PipeDream-2BW: A Powerful Method for Parallelizing Deep Learning Models
If you're at all involved in the world of deep learning, you know that training a large neural network can take hours or even days. The reason for this is that neural networks require a lot of computation, and even with specialized hardware like GPUs or TPUs, it can be difficult to get the job done quickly. That's where parallelization comes in - by breaking up the work and distributing it across multiple machines, we can s
What is PipeDream?
PipeDream is a parallel strategy used for training large neural networks. It is an asynchronous pipeline parallel strategy that helps improve the parallel training throughput, by adding inter-batch pipelining to intra-batch parallelism. This strategy helps reduce the amount of communication needed during training, while also better overlapping computation with communication.
How does PipeDream work?
PipeDream was developed to help with the training of very large neural net
Pipelined Backpropagation is a special technique used in machine learning to train neural networks. It is a computational algorithm that helps in weight updates and makes the process faster and more efficient. The main objective of this algorithm is to reduce overhead by updating weights without draining the pipeline first.
What is Pipelined Backpropagation?
Pipelined Backpropagation is an asynchronous pipeline parallel training algorithm that was first introduced by Petrowski et al in 1993.
What is PipeMare?
PipeMare is a method for training large neural networks that use two distinct techniques to optimize their performance. The first technique is called learning rate rescheduling, and the second technique is called discrepancy correction. Together, these two techniques help to create an asynchronous (bubble-free) pipeline parallel method for training large neural networks.
How Does PipeMare Work?
PipeMare works by optimizing the training of large neural networks through a com