Sync Docs
Sync HomeLaunch GradientBook Demo
  • Sync Gradient
    • The Gradient Platform
      • How Does it Work?
    • Discover Quickstart
    • Add Workspace
      • Create Sync API Key
      • Add Databricks Workspace
        • AWS Databricks Setup
          • EventBridge Setup
        • Azure Databricks Setup
      • Webhook Setup
    • Project Setup
      • Import Jobs to Projects
      • Verify and Run Jobs
      • Generate and Apply Recommendation
    • Advanced Use Cases
      • Install the Sync-CLI
      • Manual Workspace Setup
        • AWS Instance Profile
      • Apache Airflow for Databricks
      • Gradient Terraform Integration
    • Project Settings
    • Account Settings
    • ROI Reporting
    • FAQ
  • Tutorials & Best Practices
    • Running Gradient in Production
      • Production Auto-Enabled
      • Optimization Windows
      • Development Clones
    • Demos
  • Developer Docs
    • Resources
    • Sync Python Library
    • Gradient CLI Walkthrough
  • Security
    • Privacy and Security Compliance
  • Trust Center
    • Portal
  • Product Announcements
    • Product Updates
  • Need Help?
    • Troubleshooting Guide
Powered by GitBook
On this page
  • Step 1: Clone your Production jobs into your DEV environment
  • Step 2: Onboard those DEV jobs onto Gradient
  • Step 3: Enable auto-apply
  • Step 4: Run your jobs 5-10 times to find an optimal point
  • Step 5: Review and select your ideal configuration

Was this helpful?

Export as PDF
  1. Tutorials & Best Practices
  2. Running Gradient in Production

Development Clones

PreviousOptimization WindowsNextDemos

Last updated 1 year ago

Was this helpful?

Step 1: Clone your Production jobs into your DEV environment

Clone your current Production jobs into a development environment, including the input data and any other dependencies.

Step 2: Onboard those DEV jobs onto Gradient

Onboard these development jobs into Gradient through the standard job import methods.

Step 3: Enable auto-apply

Be sure to enable auto-apply so the Gradient recommendations can be automatically applied during each iteration.

Step 4: Run your jobs 5-10 times to find an optimal point

Run each of yoru jobs up to 10 times to complete both learning and optimizing phases of the Gradient algorithm. Support scripts like the can be used to speed up this process.

Step 5: Review and select your ideal configuration

Once optimization is complete, review the configurations and select the one that matches your busines needs. Copy the cluster configurations to your production jobs.

auto-training notebook