AWS sagemaker documentation change

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

File: sagemaker/latest/dg/processing-container-run-scripts.md

Summary

Replaced ScriptProcessor usage with direct ProcessingJob API calls, added SDK v3 examples, and restructured processing job configuration.

Security assessment

Documentation updates focus on API changes and new SDK patterns without addressing security vulnerabilities or introducing security features. The changes improve clarity on job configuration but contain no security-specific content or mitigations.

Diff

diff --git a/sagemaker/latest/dg/processing-container-run-scripts.md b/sagemaker/latest/dg/processing-container-run-scripts.md
index 99faa8daa..c0ff5e8d3 100644
--- a//sagemaker/latest/dg/processing-container-run-scripts.md
+++ b//sagemaker/latest/dg/processing-container-run-scripts.md
@@ -11 +11 @@ You can use scikit-learn scripts to preprocess data and evaluate your models. To
-The following example shows a general workflow for using a `ScriptProcessor` class with your own processing container. The workflow shows how to create your own image, build your container, and use a `ScriptProcessor` class to run a Python preprocessing script with the container. The processing job processes your input data and saves the processed data in Amazon Simple Storage Service (Amazon S3).
+The following example shows a general workflow for using your own processing container. The workflow shows how to create your own image, build your container, and run a Python preprocessing script with the container. The processing job processes your input data and saves the processed data in Amazon Simple Storage Service (Amazon S3).
@@ -45 +45,52 @@ Before using the following examples, you need to have your own input data and a
-  3. Set up the `ScriptProcessor` from the SageMaker Python SDK to run the script. Replace `image_uri` with the URI for the image you created, and replace `role_arn` with the ARN for an AWS Identity and Access Management role that has access to your target Amazon S3 bucket.
+  3. Set up and run the processing job. Replace `image_uri` with the URI for the image you created, and replace `role_arn` with the ARN for an AWS Identity and Access Management role that has access to your target Amazon S3 bucket. Replace `preprocessing.py` with the name of your own Python processing script, and replace `s3://path/to/my/input-data.csv` with the Amazon S3 path to your input data.
+
+SageMaker Python SDK v3
+    
+    
+        from sagemaker.core.resources import ProcessingJob
+    
+    processing_job = ProcessingJob.create(
+        processing_job_name="my-processing-job",
+        role_arn='role_arn',
+        app_specification={
+            "image_uri": 'image_uri',
+            "container_entrypoint": ["python3", "/opt/ml/processing/input/code/preprocessing.py"]
+        },
+        processing_resources={
+            "cluster_config": {
+                "instance_count": 1,
+                "instance_type": "ml.m5.xlarge",
+                "volume_size_in_gb": 30
+            }
+        },
+        processing_inputs=[
+            {
+                "input_name": "code",
+                "s3_input": {
+                    "s3_uri": 's3://path/to/preprocessing.py',
+                    "local_path": "/opt/ml/processing/input/code",
+                    "s3_data_type": "S3Prefix",
+                    "s3_input_mode": "File"
+                }
+            },
+            {
+                "input_name": "input-data",
+                "s3_input": {
+                    "s3_uri": 's3://path/to/my/input-data.csv',
+                    "local_path": "/opt/ml/processing/input",
+                    "s3_data_type": "S3Prefix",
+                    "s3_input_mode": "File"
+                }
+            }
+        ],
+        processing_output_config={
+            "outputs": [
+                {"output_name": "train", "s3_output": {"s3_uri": "s3://output/train", "local_path": "/opt/ml/processing/output/train", "s3_upload_mode": "EndOfJob"}},
+                {"output_name": "validation", "s3_output": {"s3_uri": "s3://output/validation", "local_path": "/opt/ml/processing/output/validation", "s3_upload_mode": "EndOfJob"}},
+                {"output_name": "test", "s3_output": {"s3_uri": "s3://output/test", "local_path": "/opt/ml/processing/output/test", "s3_upload_mode": "EndOfJob"}}
+            ]
+        }
+    )
+
+SageMaker Python SDK v2 (Legacy)
+    
@@ -55,2 +105,0 @@ Before using the following examples, you need to have your own input data and a
-  4. Run the script. Replace `preprocessing.py` with the name of your own Python processing script, and replace `s3://path/to/my/input-data.csv` with the Amazon S3 path to your input data.
-