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Course Outline

Introduction to Scaling Ollama

  • Overview of Ollama’s architecture and key scaling factors
  • Identifying common bottlenecks in multi-user setups
  • Best practices for preparing your infrastructure

Resource Allocation and GPU Optimization

  • Strategies for efficient CPU and GPU utilization
  • Understanding memory usage and bandwidth requirements
  • Implementing resource constraints at the container level

Deployment Using Containers and Kubernetes

  • Packaging Ollama with Docker
  • Deploying Ollama within Kubernetes clusters
  • Managing load balancing and service discovery

Autoscaling and Batching

  • Formulating autoscaling policies for Ollama
  • Utilizing batch inference techniques to boost throughput
  • Understanding the trade-offs between latency and throughput

Latency Optimization

  • Analyzing inference performance
  • Employing caching methods and model pre-warming
  • Minimizing I/O and communication overhead

Monitoring and Observability

  • Integrating Prometheus for metric collection
  • Creating visual dashboards using Grafana
  • Establishing alerting mechanisms and incident response protocols for Ollama infrastructure

Cost Management and Scaling Strategies

  • Allocating GPU resources with cost awareness
  • Evaluating cloud versus on-premises deployment options
  • Adopting strategies for sustainable scaling

Summary and Next Steps

Requirements

  • Background in Linux system administration
  • Knowledge of containerization and orchestration principles
  • Experience with deploying machine learning models

Intended Audience

  • DevOps engineers
  • Teams specializing in ML infrastructure
  • Site reliability engineers
 21 Hours

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