Transformer deep learning architecture

  1. Implementation Considerations
    1. Framework and Library Support
      1. PyTorch Implementation
        1. TensorFlow Implementation
          1. Hugging Face Transformers
            1. JAX/Flax Implementation
            2. Hardware Requirements
              1. GPU Memory Considerations
                1. Multi-GPU Training
                  1. TPU Optimization
                    1. CPU Inference
                    2. Optimization Techniques
                      1. Mixed Precision Training
                        1. Gradient Accumulation
                          1. Model Parallelism
                            1. Data Parallelism
                            2. Deployment Strategies
                              1. Model Compression
                                1. Quantization
                                  1. Pruning
                                    1. Knowledge Distillation
                                      1. ONNX Export
                                        1. TensorRT Optimization
                                        2. Monitoring and Debugging
                                          1. Training Metrics
                                            1. Attention Visualization Tools
                                              1. Memory Profiling
                                                1. Performance Optimization

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                                              12. Applications and Adaptations

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                                              1. Foundational Concepts and Predecessors