Deep learning models are generally trained using the stochastic gradient descendent (SGD) algorithm. For each iteration of SGD, we will sample a mini-batch from the training set, feed it into the training model, calculate the gradient of the loss function of the observed values and the real values, and update the model parameters (or weights). As it is well known that the SGD iterations have to be executed sequentially, it is not possible to speed up the training process by parallelizing iterations. However, as processing one single iteration for a number of commonly used models like CIFAR10 or IMAGENET takes a long time, even using the most sophisticated GPU, we can still try to parallelize the feedforward computation as well as the gradient calculation within each iteration to speed up the model training process.
In practice, we will split the mini-batch of the training data into several parts, like 4, 8, 16, etc. (in this article, we will use the term sub-batch to refer to these split parts), and each training worker takes one sub-batch. Then the training workers do feedforward, gradient computation, and model updating using the sub-batches, respectively, just as in the monolithic training mode. After these steps, a process called model average is invoked, averaging the model parameters of all the workers participating in the training, so as to make the model parameters exactly the same when a new training iteration begins. Then the new round of the training iteration starts again from the data sampling and splitting step.
Read on for the high-level explanation, followed by some Python code working in TensorFlow.
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