Transformer deep learning architecture

13.

Implementation Considerations

13.1.

Framework and Library Support

13.1.1.

PyTorch Implementation

13.1.2.

TensorFlow Implementation

13.1.3.

Hugging Face Transformers

13.1.4.

JAX/Flax Implementation

13.2.

Hardware Requirements

13.2.1.

GPU Memory Considerations

13.2.2.

Multi-GPU Training

13.2.3.

TPU Optimization

13.2.4.

13.3.

Optimization Techniques

13.3.1.

Mixed Precision Training

13.3.2.

Gradient Accumulation

13.3.3.

Model Parallelism

13.3.4.

Data Parallelism

13.4.

Deployment Strategies

13.4.1.

Model Compression

13.4.2.

13.4.3.

13.4.4.

Knowledge Distillation

13.4.5.

13.4.6.

TensorRT Optimization

13.5.

Monitoring and Debugging

13.5.1.

Training Metrics

13.5.2.

Attention Visualization Tools

13.5.3.

Memory Profiling

13.5.4.

Performance Optimization

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12. Applications and Adaptations

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1. Foundational Concepts and Predecessors