AI-ML

• 1: CloudML
• 1.1: Overview
• 1.2: How-to guides
• 1.2.1: Kubernetes Cluster Configuration
• 1.3: API Reference
• 1.4: CLI Reference
• 1.5: Release Note
• 2: AI&MLOps Platform
• 2.1: Overview
• 2.2: How-to guides
• 2.2.1: Cluster Deployment
• 2.2.2: Kubeflow Usage Guide
• 2.3: API Reference
• 2.4: CLI Reference
• 2.5: Release Note

1 - CloudML

1.1 - Overview

Service Overview

CloudML is an integrated platform that supports the entire machine learning process—from data analysis to model development, training, validation, and deployment—in a cloud environment.

Features

• Cloud ML is designed to enable users in various roles such as analysts, machine learning engineers, and developers to collaborate in a single environment and easily design and operate machine learning workflows.
• Cloud ML provides an analysis environment based on Python and R, and users with programming experience can leverage the platform more flexibly and effectively. In particular, using the generative AI–based Copilot feature allows code writing, refactoring, error correction, and function recommendation to be performed easily with natural language input, thereby increasing analytical productivity and accessibility.
• Cloud ML systematically supports each stage, including configuring the analysis environment, model development and serving, analysis automation, and visualization. It enables improvements in both productivity and model quality through repetitive experiments and operational automation.

Service Architecture Diagram

CloudML consists of an analysis environment, machine learning lifecycle management, automated analysis support, visualization, and a generative AI‑based Copilot feature, allowing users to perform the entire machine‑learning process in an integrated manner.

Provided features

CloudML provides the following features.

• Visual Modeling: Provides an intuitive interface that lets you build and deploy machine learning models without coding using a Drag&Drop approach. You can easily manage the entire process from data loading to model evaluation and deployment.
• Code-based Development: In the Jupyter Notebook environment, you can freely write and execute code using Python, R, and others. It provides powerful features for advanced users and researchers.
• Workflow Automation: It efficiently automates complex machine learning workflows such as data preprocessing, model training, evaluation, and deployment.
• Experiment Management: You can train machine learning models with various parameter combinations and systematically manage and compare the results.
• Using Copilot Features: It provides a natural-language-based AI assistant that guides and automates the model development process. It supports various tasks such as code generation, refactoring, error correction, and documentation, enhancing productivity.
• Integrated Platform: All features are integrated within CloudML for convenient use.
• Scalability and Flexibility: Supports scaling computing resources and connecting various data sources as needed.

Constraints

Before using CloudML, be sure to check the following constraints and incorporate them into your service usage plan. Since Cloud ML operates in a Kubernetes-based environment, appropriate cluster resource configuration is required for stable service operation.

• Application Basic Resources: To run the Application, a minimum of 24 vCPU cores and 96 GBi of memory are allocated by default.
• Analysis Task Resources: To perform analysis tasks, additional CPU or GPU resource configuration is required beyond the basic resources above. It should be configured appropriately, taking the workload of the analysis tasks into account.
• Copilot (CPU-based usage): To run Copilot on CPU resources, a minimum of 16 vCPU cores and 10 GiB of memory are required. In this case, the CPU resources available for analysis tasks are reduced accordingly.
• Copilot (GPU-based usage): Copilot can also be configured to use dedicated GPU resources.
• Supported LLM models: Currently, the LLM models that can be applied to Copilot are limited to Llama3.

Provision status by region

CloudML is available in the following environments.

Preliminary Service

This is a list of services that must be pre-configured before creating the service. Please refer to the guide provided for each service for details and prepare in advance.

1.2 - How-to guides

Create CloudML

Users can create the service by entering the required CloudML information and selecting detailed options through the Samsung Cloud Platform Console.

To create a CloudML, follow these steps.

1. Click the All Services > AI/ML > CloudML menu. Navigate to CloudML’s Service Home page.

2. On the Service Home page, click the Create CloudML button. You will be taken to the CloudML page.

3. On the CloudML Creation page, enter the information required to create the service and select detailed options.

   • In the Version Selection area, select the version of the service.

     Category	Required	Detailed description
     Select version	Required	Select CloudML version

     Table. CloudML service version selection options

   • SCP Kubernetes Engine deployment Select the options needed to create a service in this area.

