Deep Learning at Scale

Spis treści książki

• Preface
  • Why Scaling Matters
  • Who This Book Is For
  • How This Book Is Organized
    • Introduction
    • Part I: Foundational Concepts of Deep Learning
    • Part II: Distributed Training
    • Part III: Extreme Scaling
  • What You Need to Use This Book
  • Setting Up Your Environment for Hands-on Exercises
  • Using Code Examples
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. What Nature and History Have Taught Us About Scale
  • The Philosophy of Scaling
    • The General Law of Scaling
    • History of Scaling Law
  • Scalable Systems
    • Nature as a Scalable System
    • Our Visual System: A Biological Inspiration
  • Artificial Intelligence: The Evolution of Learnable Systems
    • It Takes Four to Tango
      • The hardware
      • The data
      • The software
      • The (deep learning) algorithms
    • Evolving Deep Learning Trends
      • General evolution of deep learning
      • Evolution in specialized domains
        • Math and compute
        • Protein folding
        • Simulated world
  • Scale in the Context of Deep Learning
    • Six Development Considerations
      • Well-defined problem
      • Domain knowledge (a.k.a. the constraints)
      • Ground truth
      • Model development
      • Deployment
      • Feedback
    • Scaling Considerations
      • Questions to ask before scaling
      • Characteristics of scalable systems
        • Reliability
        • Availability
        • Adaptability
        • Performance
      • Considerations of scalable systems
        • Avoiding single points of failure
        • Designing for high availability
        • Scaling paradigms
        • Coordination and communication
        • Caching and intermittent storage
        • Process state
        • Graceful recovery and checkpointing
        • Maintainability and observability
      • Scaling effectively
  • Summary
• I. Foundational Concepts of Deep Learning
• 2. Deep Learning
  • The Role of Data in Deep Learning
  • Data Flow in Deep Learning
  • Hands-On Exercise #1: Implementing Minimalistic Deep Learning
    • Developing the Model
      • Model input data and pipeline
      • Model
      • Training loop
        • Loss
        • Metrics
    • The Embedded/Latent Space
    • A Word of Caution
    • The Learning Rate and Loss Landscape
    • Scaling Consideration
    • Profiling
  • Hands-On Exercise #2: Getting Complex with PyTorch
    • Model Input Data and Pipeline
    • Model
    • Auxiliary Utilities
      • Callbacks
      • Loggers
      • Profilers
    • Putting It All Together
  • Computation Graphs
  • Inference
  • Summary
• 3. The Computational Side of Deep Learning
  • The Higgs Boson of the Digital World
    • Floating-Point Numbers: The Faux Continuous Numbers
      • Floating-point encoding
      • Floating-point standards
    • Units of Data Measurement
    • Data Storage Formats: The Trade-off of Latency and Throughput
  • Computer Architecture
    • The Birth of the Electromechanical Engine
    • Memory and Persistence
      • Virtual memory
      • Input/output
      • Memory and Moores law
    • Computation and Memory Combined
  • The Scaling Laws of Electronics
  • Scaling Out Computation with Parallelization
    • Threads Versus Processes: The Unit of Parallelization
      • Simultaneous multithreading
      • Scenario walkthrough: A web crawler to curate a links dataset
    • Hardware-Optimized Libraries for Acceleration
    • Parallel Computer Architectures: Flynns and Duncans Taxonomies
  • Accelerated Computing
    • Popular Accelerated Devices for Deep Learning
      • Graphics processing units (GPUs)
        • GPU microarchitecture
    • CUDA
      • NVIDIAs dominance: The competition landscape
      • Application-specific integrated circuits (ASICs)
        • Tensor Processing Units (TPUs)
        • Intelligence Processing Units (IPUs)
      • Field programmable gate arrays (FPGAs)
      • Wafer Scale Engines (WSEs)
    • Accelerator Benchmarking
