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  • Welcome
  • INTRODUCTION
    • What Is Aizen?
    • Aizen Platform Interfaces
    • Typical ML Workflow
    • Datasets and Features
    • Resources and GPUs
    • LLM Operations
    • Glossary
  • INSTALLATION
    • Setting Up Your Environment
      • Hardware Requirements
      • Deploying Kubernetes On Prem
      • Deploying Kubernetes on AWS
      • Deploying Kubernetes on GCP
        • GCP and S3 API Interoperability
        • Provisioning the Cloud Service Mesh
        • Installing Ingress Gateways with Istio
      • Deploying Kubernetes on Azure
        • Setting Up Azure Blob Storage
    • Installing Aizen
      • Software Requirements
      • Installing the Infrastructure Components
      • Installing the Core Components
      • Virtual Services and Gateways Command Script (GCP)
      • Helpful Deployment Commands
    • Installing Aizen Remote Components
      • Static Remote Deployment
      • Dynamic Remote Deployment
    • Installing Optional Components
      • MinIO
      • OpenLDAP
      • OpenEBS Operator
      • NGINX Ingress Controller
      • Airbyte
  • GETTING STARTED
    • Managing Users and Roles
      • Aizen Security
      • Adding Users
      • Updating Users
      • Listing Users and Roles
      • Granting or Revoking Roles
      • Deleting Users
    • Accessing the Aizen Platform
    • Using the Aizen Jupyter Console
  • MANAGING ML WORKFLOWS
    • ML Workflow
    • Configuring Data Sources
    • Configuring Data Sinks
    • Creating Training Datasets
    • Performing ML Data Analysis
    • Training an ML Model
    • Adding Real-Time Data Sources
    • Serving an ML Model
    • Training and Serving Custom ML Models
  • MANAGING LLM WORKFLOWS
    • LLM Workflow
    • Configuring Data Sources
    • Creating Training Datasets for LLMs
    • Fine-Tuning an LLM
    • Serving an LLM
    • Adding Cloud Providers
    • Configuring Vector Stores
    • Running AI Agents
  • Notebook Commands Reference
    • Notebook Commands
  • SYSTEM CONFIGURATION COMMANDS
    • License Commands
      • check license
      • install license
    • Authorization Commands
      • add users
      • alter users
      • list users
      • grant role
      • list roles
      • revoke role
      • delete users
    • Cloud Provider Commands
      • add cloudprovider
      • list cloudproviders
      • list filesystems
      • list instancetypes
      • status instance
      • list instance
      • list instances
      • delete cloudprovider
    • Project Commands
      • create project
      • alter project
      • exportconfig project
      • importconfig project
      • list projects
      • show project
      • set project
      • listconfig all
      • status all
      • stop all
      • delete project
      • shutdown aizen
    • File Commands
      • install credentials
      • list credentials
      • delete credentials
      • install preprocessor
  • MODEL BUILDING COMMANDS
    • Data Source Commands
      • configure datasource
      • describe datasource
      • listconfig datasources
      • delete datasource
    • Data Sink Commands
      • configure datasink
      • describe datasink
      • listconfig datasinks
      • alter datasink
      • start datasink
      • status datasink
      • stop datasink
      • list datasinks
      • display datasink
      • delete datasink
    • Dataset Commands
      • configure dataset
      • describe dataset
      • listconfig datasets
      • exportconfig dataset
      • importconfig dataset
      • start dataset
      • status dataset
      • stop dataset
      • list datasets
      • display dataset
      • export dataset
      • import dataset
      • delete dataset
    • Data Analysis Commands
      • loader
      • show stats
      • show datatypes
      • show data
      • show unique
      • count rows
      • count missingvalues
      • plot
      • run analysis
      • run pca
      • filter dataframe
      • list dataframes
      • set dataframe
      • save dataframe
    • Training Commands
      • configure training
      • describe training
      • listconfig trainings
      • start training
      • status training
      • list trainings
      • list tensorboard
      • start tensorboard
      • stop tensorboard
      • stop training
      • restart training
      • delete training
      • list mlflow
      • save embedding
      • list trained-models
      • list trained-model
      • export trained-model
      • import trained-model
      • delete trained-model
      • register model
      • update model
      • list registered-models
      • list registered-model
  • MODEL SERVING COMMANDS
    • Resource Commands
      • configure resource
      • describe resource
      • listconfig resources
      • alter resource
      • delete resource
    • Prediction Commands
      • configure prediction
      • describe prediction
      • listconfig predictions
      • start prediction
      • status prediction
      • test prediction
      • list predictions
      • stop prediction
      • list prediction-logs
      • display prediction-log
      • delete prediction
    • Data Report Commands
      • configure datareport
      • describe datareport
      • listconfig datareports
      • start datareport
      • list data-quality
      • list data-drift
      • list target-drift
      • status data-quality
      • display data-quality
      • status data-drift
      • display data-drift
      • status target-drift
      • display target-drift
      • delete datareport
    • Runtime Commands
      • configure runtime
      • describe runtime
      • listconfig runtimes
      • start runtime
      • status runtime
      • stop runtime
      • delete runtime
  • LLM AND EMBEDDINGS COMMANDS
    • LLM Commands
      • configure llm
      • listconfig llms
      • describe llm
      • start llm
      • status llm
      • stop llm
      • delete llm
    • Vector Store Commands
      • configure vectorstore
      • describe vectorstore
      • listconfig vectorstores
      • start vectorstore
      • status vectorstore
      • stop vectorstore
      • delete vectorstore
    • LLM Application Commands
      • configure llmapp
      • describe llmapp
      • listconfig llmapps
      • start llmapp
      • status llmapp
      • stop llmapp
      • delete llmapp
  • TROUBLESHOOTING
    • Installation Issues
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© 2025 Aizen Corporation

