Often it’s a multiple of 8, but it can be Input/output neuron counts that are of size 2^N. To achieve optimal performance, start by identifying the appropriate batch size. Techniques outlined in the multi-GPU section. All these approaches are still valid in a multi-GPU setup, plus you can leverage additional parallelism Convert your model to BetterTransformer to leverage PyTorch native attentionįinally, if all of the above is still not enough, even after switching to a server-grade GPU like A100, consider moving.Consider a model that uses Mixture of Experts (MoE).Look into building your own custom Docker container with efficient softare prebuilds.If these methods do not result in sufficient gains, you can explore the following options: Training your model with Trainer or writing a pure PyTorch loop, in which case you can configure these optimizations These techniques are available to you whether you are You can combine the above methods to get a cumulative effect. Large model and a small batch size, the memory use will be larger. 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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