PyTorch Library

  1. Advanced PyTorch Features
    1. GPU Computing and Acceleration
      1. CUDA Integration
        1. Device Management
          1. Memory Allocation
            1. Stream Processing
            2. Multi-GPU Training
              1. Data Parallelism
                1. nn.DataParallel
                  1. Simple Multi-GPU Usage
                  2. Distributed Data Parallelism
                    1. nn.parallel.DistributedDataParallel
                      1. Process Group Management
                        1. Gradient Synchronization
                      2. Memory Management
                        1. GPU Memory Profiling
                          1. Out-of-memory Handling
                            1. Memory Pool Management
                            2. Performance Optimization
                              1. Kernel Fusion
                                1. Memory Access Patterns
                                  1. Asynchronous Execution
                                2. Distributed Training
                                  1. Distributed Computing Concepts
                                    1. Data Parallelism vs Model Parallelism
                                      1. Communication Patterns
                                        1. Synchronization Strategies
                                        2. Process Group Setup
                                          1. Backend Selection
                                            1. Process Initialization
                                              1. Communication Topology
                                              2. Distributed Data Loading
                                                1. Data Sharding
                                                  1. Sampler Configuration
                                                    1. Load Balancing
                                                    2. Fault Tolerance
                                                      1. Checkpoint Synchronization
                                                        1. Process Recovery
                                                          1. Dynamic Scaling
                                                        2. Model Optimization Techniques
                                                          1. Quantization
                                                            1. Post-training Quantization
                                                              1. Quantization-aware Training
                                                                1. Dynamic Quantization
                                                                2. Pruning
                                                                  1. Structured Pruning
                                                                    1. Unstructured Pruning
                                                                      1. Magnitude-based Pruning
                                                                      2. Knowledge Distillation
                                                                        1. Teacher-Student Framework
                                                                          1. Soft Target Training
                                                                            1. Feature Matching
                                                                            2. Neural Architecture Search
                                                                              1. Differentiable NAS
                                                                                1. Evolutionary Approaches
                                                                                  1. Efficiency Optimization
                                                                                2. Debugging and Profiling
                                                                                  1. Debugging Tools
                                                                                    1. Gradient Checking
                                                                                      1. Tensor Inspection
                                                                                        1. Graph Visualization
                                                                                        2. Performance Profiling
                                                                                          1. CPU Profiling
                                                                                            1. GPU Profiling
                                                                                              1. Memory Profiling
                                                                                              2. Hook System
                                                                                                1. Forward Hooks
                                                                                                  1. Backward Hooks
                                                                                                    1. Activation Monitoring
                                                                                                    2. Error Diagnosis
                                                                                                      1. Common Error Patterns
                                                                                                        1. Debugging Strategies
                                                                                                          1. Performance Bottlenecks

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                                                                                                      8. Model Persistence and Deployment

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                                                                                                      10. PyTorch Ecosystem Integration