Gradient Checkpointing: Trading Compute for Memory
📰 Medium · Deep Learning
Learn how gradient checkpointing trades compute for memory in deep learning, enabling larger models with limited memory
Action Steps
- Apply gradient checkpointing to your deep learning model to reduce memory usage
- Run a forward pass and store only the necessary intermediate activations
- Re-run parts of the forward pass during backprop to compute gradients
- Configure your model to use gradient checkpointing, balancing compute and memory usage
- Test the impact of gradient checkpointing on your model's performance and memory usage
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this technique to optimize model training, especially when working with large models and limited computational resources
Key Insight
💡 Gradient checkpointing reduces memory usage by re-running parts of the forward pass during backprop, enabling larger models to be trained with limited memory
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🚀 Optimize your deep learning models with gradient checkpointing! Trade compute for memory and train larger models 🤖
Full Article
Gradient checkpointing re-runs parts of the forward pass during backprop instead of keeping all intermediate activation tensors in memory… Continue reading on Data Science Collective »
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