replace all chinese with english

docs/en/docs/admin/baize/best-practice/add-scheduler.md

@@ -5,7 +5,7 @@ Date: 2024-07-30
 
 # Add Job Scheduler
 
-DCE 5.0 AI Lab provides a job scheduler to help you better manage jobs.
+AI Lab provides a job scheduler to help you better manage jobs.
 In addition to the basic scheduler, it also supports custom schedulers.
 
 ## Introduction to Job Scheduler
@@ -54,7 +54,7 @@ including `Coscheduling (Gang Scheduling)` and other features.
 ### Deploy Scheduler Plugins
 
 To deploy a secondary scheduler plugin in a worker cluster, refer to
-[Deploying Secondary Scheduler Plugin](../../kpanda/user-guide/clusters/cluster-scheduler-plugin.md).
+[Deploying Secondary Scheduler Plugin](../../kpanda/clusters/cluster-scheduler-plugin.md).
 
 ### Enable Scheduler Plugins in AI Lab
 

docs/en/docs/admin/baize/best-practice/change-notebook-image.md

@@ -27,7 +27,7 @@ This Notebook includes basic development tools. Taking `baize-notebook:v0.5.0` (
 
 !!! note
 
-    With each version iteration, DCE 5.0 will proactively maintain and update.
+    With each version iteration, AI platform will proactively maintain and update.
 
 However, sometimes users may need custom images. This page explains how to update images and add them to the Notebook creation interface for selection.
 

docs/en/docs/admin/baize/best-practice/checkpoint.md

@@ -99,7 +99,7 @@ checkpoint.save(file_prefix=checkpoint_prefix)
 
 !!!note
 
-    Users of DCE 5.0 AI Lab can directly mount high-performance storage as the checkpoint directory to improve the speed of saving and restoring checkpoints.
+    Users of AI Lab can directly mount high-performance storage as the checkpoint directory to improve the speed of saving and restoring checkpoints.
 
 ### Restore Checkpoints in TensorFlow
 

docs/en/docs/admin/baize/best-practice/deploy-nfs-in-worker.md

@@ -4,7 +4,7 @@ A **Network File System (NFS)** allows remote hosts to mount file systems over a
 interact with those file systems as though they are mounted locally. This enables
 system administrators to consolidate resources onto centralized servers on the network.
 
-**Dataset** is a core feature provided by DCE 5.0 AI Lab.
+**Dataset** is a core feature provided by AI Lab.
 By abstracting the dependency on data throughout the entire lifecycle of MLOps
 into datasets, users can manage various types of data in datasets so that
 training tasks can directly use the data in the dataset.

docs/en/docs/admin/baize/best-practice/finetunel-llm.md

@@ -1,7 +1,7 @@
 # Fine-tune the ChatGLM3 Model by Using AI Lab
 
 This page uses the `ChatGLM3` model as an example to demonstrate how to use LoRA (Low-Rank Adaptation)
-to fine-tune the ChatGLM3 model within the DCE 5.0 AI Lab environment. The demo program is from the
+to fine-tune the ChatGLM3 model within the AI Lab environment. The demo program is from the
 [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/finetune_demo/lora_finetune.ipynb) official example.
 
 The general process of fine-tuning is as follows:
@@ -17,12 +17,12 @@ The general process of fine-tuning is as follows:
 
 !!! info
 
-    Before starting, ensure DCE 5.0 and [AI Lab](../intro/install.md) are correctly installed,
+    Before starting, ensure AI platform and [AI Lab](../intro/install.md) are correctly installed,
     GPU queue resources are successfully initialized, and computing resources are sufficient.
 
 ## Prepare Data
 
-Utilize the dataset management feature provided by DCE 5.0 AI Lab to quickly preheat
+Utilize the dataset management feature provided by AI Lab to quickly preheat
 and persist the data required for fine-tuning large models, reducing GPU resource occupation
 due to data preparation, and improving resource utilization efficiency.
 
@@ -40,7 +40,7 @@ First, pull the ChatGLM3 code repository and download the pre-training model for
 
 <!-- add image later -->
 
-DCE 5.0 AI Lab will automatically preheat the data in the background to ensure
+AI Lab will automatically preheat the data in the background to ensure
 quick data access for subsequent tasks.
 
 ### AdvertiseGen Dataset
@@ -68,11 +68,11 @@ Traditionally, environment dependencies are either packaged directly into the de
 installed in the local environment, which can lead to inconsistency in environment dependencies and
 difficulties in managing and updating dependencies.
 
-DCE 5.0 AI Lab provides environment management capabilities, decoupling Python environment
+AI Lab provides environment management capabilities, decoupling Python environment
 dependency package management from development tools and task images, solving dependency management
 chaos and environment inconsistency issues.
 
