AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples and configuration instructions
Security assessment
The diff only removes deprecated SDK examples without introducing security content or addressing vulnerabilities. No security context or threat mitigation is mentioned in the changes.
Diff
diff --git a/sagemaker/latest/dg/text-classification-tensorflow-how-to-use.md b/sagemaker/latest/dg/text-classification-tensorflow-how-to-use.md index babd466ce..55c6ad8e5 100644 --- a//sagemaker/latest/dg/text-classification-tensorflow-how-to-use.md +++ b//sagemaker/latest/dg/text-classification-tensorflow-how-to-use.md @@ -21,3 +20,0 @@ This example uses the [`SST2`](https://www.tensorflow.org/datasets/catalog/glue# -SageMaker Python SDK v3 - - @@ -79,52 +75,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker import image_uris, model_uris, script_uris, hyperparameters - from sagemaker.estimator import Estimator - - model_id, model_version = "tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2", "*" - training_instance_type = "ml.p3.2xlarge" - - # Retrieve the Docker image - train_image_uri = image_uris.retrieve(model_id=model_id,model_version=model_version,image_scope="training",instance_type=training_instance_type,region=None,framework=None) - - # Retrieve the training script - train_source_uri = script_uris.retrieve(model_id=model_id, model_version=model_version, script_scope="training") - - # Retrieve the pretrained model tarball for transfer learning - train_model_uri = model_uris.retrieve(model_id=model_id, model_version=model_version, model_scope="training") - - # Retrieve the default hyperparameters for fine-tuning the model - hyperparameters = hyperparameters.retrieve_default(model_id=model_id, model_version=model_version) - - # [Optional] Override default hyperparameters with custom values - hyperparameters["epochs"] = "5" - - # Sample training data is available in this bucket - training_data_bucket = f"jumpstart-cache-prod-{aws_region}" - training_data_prefix = "training-datasets/SST2/" - - training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}" - - output_bucket = sess.default_bucket() - output_prefix = "jumpstart-example-tc-training" - s3_output_location = f"s3://{output_bucket}/{output_prefix}/output" - - # Create an Estimator instance - tf_tc_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 training job - tf_tc_estimator.fit({"training": training_dataset_s3_path}, logs=True) -