What is gradient accumulation step?

Gradient accumulation means running a configured number of steps without updating the model variables while accumulating the gradients of those steps and then using the accumulated gradients to compute the variable updates.

What is accumulate grad batches?

It becomes difficult to fit such networks in the GPU memory. Simply speaking, gradient accumulation means that we will use a small batch size but save the gradients and update network weights once every couple of batches.

What is gradient descent batch size?

The batch size is a hyperparameter of gradient descent that controls the number of training samples to work through before the model’s internal parameters are updated. The number of epochs is a hyperparameter of gradient descent that controls the number of complete passes through the training dataset.

What is the gradient in deep learning?

A gradient simply measures the change in all weights with regard to the change in error. You can also think of a gradient as the slope of a function. The higher the gradient, the steeper the slope and the faster a model can learn. But if the slope is zero, the model stops learning.

What is gradient clipping?

Gradient clipping involves forcing the gradient values (element-wise) to a specific minimum or maximum value if the gradient exceeded an expected range. Together, these methods are often simply referred to as “gradient clipping.”

What is gradient checkpointing?

Gradient Checkpointing is a method used for reducing the memory footprint when training deep neural networks, at the cost of having a small increase in computation time.

What is the difference between SGD and gradient descent?

The only difference comes while iterating. In Gradient Descent, we consider all the points in calculating loss and derivative, while in Stochastic gradient descent, we use single point in loss function and its derivative randomly.