  • Summary
• 4. Putting It All Together: Efficient Deep Learning
  • Hands-On Exercise #1: GPT-2
    • Exercise Objectives
    • Model Architecture
      • Key contributors to scale
        • Transformer attention block
        • Unsupervised training
        • Zero-shot learning
        • Parameter scale
    • Implementation
      • model.py
      • dataset.py
      • app.py
    • Running the Example
    • Experiment Tracking
    • Measuring to Understand the Limitations and Scale Out
      • Running on a CPU
      • Running on a GPU
    • Transitioning from Language to Vision
  • Hands-On Exercise #2: Vision Model with Convolution
    • Model Architecture
      • Key contributors to scale in the scene parsing exercise
        • Scaling with convolutions
        • Scaling with EfficientNet
      • Implementation
    • Running the Example
    • Observations
  • Graph Compilation Using PyTorch 2.0
    • New Components of PyTorch 2.0
    • Graph Execution in PyTorch 2.0
      • Graph acquisition
      • Graph lowering
      • Graph compilation
  • Modeling Techniques to Scale Training on a Single Device
    • Graph Compilation
    • Reduced- and Mixed-Precision Training
      • Mixed precision
      • The effect of precision on gradients
        • Gradient scaling
        • Gradient clipping
        • 8-bit optimizers and quantization
      • A mixed-precision algorithm
    • Memory Tricks for Efficiency
      • Memory layout
      • Feature compression
      • Meta and fake tensors
    • Optimizer Efficiencies
      • Stochastic gradient descent (SGD)
      • Gradient accumulation
      • Gradient checkpointing
      • Patch Gradient Descent
      • Learning rate and weight decay
    • Model Input Pipeline Tricks
    • Writing Custom Kernels in PyTorch 2.0 with Triton
  • Summary
• II. Distributed Training
• 5. Distributed Systems and Communications
  • Distributed Systems
    • The Eight Fallacies of Distributed Computing
    • The Consistency, Availability, and Partition Tolerance (CAP) Theorem
    • The Scaling Law of Distributed Systems
    • Types of Distributed Systems
      • Centralized
      • Decentralized
  • Communication in Distributed Systems
    • Communication Paradigm
    • Communication Patterns
      • Basic communication patterns
      • Collective communication patterns
    • Communication Technologies
      • RPC
    • MPI
      • NCCL
      • Communication technology summary
    • Communication Initialization: Rendezvous
    • Hands-On Exercise
  • Scaling Compute Capacity
    • Infrastructure Setup Options
      • Private cloud (on-premise/DIY data centers)
      • Public cloud
      • Hybrid cloud
      • Multicloud
      • Federation
    • Provisioning of Accelerated Devices
    • Workload Management
      • Slurm
      • Kubernetes
      • Ray
        • Distributed memory layer
        • Asynchronous model
      • Amazon SageMaker
      • Google Vertex AI
  • Deep Learning Infrastructure Review
    • Overview of Leading Deep Learning Clusters
    • Similarities Between Todays Most Powerful Systems
  • Summary
• 6. Theoretical Foundations of Distributed Deep Learning
  • Distributed Deep Learning
    • Centralized DDL
      • Parameter server configurations
      • Subtypes of centralized DDL
        • Synchronous centralized DDL
        • Asynchronous centralized DDL
    • Decentralized DDL
      • Limiting divergence
      • Subtypes of decentralized DDL
        • Synchronous decentralized DDL
        • Asynchronous decentralized DDL
  • Dimensions of Scaling Distributed Deep Learning
    • Partitioning Dimensions of Distributed Deep Learning
    • Types of Distributed Deep Learning Techniques
      • Ensembling
      • Data parallelism
      • Model parallelism
      • Pipeline parallelism
      • Tensor parallelism
      • Hybrid parallelism
      • Federation/collaborative learning
    • Choosing a Scaling Technique
  • Measuring Scale
    • End-to-End Metrics and Benchmarks
      • Time to convergence
