Overview
Training and inference are the two fundamental phases of machine learning workflows. Each phase has distinct computational characteristics and optimization opportunities.
Training
Training is the process of learning model parameters from data.
Key Components
- Forward Pass: Computing predictions from inputs
- Loss Computation: Measuring prediction quality
- Backward Pass: Computing gradients via backpropagation
- Parameter Update: Adjusting weights using optimizers
Training Optimizations
- Mixed Precision Training: Using FP16/BF16 for faster computation
- Gradient Checkpointing: Trading computation for memory
- Data Parallelism: Distributing batches across GPUs
- Model Parallelism: Splitting large models across devices
Inference
Inference is deploying trained models to make predictions on new data.
Inference Optimizations
- Model Quantization: Reducing precision (INT8, INT4)
- Operator Fusion: Combining multiple operations
- Batching: Processing multiple inputs together
- Caching: Reusing intermediate results (KV cache for transformers)
Performance Comparison
| Aspect | Training | Inference |
|---|---|---|
| Compute | Higher | Lower |
| Memory | Activations + Gradients | Activations only |
| Precision | FP32/FP16 | INT8/INT4 possible |
| Batch Size | Large | Variable |
Conclusion
Understanding both training and inference is essential for building efficient ML systems. Each phase requires different optimization strategies and system designs.