On this page
  • JupyterLab Authentication
  • Adding System-Level User Information
  • Example system_users_config.json
  • Adding Project Users
  • Example project_users_config.json
  1. GETTING STARTED
  2. Managing Users and Roles

Adding Users

Providing LDAP or OAuth users with access to the Aizen platform is a two-step process:

  1. Users must be authenticated by JupyterLab.

  2. Users must be added to Aizen.

JupyterLab Authentication

At Aizen installation time, the installer applies these authentication settings:

  • The authentication type (LDAP or OAuth) (LDAP by default)

  • The external user ID that will serve as the Aizen JupyterLab admin user

  • The LDAP or OAuth group or organization that permits all users within that group to be auto-authenticated with the external authentication database

The external user ID that is specified as admin_user during Aizen installation will be granted the AIZEN_ADMIN role as part of the installation process. Only users within the LDAP or OAuth group or organization specified during installation will be able to log in to the Aizen notebook and run commands.

Adding System-Level User Information

Aizen users are registered into the Aizen database with their system-level information:

  • User ID, name, and contact information

  • Aizen alert delivery preferences

  • System-level Aizen roles

  1. Log in to the Jupyter console as a user with the AIZEN_ADMIN role.

  2. Create a json file with the system-level user details. The json file should contain a list of users with their login ID, name, and alert preferences. An email address or phone number is required to receive alerts. See the Example system_users_config.json below.

  3. For each user, specify any system-level Aizen roles (AIZEN_ADMIN and PROJECT_CREATOR) as applicable.

    • You may want to designate one or more users as additional AIZEN_ADMINs.

    • You will want to designate one or more users to be PROJECT_CREATOR, so they can create projects and manage those projects.

    • You can leave the roles as an empty list or omit the roles property for users that will only have project-specific privileges (PROJECT_ADMIN, PROJECT_EXECUTOR, or PROJECT_READER).

  4. Create or open a notebook.

  5. Execute the following command to add the system-level users:

    add users <system users config json file>

Users with the PROJECT_CREATOR role can now log in and add other project users as part of the project creation and configuration.

Example system_users_config.json

[
   {
      "id": "user1",
      "name": "user1",
      "email_id": "user1@demo.net",
      "phone_number": "1231231234",
      "roles": ["AIZEN_ADMIN", "PROJECT_CREATOR"],
      "alert_options": [
         {
            "severity": "error",
            "type": ["email"]
         },
         {
            "severity": "info",
            "type": ["email"]
         }
      ]
   },
   {
      "id": "user2",
      "name": "user2",
      "email_id": "user2@demo.net",
      "phone_number": "3213214321",
      "roles": [],
      "alert_options": [
         {
            "severity": "error",
            "type": ["email"]
         }
      ]      
   }
]

For severity of the alerts, you can specify one of these options:

  • critical

  • error

  • warning

  • info

For the type of the alerts, you can specify email or text.

Adding Project Users

The Aizen ML pipeline is organized by projects. A user with the PROJECT_CREATOR role can create a project and add additional users who can access objects within that project.

  1. Log in to the Jupyter console as a user with the PROJECT_CREATOR role.

  2. Create a json file with the project user details:

    • The project user details consist of a project description and a list of project-level users.

    • Each user entry requires the user ID, a list of project-level roles (PROJECT_ADMIN, PROJECT_EXECUTOR, or PROJECT_READER), and a list of services for which the user wants to receive alerts. See the Example project_users_config.json below.

  3. Create or open a notebook.

  4. Execute the following command to create a project using the input json file to define users for the project and their roles and alert preferences:

    create project <project name>,config=<project users config json file>

Now, the project is accessible by all users specified in the project configuration file.

Example project_users_config.json

{
    "description": "trip fare",
    "name": "trip_fare",
    "users": [
        {
            "id": "user1",
            "name": "user1",
            "roles": ["PROJECT_ADMIN"],
            "alerts_for_services": ["all"]
        },
		{
            "id": "user2",
            "name": "user2",
            "roles": ["PROJECT_EXECUTOR"],
            "alerts_for_services": ["all"]
        }
    ]
}

A user can receive alerts for these services (alerts_for_services):

  • datasink

  • dataset

  • training

  • prediction

  • featureview

  • storage

  • jobcontroller

  • metaserver

  • all

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Last updated 3 months ago