AWS sagemaker documentation change
Summary
Updated TensorFlow object detection documentation to replace SageMaker AI Estimator references with ModelTrainer. Added detailed code examples for SageMaker Python SDK v3 implementation.
Security assessment
Changes involve API terminology updates (Estimator → ModelTrainer) and added training code examples. No security vulnerabilities, configurations, or security features are mentioned or modified. The changes are routine documentation updates for SDK version migration.
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 4ebb148be..ededc3779 100644 --- a//sagemaker/latest/dg/object-detection-tensorflow-how-to-use.md +++ b//sagemaker/latest/dg/object-detection-tensorflow-how-to-use.md @@ -11 +11 @@ You can use Object Detection - TensorFlow as an Amazon SageMaker AI built-in alg -The Object Detection - TensorFlow algorithm supports transfer learning using any of the compatible pretrained TensorFlow models. For a list of all available pretrained models, see [TensorFlow Models](./object-detection-tensorflow-Models.html). Every pretrained model has a unique `model_id`. The following example uses ResNet50 (`model_id`: `tensorflow-od1-ssd-resnet50-v1-fpn-640x640-coco17-tpu-8`) to fine-tune on a custom dataset. The pretrained models are all pre-downloaded from the TensorFlow Hub and stored in Amazon S3 buckets so that training jobs can run in network isolation. Use these pre-generated model training artifacts to construct a SageMaker AI Estimator. +The Object Detection - TensorFlow algorithm supports transfer learning using any of the compatible pretrained TensorFlow models. For a list of all available pretrained models, see [TensorFlow Models](./object-detection-tensorflow-Models.html). Every pretrained model has a unique `model_id`. The following example uses ResNet50 (`model_id`: `tensorflow-od1-ssd-resnet50-v1-fpn-640x640-coco17-tpu-8`) to fine-tune on a custom dataset. The pretrained models are all pre-downloaded from the TensorFlow Hub and stored in Amazon S3 buckets so that training jobs can run in network isolation. Use these pre-generated model training artifacts to construct a SageMaker AI ModelTrainer. @@ -13 +13 @@ The Object Detection - TensorFlow algorithm supports transfer learning using any -First, retrieve the Docker image URI, training script URI, and pretrained model URI. Then, change the hyperparameters as you see fit. You can see a Python dictionary of all available hyperparameters and their default values with `hyperparameters.retrieve_default`. For more information, see [Object Detection - TensorFlow Hyperparameters](./object-detection-tensorflow-Hyperparameter.html). Use these values to construct a SageMaker AI Estimator. +First, retrieve the Docker image URI, training script URI, and pretrained model URI. Then, change the hyperparameters as you see fit. You can see a Python dictionary of all available hyperparameters and their default values with `hyperparameters.retrieve_default`. For more information, see [Object Detection - TensorFlow Hyperparameters](./object-detection-tensorflow-Hyperparameter.html). Use these values to construct a SageMaker AI ModelTrainer. @@ -19 +19,62 @@ Default hyperparameter values are different for different models. For example, f -This example uses the [`PennFudanPed`](https://www.cis.upenn.edu/~jshi/ped_html/#pub1) dataset, which contains images of pedestriants in the street. We pre-downloaded the dataset and made it available with Amazon S3. To fine-tune your model, call `.fit` using the Amazon S3 location of your training dataset. +This example uses the [`PennFudanPed`](https://www.cis.upenn.edu/~jshi/ped_html/#pub1) dataset, which contains images of pedestriants in the street. We pre-downloaded the dataset and made it available with Amazon S3. To fine-tune your model, call `.train()` using the Amazon S3 location of your training dataset. + +SageMaker Python SDK v3 + + + + from sagemaker.core import image_uris + from sagemaker.core import model_uris, script_uris, hyperparameters + from sagemaker.train import ModelTrainer + from sagemaker.train.configs import InputData + from sagemaker.train.configs import SourceCode, Compute, StoppingCondition, OutputDataConfig + + 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 a ModelTrainer instance + tf_od_model_trainer = ModelTrainer( + role=aws_role, + training_image=train_image_uri, + source_code=SourceCode(source_dir=train_source_uri, entry_script="transfer_learning.py"), + # In V3, pre-trained model artifacts are passed via input_data_config + compute=Compute(instance_type=training_instance_type, instance_count=1), + stopping_condition=StoppingCondition(max_runtime_in_seconds=360000), + hyperparameters=hyperparameters, + output_data_config=OutputDataConfig(s3_output_path=s3_output_location), + ) + + # Launch a training job + tf_od_model_trainer.train( + input_data_config=[ + InputData(channel_name="training", data_source=training_dataset_s3_path), + InputData(channel_name="model", data_source=train_model_uri), + ] + ) + +SageMaker Python SDK v2 (Legacy) +