AWS sagemaker documentation change
Summary
Updated documentation links to include '-slurm' suffix in multiple references, likely indicating Slurm-specific documentation restructuring
Security assessment
Changes only modify internal documentation links by adding '-slurm' to filenames. No security-related content modifications or vulnerability fixes are present in the text changes.
Diff
diff --git a/sagemaker/latest/dg/sagemaker-hyperpod-slurm.md b/sagemaker/latest/dg/sagemaker-hyperpod-slurm.md index 14149eb62..ad855d26d 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.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 operation](./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.html) and [Customize SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices.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 [Customize SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices-slurm.html). @@ -19 +19 @@ SageMaker HyperPod runs [SageMaker HyperPod DLAMI](./sagemaker-hyperpod-ref.html -After you successfully create a HyperPod cluster, cluster users can log into the cluster nodes (such as head or controller node, log-in node, and worker node) and schedule jobs for running machine learning workloads. To learn more, see [Jobs on SageMaker HyperPod clusters](./sagemaker-hyperpod-run-jobs.html). +After you successfully create a HyperPod cluster, cluster users can log into the cluster nodes (such as head or controller node, log-in node, and worker node) and schedule jobs for running machine learning workloads. To learn more, see [Jobs on SageMaker HyperPod clusters](./sagemaker-hyperpod-run-jobs-slurm.html). @@ -23 +23 @@ After you successfully create a HyperPod cluster, cluster users can log into the -SageMaker HyperPod runs health checks on cluster nodes and provides a workload auto-resume functionality. With the cluster resiliency features of HyperPod, you can resume your workload from the last checkpoint you saved, after faulty nodes are replaced with healthy ones in clusters with more than 16 nodes. To learn more, see [SageMaker HyperPod cluster resiliency](./sagemaker-hyperpod-resiliency.html). +SageMaker HyperPod runs health checks on cluster nodes and provides a workload auto-resume functionality. With the cluster resiliency features of HyperPod, you can resume your workload from the last checkpoint you saved, after faulty nodes are replaced with healthy ones in clusters with more than 16 nodes. To learn more, see [SageMaker HyperPod cluster resiliency](./sagemaker-hyperpod-resiliency-slurm.html). @@ -27 +27 @@ SageMaker HyperPod runs health checks on cluster nodes and provides a workload a -You can find SageMaker HyperPod resource utilization metrics and lifecycle logs in Amazon CloudWatch, and manage SageMaker HyperPod resources by tagging them. Each `CreateCluster` API run creates a distinct log stream, named in `<cluster-name>-<timestamp>` format. In the log stream, you can check the host names, the name of failed lifecycle scripts, and outputs from the failed scripts such as `stdout` and `stderr`. For more information, see [SageMaker HyperPod cluster management](./sagemaker-hyperpod-cluster-management.html). +You can find SageMaker HyperPod resource utilization metrics and lifecycle logs in Amazon CloudWatch, and manage SageMaker HyperPod resources by tagging them. Each `CreateCluster` API run creates a distinct log stream, named in `<cluster-name>-<timestamp>` format. In the log stream, you can check the host names, the name of failed lifecycle scripts, and outputs from the failed scripts such as `stdout` and `stderr`. For more information, see [SageMaker HyperPod cluster management](./sagemaker-hyperpod-cluster-management-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-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 [Run distributed training workloads with Slurm on HyperPod](./sagemaker-hyperpod-run-jobs-slurm-distributed-training-workload.html). @@ -35 +35 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [Tutorial for getting started with SageMaker HyperPod](./smcluster-getting-started.html) + * [Tutorial for getting started with SageMaker HyperPod](./smcluster-getting-started-slurm.html) @@ -37 +37 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod operation](./sagemaker-hyperpod-operate.html) + * [SageMaker HyperPod operation](./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.html) + * [Customize SageMaker HyperPod clusters using lifecycle scripts](./sagemaker-hyperpod-lifecycle-best-practices-slurm.html) @@ -41 +41 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod multi-head node support](./sagemaker-hyperpod-multihead.html) + * [SageMaker HyperPod multi-head node support](./sagemaker-hyperpod-multihead-slurm.html) @@ -43 +43 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [Jobs on SageMaker HyperPod clusters](./sagemaker-hyperpod-run-jobs.html) + * [Jobs on SageMaker HyperPod clusters](./sagemaker-hyperpod-run-jobs-slurm.html) @@ -45 +45 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod cluster resources monitoring](./sagemaker-hyperpod-cluster-observability.html) + * [SageMaker HyperPod cluster resources monitoring](./sagemaker-hyperpod-cluster-observability-slurm.html) @@ -47 +47 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod cluster resiliency](./sagemaker-hyperpod-resiliency.html) + * [SageMaker HyperPod cluster resiliency](./sagemaker-hyperpod-resiliency-slurm.html) @@ -49 +49 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod cluster management](./sagemaker-hyperpod-cluster-management.html) + * [SageMaker HyperPod cluster management](./sagemaker-hyperpod-cluster-management-slurm.html) @@ -51 +51 @@ Using SageMaker HyperPod, you can configure clusters with AWS optimized collecti - * [SageMaker HyperPod FAQ](./sagemaker-hyperpod-faq.html) + * [SageMaker HyperPod FAQ](./sagemaker-hyperpod-faq-slurm.html)