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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Ollama's architecture and key scaling factors
- Typical bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU usage
- Considerations for memory and bandwidth
- Managing resource constraints at the container level
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Operationalizing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Developing autoscaling policies for Ollama
- Techniques for batch inference to improve throughput
- Balancing latency against throughput
Latency Optimization
- Analyzing inference performance
- Employing caching strategies and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Connecting Prometheus for metrics collection
- Creating dashboards using Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-effective GPU allocation
- Comparing cloud versus on-premises deployment
- Strategies for sustainable scaling
Conclusion and Next Steps
Requirements
- Proficiency in Linux system administration
- Solid understanding of containerization and orchestration
- Experience with deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