-Here, use the environment management feature provided by DCE 5.0 AI Lab to
+Here, use the environment management feature provided by AI Lab to
 create the environment required for ChatGLM3 fine-tuning for subsequent use.
 
 !!! warning
@@ -97,7 +97,7 @@ may vary based on your location. Using a domestic mirror for acceleration can sp
 
 ## Use Notebook as IDE
 
-DCE 5.0 AI Lab provides Notebook as an IDE feature, allowing users to write, run, and view
+AI Lab provides Notebook as an IDE feature, allowing users to write, run, and view
 code results directly in the browser. This is very suitable for development in data analysis,
 machine learning, and deep learning fields.
 
@@ -289,7 +289,7 @@ data output dataset for subsequent inference tasks.
 
 ### Submit Tasks via `baizectl`
 
-DCE 5.0 AI Lab's Notebook supports using the `baizectl` command-line tool without authentication.
+AI Lab's Notebook supports using the `baizectl` command-line tool without authentication.
 If you prefer using CLI, you can directly use the `baizectl` command-line tool to submit tasks.
 
 ```bash
@@ -329,7 +329,7 @@ the resource configuration of the previous fine-tuning tasks.
 
 ### Configure Model Runtime
 
-Configuring the model runtime is crucial. Currently, DCE 5.0 AI Lab supports
+Configuring the model runtime is crucial. Currently, AI Lab supports
 `vLLM` as the model inference service runtime, which can be directly selected.
 
 !!! tip
@@ -359,6 +359,6 @@ curl -X POST http://10.20.100.210:31118/v2/models/chatglm3-6b/generate \
 This page used `ChatGLM3` as an example to quickly introduce and get you started with
 the **AI Lab** for model fine-tuning, using `LoRA` to fine-tune the ChatGLM3 model.
 
-DCE 5.0 AI Lab provides a wealth of features to help model developers quickly conduct
+AI Lab provides a wealth of features to help model developers quickly conduct
 model development, fine-tuning, and inference tasks. It also offers rich OpenAPI interfaces,
 facilitating integration with third-party application ecosystems.

docs/en/docs/admin/baize/best-practice/label-studio.md

@@ -21,10 +21,10 @@ machine learning and artificial intelligence jobs. Here is a brief introduction
 Label Studio offers a powerful data labeling solution for data scientists and
 machine learning engineers due to its flexibility and rich features.
 
-## Deploy to DCE 5.0
+## Deploy to AI platform
 
 To use Label Studio in AI Lab, it needs to be deployed to the
-[Global Service Cluster](../../kpanda/user-guide/clusters/cluster-role.md#global-service-cluster).
+[Global Service Cluster](../../kpanda/clusters/cluster-role.md#global-service-cluster).
 You can quickly deploy it using Helm.
 
 !!! note
@@ -57,7 +57,7 @@ You can quickly deploy it using Helm.
       image:
         repository: heartexlabs/label-studio   # Configure proxy address here if docker.io is inaccessible
       extraEnvironmentVars:
-        LABEL_STUDIO_HOST: https://{DCE_Access_Address}/label-studio    # Use the DCE 5.0 login address, refer to the current webpage URL
+        LABEL_STUDIO_HOST: https://{Access_Address}/label-studio    # Use the AI platform login address, refer to the current webpage URL
         LABEL_STUDIO_USERNAME: {User_Email}    # Must be an email, replace with your own
         LABEL_STUDIO_PASSWORD: {User_Password}
     app:
@@ -84,7 +84,7 @@ global:
   image:
     repository: heartexlabs/label-studio   # Configure proxy address here if docker.io is inaccessible
   extraEnvironmentVars:
-    LABEL_STUDIO_HOST: https://{DCE_Access_Address}/label-studio    # Use the DCE 5.0 login address, refer to the current webpage URL
+    LABEL_STUDIO_HOST: https://{Access_Address}/label-studio    # Use the AI platform login address, refer to the current webpage URL
     LABEL_STUDIO_USERNAME: {User_Email}    # Must be an email, replace with your own
     LABEL_STUDIO_PASSWORD: {User_Password}
 app:
@@ -105,7 +105,7 @@ externalPostgresql:
 
 ## Add GProduct to Navigation Bar
 
-To add Label Studio to the DCE 5.0 navigation bar, you can refer to the method in
+To add Label Studio to the AI platform navigation bar, you can refer to the method in
 [Global Management OEM IN](../../ghippo/best-practice/oem/oem-in.md).
 The following example shows how to add it to the secondary navigation of AI Lab.
 
@@ -178,7 +178,7 @@ spec:
       name: label-studio
       order: 1
       target: blank    # Control new blank page
-      url: https://{DCE_Access_Address}/label-studio    # url to access
+      url: https://{Access_Address}/label-studio    # url to access
