![]() ![]() Tensor parallelism-break up the math for a single operation such as a matrix multiplication to be split across GPUs.Pipeline parallelism-run different layers of the model on different GPUs.Data parallelism-run different subsets of the batch on different GPUs. ![]() Various parallelism techniques slice this training process across different dimensions, including: As the training iterates over batches of data, the model evolves to produce increasingly accurate outputs. The average gradient for the batch, the parameters, and some per-parameter optimization state is passed to an optimization algorithm, such as Adam, which computes the next iteration's parameters (which should have slightly better performance on your data) and new per-parameter optimization state. ![]() Then another pass proceeds backward through the layers, propagating how much each parameter affects the final output by computing a gradient with respect to each parameter. In every iteration, we do a pass forward through a model's layers to compute an output for each training example in a batch of data. Training a neural network is an iterative process. Each color refers to one layer and dashed lines separate different GPUs. An illustration of various parallelism strategies on a three-layer model. ![]()
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