AWS sagemaker documentation change
Summary
Updated SDK reference (removed get_image_uri mention) and removed legacy SageMaker Python SDK v2 code examples
Security assessment
Changes update API references and remove outdated code samples. No evidence of security vulnerability fixes or security feature documentation.
Diff
diff --git a/sagemaker/latest/dg/autogluon-tabular-modes.md b/sagemaker/latest/dg/autogluon-tabular-modes.md index 28f52f5e5..9a8f34ab3 100644 --- a//sagemaker/latest/dg/autogluon-tabular-modes.md +++ b//sagemaker/latest/dg/autogluon-tabular-modes.md @@ -13 +13 @@ You can use AutoGluon-Tabular as an Amazon SageMaker AI built-in algorithm. The -Use the AutoGluon-Tabular built-in algorithm to build an AutoGluon-Tabular training container as shown in the following code example. You can automatically spot the AutoGluon-Tabular built-in algorithm image URI using the SageMaker AI `image_uris.retrieve` API (or the `get_image_uri` API if using [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) version 2). +Use the AutoGluon-Tabular built-in algorithm to build an AutoGluon-Tabular training container as shown in the following code example. You can automatically spot the AutoGluon-Tabular built-in algorithm image URI using the SageMaker AI `image_uris.retrieve` API. @@ -17,3 +16,0 @@ After specifying the AutoGluon-Tabular image URI, you can use the AutoGluon-Tabu -SageMaker Python SDK v3 - - @@ -98,79 +94,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - from sagemaker import image_uris, model_uris, script_uris - - train_model_id, train_model_version, train_scope = "autogluon-classification-ensemble", "*", "training" - training_instance_type = "ml.p3.2xlarge" - - # Retrieve the docker image - train_image_uri = image_uris.retrieve( - region=None, - framework=None, - model_id=train_model_id, - model_version=train_model_version, - image_scope=train_scope, - instance_type=training_instance_type - ) - - # Retrieve the training script - train_source_uri = script_uris.retrieve( - model_id=train_model_id, model_version=train_model_version, script_scope=train_scope - ) - - train_model_uri = model_uris.retrieve( - model_id=train_model_id, model_version=train_model_version, model_scope=train_scope - ) - - # Sample training data is available in this bucket - training_data_bucket = f"jumpstart-cache-prod-{aws_region}" - training_data_prefix = "training-datasets/tabular_binary/" - - training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/train" - validation_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/validation" - - output_bucket = sess.default_bucket() - output_prefix = "jumpstart-example-tabular-training" - - s3_output_location = f"s3://{output_bucket}/{output_prefix}/output" - - from sagemaker import hyperparameters - - # Retrieve the default hyperparameters for training the model - hyperparameters = hyperparameters.retrieve_default( - model_id=train_model_id, model_version=train_model_version - ) - - # [Optional] Override default hyperparameters with custom values - hyperparameters[ - "auto_stack" - ] = "True" - print(hyperparameters) - - from sagemaker.estimator import Estimator - from sagemaker.utils import name_from_base - - training_job_name = name_from_base(f"built-in-algo-{train_model_id}-training") - - # Create SageMaker Estimator instance - tabular_estimator = Estimator( - role=aws_role, - image_uri=train_image_uri, - source_dir=train_source_uri, - model_uri=train_model_uri, - entry_point="transfer_learning.py", - instance_count=1, - instance_type=training_instance_type, - max_run=360000, - hyperparameters=hyperparameters, - output_path=s3_output_location - ) - - # Launch a SageMaker Training job by passing the S3 path of the training data - tabular_estimator.fit( - { - "training": training_dataset_s3_path, - "validation": validation_dataset_s3_path, - }, logs=True, job_name=training_job_name - ) -