AWS sagemaker documentation change
Summary
Updated documentation links for AWS Deep Learning Containers to point to GitHub Pages instead of AWS documentation and release notes
Security assessment
The changes only modify URLs for support policy and available images documentation. There is no evidence of security vulnerability fixes, security enhancements, or new security guidance. The updates appear to be routine documentation maintenance for link consistency.
Diff
diff --git a/sagemaker/latest/dg/pre-built-containers-support-policy.md b/sagemaker/latest/dg/pre-built-containers-support-policy.md index 275a2bb4c..4ea49085f 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://github.com/aws/deep-learning-containers/blob/master/available_images.md) in the Deep Learning Containers GitHub repository. +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/). @@ -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://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html) in the _AWS Deep Learning Containers Developer Guide_. +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/). @@ -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.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`. +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`. @@ -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://docs.aws.amazon.com/deep-learning-containers/latest/devguide/support-policy.html). +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/).