     Category	Required	Detailed description
     Cluster name	Required	Select Kubernetes Engine cluster

     Table. CloudML Service Cluster Selection Options

   • In the Service Information Input area, select the options required to create the service.

     Category	required or not	Detailed description
     CloudML name	Required	Enter service name
     Explanation	Selection	Enter service description
     Domain name	Required	Enter the domain name to be used for the service Enter 2-63 characters using lowercase English letters, numbers, and special characters
     endpoint	Required	Select the endpoint to use in the service Choose between Private and Public
     Copilot	Selection	Select whether to use Copilot in the service Apply when selected requires agreement to terms in the popup window If the selected cluster is not configured with GPUs dedicated to LLMs, or the allocated LLM resources are insufficient, Copilot cannot be applied
     Resource Information	Required	Display resource information of the selected cluster
     Enter SCR information	Required	Enter SCR information to be used in the service Enter private endpoint, authentication key, secret key

     Table. CloudML service information input items

   • Additional Information Input area, please enter or select the required information.

     Category	Required	Detailed description
     tag	Selection	Add Tag Up to 50 can be added per resource After clicking the Add Tag button, enter or select Key, Value values

     Table. CloudML Additional Information Input Items

4. Summary Check the detailed information and estimated billing amount generated in the panel, and click the Complete button.

   • When creation is complete, check the created resources on the CloudML List page.

Check CloudML detailed information

You can view and edit the full list of resources and detailed information for the CloudML service. CloudML Details page consists of Details, Tags, Activity Log tabs.

To view the detailed information of CloudML, follow these steps.

1. Click the All Services > AI/ML > CloudML menu. Navigate to CloudML’s Service Home page.
2. On the Service Home page, click the resource (CloudML) to view detailed information. You will be taken to the CloudML Details page.
   • CloudML Details page displays CloudML’s status information and detailed information, and consists of Details, Tags, Activity History tabs.

     Category	Detailed description
     Service status	CloudML status Creating: Creating Deployed: Created / operating normally Updating: Updating settings Terminating: Terminating Error: Error occurred
     Connection Guide	Service Access Guide Information on host to register on the user’s PC
     Service termination	Cancel Service button

     Table. CloudML status information and additional features

Detailed Information

CloudML List page lets you view detailed information of the selected resource and modify it if necessary.

tag

On the CloudML List page, you can view the tag information of the selected resource, and add, modify, or delete it.

Job History

On the CloudML list page, you can view the operation history of the selected resource.

Terminate CloudML Service

Users can cancel the CloudML service through the Samsung Cloud Platform Console.

To cancel CloudML, follow the steps below.

1. Click the All Services > AI/ML > CloudML menu. Navigate to CloudML’s Service Home page.
2. Click the Cancel Service button on the Service Home page. A service cancellation alert window appears.
3. Enter the CloudML name to delete in the dialog and click the Confirm button.

1.2.1 - Kubernetes Cluster Configuration

Configuring a Kubernetes cluster

To apply for the CloudML service, a dedicated cluster for CloudML must be set up. A dedicated cluster means creating a Kubernetes Engine that meets or exceeds the required minimum specifications and configuring several necessary settings. Create a dedicated cluster in advance before applying for the CloudML service.

• For instructions on creating a cluster, see the Cluster Creation guide.
• CloudML exposes an HTTPS endpoint on port 443. When creating a cluster, select Public Endpoint.

Recommended specifications for cluster nodes and storage

Cluster nodes can be added or modified after the cluster is created. The following are the recommended specifications for cluster nodes and storage that should be prepared to install CloudML for five users.

Add a label to a node

Add labels to the nodes directly according to the role-specific recommendations in the cluster node and storage specifications.

• For instructions on adding labels to a node YAML, refer to the Edit Node YAML guide.

To add a label to a cluster node, follow these steps.

1. Click the All Services > Container > Kubernetes Engine menu. Navigate to the Service Home page of Kubernetes Engine.
2. On the Service Home page, click the Node menu. You will be taken to the Node List page.
3. On the Node List page, select the cluster for which you want to view detailed information from the gear button at the top left, then click the Confirm button.
4. Select the node you want to view details for and click it. You will be taken to the Node Details page.
5. On the Node Details page, click the YAML tab. You will be taken to the YAML tab page.
6. On the YAML tab page, click the Edit button. The node edit window opens.
7. In the node edit window, add a label that matches the role and click the Save button.
   • Check the following information and add a label that matches the node specifications.