       visible: true
     # End adding
   name: AI Lab

docs/en/docs/admin/baize/developer/inference/models.md

@@ -15,7 +15,7 @@ Here, you can see information about the supported models.
     Refer to the [Release Notes](../../intro/release-notes.md) to understand the latest version and update timely.
 
 You can use GPU types that have been verified by AI platform in AI Lab.
-For more details, refer to the [GPU Support Matrix](../../../kpanda/user-guide/gpu/gpu_matrix.md).
+For more details, refer to the [GPU Support Matrix](../../../kpanda/gpu/gpu_matrix.md).
 
 ![Click to Create](../../images/inference-interface.png)
 

docs/en/docs/admin/baize/troubleshoot/cluster-not-found.md

@@ -41,8 +41,8 @@ find `baize-agent` and install it.
 
 !!! note
 
-    Quickly jump to this address: `https://<dce_host>/kpanda/clusters/<cluster_name>/helm/charts/addon/baize-agent`.
-    Note to replace `<dce_host>` with the actual DCE console address, and `<cluster_name>` with the actual cluster name.
+    Quickly jump to this address: `https://<host>/kpanda/clusters/<cluster_name>/helm/charts/addon/baize-agent`.
+    Note to replace `<host>` with the actual console address, and `<cluster_name>` with the actual cluster name.
 
 ### Cluster name not configured in the process of installing `baize-agent`
 
@@ -58,6 +58,6 @@ to be unable to retrieve cluster information. Check if the platform's Insight se
 are running and configured correctly.
 
 - Check if the insight-server component is running properly in the
-  [Global Service Cluster](../../kpanda/user-guide/clusters/cluster-role.md#global-service-cluster).
+  [Global Service Cluster](../../kpanda/clusters/cluster-role.md#global-service-cluster).
 - Check if the insight-agent component is running properly in the
-  [worker cluster](../../kpanda/user-guide/clusters/cluster-role.md#worker-cluster).
+  [worker cluster](../../kpanda/clusters/cluster-role.md#worker-cluster).

docs/en/docs/admin/baize/troubleshoot/index.md

@@ -13,10 +13,10 @@ solutions for certain errors encountered during use.
 
 !!! warning
 
-    This documentation is only applicable to version DCE 5.0. If you encounter issues with
+    This documentation is only applicable to version AI platform. If you encounter issues with
     the use of AI Lab, please refer to this troubleshooting guide first.
 
-In DCE 5.0, the module name for AI Lab is `baize`,
+In AI platform, the module name for AI Lab is `baize`,
 which offers one-stop solutions for model training, inference, model management, and more.
 
 ## Common Troubleshooting Cases

docs/en/docs/admin/baize/troubleshoot/notebook-not-controlled-by-quotas.md

@@ -14,7 +14,7 @@ they find that even if the selected queue lacks resources, the Notebook can stil
 
     The queue management capability in AI Lab is provided by [Kueue](https://kueue.sigs.k8s.io/),
     and the Notebook service is provided through [JupyterHub](https://jupyter.org/hub). JupyterHub has high
-    requirements for the Kubernetes version. For versions below v1.27, even if queue quotas are set in DCE 5.0,
+    requirements for the Kubernetes version. For versions below v1.27, even if queue quotas are set in AI platform,
     and users select the quota when creating a Notebook, the Notebook will not actually be restricted by the queue quota.
 
     ![local-queue-initialization-failed](./images/kueue-k8s127.png)

docs/en/docs/admin/ghippo/access-control/iam.md

@@ -34,9 +34,9 @@ graph TD
  class login,user,auth,group,role,id cluster;
 
 click login "https://docs.daocloud.io/en/ghippo/install/login.html"
-click user "https://docs.daocloud.io/en/ghippo/user-guide/access-control/user.html"
-click auth "https://docs.daocloud.io/en/ghippo/user-guide/access-control/role.html"
-click group "https://docs.daocloud.io/en/ghippo/user-guide/access-control/group.html"
-click role "https://docs.daocloud.io/en/ghippo/user-guide/access-control/custom-role.html"
-click id "https://docs.daocloud.io/en/ghippo/user-guide/access-control/idprovider.html"
+click user "https://docs.daocloud.io/en/ghippo/access-control/user.html"
+click auth "https://docs.daocloud.io/en/ghippo/access-control/role.html"
+click group "https://docs.daocloud.io/en/ghippo/access-control/group.html"
+click role "https://docs.daocloud.io/en/ghippo/access-control/custom-role.html"
+click id "https://docs.daocloud.io/en/ghippo/access-control/idprovider.html"
 ```

docs/en/docs/admin/ghippo/access-control/user.md

@@ -36,11 +36,11 @@ Prerequisite: The user already exists.
 
