Understanding BAGUA
BAGUA is a communication framework used in machine learning that has been designed to support state-of-the-art system relaxation techniques of distributed training. Its main goal is to provide a flexible and modular system abstraction that is useful in the context of large-scale training settings.
Unlike traditional communication frameworks like parameter server and Allreduce paradigms, BAGUA offers a collection of MPI-style collective operations that can be used to facilit
Distributed deep neural network training can be a complex process, especially when it comes to communication between nodes. This is where ByteScheduler comes in. ByteScheduler is a communication scheduler designed specifically to optimize distributed DNN training acceleration.
What is ByteScheduler?
ByteScheduler is a generic communication scheduler for distributed deep neural network (DNN) training. It is based on the idea that rearranging and partitioning tensor transmissions can lead to op
DABMD: An Overview of Distributed Any-Batch Mirror Descent
If you've ever waited for slow internet to load a webpage, you know the feeling of frustration that comes with waiting for information to be transferred between nodes on a network. In distributed online optimization, this waiting can be particularly problematic. That's where Distributed Any-Batch Mirror Descent (DABMD) comes in.
DABMD is a method of distributed online optimization that uses a fixed per-round computing time to limit the
PyTorch DDP (Distributed Data Parallel) is a method for distributing the training of deep learning models across multiple machines. It is a powerful feature of PyTorch that can improve the speed and efficiency of training large models.
What is PyTorch DDP?
PyTorch DDP is a distributed data parallel implementation for PyTorch. This means that it allows a PyTorch model to be trained across multiple machines in parallel. This is important because it can significantly speed up the training proces