     Category	Purpose-specific labels
     CPU node	App: node.kubernetes.io/nodetype: ml-app Analytics: node.kubernetes.io/nodetype: ml-analytics
     GPU node	Analysis: node.kubernetes.io/nodetype: ml-analytics-gpu Copilot: node.kubernetes.io/nodetype: ml-gpu

     Table. Kubernetes node label items by purpose

1.3 - API Reference

1.4 - CLI Reference

1.5 - Release Note

CloudML

2 - AI&MLOps Platform

2.1 - Overview

Service Overview

AI&MLOps Platform is a machine learning platform that automates repetitive tasks across the entire pipeline of developing, training, and deploying machine learning models. Through the AI&MLOps Platform service, integrated management of training data, models, and operational data is possible on a Kubernetes-based AI/MLOps environment.

The AI&MLOps Platform provides an Enterprise service that adds add-on features such as distributed training job execution and monitoring to the open-source product Kubeflow.Mini, which enables development, training, tuning, and deployment of machine learning models.

Features

• Providing a Cloud Native MLOps Environment: The AI&MLOps Platform provides a cloud‑optimized machine learning model development environment, and its Kubernetes‑based architecture makes integration with various open‑source tools convenient.

• Machine Learning Development and Operations Convenience: Provides a standardized environment that supports various machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, Keras, etc. By automating the entire pipeline for developing, training, and deploying machine learning models, it makes model composition and creation easy and promotes reusability.

• Enhanced GPU Integration: By leveraging Multi‑Node GPU on a Bare Metal Server and GPUDirect RDMA (Remote Direct Memory Access), the job speed of LLM (Large Language Model) and natural language processing (NLP) can be dramatically improved.

Service Diagram

Provided features

The AI&MLOps Platform provides the following features.

• ML Model Development Environment and Features

  • Notebook Provision: Creates Jupyter Notebooks and VS Code that include ML frameworks such as Tensorflow, Pytorch, etc.
  • TensorBoard: TensorBoard(ML model training process visualization/analysis tool) creates and manages the server.
  • Volumes: When developing ML models, store datasets and models, and connect a Volume when creating a Jupyter Notebook.

• ML model distributed training Job execution/management

  • Supports execution and monitoring of distributed training jobs, as well as management and analysis of inference services. (Add-on)
  • Provides various features for configuring MLOps environments, such as Job Queue management. (Add-on)
  • Provides efficient GPU resource utilization features such as Job Scheduler (FIFO, Bin-packing, Gang-based), GPU Fraction, and GPU resource monitoring, etc. (Add-on)
  • We dramatically improved the job speed of LLM (Large Language Model) and natural language processing (NLP) by using BM-based Multi-Node GPU and GPU Direct RDMA (Remote Direct Memory Access). (Add-on)

• ML Model Experiment Management and Pipeline

  • Provides Experiments (KFP) for managing ML pipeline experiments.
  • Supports pipeline automation features for configuring and executing ML tasks in stages.

Component

Operating System version

The operating systems supported by the AI&MLOps Platform are as follows.

Provision status by region

The AI&MLOps Platform is available in the environments below.

Prior Service

This is a list of services that must be pre-configured before creating the service. For details, refer to the guide provided for each service and prepare in advance.

2.2 - How-to guides

Create AI&MLOps Platform

Users can create the service by entering the required information for the AI&MLOps Platform and selecting detailed options through the Samsung Cloud Platform Console.

To create an AI&MLOps Platform, follow these steps.

1. Click the All Services > AI/ML > AI&MLOps Platform menu. You will be taken to the Service Home page of AI&MLOps Platform.
2. Service Home page, click the AI&MLOps Platform Create button. You will be taken to the AI&MLOps Platform Create page.
3. On the AI&MLOps Platform creation Service Type Selection page, enter the information required to create the service and select detailed options.
   • Select the service type in the Service Type and Version Selection area.

