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Inference Server Core Concepts

Understanding the fundamental concepts of Inference Servers is essential for effectively deploying and managing models.

Inference Server Architecture​

An Inference Server consists of:

Server Components:

  • Model Runtime: Environment for executing trained models
  • Request Handler: Processes incoming prediction requests
  • Load Balancer: Distributes requests across instances
  • Monitoring: Tracks performance and health metrics

Architecture Benefits:

  • High Availability: Redundant instances ensure continuous service
  • Auto-Scaling: Automatically adjust capacity based on demand
  • Resource Isolation: Isolate resources for different models
  • Performance Optimization: Optimized for inference workloads

Model Deployment​

Deploying a model to an Inference Server involves:

Deployment Process:

  1. Select Model: Choose a trained model from your training runs
  2. Configure Server: Set up server configuration and resources
  3. Deploy Model: Deploy the model to the inference server
  4. Verify Deployment: Confirm the model is serving correctly
  5. Monitor Performance: Track serving performance and metrics

Deployment Options:

  • Single Instance: Deploy model to a single server instance
  • Multiple Instances: Deploy multiple instances for high availability
  • Auto-Scaling: Configure automatic scaling based on demand
  • Resource Allocation: Allocate GPU resources per instance

Model Requirements:

  • Model must be trained and have checkpoints available
  • Model architecture must be compatible with inference server
  • Model files must be accessible from the server

Instances and Scalability​

Inference Servers support multiple instances for scalability:

Instance Management:

  • Instance Count: Number of server instances running
  • GPU Allocation: GPUs allocated per instance
  • Auto-Scaling: Automatic scaling based on demand
  • Load Distribution: Requests distributed across instances

Scaling Strategies:

  • Horizontal Scaling: Add more instances to increase capacity
  • Vertical Scaling: Increase resources per instance
  • Auto-Scaling: Automatically scale based on metrics
  • Manual Scaling: Manually adjust instance count

Scaling Triggers:

  • Request Rate: Scale based on incoming request rate
  • Latency: Scale if latency exceeds thresholds
  • Resource Utilization: Scale based on GPU/CPU usage
  • Queue Depth: Scale based on request queue depth

Resource Usage (GPU Allocation)​

Inference Servers manage GPU resources efficiently:

GPU Allocation:

  • Per Instance: Each instance can use one or more GPUs
  • Total GPUs: Total GPUs available across all servers
  • GPU Utilization: Track GPU usage per instance
  • Resource Limits: Set limits on GPU usage per model

Resource Management:

  • Allocation Strategy: How GPUs are allocated to instances
  • Resource Sharing: Share GPUs across multiple instances (future)
  • Resource Isolation: Isolate GPU resources per model
  • Resource Monitoring: Track GPU utilization and performance

GPU Metrics:

  • Total GPUs: Total GPUs available in the cluster
  • Allocated GPUs: GPUs currently allocated to inference servers
  • Available GPUs: GPUs available for new deployments
  • GPU Utilization: Average GPU utilization across servers

Next Steps​