Why PyTorch?
PyTorch is my preferred deep learning framework for its intuitive API, dynamic computation graphs, and seamless path from research to production.
Key Features
Dynamic Computation Graphs
PyTorch's define-by-run approach offers:
- Intuitive debugging with Python debuggers
- Dynamic architectures (RNNs, attention)
- Easier experimentation and iteration
- Natural Python control flow
Ecosystem
Rich ecosystem of tools:
- TorchVision: Computer vision models and utilities
- TorchText: NLP tools and datasets
- TorchAudio: Audio processing
- PyTorch Lightning: High-level training framework
- TorchServe: Model serving
- TorchScript: Production deployment
Production Ready
Path to production:
- TorchScript for optimization
- ONNX export for interoperability
- Quantization for efficiency
- Mobile deployment support
My Experience
Research Projects
Weather Nowcasting
- Built U-Net architecture with attention mechanisms
- Custom loss functions for precipitation prediction
- Integrated Gradients for explainability
- Achieved 92% accuracy on 60-min forecasts
Computer Vision
- Fine-tuned models for astronomical object detection
- Transfer learning from pre-trained models
- Custom data augmentation pipelines
- Ensemble techniques for robustness
Production ML
Model Optimization
- TorchScript compilation for inference
- Dynamic quantization for CPU inference
- CUDA optimization for GPU serving
- Batch processing for throughput
MLOps Integration
- Training pipelines with PyTorch Lightning
- Experiment tracking with MLflow
- Model versioning and registry
- Automated testing and validation
Best Practices
Training
- Use DataLoader with num_workers
- Mixed precision training (torch.cuda.amp)
- Gradient clipping for stability
- Learning rate scheduling
- Early stopping and checkpointing
Model Development
- Modular architecture design
- Proper initialization
- Batch normalization and dropout
- Residual connections
- Attention mechanisms
Production Deployment
- TorchScript for performance
- Model quantization for efficiency
- Proper error handling
- Input validation
- Monitoring and logging
Advanced Techniques
- Distributed training (DDP)
- Gradient accumulation
- Custom autograd functions
- Pruning and compression
- Knowledge distillation
- Neural architecture search