     Category	Required	Detailed description
     Service type	Required	Service type selected by the user AI&MLOps Platform Kubeflow Mini
     Service type version	Required	Select version of the selected service Provide a list of versions of the offered service

     Table. AI&MLOps Platform service types and version selection items
   • Cluster Deployment Area Classification Select the options required to create a service in this area.

     Category	Required	Detailed description
     Cluster deployment area	Required	Deploy from Kubernetes Engine: Select the previously created Kubernetes Engine Deploy to a new cluster: When creating the AI&MLOps Platform, also create a Kubernetes Engine

     Table. AI&MLOps Platform Service Cluster Deployment Area Classification Items
     Reference

     The configuration elements on the following Service Information Input page vary depending on the cluster deployment settings.
4. On the Service Information Input page of AI&MLOps Platform Creation, enter the information required to create the service and select detailed options.
   • You can select the cluster deployment region.
     • For instructions on configuring Deploy to a new cluster, see the Deploy to a new cluster guide.
     • Deploy on SCP Kubernetes Engine For configuration, refer to the Deploy on SCP Kubernetes Engine guide.
   • Refer to the Kubernetes Cluster specifications guide for the Kubernetes cluster specifications required for installation.
5. On the Creation Information Check page of AI&MLOps Platform creation, review the detailed information you created and the estimated billing amount, and click the Complete button.
   • Once creation is complete, check the created resources on the AI&MLOps Platform Service List page.

Check detailed information of AI&MLOps Platform

The AI&MLOps Platform service allows you to view and edit the full list of resources and detailed information. AI&MLOps Platform Service Details page consists of Details, Tags, Activity History tabs.

To view detailed information about the AI&MLOps Platform service, follow the steps below.

1. Click the All Services > AI/ML > AI&MLOps Platform Service menu. Navigate to the Service Home page of the AI&MLOps Platform Service.
2. On the Service Home page, click the AI&MLOps Platform menu. You will be taken to the AI&MLOps Platform Service List page.
3. On the AI&MLOps Platform Service List page, click the resource to view detailed information. You will be taken to the AI&MLOps Platform Service Details page.
   • AI&MLOps Platform Service Details page displays status information and additional feature information, and consists of Details, Tags, Activity History tabs.

Detailed Information

AI&MLOps Platform Service List page lets you view detailed information of the selected resource and edit the information if needed.

tag

AI&MLOps Platform Service List page lets you view the tag information of the selected resource, and you can add, modify, or delete it.

Job History

AI&MLOps Platform Service List page lets you view the operation history of the selected resource.

Access AI&MLOps Platform

To access the AI&MLOps Platform dashboard, you must complete the prerequisite steps.

Preliminary work

To access the AI&MLOps Platform, you must preconfigure the relevant ports and the IP addresses required for connection in the Security Group and Firewall (if using a firewall).

• Kubeflow Mini: port 31390 (inbound rules of Security Group, VPC firewall)

• To access the cluster’s worker node, you must set an inbound rule for port 22 on the Security Group and Firewall (when using a VPC firewall).

Access Dashboard

To access the AI&MLOps Platform service, follow these steps.

1. Click the All Services > AI/ML > AI&MLOps Platform Service menu. You will be taken to the Service Home page of the AI&MLOps Platform service.
2. Click the AI&MLOps Platform Service menu on the Service Home page. You will be taken to the AI&MLOps Platform Service List page.
3. Click the resource to view detailed information on the AI&MLOps Platform Service List page. You will be taken to the AI&MLOps Platform Details page.
4. AI&MLOps Platform Details on the page, click the Access Guide button. The Access Guide popup window opens.
5. Access Guide In the popup window, click the dashboard’s URL link. You will be taken to the corresponding dashboard page.

Terminate AI&MLOps Platform

You can cancel the unused service to reduce operating costs. However, canceling the service may cause the running service to stop immediately, so you should thoroughly consider the impact of service interruption before proceeding with the cancellation.

To cancel the AI&MLOps Platform, follow the steps below.

