Kathy.lin/gpu ga

content/en/gpu_monitoring/_index.md

@@ -1,6 +1,5 @@
 ---
 title: GPU Monitoring
-private: true
 further_reading:
 - link: "/gpu_monitoring/setup"
   tag: "Documentation"
@@ -16,10 +15,6 @@ further_reading:
 </div>
 {{% /site-region %}}
 
-{{< callout url="https://www.datadoghq.com/product-preview/gpu-monitoring/" >}}
-GPU Monitoring is in Preview. To join the preview, click <strong>Request Access</strong> and complete the form.
-{{< /callout >}}
-
 ## Overview
 Datadog's [GPU Monitoring][1] provides a centralized view into your GPU fleet's health, cost, and performance. It enables teams to make better provisioning decisions, optimize and troubleshoot AI workload performance, and eliminate idle GPU costs without having to manually set up individual vendor tools (like NVIDIA's DCGM). GPU Monitoring supports fleets deployed across the major cloud providers (AWS, GCP, Azure, Oracle Cloud), hosted on-premises, or provisioned through GPU-as-a-Service platforms like Coreweave and Lambda Labs. 
 

content/en/gpu_monitoring/fleet.md

@@ -1,7 +1,6 @@
 ---
 title: GPU Monitoring Fleet Page
 description: "An inventory of all your GPU-accelerated hosts that helps you diagnose performance issues."
-private: true
 further_reading:
     - link: "https://www.datadoghq.com/blog/datadog-gpu-monitoring/"
       tag: "Blog"

content/en/gpu_monitoring/setup.md

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 ---
 title: Set up GPU Monitoring
-private: true
 ---
 This page provides instructions on setting up Datadog's GPU Monitoring on your infrastructure. Follow the configuration instructions that match your operating environment below.
 
@@ -28,7 +27,14 @@
 - [**Datadog Operator**][5]: version 1.18, _or_ [**Datadog Helm chart**][6]: version 3.137.3
 - **Kubernetes**: 1.22 with PodResources API active
 
-## Set up GPU Monitoring on a non-Kubernetes environment or uniform Kubernetes cluster
+## Setting up GPU Monitoring 
+Setting up GPU Monitoring does not require DCGM at all. You will need to opt-in to the collection of GPU Monitoring metrics at the Agent depending on your environment: non-Kubernetes/uniform Kubernetes cluster or mixed cluster. 
+
+Once you've enabled the collection of GPU Monitoring metrics, you can opt-in to enabling several integrations for more advanced insights: 
+- For cloud costs and cloud instance-type information: enable the AWS[9], Google Cloud[10], Azure[11], or Oracle [12] cloud integrations in your Datadog UI
+- For process-level insights, set up Datadog's Live Processes[13] .
+
+### Set up GPU Monitoring on a non-Kubernetes environment or uniform Kubernetes cluster
 
 The following instructions are the basic steps to set up GPU Monitoring in the following environments:
 - In a Kubernetes cluster where **all** nodes have GPU devices
@@ -550,3 +556,8 @@
 [6]: https://github.com/DataDog/helm-charts/blob/main/charts/datadog/README.md
 [7]: /containers/docker/
 [8]: /agent/supported_platforms/linux/
+[9]: https://docs.datadoghq.com/getting_started/integrations/aws/
+[10]:https://docs.datadoghq.com/getting_started/integrations/google_cloud/?tab=orglevel
+[11]:https://docs.datadoghq.com/getting_started/integrations/azure/?tab=createanappregistration
+[12]:https://docs.datadoghq.com/getting_started/integrations/oci/
+[13]:https://docs.datadoghq.com/infrastructure/process?tab=linuxwindows#installation

content/en/gpu_monitoring/summary.md

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 ---
 title: GPU Monitoring Summary Page
-private: true
 description: "Real-time insights across your entire GPU fleet for better provisioning and cost optimization"
 further_reading:
     - link: "https://www.datadoghq.com/blog/datadog-gpu-monitoring/"