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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Examining Ollama's architecture and scaling factors
- Identifying common bottlenecks in multi-user setups
- Establishing best practices for infrastructure readiness
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU utilization
- Evaluating memory and bandwidth requirements
- Managing container-level resource limits
Deployment via Containers and Kubernetes
- Containerizing Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Formulating autoscaling policies tailored for Ollama
- Applying batch inference methods to boost throughput
- Balancing latency against throughput
Latency Optimization
- Profiling inference performance
- Employing caching techniques and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metric collection
- Creating visual dashboards using Grafana
- Setting up alerts and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Implementing cost-conscious GPU allocation
- Considering cloud versus on-premises deployment
- Developing strategies for sustainable growth
Summary and Next Steps
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration
- Exposure to deploying machine learning models
Audience
- DevOps engineers
- ML infrastructure specialists
- Site Reliability Engineers
