AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples and references from remote debugging documentation
Security assessment
Changes only remove outdated SDK version examples without modifying security-related content. No evidence of security vulnerability fixes or security feature additions.
Diff
diff --git a/sagemaker/latest/dg/train-remote-debugging.md b/sagemaker/latest/dg/train-remote-debugging.md index 80d8b16a7..7cb5bf9d1 100644 --- a//sagemaker/latest/dg/train-remote-debugging.md +++ b//sagemaker/latest/dg/train-remote-debugging.md @@ -153,5 +152,0 @@ To enable remote debugging when you create a new training job, set the `enable_r -SageMaker Python SDK v3SageMaker Python SDK v2 (Legacy) - -SageMaker Python SDK v3 - - @@ -179,20 +173,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import sagemaker - - session = sagemaker.Session() - - estimator = sagemaker.estimator.Estimator( - ..., - sagemaker_session=session, - image_uri="<your_image_uri>", #must be owned by your organization or Amazon DLCs - role=role, - instance_type="ml.m5.xlarge", - instance_count=1, - output_path=output_path, - max_run=1800, - enable_remote_debug=True - ) - @@ -203,5 +177,0 @@ Using the following ModelTrainer class methods, you can enable or disable remote -SageMaker Python SDK v3SageMaker Python SDK v2 (Legacy) - -SageMaker Python SDK v3 - - @@ -215,10 +184,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - # Enable RemoteDebug - estimator.enable_remote_debug() - - # Disable RemoteDebug - estimator.disable_remote_debug() - @@ -232,5 +191,0 @@ To enable remote debugging when you create a new training job, set the value for -SageMaker Python SDK v3SageMaker Python SDK v2 (Legacy) - -SageMaker Python SDK v3 - - @@ -267,13 +221,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import boto3 - - session = boto3.session.Session() - region = session.region_name - sm = boto3.Session(region_name=region).client("sagemaker") - - # Describe the job status - sm.describe_training_job(TrainingJobName=job_name) - @@ -362,5 +303,0 @@ To check the `SecondaryStatus` of a training job, run the following SageMaker Py -SageMaker Python SDK v3SageMaker Python SDK v2 (Legacy) - -SageMaker Python SDK v3 - - @@ -377,20 +313,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import sagemaker - - session = sagemaker.Session() - - estimator = sagemaker.estimator.Estimator( - ..., - sagemaker_session=session, - image_uri="<your_image_uri>", #must be owned by your organization or Amazon DLCs - role=role, - instance_type="ml.m5.xlarge", - instance_count=1, - output_path=output_path, - max_run=1800, - enable_remote_debug=True - ) - @@ -402,5 +318,0 @@ To check the `SecondaryStatus` of a training job, run the following SDK for Pyth -SageMaker Python SDK v3SageMaker Python SDK v2 (Legacy) - -SageMaker Python SDK v3 - - @@ -417,33 +328,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import boto3 - - sm = boto3.Session(region_name=region).client("sagemaker") - - # Start a training job - sm.create_training_job( - ..., - TrainingJobName=job_name, - AlgorithmSpecification={ - // Specify a training Docker container image URI - // (Deep Learning Container or your own training container) to TrainingImage. - "TrainingImage": "<your_image_uri>", - "TrainingInputMode": "File" - }, - RoleArn=iam_role_arn, - OutputDataConfig=output_path, - ResourceConfig={ - "InstanceType": "ml.m5.xlarge", - "InstanceCount": 1, - "VolumeSizeInGB": 30 - }, - StoppingCondition={ - "MaxRuntimeInSeconds": 86400 - }, - **RemoteDebugConfig={ - "EnableRemoteDebug": True - }** - ) -