docs: Rename aidaptivcache documents

src/content/docs/demos/07-aidaptiv-operator.mdx → src/content/docs/demos/07-aidaptivcache-operator.mdx

@@ -1,11 +1,11 @@
 ---
-title: aiDAPTIV Operator
-description: Learn how to install the aiDAPTIV Cache Operator for Phison aiDAPTIVCache device support in Kubernetes
+title: aiDAPTIVCache Operator
+description: Learn how to install the aiDAPTIVCache Operator for Phison aiDAPTIVCache device support in Kubernetes
 ---
 
 import { Steps, Aside } from '@astrojs/starlight/components';
 
-This guide demonstrates how to install the **aiDAPTIV Cache Operator**, which is required for Kubernetes to recognize and utilize Phison aiDAPTIVCache devices (`phison.com/ai100`).
+This guide demonstrates how to install the **aiDAPTIVCache Operator**, which is required for Kubernetes to recognize and utilize Phison aiDAPTIVCache devices (`phison.com/ai100`).
 
 ## Prerequisites
 
@@ -16,7 +16,7 @@ Before installing the operator, ensure you have:
 - Sufficient cluster resources for the operator deployment
 
 <Aside type="note">
-The aiDAPTIV Cache Operator must be installed **before** deploying any workloads that require Phison aiDAPTIVCache devices. Without this operator, Kubernetes will not be able to recognize or allocate aiDAPTIVCache resources.
+The aiDAPTIVCache Operator must be installed **before** deploying any workloads that require Phison aiDAPTIVCache devices. Without this operator, Kubernetes will not be able to recognize or allocate aiDAPTIVCache resources.
 </Aside>
 
 ## Installation Steps
@@ -29,7 +29,7 @@ The aiDAPTIV Cache Operator must be installed **before** deploying any workloads
    - Go to **Application** → **Store**
    - This opens the Helm chart repository
 
-2. **Add the aiDAPTIV Operator Chart**
+2. **Add the aiDAPTIVCache Operator Chart**
 
    Click **Import** button and provide the following information:
 
@@ -57,7 +57,7 @@ The aiDAPTIV Cache Operator must be installed **before** deploying any workloads
 
 ## What the Operator Does
 
-The aiDAPTIV Cache Operator performs the following functions:
+The aiDAPTIVCache Operator performs the following functions:
 
 - **Resource Manager**: Manage aiDAPTIVCache devices
 - **Device Discovery**: Automatically detects Phison aiDAPTIVCache devices on cluster nodes
@@ -86,10 +86,10 @@ If the operator pod fails to start:
 
 ## Next Steps
 
-After successfully installing the aiDAPTIV Cache Operator, you can:
+After successfully installing the aiDAPTIVCache Operator, you can:
 
-- Deploy fine-tuning jobs that utilize aiDAPTIVCache devices (see [Deploy aiDAPTIV Finetune](/demos/08-aidaptiv-finetune))
-- Deploy inference services with aiDAPTIVCache acceleration (see [Deploy aiDAPTIV Inference](/demos/09-aidaptiv-inference))
+- Deploy fine-tuning jobs that utilize aiDAPTIVCache devices (see [Deploy aiDAPTIVCache Finetune](/demos/08-aidaptivcache-finetune))
+- Deploy inference services with aiDAPTIVCache acceleration (see [Deploy aiDAPTIVCache Inference](/demos/09-aidaptivcache-inference))
 
 <Aside type="tip">
 When requesting aiDAPTIVCache devices in your workloads, use the resource key `phison.com/ai100` in both `requests` and `limits` sections of your pod specifications.

src/content/docs/demos/08-aidaptiv-finetune.mdx → src/content/docs/demos/08-aidaptivcache-finetune.mdx

@@ -1,30 +1,29 @@
 ---
-title: aiDAPTIV Finetune
-description: Fine-tune LLM models using aiDAPTIV Cache in OtterScale cluster.
+title: aiDAPTIVCache Finetune
+description: Fine-tune LLM models using aiDAPTIVCache in OtterScale cluster.
 ---
 
 import { Steps, Aside, LinkCard } from '@astrojs/starlight/components';
 
-This guide demonstrates how to fine-tune Large Language Models (LLMs) using aiDAPTIV Cache within the OtterScale cluster.
+This guide demonstrates how to fine-tune Large Language Models (LLMs) using aiDAPTIVCache within the OtterScale cluster.
 
 ## Overview
 
-aiDAPTIV Finetune provides efficient model fine-tuning capabilities with support for LoRA, full parameter training, and other training modes. Key features:
+aiDAPTIVCache finetune provides efficient model fine-tuning capabilities with support for LoRA, full parameter training, and other training modes. Key features:
 
 - Distributed training support (Multi-GPU)
-- NFS storage integration for model input/output
 - LoRA fine-tuning support
 - Customizable training datasets
 - Flexible resource allocation (vGPU, memory)
 
 ## Prerequisites
 
 <Aside type="caution">
-**Required**: Before using aiDAPTIV Finetune, you must install the **aiDAPTIV Cache Operator**.
+**Required**: Before using aiDAPTIVCache finetune, you must install the **aiDAPTIVCache Operator**.
 
 The operator enables Kubernetes to recognize and allocate Phison aiDAPTIVCache devices (`phison.com/ai100`) used by aiDAPTIV workloads.
 
