AWS sagemaker documentation change

Service: sagemaker · 2026-06-28 · Documentation low

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

Summary

Updated tutorial to use ModelTrainer instead of PyTorch estimator, added code examples for SageMaker Python SDK v3 and v2, including deployment configuration.

Security assessment

The changes update code samples to use a new SDK class (ModelTrainer) and add version-specific implementation examples. There's no evidence of security vulnerabilities being addressed or security features being documented. Changes are purely related to SDK usage patterns.

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 9ff08ec6c..df3052d64 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
@@ -95 +95,60 @@ If you are using HyperPod recipes to launch using cluster type `sm_jobs`, this w
-You can use the following Python code to run a SageMaker training job with your recipe. It leverages the PyTorch estimator from the [SageMaker AI Python SDK](https://sagemaker.readthedocs.io/en/stable/) to submit the recipe. The following example launches the llama3-8b recipe on the SageMaker AI Training platform.
+You can use the following Python code to run a SageMaker training job with your recipe. It leverages the PyTorch ModelTrainer from the [SageMaker AI Python SDK](https://sagemaker.readthedocs.io/en/stable/) to submit the recipe. The following example launches the llama3-8b recipe on the SageMaker AI Training platform.
+
+SageMaker Python SDK v3
+    
+    
+    
+    import os
+    import boto3
+    from sagemaker.core.debugger import TensorBoardOutputConfig
+    from sagemaker.core.helper.session_helper import Session, get_execution_role
+    from sagemaker.train import ModelTrainer
+    from sagemaker.train.configs import Compute, InputData, OutputDataConfig
+    
+    sagemaker_session = Session()
+    role = get_execution_role()
+    
+    bucket = sagemaker_session.default_bucket() 
+    output = os.path.join(f"s3://{bucket}", "output")
+    output_path = "<s3-URI>"
+    
+    recipe_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=recipe_overrides["exp_manager"]["explicit_log_dir"]
+    )
+    
+    model_trainer = ModelTrainer(
+        output_data_config=OutputDataConfig(s3_output_path=output_path),
+        base_job_name=f"llama-recipe",
+        role=role,
+        compute=Compute(instance_type="ml.p5.48xlarge", instance_count=1),
+        training_recipe="training/llama/hf_llama3_8b_seq8k_gpu_p5x16_pretrain",
+        recipe_overrides=recipe_overrides,
+        tensorboard_output_config=tensorboard_output_config,
+    )
+    
+    model_trainer.train(input_data_config=[
+        InputData(channel_name="train", data_source="s3 or fsx input"),
+        InputData(channel_name="val", data_source="s3 or fsx input")
+    ], wait=True)
+    
+
+SageMaker Python SDK v2 (Legacy)
+    
@@ -147 +206 @@ You can use the following Python code to run a SageMaker training job with your
-The preceding code creates a PyTorch estimator object with the training recipe and then fits the model using the `fit()` method. Use the training_recipe parameter to specify the recipe you want to use for training.
+The preceding code creates a ModelTrainer object with the training recipe and then trains the model using the `train()` method. Use the training_recipe parameter to specify the recipe you want to use for training.
@@ -155 +214,23 @@ Append `transformers==4.45.2` to `requirements.txt` in `source_dir` only when yo
-When you deploy the endpoint for a SageMaker training job, you must specify the image URI that you're using. If don't provide the image URI, the estimator uses the training image as the image for the deployment. The training images that SageMaker HyperPod provides don't contain the dependencies required for inference and deployment. The following is an example of how an inference image can be used for deployment:
+When you deploy the endpoint for a SageMaker training job, you must specify the image URI that you're using. If don't provide the image URI, the ModelTrainer uses the training image as the image for the deployment. The training images that SageMaker HyperPod provides don't contain the dependencies required for inference and deployment. The following is an example of how an inference image can be used for deployment:
+
+SageMaker Python SDK v3
+    
+    
+    
+    from sagemaker.serve import ModelBuilder
+    from sagemaker.core import image_uris
+    
+    inference_image = image_uris.retrieve(framework='pytorch',region='us-west-2',version='2.0',py_version='py310',image_scope='inference', instance_type='ml.p4d.24xlarge')
+    
+    model_builder = ModelBuilder(
+        model=model_trainer,
+        image_uri=inference_image,
+        role_arn=role,
+        instance_type='ml.p4d.24xlarge'
+    )
+    model = model_builder.build()
+    endpoint = model_builder.deploy(endpoint_name="my-endpoint")
+    
+
+SageMaker Python SDK v2 (Legacy)
+