Deploying Diffusion Models at Scale
Chapter 1: Scaling Challenges and Architectures
Computational Requirements of Diffusion Models
Latency and Throughput Considerations
Architectural Patterns for Generative AI Deployment
Synchronous vs. Asynchronous Processing
MLOps Principles for Diffusion Models
Chapter 2: Optimizing Diffusion Models for Inference
Inference Bottlenecks in Diffusion Processes
Model Quantization Techniques (INT8, FP16)
Knowledge Distillation for Diffusion Models
Sampler Optimization Strategies
Hardware Acceleration (GPUs, TPUs)
Compiler Optimization (TensorRT, OpenVINO)
Benchmarking Inference Performance
Hands-on Practical: Optimizing a Diffusion Model
Chapter 3: Infrastructure for Scalable Deployment
Containerizing Diffusion Models with Docker
GPU Resource Management in Containers
Orchestration with Kubernetes
Managing GPU Nodes in Kubernetes
Autoscaling Strategies for Inference Workloads
Serverless GPU Inference Options
Storage Considerations for Models and Data
Hands-on Practical: Deploying on Kubernetes
Chapter 4: Building Scalable Inference APIs
API Design Patterns for Generative Models
Handling Long-Running Generation Tasks
Request Batching Techniques
Implementing Request Queues
Rate Limiting and Throttling
Authentication and Authorization
API Versioning Strategies
Hands-on Practical: Building an Inference API
Chapter 5: Monitoring and Maintaining Deployed Models
Essential Metrics for Diffusion Model Deployment
Setting up Logging and Tracing
Monitoring Tools and Platforms
Detecting Performance Regressions
Monitoring Generation Quality
Cost Monitoring and Alerting
Model Retraining and Update Strategies
Hands-on Practical: Setting up Monitoring
Chapter 6: Advanced Deployment Techniques
Multi-Region and Global Deployment Strategies
Canary Releases and A/B Testing Models
Advanced Cost Optimization Strategies
Handling GPU Failures and Spot Instance Interruptions
Optimizing Data Transfer Costs
Cold Starts in Serverless and Container Environments
Load Balancing Strategies for Stateful/Long Tasks
Optimizing Data Transfer Costs
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- AWS Well-Architected Framework - Cost Optimization Pillar, Amazon Web Services, 2023 (Amazon Web Services) - Provides architectural guidance for cost efficiency across cloud resources, including strategies for optimizing data transfer, storage, and compute costs in AWS deployments.
- Amazon CloudFront Pricing, Amazon Web Services, 2023 - Details the pricing structure for Amazon CloudFront, illustrating how Content Delivery Networks can reduce data egress costs compared to direct cloud data transfer.
- Amazon S3 Pricing, Amazon Web Services, 2023 (Amazon Web Services) - Explains the cost components of Amazon S3 object storage, including charges related to data transfer in and out, which are important for managing model and output storage costs.
- Optimize your images, Google Developers, 2023 (Google) - Offers guidance on optimizing image formats and compression techniques, including modern formats like WebP and AVIF, to minimize file sizes for efficient data transfer.
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