1. Click the All Services > AI/ML > AI&MLOps Platform Service menu. Navigate to the Service Home page of the AI&MLOps Platform Service.
2. On the Service Home page, click the AI&MLOps Platform Service menu. You will be taken to the AI&MLOps Platform Service List page.
3. Click the resource to view detailed information on the AI&MLOps Platform Service List page. You will be taken to the AI&MLOps Platform Details page.
4. AI&MLOps Platform Details on the page, click the Cancel Service button. The Cancel Service popup will open.
5. After entering the service name for verification, click Confirm.
6. When termination is complete, check on the AI&MLOps Platform Service List page whether the resource has been terminated.

2.2.1 - Cluster Deployment

Cluster deployment area

In Samsung Cloud Platform, the AI&MLOps Platform creation’s service type selection provides two cloud deployment regions.

• Deploy from SCP Kubernetes Engine
• Deploy to a new cluster

Depending on the selection of the cluster deployment region, the installation details on the AI&MLOps Platform creation service information input page differ.

Deploy from SCP Kubernetes Engine

1. Click the All Services > AI/ML > AI&MLOps Platform menu. You will be taken to the Service Home page of AI&MLOps Platform.
2. On the Service Home page, click the AI&MLOps Platform Create button. It navigates to the AI&MLOps Platform Create page.
3. On the service type selection page of AI&MLOps Platform creation, enter the information required to create the service and select detailed options.
   Cluster deployment

   Select the Deploy on SCP Kubernetes Engine option.
4. On the Service Information Input page of AI&MLOps Platform Creation, enter the information required to create the service, and select detailed options.
   • In the Service Information Input area, enter or view the information required to create a service.

     Category	Required status	Detailed description
     Service name	Required	Enter AI&MLOps Platform name AI&MLOps Platform name cannot be duplicated within a project
     Storage Class	Required	Storage Class is automatically registered
     Installation node information	Lookup	View the node information of the selected Kubernetes Engine
     Admin Email Address	Required	Enter the administrator (Admin) email address to use for login
     password	Required	Enter the password to use for login
     Confirm Password	Required	Re-enter password to prevent password errors

     Table. AI&MLOps Platform service information input fields
   • Additional Information Input area: enter or select the information required to create a service.

     Category	Required status	Detailed description
     tag	Selection	Select tags to add to the AI&MLOps Platform Click ‘Add Tag’ to create a new tag or add an existing tag You can register up to 50 tags The newly added tags will be applied after the service creation is completed

     Table. AI&MLOps Platform service additional information input fields

Deploy to a new cluster

1. Click the All Services > AI/ML > AI&MLOps Platform menu. You will be taken to the Service Home page of AI&MLOps Platform.
2. Service Home page, click the Create AI&MLOps Platform button. It navigates to the Create AI&MLOps Platform page.
3. On the Service Type Selection page of the AI&MLOps Platform creation, enter the information required to create the service and select detailed options.
   Cluster deployment

   Select the Deploy to a new cluster option.
4. On the Service Information Input page of AI&MLOps Platform creation, enter the information required to create a service and select detailed options.

   • In the Service Information Input area, enter or view the information required to create a service.

     Category	Required	Detailed description
     Service name	Required	Enter AI&MLOps Platform name AI&MLOps Platform name cannot be duplicated within a project
     Storage Class	Required	Storage Class is automatically registered
     Installation node information	Lookup	View the node information of the selected Kubernetes Engine
     Admin Email Address	Required	Enter the email address of the administrator (Admin) to be used for login.
     password	Required	Enter the password to use for login
     Confirm Password	Required	Re-enter password to prevent password errors

     Table. AI&MLOps Platform Service Information Input Items

   • Kubernetes Engine Information Input Enter or select the required information in this area.

     Category	Required	Detailed description
     Cluster name	Required	Cluster name must start with an English letter and may use English letters, numbers, and special characters (-) Enter within 3 to 30 characters
     Control plane settings > Kubernetes version	Required	Select Kubernetes version
     Control Area Settings > Control Area Logging	Selection	Select whether to enable control plane logging Audit/Event logs from the cluster control plane can be viewed in Cloud Monitoring’s log analysis Log storage up to 1 GB for all services within the account is provided for free, and logs exceeding 1 GB are deleted sequentially For more details, see Cloud Monitoring > Log Analysis
     Network Settings	Required	Network connection settings for the node pool VPC: Select a pre‑created VPC Subnet: Select a standard Subnet to use from the subnets of the chosen VPC Security Group: Click the Search button, then select a Security Group in the Select Security Group popup Load Balancer: Provides the type:LoadBalancer feature in a Kubernetes Service object Select a load balancer on the same network Select whether to use Cannot be changed after configuration
     File Storage Settings	Required	Select the file storage volume to use in the cluster Default Volume (NFS): Select File Storage using the Search button The default Volume file storage provides only the NFS format

     Table. Kubernetes Engine Service Information Input Items

   • Enter or select the required information in the Node Pool Information Input area.