 1. The administrator enters __Access Control__ , selects __Users__ , enters the user list, and clicks __┇__ -> __Authorization__ .
 
-    ![Menu](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/authorize01.png)
+    ![Menu](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/authorize01.png)
 
 2. On the __Authorization__ page, check the required role permissions (multiple choices are allowed).
 
-    ![Interface](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/authorize02.png)
+    ![Interface](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/authorize02.png)
 
 3. Click __OK__ to complete the authorization for the user.
 
@@ -52,11 +52,11 @@ Prerequisite: The user already exists.
 
 1. The administrator enters __Access Control__ , selects __Users__ , enters the user list, and clicks __┇__ -> __Add to Group__ .
 
-    ![Add group menu](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/joingroup01.png)
+    ![Add group menu](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/joingroup01.png)
 
 2. On the __Add to Group__ page, check the groups to be joined (multiple choices are allowed). If there is no optional group, click __Create a new group__ to create a group, and then return to this page and click the __Refresh__ button to display the newly created group.
 
-    ![Add group interface](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/joingroup02.png)
+    ![Add group interface](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/joingroup02.png)
 
 3. Click __OK__ to add the user to the group.
 
@@ -70,11 +70,11 @@ Once a user is deactivated, that user will no longer be able to access the Platf
 
 1. The administrator enters __Access Control__ , selects __Users__ , enters the user list, and clicks a username to enter user details.
 
-    ![User details](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/createuser03.png)
+    ![User details](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/createuser03.png)
 
 2. Click __Edit__ on the upper right, turn off the status button, and make the button gray and inactive.
 
-    ![Edit](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/enableuser01.png)
+    ![Edit](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/enableuser01.png)
 
 3. Click __OK__ to finish disabling the user.
 
@@ -84,11 +84,11 @@ Premise: User mailboxes need to be set. There are two ways to set user mailboxes
 
 - On the user details page, the administrator clicks __Edit__ , enters the user's email address in the pop-up box, and clicks __OK__ to complete the email setting.
 
-    ![Edit](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/enableuser02.png)
+    ![Edit](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/enableuser02.png)
 
 - Users can also enter the __Personal Center__ and set the email address on the __Security Settings__ page.
 
-    ![User center](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/mailbox.png)
+    ![User center](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/mailbox.png)
 
 If the user forgets the password when logging in, please refer to [Reset Password](../password.md).
 
@@ -103,8 +103,8 @@ If the user forgets the password when logging in, please refer to [Reset Passwor
 
 1. The administrator enters __Access Control__ , selects __Users__ , enters the user list, and clicks __┇__ -> __Delete__ .
 
-    ![Delete user](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/deleteuser01.png)
+    ![Delete user](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/deleteuser01.png)
 
 2. Click __Delete__ to finish deleting the user.
 
-    ![Confirm deletion](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/user-guide/images/deleteuser02.png)
+    ![Confirm deletion](https://docs.daocloud.io/daocloud-docs-images/docs/en/docs/ghippo/images/deleteuser02.png)

docs/en/docs/admin/ghippo/best-practice/authz-plan.md

@@ -1,6 +1,6 @@
 # Ordinary user authorization plan
 
-Ordinary users refer to those who can use most of DCE's product modules and features (except management features), have certain operation rights to resources within the scope of authority, and can independently use resources to deploy applications.
+Ordinary users refer to those who can use most product modules and features (except management features), have certain operation rights to resources within the scope of authority, and can independently use resources to deploy applications.
 
 The authorization and resource planning process for such users is shown in the following figure.
 
@@ -14,11 +14,11 @@ graph TB
     ws-to-ns --> authu[5. Authorize a user with Workspace Editor]
     authu --> complete([End])
     
-click user "https://docs.daocloud.io/en/ghippo/user-guide/access-control/user/"
-click ns "https://docs.daocloud.io/en/kpanda/user-guide/namespaces/createns/"
-click ws "https://docs.daocloud.io/en/ghippo/user-guide/workspace/workspace/"
-click ws-to-ns "https://docs.daocloud.io/en/ghippo/user-guide/workspace/ws-to-ns-across-clus/"
-click authu "https://docs.daocloud.io/en/ghippo/user-guide/workspace/wspermission/"
+click user "https://docs.daocloud.io/en/ghippo/access-control/user/"
+click ns "https://docs.daocloud.io/en/kpanda/namespaces/createns/"
+click ws "https://docs.daocloud.io/en/ghippo/workspace/workspace/"
+click ws-to-ns "https://docs.daocloud.io/en/ghippo/workspace/ws-to-ns-across-clus/"
+click authu "https://docs.daocloud.io/en/ghippo/workspace/wspermission/"
 
  classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000;
  classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff;