How to Train Really Large Models on Many GPUs?
📰 Lilian Weng's Blog
Training large neural networks requires parallelism and memory-saving designs to overcome GPU memory limitations
Action Steps
- Understand data parallelism to split data across multiple GPUs
- Implement model parallelism to split models across multiple GPUs
- Apply pipeline parallelism to split computation across multiple GPUs
- Use tensor parallelism to split tensor operations across multiple GPUs
Who Needs to Know This
AI engineers and researchers can benefit from understanding parallelism techniques to train large models, while software engineers can implement these techniques in practice
Key Insight
💡 Parallelism is necessary to train large neural networks due to GPU memory limitations
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💡 Train large neural networks with parallelism and memory-saving designs!
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