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
Knowledge Distillation for Diffusion Models
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- Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015 arXiv:1503.02531 DOI: 10.48550/arXiv.1503.02531 - Introduces the concept of knowledge distillation for transferring knowledge from a large teacher model to a smaller student model.
- Progressive Distillation for Fast Sampling of Diffusion Models, Tim Salimans, Jonathan Ho, 2022 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2202.00512 - Presents a method to progressively distill a diffusion model into a student that requires fewer sampling steps, significantly speeding up inference.
- Consistency Models, Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever, 2023 International Conference on Machine Learning (ICML) DOI: 10.48550/arXiv.2303.01469 - Proposes a new family of generative models that enable one-step generation by distilling the score function, effectively learning a direct mapping for fast sampling.
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