     Category	Required status	Detailed description
     Node pool configuration	Required	Select node pool information * Items marked with an asterisk are required fields and must be entered For the AI&MLOps Platform, image size may continuously increase depending on usage, so setting Block Storage to at least 200 GB enables smooth system configuration

     Table. AI&MLOps Platform Service Information Input Items
     Reference

     • A Windows OS node pool can be created only when an additional storage (CIFS) volume is in use in the cluster.
     • Volume encryption for node pool Block Storage can only be set at initial creation.
       • Enabling encryption may cause performance degradation in some features.
     • Only when you have selected the node pool auto‑scaling or shrinking feature can you input node count, minimum node count, maximum node count.

   • In the Additional Information Input area, enter or select the required information.

     Category	Required	Detailed description
     tag	Selection	Select tags to add to the AI&MLOps Platform Click ‘Add Tag’ to create a new tag or add an existing tag You can register up to 50 tags The newly added tags will be applied after the service creation is completed

     Table. AI&MLOps Platform Service Information Input Items

Cluster specifications

To use the AI&MLOps Platform, you need a Kubernetes Engine to install the AI&MLOps Platform. You can select an existing Kubernetes Engine, or you can create a Kubernetes Engine together when creating the AI&MLOps Platform.

The specifications of the Kubernetes cluster required for installation are as follows.

• Node pool resource size (composed of 2 or more nodes)

  • AI&MLOps Platform: vCPU 32, Memory 128G or more
  • Kubeflow Mini: vCPU 24, Memory 96G or more

• Kubernetes version

  • AI&MLOps Platform v1.9.1 (k8s v1.30)
  • Kubeflow Mini v1.9.1 (k8s v1.30)

2.2.2 - Kubeflow Usage Guide

Below, we guide you on how to use Kubeflow after creating it.

Add Kubeflow User

Below is a guide on how to use Kubeflow after it has been created.

Kubeflow only creates the account of the single Admin User entered on the initial installation screen.

When using the Kubeflow Dashboard, to add users other than the initial user, you must modify the settings of Dex (the authentication integration component of Kubeflow).

• Dex is deployed in the auth namespace, and its configuration is stored in a configmap named dex.

The following is an example of Dex configuration.

apiVersion: v1
kind: ConfigMap
metadata:
  name: dex
  namespace: auth
data:
  config.yaml: |
    issuer: http://dex.auth.svc.cluster.local:5556/dex
    storage:
      type: kubernetes
      config:
        inCluster: true
    web:
      http: 0.0.0.0:5556
    logger:
      level: "debug"
      format: text
    oauth2:
      skipApprovalScreen: true
    enablePasswordDB: true
    staticPasswords:
    - email: admin@kubeflow.org
      hash: $2y$10$Yb9WVbn8pzVSM6fBgKdFae1Bh6Z.XTihi7bNu3sB6/h5bt1JuUOgq
      username: admin
      userID: 9cb67307-fd6d-4441-9b59-52acd78f4c9e
    staticClients:
    - id: kubeflow-oidc-authservice
      redirectURIs: ["/login/oidc"]
      name: 'Dex Login Application'
      secret: pUBnBOY80SnXgjibTYM9ZWNzY2xreNGQok    

apiVersion: v1
kind: ConfigMap
metadata:
  name: dex
  namespace: auth
data:
  config.yaml: |
    issuer: http://dex.auth.svc.cluster.local:5556/dex
    storage:
      type: kubernetes
      config:
        inCluster: true
    web:
      http: 0.0.0.0:5556
    logger:
      level: "debug"
      format: text
    oauth2:
      skipApprovalScreen: true
    enablePasswordDB: true
    staticPasswords:
    - email: admin@kubeflow.org
      hash: $2y$10$Yb9WVbn8pzVSM6fBgKdFae1Bh6Z.XTihi7bNu3sB6/h5bt1JuUOgq
      username: admin
      userID: 9cb67307-fd6d-4441-9b59-52acd78f4c9e
    staticClients:
    - id: kubeflow-oidc-authservice
      redirectURIs: ["/login/oidc"]
      name: 'Dex Login Application'
      secret: pUBnBOY80SnXgjibTYM9ZWNzY2xreNGQok    

