AWS sagemaker documentation change

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

File: sagemaker/latest/dg/byoc-run.md

Summary

Updated BYOC processing documentation with SDK v3 examples using ProcessingJob.create() and maintained legacy v2 references

Security assessment

The changes focus on updating code samples to new SDK syntax without any security-specific content. No vulnerabilities, security controls, or security incidents are mentioned.

Diff

diff --git a/sagemaker/latest/dg/byoc-run.md b/sagemaker/latest/dg/byoc-run.md
index dd6561b92..025d82d5a 100644
--- a//sagemaker/latest/dg/byoc-run.md
+++ b//sagemaker/latest/dg/byoc-run.md
@@ -9 +9,48 @@
-You can use the SageMaker Python SDK to run your own processing image by using the `Processor` class. The following example shows how to run your own processing container with one input from Amazon Simple Storage Service (Amazon S3) and one output to Amazon S3.
+You can use the SageMaker Python SDK to run your own processing image. The following example shows how to run your own processing container with one input from Amazon Simple Storage Service (Amazon S3) and one output to Amazon S3.
+
+SageMaker Python SDK v3
+    
+    
+    
+    from sagemaker.core.resources import ProcessingJob
+    
+    processing_job = ProcessingJob.create(
+        processing_job_name="my-byoc-processing",
+        role_arn=role,
+        app_specification={
+            "image_uri": '<your_ecr_image_uri>'
+        },
+        processing_resources={
+            "cluster_config": {
+                "instance_count": 1,
+                "instance_type": "ml.m5.xlarge",
+                "volume_size_in_gb": 30
+            }
+        },
+        processing_inputs=[
+            {
+                "input_name": "input-data",
+                "s3_input": {
+                    "s3_uri": '<s3_uri or local path>',
+                    "local_path": "/opt/ml/processing/input_data",
+                    "s3_data_type": "S3Prefix",
+                    "s3_input_mode": "File"
+                }
+            }
+        ],
+        processing_output_config={
+            "outputs": [
+                {
+                    "output_name": "processed-data",
+                    "s3_output": {
+                        "s3_uri": '<s3_uri>',
+                        "local_path": "/opt/ml/processing/processed_data",
+                        "s3_upload_mode": "EndOfJob"
+                    }
+                }
+            ]
+        }
+    )
+
+SageMaker Python SDK v2 (Legacy)
+    
@@ -29 +76 @@ Instead of building your processing code into your processing image, you can pro
-You can also use the scikit-learn image that Amazon SageMaker Processing provides through `SKLearnProcessor` to run scikit-learn scripts. For an example, see [Run a Processing Job with scikit-learn](./use-scikit-learn-processing-container.html). 
+You can also use the scikit-learn image that Amazon SageMaker Processing provides to run scikit-learn scripts. For an example, see [Run a Processing Job with scikit-learn](./use-scikit-learn-processing-container.html).