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Alauda AI
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Alauda AI
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Overview

Introduction
Quick Start
Release Notes

Install

Pre-installation Configuration
Install Alauda AI Essentials
Install Alauda AI

Upgrade

Upgrade from AI 1.3

Uninstall

Uninstall

Infrastructure Management

Device Management

About Alauda Build of Hami
About Alauda Build of NVIDIA GPU Device Plugin

Multi-Tenant

Guides

Namespace Management

Workbench

Overview

Introduction
Install
Upgrade

How To

Create WorkspaceKind
Create Workbench

Model Deployment & Inference

Overview

Introduction
Features

Inference Service

Introduction

Guides

Inference Service

How To

Extend Inference Runtimes
Configure External Access for Inference Services
Configure Scaling for Inference Services

Troubleshooting

Experiencing Inference Service Timeouts with MLServer Runtime
Inference Service Fails to Enter Running State

Model Management

Introduction

Guides

Model Repository

Monitoring & Ops

Overview

Introduction
Features Overview

Logging & Tracing

Introduction

Guides

Logging

Resource Monitoring

Introduction

Guides

Resource Monitoring

API Reference

Introduction

Kubernetes APIs

Inference Service APIs

ClusterServingRuntime [serving.kserve.io/v1alpha1]
InferenceService [serving.kserve.io/v1beta1]

Workbench APIs

Workspace Kind [kubeflow.org/v1beta1]
Workspace [kubeflow.org/v1beta1]

Manage APIs

AmlNamespace [manage.aml.dev/v1alpha1]

Operator APIs

AmlCluster [amlclusters.aml.dev/v1alpha1]
Glossary
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#Pre-installation Configuration

#TOC

#Deploy Service Mesh

Since Alauda AI leverages Service Mesh capabilities for model inference services, Service Mesh must be deployed in the cluster before deploying Alauda AI. For detailed deployment procedures, refer to .

INFO

After completing the Prerequisites on the Create Service Mesh page, proceed to the Creating a Service Mesh page and follow the on-screen instructions to finalize the deployment of the Service Mesh.

#Preparing the GitLab Service

In Alauda AI, GitLab is the core component for Model Management. Before deploying Alauda AI, you must prepare a GitLab service.

#Deployment Options

#1. GitLab service requirements

Regardless of deployment method, all GitLab instances must satisfy:

  • Version: Must be v15 or later.
  • Protocol: Must use HTTPS. For setup instructions, refer to .
  • Git LFS: Must be enabled. For setup instructions, refer to .
  • Hosting: Must be self-hosted (public cloud-hosted GitLab services are not supported).
  • Access Tokens: Disable expiration dates for access tokens.

#2. Use the platform-provided plugin

Deploy a new GitLab service using the 'Alauda Build of GitLab' plugin. For instructions, refer to: .

#3. Use your own GitLab service

Alternatively, you can use a self-managed GitLab instance, but it must meet the GitLab service requirements.

#GitLab Configuration

Before deploying Alauda AI, perform these GitLab configuration steps after service acquisition。

#1. Disable expiration dates for access tokens

If GitLab is running v17.0 or greater, we need to disable expiration dates for access tokens.

WARNING

If expiration date for access token keeps enabled, we have to refresh admin token manually at least yearly, or Alauda AI may stop functionally.

To disable expiration dates for new access tokens:

  1. On the left sidebar, at the bottom, select Admin.
  2. Select Settings > General.
  3. Expand Account and limit.
  4. Uncheck the Personal / Project / Group access token expiration checkbox.
  5. Select Save changes.

#2. Generate new token

To generate impersonation token for admin:

  1. On the left sidebar, at the bottom, select Admin.
  2. Select Overview > Users.
  3. Select the admin user (Administrator for example).
  4. On the top navigation bar, select Impersonation Tokens.
  5. Select Add new token.
  6. In the popup form:
    1. Input a Token name for Alauda AI (aml-root, for example).
    2. Remove Expiration Date (select "x" icon to remove expiration date).
    3. Check ALL scopes (especially api scope) for Select scopes.
  7. Select Create impersonation token.
  8. Save the newly generated token under Your new impersonation token, we need to use it later.
WARNING

Make sure you save the newly generate token - you won't be able to access it again.

#3. Create kubernetes secret for admin token

Then we create secret for gitlab admin token named aml-gitlab-admin-token under cpaas-system namespace:

# Please replace ${TOKEN} with real token saved previously
kubectl create secret generic aml-gitlab-admin-token \
  --from-literal="password=${TOKEN}" \
  -n cpaas-system
  1. Create a gitlab admin token secret named aml-gitlab-admin-token
  2. The token is saved under password key, please replace ${TOKEN} with the real token saved previously.
  3. The secret is created under cpaas-system namespace.