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Inference Server Usage Guide

Complete guide to deploying models, configuring servers, and troubleshooting common issues.

Step-by-Step Deployment​

Complete guide to deploying a model:

Step 1: Prepare Your Model

  • Ensure your model is trained and has checkpoints
  • Verify model files are accessible
  • Check model compatibility with inference server

Step 2: Access Inference Server

  • Navigate to the Inference Server section
  • Click "Deploy Model" button
  • Review available models

Step 3: Configure Deployment

  • Select the model to deploy
  • Set instance count (start with 1-2 instances)
  • Configure GPU allocation
  • Set resource limits if needed

Step 4: Deploy

  • Review all configuration settings
  • Click "Deploy" to start deployment
  • Monitor deployment progress
  • Wait for deployment to complete

Step 5: Verify Deployment

  • Check that model status is "Active"
  • Test prediction endpoint
  • Verify response times are acceptable
  • Monitor initial performance

Step 6: Scale (if needed)

  • Monitor request rate and latency
  • Add more instances if needed
  • Configure auto-scaling if available
  • Optimize resource allocation

Best practices for inference server configuration:

Small Workloads:

  • Instances: 1-2 instances
  • GPUs: 1 GPU per instance
  • Use Case: Development, testing, low-traffic production

Medium Workloads:

  • Instances: 2-4 instances
  • GPUs: 1-2 GPUs per instance
  • Use Case: Moderate traffic production workloads

Large Workloads:

  • Instances: 4+ instances
  • GPUs: 2+ GPUs per instance
  • Use Case: High-traffic production, mission-critical applications

Auto-Scaling:

  • Enable auto-scaling for variable workloads
  • Set minimum and maximum instance counts
  • Configure scaling based on request rate or latency
  • Monitor scaling behavior

Resource Allocation:

  • Allocate GPUs based on model size and expected load
  • Reserve resources for peak usage
  • Monitor and adjust based on actual usage
  • Consider cost vs. performance trade-offs

Common Troubleshooting​

Issue: Deployment Fails

  • Symptom: Model deployment fails to start
  • Possible Causes:
    • Insufficient GPU resources
    • Model files not accessible
    • Invalid model configuration
  • Solutions:
    • Check available GPU resources
    • Verify model files are accessible
    • Review model configuration
    • Check deployment logs

Issue: High Latency

  • Symptom: Response times are too high
  • Possible Causes:
    • Insufficient instances
    • Model too large for allocated resources
    • Network issues
  • Solutions:
    • Add more instances
    • Increase GPU allocation
    • Optimize model architecture
    • Check network connectivity

Issue: Low Throughput

  • Symptom: Not processing enough requests per second
  • Possible Causes:
    • Insufficient instances
    • Resource constraints
    • Inefficient batching
  • Solutions:
    • Scale up instances
    • Increase resource allocation
    • Optimize request batching
    • Review model optimization

Issue: Instance Failures

  • Symptom: Instances failing or restarting
  • Possible Causes:
    • Resource exhaustion
    • Model errors
    • Infrastructure issues
  • Solutions:
    • Check resource usage
    • Review model logs
    • Verify infrastructure health
    • Contact support if persistent

Issue: Resource Exhaustion

  • Symptom: Cannot deploy new models or scale
  • Possible Causes:
    • All GPUs allocated
    • Cluster capacity reached
  • Solutions:
    • Undeploy unused models
    • Reduce instance counts
    • Add more cluster capacity
    • Optimize resource allocation

Best Practices​

Deployment Best Practices​

  • Start Small: Start with minimal instances and scale up
  • Monitor Closely: Monitor performance during initial deployment
  • Test Thoroughly: Test models before production deployment
  • Version Control: Keep track of model versions
  • Rollback Plan: Have a plan to rollback if issues occur

Performance Optimization​

  • Model Optimization: Optimize models for inference
  • Batch Processing: Use batch processing when possible
  • Caching: Implement prediction caching for repeated queries
  • Load Balancing: Distribute requests evenly across instances
  • Resource Right-Sizing: Allocate appropriate resources

Monitoring and Alerts​

  • Set Up Alerts: Configure alerts for critical metrics
  • Monitor Trends: Track performance trends over time
  • Capacity Planning: Plan capacity based on usage patterns
  • Regular Reviews: Regularly review and optimize configurations

Next Steps​

  • Learn about Clusters that provide compute infrastructure
  • Explore Training to understand model training
  • Check Datasets to see how data is prepared