Distributed Training Pitfall: The Hidden Dangers of Inadequate Gradient Accumulation
As machine learning engineers, we're well aware of the benefits of distributed training for scaling deep neural networks. However, there's a common pitfall that can lead to reduced model performance if not addressed: inadequate gradient accumulation.
What is Gradient Accumulation?
Gradient accumulation is the process of collecting gradients from multiple batches of data before updating the model's parameters. This is done to reduce communication overhead between devices and increase overall training efficiency.
The Problem with Fixed-Size Gradient Accumulation
When using fixed-size gradient accumulation, gradients are accumulated across a fixed number of batches, regardless of the batch size. This can lead to inconsistent gradient updates, causing the model to converge slowly or even diverge.
Inconsistent Gradient Updates
Consider a scenario where the batch size varies signi...
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