Demystifying Gradient Accumulation: A Beginner’s Guide to Training Large Models on Small GPUs
📰 Medium · Deep Learning
Learn to train large models on small GPUs using gradient accumulation, a technique to overcome CUDA Out of Memory errors
Action Steps
- Identify the CUDA Out of Memory error in your model training process
- Understand the concept of gradient accumulation and its benefits
- Implement gradient accumulation in your model training code
- Configure the accumulation steps and batch size to optimize performance
- Test and evaluate the trained model's performance
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this technique to train larger models on limited GPU resources, improving their workflow efficiency
Key Insight
💡 Gradient accumulation allows you to train larger models on smaller GPUs by accumulating gradients from multiple mini-batches, reducing memory requirements
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Train large models on small GPUs with gradient accumulation!
Key Takeaways
Learn to train large models on small GPUs using gradient accumulation, a technique to overcome CUDA Out of Memory errors
Full Article
If you’ve ever tried to train a neural network and been greeted by the dreaded CUDA Out of Memory error, you’ve hit the physical limits of… Continue reading on Medium »
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