Efficient Training on Multiple GPUs
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Multi-GPU setups are effective for accelerating training and fitting large models in memory that otherwise wouldn’t fit on a single GPU. It relies on parallelizing the workload across GPUs. There are several types of parallelism such as data parallelism, tensor parallelism, pipeline parallelism, and model parallelism. Each type of parallelism splits the workload differently, whether it’s the data or the model. This guide will discuss the various parallelism methods, combining them, and choosing an appropriate strategy for your setup. For more details about distributed training, refer to the…
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