Term
Gradient Checkpointing
Definition
Gradient Checkpointing is a technique used in AI to optimize the training of models by saving memory. It recalculates intermediate results when needed instead of keeping all computations stored in memory throughout the training process.
Where you’ll find it
Gradient Checkpointing is typically employed in the training settings or optimization configurations of AI model development environments. It is more commonly used in high-memory-consumption models and may not be available on all AI platforms.
Common use cases
- Improving the efficiency of training deep learning models.
- Managing memory usage effectively when training complex AI models with limited resources.
- Allowing the training of larger models on hardware with memory constraints.
Things to watch out for
- Implementing Gradient Checkpointing requires careful planning as it can increase computational overhead due to the need for re-computation.
- It may not be suitable for all kinds of models; its effectiveness depends on the model architecture.
- Users must ensure their specific AI platform supports Gradient Checkpointing, as its availability can vary.
Related terms
- Memory Optimization
- Deep Learning
- Model Training
- Computational Overhead
- AI Platforms