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Deep Learning
Distributed Deep Learning Training
1. Introduction to Distributed Deep Learning
2. Data Parallelism
3. Model Parallelism
4. Hybrid Parallelism Strategies
5. Communication in Distributed Training
6. Communication Optimization
7. System and Hardware Considerations
8. Frameworks and Libraries
9. Performance Optimization and Tuning
10. Practical Implementation
11. Advanced Topics and Future Directions
Frameworks and Libraries
PyTorch Distributed Training
DistributedDataParallel
Initialization and Setup
Process Group Management
Gradient Synchronization
Performance Optimization
Fully Sharded Data Parallel
Sharding Strategies
Memory Efficiency
Communication Optimization
RPC Framework
Remote Procedure Calls
Distributed Autograd
Parameter Server Implementation
TensorFlow Distributed Strategies
MirroredStrategy
Single-Machine Multi-GPU
Synchronous Training
MultiWorkerMirroredStrategy
Multi-Node Training
Fault Tolerance Features
ParameterServerStrategy
Asynchronous Training
Worker-Server Architecture
TPUStrategy
Tensor Processing Unit Integration
Specialized Libraries
Horovod
All-Reduce Implementation
Framework Integration
Performance Optimization
DeepSpeed
ZeRO Optimizer
Stage 1 Implementation
Stage 2 Implementation
Stage 3 Implementation
ZeRO-Offload
Pipeline Parallelism
Model Compression
Megatron-LM
Large Language Model Training
Tensor Parallelism Implementation
Pipeline Parallelism Integration
FairScale
Sharded Data Parallel
Pipeline Parallelism
Activation Checkpointing
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9. Performance Optimization and Tuning