📄 中文摘要
在深度学习中，利用多GPU进行训练可以显著提高模型的训练速度和效率。梯度累积和数据并行是两种常用的技术，以优化多GPU的使用。梯度累积允许在多个小批量上进行梯度计算，从而有效地模拟更大的批量大小，而不需要增加显存的消耗。数据并行则通过将数据划分到不同的GPU上并行处理，进一步加速训练过程。通过在PyTorch中实现这两种方法，用户能够更好地理解和应用这些技术，从而提升模型训练的性能和效率。
多GPU中的AI：梯度累积与数据并行
出处: AI in Multiple GPUs: Gradient Accumulation & Data Parallelism
发布: 2026年2月23日
📄 English Summary
AI in Multiple GPUs: Gradient Accumulation & Data Parallelism
Utilizing multiple GPUs for training in deep learning can significantly enhance the speed and efficiency of model training. Gradient accumulation and data parallelism are two commonly used techniques to optimize the use of multiple GPUs. Gradient accumulation allows for gradient computation over several smaller batches, effectively simulating a larger batch size without increasing memory consumption. Data parallelism accelerates the training process by distributing data across different GPUs for parallel processing. Implementing these two methods in PyTorch enables users to better understand and apply these techniques, thereby improving the performance and efficiency of model training.