AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples and redundant section headers
Security assessment
Deletion of outdated SDK examples without security context. No references to vulnerabilities, exploits, or security features in the changes.
Diff
diff --git a/sagemaker/latest/dg/object-detection-tensorflow-how-to-use.md b/sagemaker/latest/dg/object-detection-tensorflow-how-to-use.md index ededc3779..82db9b42a 100644 --- a//sagemaker/latest/dg/object-detection-tensorflow-how-to-use.md +++ b//sagemaker/latest/dg/object-detection-tensorflow-how-to-use.md @@ -21,3 +20,0 @@ This example uses the [`PennFudanPed`](https://www.cis.upenn.edu/~jshi/ped_html/ -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-od1-ssd-resnet50-v1-fpn-640x640-coco17-tpu-8", "*" - 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/PennFudanPed_COCO_format/" - - training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}" - - output_bucket = sess.default_bucket() - output_prefix = "jumpstart-example-od-training" - s3_output_location = f"s3://{output_bucket}/{output_prefix}/output" - - # Create an Estimator instance - tf_od_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_od_estimator.fit({"training": training_dataset_s3_path}, logs=True) -