> ## Documentation Index > Fetch the complete documentation index at: https://newtorch.aboneda.com/llms.txt > Use this file to discover all available pages before exploring further. # Study Plan 1 **Objective:** Build comprehensive expertise in PyTorch and deep learning engineering, from fundamentals through production deployment. *** ## Phase 1: Foundations ### 1.1 Tensor Operations * Master `torch.tensor` API and core operations * Build custom tensor implementation to understand CUDA integration * Optimize tensor operations between CPU and GPU * Study memory management and performance optimization ### 1.2 Automatic Differentiation * Master `torch.autograd` for gradient computation * Build a custom autograd engine from scratch * Understand computational graphs and backpropagation * Optimize gradient computation for complex models ### 1.3 Neural Network Modules * Master `torch.nn` module architecture * Build custom neural network framework * Understand internal mathematics of layers * Implement various network architectures (feedforward, convolutional, recurrent) ### 1.4 Data Pipeline Engineering * Master `torch.utils.data` for efficient data loading * Design end-to-end ETL processes * Implement batching, sampling, and augmentation strategies * Optimize data pipelines for performance * Build real-world streaming data demos Outcome: Deep understanding of PyTorch internals, ability to implement custom components, and optimized data pipelines for real-world applications. *** ## Phase 2: Model Development ### 2.1 Architecture Design * Structure custom model architectures * Design and implement custom layers * Understand architectural patterns and best practices ### 2.2 Training Components * Build custom loss functions and evaluation metrics * Implement custom optimizers and learning rate schedulers * Design complete training and evaluation loops * Optimize training workflow for efficiency ### 2.3 Development Tools * Master debugging techniques * Use visualization and monitoring tools * Profile model performance * Implement advanced techniques: parameterization, pruning, distillation ### 2.4 Distributed Computing * Study parallel computing fundamentals * Master tools: DeepSpeed, Ray, FSDP * Implement data and model parallelism * Optimize multi-device training workflows ### 2.5 Device Management * Optimize performance across devices (CPU, GPU, MPS, XPU) * Understand device-specific optimizations * Implement efficient device allocation strategies Outcome: Ability to train complex models reliably and efficiently. *** ## Phase 3: Performance Optimization ### 3.1 Compilation and Export * Master `torch.compile` for performance gains * Use `torch.export` for model optimization * Understand JIT compilation and graph optimization ### 3.2 Quantization * Implement post-training quantization * Apply quantization-aware training * Optimize model size and inference speed ### 3.3 Distributed Training * Implement distributed data parallelism (DDP) * Use fully sharded data parallelism (FSDP) * Configure multi-node training ### 3.4 Hardware Acceleration * Optimize CUDA kernels * Implement mixed precision training (AMP) * Leverage specialized hardware (TPU, XPU, MPS) ### 3.5 Memory Optimization * Apply gradient checkpointing * Implement activation checkpointing * Optimize batch sizes and memory usage ### 3.6 Profiling and Benchmarking * Use PyTorch Profiler for bottleneck identification * Benchmark model performance * Optimize code based on profiling results ### 3.7 Advanced Transformations * Apply code transforms and graph optimizations * Implement fusion patterns * Understand compiler internals Outcome: Performance engineering mindset. *** ## Phase 4: Domain Specialization ### 4.1 Natural Language Processing * Implement transformer architectures from scratch * Build models: BERT, GPT, T5 * Optimize for text generation and understanding ### 4.2 Computer Vision * Implement CNN architectures (ResNet, EfficientNet, Vision Transformers) * Build object detection and segmentation models * Optimize image processing pipelines ### 4.3 Audio and Speech * Implement speech recognition models * Build audio generation systems * Process and augment audio data ### 4.4 Diffusion and Generative Models * Implement diffusion models (DDPM, DDIM) * Build GANs and VAEs * Optimize generation quality and speed ### 4.5 Reinforcement Learning * Implement policy gradient methods * Build Q-learning and actor-critic models * Design RL training environments ### 4.6 Graph Neural Networks * Implement GNN architectures (GCN, GAT, GraphSAGE) * Process graph-structured data * Apply GNNs to real-world problems ### 4.7 Time Series Analysis * Build forecasting models (LSTM, Transformer-based) * Implement anomaly detection * Handle temporal dependencies ### 4.8 Video and Multimodal Models * Process video data efficiently * Build multimodal fusion architectures * Implement vision-language models ### 4.9 Recommender Systems * Build collaborative and content-based systems * Implement neural collaborative filtering * Optimize recommendation pipelines ### 4.10 Anomaly Detection * Implement unsupervised anomaly detection * Build autoencoders and isolation forests * Apply to real-world detection tasks Outcome: Domain-level competence. *** ## Phase 5: Production Deployment ### 5.1 Model Export and Serialization * Master TorchScript for model serialization * Export models to ONNX format * Use TorchServe for model serving * Optimize deployment workflows ### 5.2 Inference Optimization * Implement batching strategies * Optimize serving latency and throughput * Use model compression techniques * Deploy on edge devices ### 5.3 Experiment Tracking * Use MLflow, Weights & Biases, or TensorBoard * Track metrics, hyperparameters, and artifacts * Organize and compare experiments ### 5.4 MLOps and CI/CD * Build ML pipelines with Kubeflow or MLflow * Implement continuous training and deployment * Monitor model drift and performance * Automate testing and validation ### 5.5 Ecosystem Integration * Master Hugging Face Transformers and Accelerate * Use PyTorch Lightning for structured training * Leverage Triton Inference Server * Integrate with cloud platforms (AWS, GCP, Azure) ### 5.6 Production Monitoring * Implement logging and alerting * Monitor model performance in production * Debug production issues * Handle model retraining triggers Outcome: Production-ready AI engineer. *** ## Phase 6: Advanced Topics ### 6.1 Transfer Learning and Fine-tuning * Implement domain adaptation techniques * Fine-tune pre-trained models efficiently * Use parameter-efficient methods (LoRA, Adapters) ### 6.2 Custom CUDA Extensions * Write custom CUDA kernels * Implement efficient custom operators * Integrate C++/CUDA extensions with PyTorch ### 6.3 Advanced Parallelism * Master Megatron-LM for large models * Use DeepSpeed ZeRO optimization * Implement FairScale strategies ### 6.4 Research and Development Tools * Use `torch.func` for functional transformations * Apply `torch.fx` for symbolic tracing * Understand compiler internals (`torch._dynamo`, `torch._inductor`) * Implement AOT compilation strategies ### 6.5 Extending PyTorch * Contribute to PyTorch core * Build custom PyTorch extensions * Understand PyTorch internals and architecture ### 6.6 Advanced Features * Work with complex numbers and complex-valued models * Implement sparse, Complex tensor operations * Low-level memory control * Optimize for specialized data types ### 6.7 Low-Level Internals * Understand dispatcher and operator registration * Study memory allocators and caching * Explore autograd engine implementation Outcome: Systems-level AI engineer. *** ## Phase 7: API Mastery and Continuous Learning ### 7.1 Comprehensive API Review * Review and master all PyTorch modules * Stay updated with new API releases * Understand deprecations and migrations ### 7.2 Best Practices * Follow PyTorch coding conventions * Write efficient and maintainable code * Document and test implementations ### 7.3 Community Engagement * Contribute to open-source projects * Participate in PyTorch forums and discussions * Share knowledge through blogs and tutorials