Distributed Deep Learning Training

8.

Frameworks and Libraries

8.1.

PyTorch Distributed Training

8.1.1.

DistributedDataParallel

8.1.1.1.

Initialization and Setup

8.1.1.2.

Process Group Management

8.1.1.3.

Gradient Synchronization

8.1.1.4.

Performance Optimization

8.1.2.

Fully Sharded Data Parallel

8.1.2.1.

Sharding Strategies

8.1.2.2.

Memory Efficiency

8.1.2.3.

Communication Optimization

8.1.3.

8.1.3.1.

Remote Procedure Calls

8.1.3.2.

Distributed Autograd

8.1.3.3.

Parameter Server Implementation

8.2.

TensorFlow Distributed Strategies

8.2.1.

MirroredStrategy

8.2.1.1.

Single-Machine Multi-GPU

8.2.1.2.

Synchronous Training

8.2.2.

MultiWorkerMirroredStrategy

8.2.2.1.

Multi-Node Training

8.2.2.2.

Fault Tolerance Features

8.2.3.

ParameterServerStrategy

8.2.3.1.

Asynchronous Training

8.2.3.2.

Worker-Server Architecture

8.2.4.

8.2.4.1.

Tensor Processing Unit Integration

8.3.

Specialized Libraries

8.3.1.

8.3.1.1.

All-Reduce Implementation

8.3.1.2.

Framework Integration

8.3.1.3.

Performance Optimization

8.3.2.

8.3.2.1.

8.3.2.1.1.

Stage 1 Implementation

8.3.2.1.2.

Stage 2 Implementation

8.3.2.1.3.

Stage 3 Implementation

8.3.2.2.

8.3.2.3.

Pipeline Parallelism

8.3.2.4.

Model Compression

8.3.3.

8.3.3.1.

Large Language Model Training

8.3.3.2.

Tensor Parallelism Implementation

8.3.3.3.

Pipeline Parallelism Integration

8.3.4.

8.3.4.1.

Sharded Data Parallel

8.3.4.2.

Pipeline Parallelism

8.3.4.3.

Activation Checkpointing

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9. Performance Optimization and Tuning