AWS sagemaker documentation change

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

File: sagemaker/latest/dg/sagemaker-hyperpod-gpu-sagemaker-training-jobs-pretrain-tutorial.md

Summary

Removed legacy SageMaker Python SDK v2/v3 code examples from training job tutorial

Security assessment

Removal of outdated code samples doesn't indicate security issues. No security features added or vulnerabilities addressed in the change.

Diff

diff --git a/sagemaker/latest/dg/sagemaker-hyperpod-gpu-sagemaker-training-jobs-pretrain-tutorial.md b/sagemaker/latest/dg/sagemaker-hyperpod-gpu-sagemaker-training-jobs-pretrain-tutorial.md
index df3052d64..2ccab3833 100644
--- a//sagemaker/latest/dg/sagemaker-hyperpod-gpu-sagemaker-training-jobs-pretrain-tutorial.md
+++ b//sagemaker/latest/dg/sagemaker-hyperpod-gpu-sagemaker-training-jobs-pretrain-tutorial.md
@@ -97,3 +96,0 @@ You can use the following Python code to run a SageMaker training job with your
-SageMaker Python SDK v3
-    
-    
@@ -153,53 +149,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import os
-    import sagemaker,boto3
-    from sagemaker.debugger import TensorBoardOutputConfig
-    
-    from sagemaker.pytorch import PyTorch
-    
-    sagemaker_session = sagemaker.Session()
-    role = sagemaker.get_execution_role()
-    
-    bucket = sagemaker_session.default_bucket() 
-    output = os.path.join(f"s3://{bucket}", "output")
-    output_path = "<s3-URI>"
-    
-    overrides = {
-        "run": {
-            "results_dir": "/opt/ml/model",
-        },
-        "exp_manager": {
-            "exp_dir": "",
-            "explicit_log_dir": "/opt/ml/output/tensorboard",
-            "checkpoint_dir": "/opt/ml/checkpoints",
-        },   
-        "model": {
-            "data": {
-                "train_dir": "/opt/ml/input/data/train",
-                "val_dir": "/opt/ml/input/data/val",
-            },
-        },
-    }
-    
-    tensorboard_output_config = TensorBoardOutputConfig(
-        s3_output_path=os.path.join(output, 'tensorboard'),
-        container_local_output_path=overrides["exp_manager"]["explicit_log_dir"]
-    )
-    
-    estimator = PyTorch(
-        output_path=output_path,
-        base_job_name=f"llama-recipe",
-        role=role,
-        instance_type="ml.p5.48xlarge",
-        training_recipe="training/llama/hf_llama3_8b_seq8k_gpu_p5x16_pretrain",
-        recipe_overrides=recipe_overrides,
-        sagemaker_session=sagemaker_session,
-        tensorboard_output_config=tensorboard_output_config,
-    )
-    
-    estimator.fit(inputs={"train": "s3 or fsx input", "val": "s3 or fsx input"}, wait=True)
-    
-
@@ -216,3 +159,0 @@ When you deploy the endpoint for a SageMaker training job, you must specify the
-SageMaker Python SDK v3
-    
-    
@@ -235,9 +175,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker import image_uris
-    container=image_uris.retrieve(framework='pytorch',region='us-west-2',version='2.0',py_version='py310',image_scope='inference', instance_type='ml.p4d.24xlarge')
-    predictor = estimator.deploy(initial_instance_count=1,instance_type='ml.p4d.24xlarge',image_uri=container)
-    
-