When the enablePasswordDB value in the configuration is true, Dex stores the list of users defined in staticPasswords from the configmap into its internal storage when the service starts. Therefore, by adding new user entries composed of email, hash, username, and userID to staticPasswords, you can freely add users beyond the initial ones and use the Kubeflow service.

The attribute values for adding a user can be defined as follows.

From a node where you can use kubectl, use the following command to enter the edit screen of dex configmap.

kubectl edit configmap dex -n auth

kubectl edit configmap dex -n auth

staticPasswords:
    - email: admin@kubeflow.org
      hash: $2y$10$Yb9WVbn8pzVSM6fBgKdFae1Bh6Z.XTihi7bNu3sB6/h5bt1JuUOgq
      username: admin
      userID: 9cb67307-fd6d-4441-9b59-52acd78f4c9e
    - email: sds@samsung.com
      hash: $2y$12$0g5.y86jnrt0v6In5NRCZ.YVuvrAUQ6j/RJYO3rV.kNulaDALOKfq
      username: sds
      userID: 8961d517-3498-4148-90c9-7e442ee91154

staticPasswords:
    - email: admin@kubeflow.org
      hash: $2y$10$Yb9WVbn8pzVSM6fBgKdFae1Bh6Z.XTihi7bNu3sB6/h5bt1JuUOgq
      username: admin
      userID: 9cb67307-fd6d-4441-9b59-52acd78f4c9e
    - email: sds@samsung.com
      hash: $2y$12$0g5.y86jnrt0v6In5NRCZ.YVuvrAUQ6j/RJYO3rV.kNulaDALOKfq
      username: sds
      userID: 8961d517-3498-4148-90c9-7e442ee91154

Since the staticPasswords value in the configmap is applied when the Dex service starts, restart the Dex service using the following command.

kubectl rollout restart deployment dex -n auth

kubectl rollout restart deployment dex -n auth

Attempt to log in using new user information.

Verify that after successful login, it transitions to the screen for creating a new Namespace(profile).

The above content was written with reference to the official Kubeflow site. For more details, see Kubeflow Profiles.

How to use Custom Image in Kubeflow Jupyter Notebook

To use a custom image in the Kubeflow Notebook Controller that manages the Notebook life cycle of Kubeflow, you must meet several requirements.

Kubeflow assumes that Jupyter will start automatically when a Notebook image is run. Therefore, you need to set the default command to start Jupyter in the container image.

The following is an example of what should be included in a Dockerfile.

ENV NB_PREFIX

CMD ["sh","-c", "jupyter notebook --notebook-dir=/home/${NB_USER} --ip=0.0.0.0 --no-browser --allow-root --port=8888 --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.allow_origin='*' --NotebookApp.base_url=${NB_PREFIX}"]

ENV NB_PREFIX

CMD ["sh","-c", "jupyter notebook --notebook-dir=/home/${NB_USER} --ip=0.0.0.0 --no-browser --allow-root --port=8888 --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.allow_origin='*' --NotebookApp.base_url=${NB_PREFIX}"]

The above items are explained as follows.

You can create a Custom Image by referring to the Dockerfile that builds the tesorflow notebook image.

• https://github.com/kubeflow/kubeflow/blob/v1.2.0/components/tensorflow-notebook-image/Dockerfile Please refer to it.

1. On the Notebook Servers page, click the +NEW SERVER button.

   

2. If you have created a Custom Image, check Custom Image on the Kubeflow Notebook Server screen and enter the Custom Image address to create a new Notebook Server.

   

2.3 - API Reference

2.4 - CLI Reference

2.5 - Release Note

AI&MLOps Platform