Vectice Docs
API Reference (Latest)Vectice WebsiteStart Free Trial
Latest
Latest
  • 🏠Introduction
    • Vectice overview
      • Autolog
      • Next-Gen Autolog [BETA]
      • AskAI
      • Vectice for financial services
  • 🏁Quickstart
    • Getting started
    • Quickstart project
    • Tutorial project
    • FAQ
  • ▶️Demo Center
    • Feature videos
  • 📊Manage AI/ML projects
    • Organize workspaces
      • Create a workspace
      • Workspace Dashboard
    • Organize projects
      • Create a project
      • Project templates best practices
    • Invite colleagues
    • Define phase requirements
    • Collaborate with your team
  • 🚀Log and Manage Assets with Vectice API
    • API cheatsheets
      • Vectice Python API cheatsheet
      • Vectice R API cheatsheet
    • Connect to API
    • Log assets to Vectice
      • Autolog your assets
      • Log datasets
      • Log models
      • Log attachments and notes
      • Log code
      • Log a custom data source
      • Log assets using Vectice IDs
      • Log dataset structure and statistics
      • Log custom metadata in a table format
      • Log MLFLow runs
    • Retrieve assets from app
    • Manage your assets
    • Manage your iteration
    • Preserve your code and asset lineage
  • 🤝Create Model documentation and reports
    • Create model documentation with Vectice Reports
    • Streamline documentation with Macros
    • Auto-document Models and Datasets with AskAI Prompts
    • Document phase outcomes
  • 🗂️Admin Guides
    • Organization management
    • Workspace management
    • Teams management
    • User management
      • User roles and permissions
      • Update a user role in your organization
      • Activate and deactivate users
      • Reset a user's password
    • Manage report templates
  • 🔗Integrations
    • Integrations Overview
    • Integrate Vectice with your data platform
  • 💻IT & Security
    • IT & Security Overview
    • Secure Evaluation Environment Overview
    • Deployment
      • SaaS offering (Multi-Tenant SaaS)
      • Kubernetes self-hosted offering
        • General Architecture & Infrastructure
        • Kubernetes on GCP
          • Appendices
        • Kubernetes on AWS
          • Appendices
        • Kubernetes on Azure
          • Appendices
        • GCP Marketplace deployment
        • On premise
        • Configuration
      • Bring Your Own LLM Guide
    • Data privacy
    • User management
    • SSO management
      • Generic SAML integration
      • Okta SSO integration
    • Security
      • Data storage security
      • Network Security
        • HTTPS communication
        • Reverse proxy
        • CORS/CSRF
        • VPC segregation
      • Sessions
      • Secrets and certificates
      • Audit logs
      • SOC2
      • Security updates
      • Best practices
      • Business continuity
    • Monitoring
      • Installation guide
      • Customizing the deployments
    • Maintenance & upgrades
    • Integrating Vectice Securely
  • ⭐Glossary
    • Concepts
      • Workspaces
      • Projects
        • Setup a project
      • Phases
      • Iterations
        • Iterative development
      • Datasets
        • Dataset resources
        • Dataset properties
        • Dataset lineage and versions
      • Models
      • Reports
  • 🎯Release notes
    • Release notes
  • ↗️References
    • Vectice Python API Reference
    • Vectice R API Cheatsheet
    • Notebooks and code samples
    • Vectice website
Powered by GitBook
On this page
  • Retrieve dataset version metadata from app
  • Retrieve model version metadata from app

Was this helpful?

  1. Log and Manage Assets with Vectice API

Retrieve assets from app

PreviousLog MLFLow runsNextManage your assets

Last updated 11 months ago

Was this helpful?

Learn how to effortlessly retrieve your assets from the Vectice app using the Vectice API.

Retrieve dataset version metadata from app

After logging your datasets to Vectice, you can retrieve the dataset's metadata using the following code which will return a Dataset Version Representation.

A Dataset Version Representation shows information about a specific version of a dataset from the Vectice app. It makes it easier to get and read this information through the API.

A dataset version ID starts with 'DTV-XXX'. Retrieve the ID in the Vectice App, then use the ID with the following methods to get the dataset version:

connect.dataset_version('DTV-XXX') or connect.browse('DTV-XXX')

import vectice
connect = vectice.connect(
    api_token = 'your-api-key',        # Paste your api key
    host = 'https://app.vectice.com',  # Paste your host
)

# Retrieve a specific dataset version metadata
connect.dataset_version('DTV-XXXX')

For more information on return values and other associated methods, view the .

Retrieve model version metadata from app

After logging your models to Vectice, you can retrieve the model's metadata using the following code which will return a Model Version Representation.

A Model Version Representation shows information about a specific version of a model from the Vectice app. It makes it easier to get and read this information through the API.

A model version ID starts with 'MDV-XXX'. Retrieve the ID in the Vectice App, then use the ID with the following methods to get the model version:

connect.model_version('MDV-XXX') or connect.browse('MDV-XXX')

import vectice
connect = vectice.connect(
    api_token = 'your-api-key',        # Paste your api key
    host = 'https://app.vectice.com',  # Paste your host
)

# Retrieve a specific model version metadata
connect.model_version('MDV-XXXX')

For more information on return values and other associated methods, view the .

🚀
Dataset Version Representation API documentation
Model Version Representation API documentation