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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Examining Ollama’s architecture and the specific considerations required for scaling.
- Identifying typical bottlenecks encountered in multi-user deployment scenarios.
- Reviewing best practices for ensuring infrastructure readiness.
Resource Allocation and GPU Optimization
- Developing strategies for efficient utilization of CPU and GPU resources.
- Assessing key factors related to memory and bandwidth constraints.
- Defining resource limits at the container level.
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker.
- Executing Ollama workloads within Kubernetes clusters.
- Implementing load balancing and service discovery mechanisms.
Autoscaling and Batching
- Designing effective autoscaling policies for Ollama.
- Applying batch inference techniques to enhance overall throughput.
- Managing the balance between latency and throughput requirements.
Latency Optimization
- Profiling performance metrics during inference tasks.
- Utilizing caching strategies and model warm-up procedures.
- Minimizing overhead related to I/O operations and communication.
Monitoring and Observability
- Integrating Prometheus for comprehensive metrics collection.
- Creating visual dashboards using Grafana.
- Establishing alerting mechanisms and incident response protocols for Ollama infrastructure.
Cost Management and Scaling Strategies
- Implementing cost-aware approaches to GPU allocation.
- Evaluating the trade-offs between cloud and on-premises deployment options.
- Adopting strategies for long-term, sustainable scaling.
Summary and Next Steps
Requirements
- Proficiency in Linux system administration.
- Solid understanding of containerization and orchestration concepts.
- Familiarity with the processes involved in deploying machine learning models.
Audience
- DevOps engineers.
- Machine learning infrastructure teams.
- Site reliability engineers.
