AWS sagemaker documentation change
Summary
Removed SageMaker Python SDK v2 (legacy) code examples and references
Security assessment
Change involves removing outdated SDK version references without any security context. No vulnerability fixes or security features are mentioned in the diff.
Diff
diff --git a/sagemaker/latest/dg/lightgbm-modes.md b/sagemaker/latest/dg/lightgbm-modes.md index 882d90917..325650c11 100644 --- a//sagemaker/latest/dg/lightgbm-modes.md +++ b//sagemaker/latest/dg/lightgbm-modes.md @@ -13 +13 @@ You can use LightGBM as an Amazon SageMaker AI built-in algorithm. The following -Use the LightGBM built-in algorithm to build a LightGBM training container as shown in the following code example. You can automatically spot the LightGBM 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 LightGBM built-in algorithm to build a LightGBM training container as shown in the following code example. You can automatically spot the LightGBM built-in algorithm image URI using the SageMaker AI `image_uris.retrieve` API. @@ -17,3 +16,0 @@ After specifying the LightGBM image URI, you can use the LightGBM container to c -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 = "lightgbm-classification-model", "*", "training" - training_instance_type = "ml.m5.xlarge" - - # 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_multiclass/" - - 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[ - "num_boost_round" - ] = "500" - 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, # for distributed training, specify an instance_count greater than 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( - { - "train": training_dataset_s3_path, - "validation": validation_dataset_s3_path, - }, logs=True, job_name=training_job_name - ) -