Distributed Deep Learning Training

Distributed Deep Learning Training is a method used to accelerate the computationally intensive process of training large-scale deep learning models by distributing the workload across multiple processors, often GPUs, spread across multiple machines. This approach is critical for handling massive datasets and increasingly complex model architectures, which would be impractical or take an prohibitive amount of time to train on a single device. The primary strategies employed are data parallelism, where each processor trains on a different subset of the data with a replica of the model, and model parallelism, where different parts of a very large model are placed on different processors, allowing for the training of models that exceed the memory capacity of a single machine.

1. Introduction to Distributed Deep Learning
   1. Overview of Deep Learning Workloads
      1. Growth of Data and Model Sizes
      2. Computational Demands of Modern Architectures
      3. Memory Requirements Evolution
      4. Training Time Challenges
   2. The Need for Distributed Training
      1. Single-Device Limitations
         1. Memory Constraints
         2. Computational Bottlenecks
         3. Training Time Limitations
      2. Big Data Challenges
         1. Dataset Size Growth
         2. Storage Constraints
         3. Data Loading Bottlenecks
         4. Preprocessing Overhead
      3. Big Model Challenges
         1. Parameter Count Explosion
         2. Memory Requirements
         3. Computational Complexity
   3. Core Concepts of Parallelism
      1. Task Parallelism
         1. Independent Task Execution
         2. Hyperparameter Search Applications
         3. Ensemble Training
      2. Data Parallelism
         1. Model Replication Strategy
         2. Data Batch Partitioning
         3. Gradient Synchronization
      3. Model Parallelism
         1. Model Partitioning Approaches
         2. Inter-Device Communication Requirements
         3. Memory Distribution Benefits
   4. Distributed Training Architectures
      1. Parameter Server Architecture
         1. Centralized Parameter Server
         2. Decentralized Parameter Server
         3. Worker-Server Communication Patterns
      2. All-Reduce Architecture
         1. Peer-to-Peer Communication
         2. Collective Operations
         3. Scalability Characteristics