AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 documentation and empty SageMaker Python SDK v3 section
Security assessment
Change removes outdated SDK documentation without any security context. No vulnerabilities, weaknesses, or security incidents are mentioned.
Diff
diff --git a/sagemaker/latest/dg/model-checkpoints-enable.md b/sagemaker/latest/dg/model-checkpoints-enable.md index 05fe70f1d..a7847d864 100644 --- a//sagemaker/latest/dg/model-checkpoints-enable.md +++ b//sagemaker/latest/dg/model-checkpoints-enable.md @@ -15,3 +14,0 @@ The following example shows how to configure checkpoint paths when you construct -SageMaker Python SDK v3 - - @@ -58,45 +54,0 @@ To find a complete list of SageMaker AI `ModelTrainer` parameters, see the [Mode -SageMaker Python SDK v2 (Legacy) - - -To enable checkpointing, add the `checkpoint_s3_uri` and `checkpoint_local_path` parameters to your estimator. The following example template shows how to create a generic SageMaker AI estimator and enable checkpointing. You can use this template for the supported algorithms by specifying the `image_uri` parameter. To find Docker image URIs for algorithms with checkpointing supported by SageMaker AI, see [Docker Registry Paths and Example Code](https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths). You can also replace `estimator` and `Estimator` with other SageMaker AI frameworks' estimator parent classes and estimator classes, such as `[TensorFlow](https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html#create-an-estimator)`, `[PyTorch](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#create-an-estimator)`, `[MXNet](https://sagemaker.readthedocs.io/en/stable/frameworks/mxnet/using_mxnet.html#create-an-estimator)`, `[HuggingFace](https://huggingface.co/docs/sagemaker/train#create-a-hugging-face-estimator)` and `[XGBoost](https://sagemaker.readthedocs.io/en/stable/frameworks/xgboost/using_xgboost.html#create-an-estimator)`. - - - import sagemaker - from sagemaker.estimator import Estimator - - bucket=sagemaker.Session().default_bucket() - base_job_name="sagemaker-checkpoint-test" - checkpoint_in_bucket="checkpoints" - - # The S3 URI to store the checkpoints - checkpoint_s3_bucket="s3://{}/{}/{}".format(bucket, base_job_name, checkpoint_in_bucket) - - # The local path where the model will save its checkpoints in the training container - checkpoint_local_path="/opt/ml/checkpoints" - - estimator = Estimator( - ... - image_uri="<ecr_path>/<algorithm-name>:<tag>" # Specify to use built-in algorithms - output_path=bucket, - base_job_name=base_job_name, - - # Parameters required to enable checkpointing - checkpoint_s3_uri=checkpoint_s3_bucket, - checkpoint_local_path=checkpoint_local_path - ) - -The following two parameters specify paths for checkpointing: - - * `checkpoint_local_path` – Specify the local path where the model saves the checkpoints periodically in a training container. The default path is set to `'/opt/ml/checkpoints'`. If you are using other frameworks or bringing your own training container, ensure that your training script's checkpoint configuration specifies the path to `'/opt/ml/checkpoints'`. - -###### Note - -We recommend specifying the local paths as `'/opt/ml/checkpoints'` to be consistent with the default SageMaker AI checkpoint settings. If you prefer to specify your own local path, make sure you match the checkpoint saving path in your training script and the `checkpoint_local_path` parameter of the SageMaker AI estimators. - - * `checkpoint_s3_uri` – The URI to an S3 bucket where the checkpoints are stored in real time. You can specify either an S3 general purpose or S3 directory bucket to store your checkpoints. For more information on S3 directory buckets, see [Directory buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/directory-buckets-overview.html) in the _Amazon Simple Storage Service User Guide_. - - - - -To find a complete list of SageMaker AI estimator parameters, see the [Estimator API](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Estimator) in the _[Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) documentation_. -