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Workload Assets | Run:ai Documentation

• Home
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• Getting Started
  • Overview
  • What's New
    • What's New in Version 2.22
    • Hotfixes for Version 2.22
  • Installation
    • Control Plane System Requirements
    • Preparations
    • Network Requirements
    • Install the Control Plane
    • Cluster System Requirements
    • Install Using Helm
    • Customized Installation
    • Upgrade
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• Infrastructure setup
  • Authentication and Authorization
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    • Users
    • SSO
      • Set Up SSO with SAML
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    • Roles
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    • Cluster Authentication
  • Advanced Setup
    • Node Roles
    • Advanced Control Plane Configurations
    • Advanced Cluster Configurations
    • Container Access
      • External Access to Containers
      • User Identity in Containers
    • Service Mesh
    • Integrations
      • Interworking with Karpenter
  • Infrastructure Procedures
    • NVIDIA Run:ai at Scale
    • Monitoring and Maintenance
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    • Clusters
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    • Backup and Restore
    • Secure Your Cluster
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• Platform management
  • Manage AI Initiatives
    • Adapting AI Initiatives to Your Organization
    • Managing Your Organization
      • Projects
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    • Managing Your Resources
      • Nodes
      • Configuring NVIDIA MIG Profiles
      • Using GB200 NVL72 and Multi-Node NVLink Domains
      • Node Pools
  • Scheduling and Resource Optimization
    • Scheduling
      • The NVIDIA Run:ai Scheduler: Concepts and Principles
      • How the Scheduler Works
      • Using the Scheduler with Third-Party Workloads
      • Workload Priority Control
      • Quick Starts
        • Over Quota, Fairness and Preemption
    • Resource Optimization
      • GPU Fractions
      • Dynamic GPU Fractions
      • Optimize Performance with Node Level Scheduler
      • GPU Time-Slicing
      • GPU Memory Swap
      • CPU Compute and Memory Allocation
      • Quick Starts
        • Launching Workloads with GPU Fractions
        • Launching Workloads with Dynamic GPU Fractions
        • Launching Workloads with GPU Memory Swap
  • Policies
    • Policies and Rules
    • Workload Policies
    • Policy YAML Examples
    • Policy YAML Reference
    • Scheduling Rules
  • Monitor Performance and Health
    • Before You Start
    • Monitor Workloads by Category
    • Metrics and Telemetry
    • Reports
• Workloads in NVIDIA Run:ai
  • Introduction to Workloads
  • NVIDIA Run:ai Workload Types
  • Workloads
  • Workload Assets
    • Workload Assets
    • Environments
    • Data Sources
    • Data Volumes
    • Compute Resources
    • Credentials
  • Workload Templates
    • Workspace Templates
      • Workspace Templates (Legacy)
    • Training Templates
      • Standard Training Templates
      • Distributed Training Templates
  • Experiment Using Workspaces
    • Running Workspaces
    • Quick Starts
      • Running Jupyter Notebooks Using Workspaces
  • Train Models Using Training
    • Standard Training
      • Train Models Using a Standard Training Workload
      • Quick Starts
        • Run Your First Standard Training
    • Distributed Training
      • Train Models Using a Distributed Training Workload
      • Quick Starts
        • Run Your First Distributed Training
    • Best Practices: Checkpointing Preemptible Training Workloads
  • Deploy Models Using Inference
    • Deploy a Custom Inference Workload
    • Deploy Inference Workloads from Hugging Face
    • Deploy Inference Workloads with NVIDIA NIM
    • Deploy NVIDIA Cloud Functions (NVCF) in NVIDIA Run:ai
    • Quick Starts
      • Run Your First Custom Inference Workload
• Settings
  • General Settings
  • User Settings
    • Email Notifications
    • User Applications
    • User Credentials
• Reference
  • CLI Reference
    • Install and Configure CLI
    • Add NVIDIA Run:ai Authorization to Kubeconfig
    • Auto Update CLI Mechanism
    • CLI Commands Reference
      • runai cluster
      • runai config
      • runai department
      • runai inference
      • runai jax
      • runai kubeconfig
      • runai login
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      • runai node
      • runai nodepool
      • runai project
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      • runai pytorch
      • runai report
      • runai tensorflow
      • runai training
      • runai upgrade
      • runai version
      • runai whoami
      • runai workload
      • runai workspace
      • runai xgboost
    • CLI Commands Examples
    • Administrator CLI
  • API Reference
    • How to Authenticate to the API
    • NVIDIA Run:ai REST API
      • Configuring Slack Notifications
  • API Python Client Reference
    • API Python Client (runapy)
    • Install and Configure the Client
• Support Policy
  • Product Support Policy
  • Product Version Life Cycle

1. Workloads in NVIDIA Run:ai

Last updated 12 days ago

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