Grandient Accumulation

10/13/2024

• Gradient Accumulation
• Gradient Accumulation is a technique used when training neural networks to support larger batch sizes given limited available GPU memory.
• In Gradient Accumulation, instead of updating the model parameters after processing each individual batch of training data, the gradients are accumulated over multiple batches before updating.
  This means that rather than immediately incorporating the information from a single batch into the model's parameters, the gradients are summed up over multiple batches
• This approach reduces the amount of memory needed for training and can help stabilize the training process, particularly when working with the batch size is too large to fit into the memory


• Implementation