Phase 1: Foundations
1.1 Tensor Operations
- Master
torch.tensorAPI 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.autogradfor 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.nnmodule 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.datafor 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
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
Phase 3: Performance Optimization
3.1 Compilation and Export
- Master
torch.compilefor performance gains - Use
torch.exportfor 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
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
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
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.funcfor functional transformations - Apply
torch.fxfor 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
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