AWS sagemaker documentation change
Summary
Added deprecation note and removed all SageMaker Python SDK v2 (Legacy) code examples while preserving v3 examples.
Security assessment
Removal of legacy SDK examples simplifies documentation but shows no evidence of security vulnerability remediation. Deprecation note mentions security improvements but doesn't document new security features.
Diff
diff --git a/sagemaker/latest/dg/debugger-save-tensors.md b/sagemaker/latest/dg/debugger-save-tensors.md index f3706b078..baef78cdd 100644 --- a//sagemaker/latest/dg/debugger-save-tensors.md +++ b//sagemaker/latest/dg/debugger-save-tensors.md @@ -10,0 +11,4 @@ Tensor visualization example notebooksSave tensors using Debugger built-in colle +###### Note + +After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Debugger, effective 6/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Debugger, but we do not plan to introduce new features. For more information, see [Debugger availability change](./debugger-availability-change.html). + @@ -64,3 +67,0 @@ In the following example code, the `s3_output_path` parameter for `DebuggerHookC -SageMaker Python SDK v3 - - @@ -121,45 +121,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import sagemaker - from sagemaker.tensorflow import TensorFlow - from sagemaker.debugger import DebuggerHookConfig, CollectionConfig - - **# use Debugger CollectionConfig to call built-in collections** - **collection_configs** =[ - CollectionConfig(name="weights"), - CollectionConfig(name="gradients"), - CollectionConfig(name="losses"), - CollectionConfig(name="biases") - ] - - # configure Debugger hook - # set a target S3 bucket as you want - sagemaker_session=sagemaker.Session() - BUCKET_NAME=sagemaker_session.default_bucket() - LOCATION_IN_BUCKET='debugger-built-in-collections-hook' - - **hook_config** =DebuggerHookConfig( - s3_output_path='s3://{BUCKET_NAME}/{LOCATION_IN_BUCKET}'. - format(BUCKET_NAME=BUCKET_NAME, - LOCATION_IN_BUCKET=LOCATION_IN_BUCKET), - collection_configs=**collection_configs** - ) - - # construct a SageMaker TensorFlow estimator - sagemaker_estimator=TensorFlow( - entry_point='directory/to/your_training_script.py', - role=sm.get_execution_role(), - base_job_name='debugger-demo-job', - instance_count=1, - instance_type="ml.p3.2xlarge", - framework_version="2.9.0", - py_version="py39", - - # debugger-specific hook argument below - debugger_hook_config=**hook_config** - ) - - sagemaker_estimator.fit() - @@ -172,3 +127,0 @@ You can modify the Debugger built-in collections using the `CollectionConfig` AP -SageMaker Python SDK v3 - - @@ -227,43 +179,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import sagemaker - from sagemaker.tensorflow import TensorFlow - from sagemaker.debugger import DebuggerHookConfig, CollectionConfig - - # use Debugger CollectionConfig to call and modify built-in collections - **collection_configs** =[ - CollectionConfig( - name="losses", - parameters={"save_interval": "50"})] - - # configure Debugger hook - # set a target S3 bucket as you want - sagemaker_session=sagemaker.Session() - BUCKET_NAME=sagemaker_session.default_bucket() - LOCATION_IN_BUCKET='debugger-modified-collections-hook' - - **hook_config** =DebuggerHookConfig( - s3_output_path='s3://{BUCKET_NAME}/{LOCATION_IN_BUCKET}'. - format(BUCKET_NAME=BUCKET_NAME, - LOCATION_IN_BUCKET=LOCATION_IN_BUCKET), - collection_configs=**collection_configs** - ) - - # construct a SageMaker TensorFlow estimator - sagemaker_estimator=TensorFlow( - entry_point='directory/to/your_training_script.py', - role=sm.get_execution_role(), - base_job_name='debugger-demo-job', - instance_count=1, - instance_type="ml.p3.2xlarge", - framework_version="2.9.0", - py_version="py39", - - # debugger-specific hook argument below - debugger_hook_config=**hook_config** - ) - - sagemaker_estimator.fit() - @@ -276,3 +185,0 @@ You can also save a reduced number of tensors instead of the full set of tensors -SageMaker Python SDK v3 - - @@ -335,47 +241,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import sagemaker - from sagemaker.tensorflow import TensorFlow - from sagemaker.debugger import DebuggerHookConfig, CollectionConfig - - # use Debugger CollectionConfig to create a custom collection - **collection_configs** =[ - CollectionConfig( - name="custom_activations_collection", - parameters={ - "include_regex": "relu|tanh", # Required - "reductions": "mean,variance,max,abs_mean,abs_variance,abs_max" - }) - ] - - # configure Debugger hook - # set a target S3 bucket as you want - sagemaker_session=sagemaker.Session() - BUCKET_NAME=sagemaker_session.default_bucket() - LOCATION_IN_BUCKET='debugger-custom-collections-hook' - - **hook_config** =DebuggerHookConfig( - s3_output_path='s3://{BUCKET_NAME}/{LOCATION_IN_BUCKET}'. - format(BUCKET_NAME=BUCKET_NAME, - LOCATION_IN_BUCKET=LOCATION_IN_BUCKET), - collection_configs=**collection_configs** - ) - - # construct a SageMaker TensorFlow estimator - sagemaker_estimator=TensorFlow( - entry_point='directory/to/your_training_script.py', - role=sm.get_execution_role(), - base_job_name='debugger-demo-job', - instance_count=1, - instance_type="ml.p3.2xlarge", - framework_version="2.9.0", - py_version="py39", - - # debugger-specific hook argument below - debugger_hook_config=**hook_config** - ) - - sagemaker_estimator.fit() -