      • Cost to train
      • Multilevel benchmarks
    • Measuring Incrementally in a Reproducible Environment
  • Summary
• 7. Data Parallelism
  • Data Partitioning
    • Implications of Data Sampling Strategies
    • Working with Remote Datasets
  • Introduction to Data Parallel Techniques
    • Hands-On Exercise #1: Centralized Parameter Server Using RCP
      • Setup
      • Observations
        • Inspecting involved processes
        • Inspecting connections
        • Communication patterns
      • Discussion
    • Hands-On Exercise #2: Centralized Gradient-Partitioned Joint Worker/Server Distributed Training
      • Setup
      • Observations
        • Communication patterns
      • Discussion
    • Hands-On Exercise #3: Decentralized Asynchronous Distributed Training
      • Setup
      • Observations
        • Communication patterns
      • Discussion
  • Centralized Synchronous Data Parallel Strategies
    • Data Parallel (DP)
    • Distributed Data Parallel (DDP)
      • Devil in the details
      • Distributed Data Parallel 2 (DDP2)
    • Zero Redundancy OptimizerPowered Data Parallelism (ZeRO-DP)
    • Fault-Tolerant Training
    • Hands-On Exercise #4: Scene Parsing with DDP
      • Setup
      • Observations
        • Baseline
        • Multi-GPU training
        • Multinode
        • Mixed-precision training
    • Hands-On Exercise #5: Distributed Sharded DDP (ZeRO)
      • Setup
        • Runtime configuration
      • Observations
      • Discussion
  • Building Efficient Pipelines
    • Dataset Format
    • Local Versus Remote
    • Staging
    • Threads Versus Processes: Scaling Your Pipelines
    • Memory Tricks
    • Data Augmentations: CPU Versus GPU
    • JIT Acceleration
    • Hands-On Exercise #6: Pipeline Efficiency with FFCV
      • Setup
        • Runtime configuration
      • Observations
  • Summary
• 8. Scaling Beyond Data Parallelism: Model, Pipeline, Tensor, and Hybrid Parallelism
  • Questions to Ask Before Scaling Vertically
  • Theoretical Foundations of Vertical Scaling
    • Revisiting the Dimensions of Scaling
      • Implementing tensor parallelism
      • Implementing model parallelism
      • Choosing a scaling dimension
    • Operators Perspective of Parallelism Dimensions
    • Data Flow and Communications in Vertical Scaling
      • Tensor parallelism
      • Model parallelism
      • Pipeline parallelism: An evolution of model parallelism
        • GPipe
        • PipeDream
      • Hybrid parallelism
        • 2D hybrid parallelism
        • 3D hybrid parallelism
  • Basic Building Blocks for Scaling Beyond DP
    • PyTorch Primitives for Vertical Scaling
      • Device mesh: Mapping model architecture to physical devices
      • Distributed tensors: Tensors with sharding and replication
        • Sharding and replication examples
        • Partial tensors
      • Logical tensors: Representation without materialization
        • Meta tensors
        • Fake tensors
    • Working with Larger Models
    • Distributed Checkpointing: Saving the Partitioned Model
  • Summary
• 9. Gaining Practical Expertise with Scaling Across All Dimensions
  • Hands-On Exercises: Model, Tensor, Pipeline, and Hybrid Parallelism
    • The Dataset
    • Hands-On Exercise #1: Baseline DeepFM
      • Training
      • Observations
    • Hands-On Exercise #2: Model Parallel DeepFM
      • Implementation details
      • Observations
    • Hands-On Exercise #3: Pipeline Parallel DeepFM
      • Implementation details
      • Observations
    • Hands-On Exercise #4: Pipeline Parallel DeepFM with RPC
      • Implementation details
      • Observations
    • Hands-On Exercise #5: Tensor Parallel DeepFM
      • Implementation details
      • Observations
    • Hands-On Exercise #6: Hybrid Parallel DeepFM
      • Implementation details
      • Observations
  • Tools and Libraries for Vertical Scaling
    • OneFlow
    • FairScale
    • DeepSpeed
    • FSDP
    • Overview and Comparison
    • Hands-On Exercise #7: Automatic Vertical Scaling with DeepSpeed