-See [aiDAPTIV Operator](/demo/07-aidaptiv-operator) for installation instructions.
+See [aiDAPTIVCache Operator](/demos/07-aidaptivcache-operator) for installation instructions.
 </Aside>
 
 ## Prepare Model and Data
@@ -40,7 +39,7 @@ This method uses OtterScale's NFS File System to store and access model files.
 1. **Create NFS File System in OtterScale**:
 
    Navigate to the Storage section:
-   - Go to `https://<your-domain>/scope/<scope-name>/service/file-system`
+   - Go to **Storage** → **File System**
    - Create a new NFS File System or use an existing one
    - Record the NFS server address (format: `10.102.197.0:/volumes/_nogroup/xxx`)
 
@@ -192,7 +191,7 @@ job:
   name: finetune-job
   backoffLimit: 1
   restartPolicy: Never
-  ttlSecondsAfterFinished: 3600
+  ttlSecondsAfterFinished: 60
 ```
 
 - `name`: Kubernetes Job name
@@ -415,7 +414,7 @@ job:
   name: finetune-llama-job
   backoffLimit: 1
   restartPolicy: Never
-  ttlSecondsAfterFinished: 3600
+  ttlSecondsAfterFinished: 60
 
 securityContext:
   privileged: true
@@ -509,11 +508,8 @@ resources:
 1. **Check Job Status**:
 
    Navigate to the Jobs page:
-   ```
-   https://<your-domain>/scope/<scope-name>/service/job
-   ```
-
-   Find your fine-tune job under **Application → Job** and check its status.
+   - Go to **Applications** → **Jobs**
+   - Find your fine-tune job under your namespace and check its status.
 
 2. **Retrieve Output Model**:
 
@@ -531,9 +527,9 @@ resources:
 ## Related Resources
 
 <LinkCard
-  title="AiDaptiv Inference"
+  title="aiDaptivCache Inference"
   description="Deploy fine-tuned models for inference"
-  href="/demos/09-aidaptiv-inference"
+  href="/demos/09-aidaptivcache-inference"
 />
 
 <LinkCard

src/content/docs/demos/09-aidaptiv-inference.mdx → src/content/docs/demos/09-aidaptivcache-inference.mdx

@@ -1,5 +1,5 @@
 ---
-title: aiDAPTIV Inference
+title: aiDAPTIVCache Inference
 description: Deploy high-performance LLM inference service using aiDAPTIV Cache.
 ---
 
@@ -9,24 +9,22 @@ This guide demonstrates how to deploy a high-performance LLM inference service u
 
 ## Overview
 
-aiDAPTIV Inference provides high-performance LLM inference service based on vLLM, supporting:
+aiDAPTIVCache Inference provides high-performance LLM inference service based on vLLM, supporting:
 
 - High-throughput serving
 - Tensor parallelism (Multi-GPU)
-- PagedAttention memory optimization
-- KV Cache offloading (DRAM/SSD)
+- KV Cache offloading
 - Dynamic LoRA loading
-- Prefix Caching
 - OpenAI-compatible API
 
 ## Prerequisites
 
 <Aside type="caution">
-**Required**: Before using aiDAPTIV Inference, you must install the **aiDAPTIV Cache Operator**.
+**Required**: Before using aiDAPTIVCache Inference, you must install the **aiDAPTIVCache Operator**.
 
 The operator enables Kubernetes to recognize and allocate Phison aiDAPTIVCache devices (`phison.com/ai100`) used by aiDAPTIV workloads.
 
-See [aiDAPTIV Operator](/demo/07-aidaptiv-operator) for installation instructions.
+See [aiDAPTIVCache Operator](/demos/07-aidaptivcache-operator) for installation instructions.
 </Aside>
 
 ## Prepare Model
@@ -42,7 +40,7 @@ This method uses OtterScale's NFS File System to store and access model files.
 1. **Create NFS File System in OtterScale**:
 
    Navigate to the Storage section:
-   - Go to `https://<your-domain>/scope/<scope-name>/service/file-system`
+   - Go to **Storage** → **File System**
    - Create a new NFS File System or use an existing one
    - Record the NFS server address (format: `10.102.197.0:/volumes/_nogroup/xxx`)
 
@@ -467,12 +465,8 @@ resources:
 
 1. **Get Service Address**:
 
-   Navigate to the Services page:
-   ```
-   https://<your-domain>/scope/<scope-name>/service/services
-   ```
-
-   Find your inference service and note the NodePort.
+   - Go to **Application** → **Services**
+   - Find your inference service and note the NodePort.
 
 2. **Test API Connection**:
 
@@ -546,9 +540,8 @@ resources:
 1. **Check Deployment Status**:
 
    Navigate to the Workloads page:
-   ```
-   https://<your-domain>/scope/<scope-name>/service/workload
-   ```
+   - Go to **Application** → **Workloads**
+   - Find your deployment
 
 2. **View Pod Logs**:
 
@@ -601,9 +594,9 @@ resources:
 ## Related Resources
 
 <LinkCard
-  title="aiDAPTIV Finetune"
+  title="aiDAPTIVCache Finetune"
   description="Train and fine-tune LLM models"
-  href="/demos/08-aidaptiv-finetune"
+  href="/demos/08-aidaptivcache-finetune"
 />
 
 <LinkCard