![]() Note: when using mixed precision with a small model and a large batch size, there will be some memory savings but with a The methods and tools covered in this guide can be classified based on the effect they have on the training process: Method/tool Hyperparameter tuning, you should determine which batch size yields the best results and then optimize resources accordingly. ![]() Just because one can use a large batch size, does not necessarily mean they should. However, if the preferred batch size fits into memory, there’s no reason to apply memory-optimizing techniques because they can The memory optimization techniques, such as gradient accumulation, can help. If the desired batch size exceeds the limits of the GPU memory, This is generally achieved by utilizing the GPUĪs much as possible and thus filling GPU memory to its limit. Maximizing the throughput (samples/second) leads to lower training cost. When training large models, there are two aspects that should be considered at the same time: If you have access to a machine with multiple GPUs, these approaches are still valid, plus you can leverage additional methods outlined in the multi-GPU section.
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