    • Observations
  • Summary
• III. Extreme Scaling
• 10. Data-Centric Scaling
  • The Seven Vs of Data Through a Deep Learning Lens
  • The Scaling Law of Data
  • Data Quality
    • Validity
    • Variety
      • Handling too much variety
        • Heuristic-based pruning
        • Algorithmic outlier pruning
        • Hands-on exercise #1: Outlier detection
        • Scaling outlier detection
      • Handling too-low variety
        • Data augmentation
        • Advanced data augmentation
        • Automated augmentation
        • Synthetic data generation
      • Handling imbalance
        • Sampling
        • Hands-on exercise #2: Handling imbalance in a multilabel dataset
        • Loss tricks
    • Veracity
      • Reasons for error in labels
      • Approaches to labeling
      • Techniques to increase veracity/decrease noise
        • Using heuristics to identify noise
        • Using inter-label information, such as ontology
        • Continuous feedback
        • Handling disagreements from multiple annotators
        • Identifying noisy samples by loss gradients
        • Hands-on exercise #3: Loss tricks to find noisy samples
        • Using confident learning
        • Summary of veracity tactics
    • Value and Volume
      • Core principles driving value
      • Volume reduction via compression and pruning
      • Volume reduction via dimensionality reduction
      • Volume reduction via approximation
      • Volume reduction via distillation
      • Value via regularization
  • The Data Engine and Continual Learning
    • Volatility
    • Velocity
  • Summary
• 11. Scaling Experiments: Effective Planning and Management
  • Model Development Is Iterative
  • Planning for Experiments and Execution
    • Simplify the Complex
    • Fast Iteration for Fast Feedback
    • Decoupled Iterations
    • Feasibility Testing
    • Developing and Scaling a Minimal Viable Solution
    • Setting Up for Iterative Execution
  • Techniques to Scale Your Experiments
    • Accelerating Model Convergence
      • Using transfer learning
        • Retraining
        • Fine tuning
        • Pretraining
      • Knowledge distillation
    • Accelerating Learning Via Optimization and Automation
      • Hyperparameter optimization
      • AutoML
        • Neural architecture search
        • Model validation
      • Simulating optimization behavior with Daydream
    • Accelerating Learning by Increasing Expertise
      • Continuous learning
      • Learning to learn via meta-learning
      • Curriculum learning
      • Mixture of experts
    • Learning with Scarce Supervision
      • Self-supervised learning
      • Contrastive learning
  • Hands-On Exercises
    • Hands-On Exercise #1: Transfer Learning
    • Hands-On Exercise #2: Hyperparameter Optimization
    • Hands-On Exercise #3: Knowledge Distillation
    • Hands-On Exercise #4: Mixture of Experts
      • Mock MoE
      • DeepSpeed-MoE
    • Hands-On Exercise #5: Contrastive Learning
    • Hands-On Exercise #6: Meta-Learning
  • Summary
• 12. Efficient Fine-Tuning of Large Models
  • Review of Fine-Tuning Techniques
    • Standard Fine Tuning
    • Meta-Learning (Zero-/Few-Shot Learning)
    • Adapter-Based Fine Tuning
    • Low-Rank Tuning
  • LoRAParameter-Efficient Fine Tuning
  • Quantized LoRA (QLoRA)
  • Hands-on Exercise: QLoRA-Based Fine Tuning
    • Implementation Details
    • Inference
    • Exercise Summary
  • Summary
• 13. Foundation Models
  • What Are Foundation Models?
  • The Evolution of Foundation Models
  • Challenges Involved in Developing Foundation Models
    • Measurement Complexity
    • Deployment Challenges
    • Propagation of Defects to All Downstream Models
    • Legal and Ethical Considerations
    • Ensuring Consistency and Coherency
  • Multimodal Large Language Models
    • Projection
    • Gated Cross-Attention
    • Query-Based Encoding
    • Further Exploration
  • Summary
• Index

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