AWS sagemaker documentation change
Summary
Updated documentation links and section titles for consistency (e.g., 'operation' to 'operations', 'Customize' to 'Customizing', 'FAQ' to 'FAQs')
Security assessment
Changes are editorial improvements to documentation structure and wording. No security-related content was added or modified. The existing security context about VPCs and IAM roles remains unchanged.
Diff
diff --git a/sagemaker/latest/dg/sagemaker-hyperpod-slurm.md b/sagemaker/latest/dg/sagemaker-hyperpod-slurm.md index ad855d26d..62873577b 100644 --- a//sagemaker/latest/dg/sagemaker-hyperpod-slurm.md +++ b//sagemaker/latest/dg/sagemaker-hyperpod-slurm.md @@ -11 +11 @@ Slurm support in SageMaker HyperPod helps you provision resilient clusters for r -You can create, configure, and maintain SageMaker HyperPod clusters graphically through the console user interface (UI) and programmatically through the AWS command line interface (CLI) or AWS SDK for Python (Boto3). With Amazon VPC, you can secure the cluster network and also take advantage of configuring your cluster with resources in your VPC, such as Amazon FSx for Lustre, which offers the fastest throughput. You can also give different IAM roles to cluster instance groups, and limit actions that your cluster resources and users can operate. To learn more, see [SageMaker HyperPod operation](./sagemaker-hyperpod-operate-slurm.html). +You can create, configure, and maintain SageMaker HyperPod clusters graphically through the console user interface (UI) and programmatically through the AWS command line interface (CLI) or AWS SDK for Python (Boto3). With Amazon VPC, you can secure the cluster network and also take advantage of configuring your cluster with resources in your VPC, such as Amazon FSx for Lustre, which offers the fastest throughput. You can also give different IAM roles to cluster instance groups, and limit actions that your cluster resources and users can operate. To learn more, see [SageMaker HyperPod Slurm cluster operations](./sagemaker-hyperpod-operate-slurm.html). @@ -15 +15 @@ You can create, configure, and maintain SageMaker HyperPod clusters graphically -SageMaker HyperPod runs [SageMaker HyperPod DLAMI](./sagemaker-hyperpod-ref.html#sagemaker-hyperpod-ref-hyperpod-ami), which sets up an ML environment on the HyperPod clusters. You can configure additional customizations to the DLAMI by providing lifecycle scripts to support your use case. To learn more about how to set up lifecycle scripts, see [Tutorial for getting started with SageMaker HyperPod](./smcluster-getting-started-slurm.html) and [Customize SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices-slurm.html). +SageMaker HyperPod runs [SageMaker HyperPod DLAMI](./sagemaker-hyperpod-ref.html#sagemaker-hyperpod-ref-hyperpod-ami), which sets up an ML environment on the HyperPod clusters. You can configure additional customizations to the DLAMI by providing lifecycle scripts to support your use case. To learn more about how to set up lifecycle scripts, see [Tutorial for getting started with SageMaker HyperPod](./smcluster-getting-started-slurm.html) and [Customizing SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices-slurm.html). @@ -31 +31 @@ You can find SageMaker HyperPod resource utilization metrics and lifecycle logs -Using SageMaker HyperPod, you can configure clusters with AWS optimized collective communications libraries offered by SageMaker AI, such as the [SageMaker AI distributed data parallelism (SMDDP) library](./data-parallel.html). The SMDDP library implements the `AllGather` operation optimized to the AWS compute and network infrastructure for the most performant SageMaker AI machine learning instances powered by NVIDIA A100 GPUs. To learn more, see [Run distributed training workloads with Slurm on HyperPod](./sagemaker-hyperpod-run-jobs-slurm-distributed-training-workload.html). +Using SageMaker HyperPod, you can configure clusters with AWS optimized collective communications libraries offered by SageMaker AI, such as the [SageMaker AI distributed data parallelism (SMDDP) library](./data-parallel.html). The SMDDP library implements the `AllGather` operation optimized to the AWS compute and network infrastructure for the most performant SageMaker AI machine learning instances powered by NVIDIA A100 GPUs. To learn more, see [Running distributed training workloads with Slurm on HyperPod](./sagemaker-hyperpod-run-jobs-slurm-distributed-training-workload.html). @@ -37 +37 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod operation](./sagemaker-hyperpod-operate-slurm.html) + * [SageMaker HyperPod Slurm cluster operations](./sagemaker-hyperpod-operate-slurm.html) @@ -39 +39 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [Customize SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices-slurm.html) + * [Customizing SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices-slurm.html) @@ -51 +51 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod FAQ](./sagemaker-hyperpod-faq-slurm.html) + * [SageMaker HyperPod FAQs](./sagemaker-hyperpod-faq-slurm.html) @@ -64 +64 @@ Appendix -Getting started with SageMaker HyperPod +Getting started