AWS sagemaker documentation change
Summary
Updated documentation links from training/processing.html to sagemaker_core.html and maintained content about SHAP configuration and instance scaling recommendations
Security assessment
The changes only update URL paths and maintain existing content. The recommendation to increase instance_count for load balancing is a performance best practice, not a security fix. No security vulnerabilities or features are documented.
Diff
diff --git a/sagemaker/latest/dg/clarify-processing-job-run.md b/sagemaker/latest/dg/clarify-processing-job-run.md index e5928f4ad..de6f1562b 100644 --- a//sagemaker/latest/dg/clarify-processing-job-run.md +++ b//sagemaker/latest/dg/clarify-processing-job-run.md @@ -19 +19 @@ The API acts as a high-level wrapper of the SageMaker AI `CreateProcessingJob` A - * The input dataset and output location: [DataConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.DataConfig). + * The input dataset and output location: [DataConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html). @@ -21 +21 @@ The API acts as a high-level wrapper of the SageMaker AI `CreateProcessingJob` A - * The model or endpoint to be analyzed: [ModelConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.ModelConfig). + * The model or endpoint to be analyzed: [ModelConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html). @@ -23 +23 @@ The API acts as a high-level wrapper of the SageMaker AI `CreateProcessingJob` A - * Bias analysis parameters: [BiasConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.BiasConfig). + * Bias analysis parameters: [BiasConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html). @@ -25 +25 @@ The API acts as a high-level wrapper of the SageMaker AI `CreateProcessingJob` A - * SHapley Additive exPlanations (SHAP) analysis parameters: [SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SHAPConfig). + * SHapley Additive exPlanations (SHAP) analysis parameters: [SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html). @@ -27 +27 @@ The API acts as a high-level wrapper of the SageMaker AI `CreateProcessingJob` A - * Asymmetric Shapley value analysis parameters (for time series only): [AsymmetricShapleyValueConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.AsymmetricShapleyValueConfig). + * Asymmetric Shapley value analysis parameters (for time series only): [AsymmetricShapleyValueConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html). @@ -44 +44 @@ The following code example shows how to create a `SageMakerClarifyProcessor` obj - 3. Call the specific run method of the [SageMakerClarifyProcessor](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SageMakerClarifyProcessor.run) object with the configuration objects for your use case to launch the job. These run methods include the following: + 3. Call the specific run method of the [SageMakerClarifyProcessor](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html) object with the configuration objects for your use case to launch the job. These run methods include the following: @@ -647 +647 @@ The following configuration example shows how to use `SageMakerClarifyProcessor` -If you set the `save_local_shap_values` parameter of [SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SHAPConfig) to `True`, the SageMaker Clarify processing job saves the local SHAP value as multiple part files in the job output location. +If you set the `save_local_shap_values` parameter of [SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html) to `True`, the SageMaker Clarify processing job saves the local SHAP value as multiple part files in the job output location. @@ -649 +649 @@ If you set the `save_local_shap_values` parameter of [SHAPConfig](https://sagema -To associate the local SHAP values to the input dataset instances, use the `joinsource` parameter of `DataConfig`. If you add more compute instances, we recommend that you also increase the `instance_count` of [ModelConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.ModelConfig) for the ephemeral endpoint. This prevents Spark workers' concurrent inference requests from overwhelming the endpoint. Specifically, we recommend that you use a one-to-one ratio of endpoint-to-processing instances. +To associate the local SHAP values to the input dataset instances, use the `joinsource` parameter of `DataConfig`. If you add more compute instances, we recommend that you also increase the `instance_count` of [ModelConfig](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_core.html) for the ephemeral endpoint. This prevents Spark workers' concurrent inference requests from overwhelming the endpoint. Specifically, we recommend that you use a one-to-one ratio of endpoint-to-processing instances.