AWS sagemaker documentation change
Summary
Updated documentation links for AWS Deep Learning Containers, modified a code import path, and removed outdated information about Scikit-Learn image variants.
Security assessment
The changes primarily involve updating URLs to point to current documentation locations and modifying a code example to reflect SDK changes. The removal of the Scikit-Learn note suggests deprecation of older versions but doesn't indicate any security vulnerability. No security advisories, vulnerability fixes, or new security features are mentioned.
Diff
diff --git a/sagemaker/latest/dg/pre-built-containers-support-policy.md b/sagemaker/latest/dg/pre-built-containers-support-policy.md index 4ea49085f..3c7a0fb70 100644 --- a//sagemaker/latest/dg/pre-built-containers-support-policy.md +++ b//sagemaker/latest/dg/pre-built-containers-support-policy.md @@ -40 +40 @@ As of August 2024, the `forecasting-deepar` container is no longer receiving sec -AWS Deep Learning Containers are a set of Docker images for training and serving deep learning models. To view available images, see [Available Deep Learning Containers Images](https://aws.github.io/deep-learning-containers/reference/available_images/). +AWS Deep Learning Containers are a set of Docker images for training and serving deep learning models. To view available images, see [Available Deep Learning Containers Images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) in the Deep Learning Containers GitHub repository. @@ -42 +42 @@ AWS Deep Learning Containers are a set of Docker images for training and serving -DLCs hit their end of patch date 365 days after their GitHub release date. Patch updates for DLCs are not “in-place” updates. You must delete the existing image on your instance and pull the latest container image without terminating your instance. For more information, see [Framework Support Policy](https://aws.github.io/deep-learning-containers/reference/support_policy/). +DLCs hit their end of patch date 365 days after their GitHub release date. Patch updates for DLCs are not “in-place” updates. You must delete the existing image on your instance and pull the latest container image without terminating your instance. For more information, see [Framework Support Policy](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html) in the _AWS Deep Learning Containers Developer Guide_. @@ -44 +44 @@ DLCs hit their end of patch date 365 days after their GitHub release date. Patch -Reference the [AWS Deep Learning Containers Framework Support Policy table](https://aws.github.io/deep-learning-containers/reference/support_policy/) to check which frameworks and versions are actively supported for AWS DLCs. You can reference the framework associated with a DLC in the support policy table for any images that are not explicitly listed. For example, you can reference **PyTorch** in the support policy table for DLC images such as `huggingface-pytorch-inference` and `stabilityai-pytorch-inference`. +Reference the [AWS Deep Learning Containers Framework Support Policy table](https://aws.amazon.com/releasenotes/dlc-support-policy/) to check which frameworks and versions are actively supported for AWS DLCs. You can reference the framework associated with a DLC in the support policy table for any images that are not explicitly listed. For example, you can reference **PyTorch** in the support policy table for DLC images such as `huggingface-pytorch-inference` and `stabilityai-pytorch-inference`. @@ -52 +52 @@ If a DLC uses the HuggingFace [Transformers](https://huggingface.co/docs/transfo -The SageMaker AI ML Framework Containers are a set of Docker images for training and serving machine learning workloads with environments optimized for common frameworks such as XGBoost and Scikit Learn. To view available SageMaker AI ML Framework Containers, see [Docker Registry Paths and Example Code](https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths.html). Navigate to the AWS Region of your choice, and browse images with the **(algorithm)** tag. SageMaker AI ML Framework Containers also adhere to the [AWS Deep Learning Containers framework support policy](https://aws.github.io/deep-learning-containers/reference/support_policy/). +The SageMaker AI ML Framework Containers are a set of Docker images for training and serving machine learning workloads with environments optimized for common frameworks such as XGBoost and Scikit Learn. To view available SageMaker AI ML Framework Containers, see [Docker Registry Paths and Example Code](https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths.html). Navigate to the AWS Region of your choice, and browse images with the **(algorithm)** tag. SageMaker AI ML Framework Containers also adhere to the [AWS Deep Learning Containers framework support policy](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html). @@ -57 +57 @@ To retrieve the latest image version for XGBoost 1.7-1 in framework mode, use th - from sagemaker import image_uris + from sagemaker.core import image_uris @@ -75,4 +74,0 @@ Scikit-Learn | 0.20-1 | >4 years | Not supported -###### Note - -Scikit-Learn 1.4-2 is available in both Python 3.10 (`1.4-2`) and Python 3.12 (`1.4-2-py312`) image variants. The Python 3.12 image does not include [ml-io](https://github.com/awslabs/ml-io). Customers using mlio should continue using the 1.4-2 (Python 3.10) image. -