Next-Level Training Strategies
Implement production-grade techniques that maximize model performance and efficiency
Model Optimization Techniques
Weight Pruning
Remove redundant connections to reduce model size
Quantization
Convert 32-bit weights to 8-bit or lower precision
Knowledge Distillation
Train smaller models to mimic larger teacher models
Distributed Training Patterns
Data Parallelism
Distribute input data across multiple devices simultaneously
Use cases: Large-scale image classification, NLP pretraining
torch.nn.DataParallel(model)
Implementation Guide →
Model Parallelism
Distribute model components across different devices
Advanced Tools & Frameworks
NVIDIA Apex
Mixed precision training and distributed utilities
DeepSpeed
Deep learning optimization for large models
Hugging Face Accelerate
Seamless distributed training implementation