AWS sagemaker documentation change
Summary
Added deprecation notice and removed legacy SageMaker Python SDK v2/v3 code examples
Security assessment
Removes outdated SDK examples and adds general deprecation notice. No evidence of security fixes or new security documentation.
Diff
diff --git a/sagemaker/latest/dg/debugger-htb-prepare-training-job.md b/sagemaker/latest/dg/debugger-htb-prepare-training-job.md index da1dbb15a..c8a97e69d 100644 --- a//sagemaker/latest/dg/debugger-htb-prepare-training-job.md +++ b//sagemaker/latest/dg/debugger-htb-prepare-training-job.md @@ -76,3 +75,0 @@ You can also use a different container local output path. However, in Step 2: Cr -SageMaker Python SDK v3 - - @@ -81,5 +77,0 @@ Use the `sagemaker.debugger.TensorBoardOutputConfig` while configuring a SageMak -SageMaker Python SDK v2 (Legacy) - - -Use the `sagemaker.debugger.TensorBoardOutputConfig` while configuring a SageMaker AI framework estimator. This configuration API maps the S3 bucket you specify for saving TensorBoard data with the local path in the training container (`/opt/ml/output/tensorboard`). Pass the object of the module to the `tensorboard_output_config` parameter of the estimator class. The following code snippet shows an example of preparing a TensorFlow estimator with the TensorBoard output configuration parameter. - @@ -96,3 +87,0 @@ This example assumes that you use the SageMaker Python SDK. If you use the low-l -SageMaker Python SDK v3 - - @@ -131,34 +119,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker.tensorflow import TensorFlow - from sagemaker.debugger import TensorBoardOutputConfig - - # Set variables for training job information, - # such as s3_out_bucket and other unique tags. - ... - - LOG_DIR="/opt/ml/output/tensorboard" - - output_path = os.path.join( - "s3_output_bucket", "sagemaker-output", "date_str", "your-training_job_name" - ) - - **tensorboard_output_config = TensorBoardOutputConfig( - s3_output_path=os.path.join(output_path, 'tensorboard'), - container_local_output_path=LOG_DIR - )** - - estimator = TensorFlow( - entry_point="train.py", - source_dir="src", - role=role, - image_uri=image_uri, - instance_count=1, - instance_type="ml.c5.xlarge", - base_job_name="your-training_job_name", - **tensorboard_output_config=tensorboard_output_config,** - hyperparameters=hyperparameters - ) -