AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples and references to deprecated get_image_uri API
Security assessment
The changes involve removing outdated code samples and API references without any mention of security vulnerabilities, patches, or security-related configurations. This appears to be routine documentation cleanup.
Diff
diff --git a/sagemaker/latest/dg/tabtransformer-modes.md b/sagemaker/latest/dg/tabtransformer-modes.md index 22d08f62f..d36607e8b 100644 --- a//sagemaker/latest/dg/tabtransformer-modes.md +++ b//sagemaker/latest/dg/tabtransformer-modes.md @@ -13 +13 @@ You can use TabTransformer as an Amazon SageMaker AI built-in algorithm. The fol -Use the TabTransformer built-in algorithm to build a TabTransformer training container as shown in the following code example. You can automatically spot the TabTransformer 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 TabTransformer built-in algorithm to build a TabTransformer training container as shown in the following code example. You can automatically spot the TabTransformer built-in algorithm image URI using the SageMaker AI `image_uris.retrieve` API. @@ -17,3 +16,0 @@ After specifying the TabTransformer image URI, you can use the TabTransformer co -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 = "pytorch-tabtransformerclassification-model", "*", "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[ - "n_epochs" - ] = "50" - 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 - ) -