-    list(mme.list_models())
-    
-    predictor = Predictor(endpoint_name=mme.endpoint_name, sagemaker_session=smsess)
-                             
-    # test the endpoint
-    import random
-    import numpy as np
-    dummy_data = {"inputs": np.random.rand(16, 1, 28, 28).tolist()}
-    
-    res = predictor.predict(date=dummy_data, target_model="mnist.tar.gz")
-