Change to tabbed view for CLI/ SDK

doc/_static/custom.css

@@ -22,22 +22,22 @@
 /* Header styling */
 header {
     background-color: white;
-    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); /* shadow-sm */
+
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
     position: sticky;
     top: 0;
     z-index: 50;
 }
 
-/* Preserving your existing typography */
 h1 {
-    font-size: 1.875rem;      /* text-3xl */
-    font-weight: 700;         /* font-bold */
-    color: #111827;           /* text-gray-900 */
+    font-size: 1.875rem;
+    font-weight: 700;
+    color: #111827;
 }
 
 h2 {
-    font-size: 1.5rem;        /* text-2xl */
-    font-weight: 700;         /* font-bold */
+    font-size: 1.5rem;
+    font-weight: 700;
     color: #111827;
 }
 
@@ -48,6 +48,6 @@ h3 {
 }
 
 p {
-    font-size: 0.875rem;     /* Tailwind's text-sm */
-    color: #4b5563;          /* Tailwind's gray-600 */
+    font-size: 0.875rem;
+    color: #4b5563;
 }

doc/getting_started.md

@@ -10,18 +10,22 @@ This guide will help you get started with the SageMaker HyperPod CLI and SDK to
 
 List all available SageMaker HyperPod clusters in your account:
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp list-cluster [--region <region>] [--namespace <namespace>] [--output <json|table>]
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.hyperpod_manager import HyperPodManager
 
 clusters = HyperPodManager.list_clusters(region='us-east-2')
 print(clusters)
 ```
+````
+`````
 
 **Parameters:**
 - `region` (string) - Optional. The AWS region where the SageMaker HyperPod and EKS clusters are located. If not specified, uses the region from your current AWS account credentials.
@@ -32,17 +36,21 @@ print(clusters)
 
 Configure your local kubectl environment to interact with a specific SageMaker HyperPod cluster and namespace:
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp set-cluster-context --cluster-name <cluster-name> [--namespace <namespace>]
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.hyperpod_manager import HyperPodManager
 
 HyperPodManager.set_context('<hyperpod-cluster-name>', region='us-east-2')
 ```
+````
+`````
 
 **Parameters:**
 - `cluster-name` (string) - Required. The SageMaker HyperPod cluster name to configure with.
@@ -52,36 +60,44 @@ HyperPodManager.set_context('<hyperpod-cluster-name>', region='us-east-2')
 
 View information about the currently configured cluster context:
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp get-cluster-context
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.hyperpod_manager import HyperPodManager
 
 # Get current context information
 context = HyperPodManager.get_context()
 print(context)
 ```
+````
+`````
 
 ## Job Management
 
 ### List Pods for a Training Job
 
 View all pods associated with a specific training job:
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp list-pods hyp-pytorch-job --job-name <job-name>
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 # List all pods created for this job
 pytorch_job.list_pods()
 ```
+````
+`````
 
 **Parameters:**
 - `job-name` (string) - Required. The name of the job to list pods for.
@@ -90,16 +106,20 @@ pytorch_job.list_pods()
 
 View logs for a specific pod within a training job:
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp get-logs hyp-pytorch-job --pod-name <pod-name> --job-name <job-name>
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 # Check the logs from pod0
 pytorch_job.get_logs_from_pod("demo-pod-0")
 ```
+````
+`````
 
 **Parameters:**
 - `job-name` (string) - Required. The name of the job to get logs for.

doc/index.md

@@ -16,6 +16,8 @@ API reference <_apidoc/modules>
 
 SageMaker HyperPod CLI and SDK provide a seamless way to manage distributed training and inference workloads on EKS-hosted SageMaker HyperPod clusters—without needing Kubernetes expertise. Use the powerful CLI to launch and monitor training jobs and endpoints, or leverage the Python SDK to do the same programmatically with minimal code, including support for JumpStart models, custom endpoints, and built-in monitoring.
 
+## Start Here
+
 ::::{container}
 ::::{grid} 1 2 4 4
 :gutter: 3

doc/inference.md

@@ -21,7 +21,8 @@ You can create inference endpoints using either JumpStart models or custom model
 
 ### JumpStart Model Endpoints
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp create hyp-jumpstart-endpoint \
     --version 1.0 \
@@ -30,8 +31,9 @@ hyp create hyp-jumpstart-endpoint \
     --endpoint-name endpoint-jumpstart \
     --tls-output-s3-uri s3://sample-bucket
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.inference import HyperPodJumpstartEndpoint
 
@@ -46,10 +48,13 @@ endpoint = HyperPodJumpstartEndpoint(
 # Deploy the endpoint
 endpoint.create()
 ```
+````
+`````
 
 ### Custom Model Endpoints
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 hyp create hyp-custom-endpoint \
     --version 1.0 \
@@ -59,8 +64,9 @@ hyp create hyp-custom-endpoint \
     --instance-type ml.g5.8xlarge \
     --tls-output-s3-uri s3://sample-bucket
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.inference import HyperPodCustomEndpoint
 
@@ -76,6 +82,8 @@ endpoint = HyperPodCustomEndpoint(
 # Deploy the endpoint
 endpoint.create()
 ```
+````
+`````
 
 ## Key Parameters
 
@@ -92,16 +100,18 @@ When creating an inference endpoint, you'll need to specify:
 
 ### List Endpoints
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 # List JumpStart endpoints
 hyp list hyp-jumpstart-endpoint
 
 # List custom endpoints
 hyp list hyp-custom-endpoint
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.inference import HyperPodJumpstartEndpoint, HyperPodCustomEndpoint
 
@@ -113,19 +123,23 @@ print(jumpstart_endpoints)
 custom_endpoints = HyperPodCustomEndpoint.list()
 print(custom_endpoints)
 ```
+````
+`````
 
 ### Describe an Endpoint
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 # Describe JumpStart endpoint
 hyp describe hyp-jumpstart-endpoint --endpoint-name <endpoint-name>
 
 # Describe custom endpoint
 hyp describe hyp-custom-endpoint --endpoint-name <endpoint-name>
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.inference import HyperPodJumpstartEndpoint, HyperPodCustomEndpoint
 
@@ -139,19 +153,23 @@ custom_endpoint = HyperPodCustomEndpoint.load(endpoint_name="endpoint-custom")
 custom_details = custom_endpoint.describe()
 print(custom_details)
 ```
+````
+`````
 
 ### Invoke an Endpoint
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 # Invoke custom endpoint
 hyp invoke hyp-custom-endpoint \
     --endpoint-name <endpoint-name> \
     --content-type "application/json" \
     --payload '{"inputs": "What is machine learning?"}'
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.inference import HyperPodCustomEndpoint
 
@@ -165,19 +183,23 @@ response = endpoint.invoke(
 )
 print(response)
 ```
+````
+`````
 
 ### Delete an Endpoint
 
-**CLI**
+`````{tab-set}
+````{tab-item} CLI
 ```bash
 # Delete JumpStart endpoint
 hyp delete hyp-jumpstart-endpoint --endpoint-name <endpoint-name>
 
 # Delete custom endpoint
 hyp delete hyp-custom-endpoint --endpoint-name <endpoint-name>
 ```
+````
 
-**SDK**
+````{tab-item} SDK
 ```python
 from sagemaker.hyperpod.inference import HyperPodJumpstartEndpoint, HyperPodCustomEndpoint
 
@@ -189,6 +211,8 @@ jumpstart_endpoint.delete()
 custom_endpoint = HyperPodCustomEndpoint.load(endpoint_name="endpoint-custom")
 custom_endpoint.delete()
 ```
+````
+`````
 
 ## Inference Example Notebooks