Model Training Optimization: Techniques and Best Practices

Optimizing Model Training

Efficient training requires understanding optimization algorithms, hyperparameter tuning, and computational strategies.

Optimization Algorithms

SGD Variants

Vanilla SGD

Momentum

Nesterov momentum

Adaptive Methods

AdaGrad

RMSprop

Adam

AdamW

Modern Methods

LAMB

Lookahead

SAM (Sharpness-Aware)

Learning Rate Strategies

Schedules

Step decay

Exponential decay

Cosine annealing

Warmup + decay

Adaptive

Learning rate finder

Cyclical learning rates

One-cycle policy

Hyperparameter Tuning

Methods

Grid search

Random search

Bayesian optimization

Population-based training

Key Hyperparameters

Learning rate

Batch size

Architecture choices

Regularization strength

Training Efficiency

Mixed Precision

FP16 training

Faster computation

Less memory

Gradient Accumulation

Effective larger batches

Memory constraints

Distributed Training

Data parallelism

Model parallelism

Pipeline parallelism

Regularization

Dropout

Weight decay

Data augmentation

Early stopping

Label smoothing

Monitoring and Debugging

Loss curves

Gradient statistics

Activation distributions

Learning rate visualization

Conclusion

Optimizing training requires understanding both algorithmic and engineering aspects.