Deka GPU Documentations
  • Starter Guide
    • Introduction
    • Sign Up
    • Choose a Package
    • Top Up
    • Create a Virtual Machine
    • Download kubeconfig
    • Create a Deka LLM
    • Create a Deka Notebook
    • Conclusion
  • Service Portal
    • Introduction
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    • Account Setting
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    • List Roles
    • Broadcast
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      • AI Security AI Infrastructure Layer
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      • Create Ticket
      • Detail Ticket
    • Billing
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    • Balance
      • Project Type: SME
        • GPU Merdeka
        • Choose Package
        • Top-Up
      • Project Type: Enterprise
      • History Balance
        • Balance
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      • Custom Resource Definition
  • Deka GPU
    • Deka GPU: Kubernetes
      • Introduction
      • GPU Type
      • Dashboard
        • Check Status Kubernetes
        • Download Kube Config
        • Access Console
      • Workloads
        • Pods
          • Create New Pod
          • Access Console
          • Configuration Pod
          • Delete Pod
          • How to Create a New Pod use CLI
        • Deployments
          • Create New Deployment
          • Configuring Deployment
          • Delete of a Deployment
          • How to Create a New Deployment use CLI
        • DaemonSets
          • Create a New DaemonSet
          • Configuring a DaemonSet
          • Delete DaemonSet
      • Services
      • Storages
        • Storage Class
        • Persistent Volume Claims
          • Create a New Persistent Volume Claim
          • How to Create a New Persistent Volume Claim use CLI
    • Deka GPU: VMs
      • Operating System
      • GPU Type
      • Machine Type
      • Namespace Type
      • Storage Class
      • How to Create a Virtual Machine on Service Portal
      • How to Manually Create a Virtual Machine
        • Download Kube Config
        • Running Kube Config
        • Configuration file dv.yaml
        • Configuration file vm.yaml
        • Configuration file svc.yaml
      • Feature Overview of Virtual Machine
        • Detail a Virtual Machine
        • Open Console
        • Turn Off a VM Instance
        • Turn On a VM Instance
        • Restart a Virtual Machine
        • How to Access Console
        • Show YAML File
      • Delete a Virtual Machine
    • Deka GPU: Registry
      • Create Registry
      • Quota
      • Detail Registry
        • Summary
        • Repository
        • Logs
        • Labels
        • Tag Immutability
        • Member
        • Resize Storage Registry
      • Delete Registry
    • Deka GPU: Security
      • Deka Guard
        • Introduction
        • Create Guard to Deny All Ingress
        • Create Guard to Allow Ingress
        • Create Guard to Allow Ingress with port
        • Create Guard to Allow Ingress with IP/CIDR
        • Create Guard to Deny All Egress
        • Create Guard to Allow Egress
        • Create guard to Allow Egress with Port
        • Create Guard to Allow Egress with IP/CIDR
    • Deka GPU: Service
      • Ingress
        • Install Ingress nginx
        • Install Cert Manager
        • Create Cluster Issuer
        • Create Ingress with TLS
    • Deka GPU: Autoscaling
      • Basic Autoscaling
    • Deka GPU: Network
      • Deka VPC
    • Deka GPU: MLOps
      • Introduction
      • Notebook
      • Tensorboards
      • Volumes
      • Endpoints
        • Create Endpoint
        • Delete Endpoint
      • Experiments (AutoML)
        • Create Experiments (AutoML)
        • Create Experiments (AutoML) using Python SDK
        • Get Experiments Results
      • Experiments (KFP)
        • Create Experiment
      • Pipelines
      • Runs
        • Create Run
        • Delete Active Run
      • Recurring Runs
        • Create Recurring Run
        • Delete Recurring Runs
        • Home
      • Artifacts
      • Executions
      • Manage Contributors
  • Deka LLM
    • Introduction
    • Check Project Type
    • Create a New LLM
    • Detail Deka LLM
      • Overview Tab
      • Keys Tab
        • Create a New Key
        • Detail a Key
        • Edit a Key
        • Get a Secret Key
        • Delete a Key
      • Usage Tab
      • Top Up Coin
    • API Deka LLM
      • Model Management
      • Completions
      • Embedding
    • Delete Deka LLM
    • How to Create Simple Prompt with Deka LLM
      • Create Deka LLM
      • Get URL API Deka LLM
      • Get Secret Key
      • Access API Deka LLM using Postman
      • Get Model
      • Post Chat Completions
  • Deka Notebook
    • Introduction
    • Namespace Type
    • Create a New Notebook
    • Detail Deka Notebook
      • Configuration Deka Notebook
      • Start Deka Notebook Service
      • Stop Deka Notebook Service
      • Get Token
      • Login Deka Notebook
      • Logout Deka Notebook
    • Delete Deka Notebook
  • Reference
    • How to use kubeconfig on Linux
    • How to use kubeconfig on Windows
    • Kubernetes Commands for Enhancing Security
    • How to add GPU in Kubernetes
    • How to Add GPU in VM
      • Download kubeconfig
      • Install kubectl
      • Add GPU
      • Install Driver NVIDIA
    • RAPIDS
      • How to Setup RAPIDS
      • How to make Custom Image
    • How to push image with Docker
    • Deployment LLaMA 3.1 70B with VLLM on Kubernetes
      • Getting the Hugging Face API Key
      • Requesting Access to the LLaMA Model
      • Connect Kubernetes on Computer
      • Create Namespace
      • Create PersistentVolumeClaim (PVC)
      • Create Secret for Hugging Face Token
      • Create Deployment
      • Create Service
      • Verify Deployment
      • Accessing the LLaMA Service
      • Troubleshooting
    • How to Get an API Key on NGC
    • Deployment LLM with Deka GPU + NIM
    • How to Upload and Download on FTP Web
  • Troubleshooting
    • Reinstall Driver NVIDIA on Linux
    • NVIDIA Driver Not Detected After Upgrade Kernel
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Artifacts

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Last updated 2 months ago

The Artifacts menu is used for tracking experimental results, further analysis, or used as input for subsequent steps in the pipeline. On the Service Portal Deka MLOps menu page, select the Artifacts menu.

In the Artifacts menu, there are two submenus.

  • Default

The Default submenu displays all artifacts that are not categorized under any specific group. This includes individual files or outputs generated by various components of your machine learning workflows. It provides a straightforward view of all artifacts, making it easy to access and manage them without any additional organization.

  • Grouped

The Grouped submenu organizes artifacts into specific categories or groups based on certain criteria, such as the experiment or pipeline they belong to. This allows users to easily navigate through related artifacts, facilitating better management and retrieval of files associated with particular projects or workflows. Grouping helps in maintaining a structured overview, especially when dealing with a large number of artifacts.

Artifacts
Artifacts
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