AWS sagemaker documentation change
Summary
Updated SageMaker Python SDK links to remove '/v2.html' suffix and modified code example from 'sagemaker.image_uris.retrieve' to 'image_uris.retrieve'.
Security assessment
Changes involve documentation URL updates and code syntax refactoring (module import simplification). No security configurations, access controls, or vulnerability mitigations are modified or added.
Diff
diff --git a/sagemaker/latest/dg/create-multi-model-endpoint.md b/sagemaker/latest/dg/create-multi-model-endpoint.md index e073ec98c..5520da187 100644 --- a//sagemaker/latest/dg/create-multi-model-endpoint.md +++ b//sagemaker/latest/dg/create-multi-model-endpoint.md @@ -63 +63 @@ The following procedure outlines the key steps used in that sample to create a C - 1. Get a container with an image that supports deploying multi-model endpoints. For a list of built-in algorithms and framework containers that support multi-model endpoints, see [Supported algorithms, frameworks, and instances for multi-model endpoints](./multi-model-support.html). For this example, we use the [K-Nearest Neighbors (k-NN) Algorithm](./k-nearest-neighbors.html) built-in algorithm. We call the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/v2.html) utility function `image_uris.retrieve()` to get the address for the K-Nearest Neighbors built-in algorithm image. + 1. Get a container with an image that supports deploying multi-model endpoints. For a list of built-in algorithms and framework containers that support multi-model endpoints, see [Supported algorithms, frameworks, and instances for multi-model endpoints](./multi-model-support.html). For this example, we use the [K-Nearest Neighbors (k-NN) Algorithm](./k-nearest-neighbors.html) built-in algorithm. We call the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) utility function `image_uris.retrieve()` to get the address for the K-Nearest Neighbors built-in algorithm image. @@ -67 +67 @@ The following procedure outlines the key steps used in that sample to create a C - image = sagemaker.image_uris.retrieve("knn",region=region) + image = image_uris.retrieve("knn",region=region) @@ -161 +161 @@ The following procedure outlines the key steps to create a GPU backed multi-mode - 1. Define the container image. To create a multi-model endpoint with GPU support for ResNet models, define the container to use the [NVIDIA Triton Server image](https://docs.aws.amazon.com/sagemaker/latest/dg/triton.html). This container supports multi-model endpoints and is optimized for running on GPU instances. We call the [SageMaker AI Python SDK](https://sagemaker.readthedocs.io/en/stable/v2.html) utility function `image_uris.retrieve()` to get the address for the image. For example: + 1. Define the container image. To create a multi-model endpoint with GPU support for ResNet models, define the container to use the [NVIDIA Triton Server image](https://docs.aws.amazon.com/sagemaker/latest/dg/triton.html). This container supports multi-model endpoints and is optimized for running on GPU instances. We call the [SageMaker AI Python SDK](https://sagemaker.readthedocs.io/en/stable/) utility function `image_uris.retrieve()` to get the address for the image. For example: