Accordion: A Simple and Effective Communication Scheduling Algorithm
If you are interested in machine learning, you might have heard about a communication scheduling algorithm called "Accordion." But what is Accordion, and how does it work?
Accordion is a gradient communication scheduling algorithm that is designed to work across different models without requiring additional parameter tuning. It is a simple yet effective algorithm that dynamically adjusts the communication schedule based on th
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
Overview of ALQ and AMQ Quantization Schemes
Many machine learning models operate on large amounts of data and require a significant amount of computational resources. For example, image classification models may have millions of parameters and require a vast amount of training data. One of the main challenges in optimizing these models is the high communication cost incurred when training them. In distributed environments, where processors are connected by a network, the cost of transferring m
Overview of Gradient Sparsification
Gradient Sparsification is a technique used in distributed machine learning to reduce the communication cost between multiple machines during training. This technique involves sparsifying stochastic gradients, which are used to calculate the weights of the machine learning model. By reducing the number of coordinates in the stochastic gradient, Gradient Sparsification can significantly decrease the amount of data that needs to be communicated between machines
Local SGD is an advanced technique used in machine learning that helps to speed up the training process by running stochastic gradient descent (SGD) on different machines in parallel. This technique allows the process to be distributed and carried out on multiple workers, effectively reducing the amount of time required to train complex machine learning models.
What is Local SGD?
Local SGD is a type of distributed training technique that can be used in machine learning to train models using s
Overview of NUQSGD
In today’s age where the size and complexity of models and datasets are constantly increasing, efficient methods for parallel model training are in high demand. One such method is Stochastic Gradient Descent (SGD) which is widely used in data-parallel settings. However, when it comes to communication costs, SGD is quite expensive since it has to communicate gradients with a large number of other nodes, especially in the case of large neural networks.
In order to combat this
Overview of PowerSGD: A Distributed Optimization Technique
If you're someone who is interested in the field of machine learning, you may have come across PowerSGD. PowerSGD is a distributed optimization technique used to approximate gradients during the training phase of a model. It was introduced in 2018 by DeepMind, an artificial intelligence research lab owned by Google.
Before understanding what PowerSGD does, you need to have a basic understanding of what an optimization algorithm is. In
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
SlowMo: Distributed Optimization for Faster Learning
SlowMo, short for Slow Momentum, is a distributed optimization method designed to help machines learn faster. It does this by periodically synchronizing workers and performing a momentum update using ALLREDUCE after several iterations of an initial optimization algorithm. This allows for better coordination among machines during the learning process, resulting in more accurate and faster results.
How SlowMo Works
SlowMo is built upon exist
What is Wavelet Distributed Training?
Wavelet distributed training is an approach to neural network training that uses an asynchronous data parallel technique to divide the training tasks into two waves. The tick-wave and tock-wave run on the same group of GPUs and are interleaved so that each wave can leverage the on-device memory of the other wave during their memory valley period.
How does Wavelet work?
Wavelet divides dataparallel training tasks into two waves, tick-wave and tock-wave. T
ZeRO-Infinity is a cutting-edge technology designed to help data scientists tackle larger and more complex machine learning projects. It is an extension of ZeRO, a sharded data parallel system that allows for parallel training of large models across multiple GPUs. However, what sets ZeRO-Infinity apart is its innovation in heterogeneous memory access, which includes the infinity offload engine and memory-centric tiling.
Infinity Offload Engine
One of the biggest challenges of training large m
What is ZeRO-Offload?
ZeRO-Offload is a method for distributed training where data is split between multiple GPUs and CPUs. It is called a sharded data parallel method because it exploits both CPU memory and compute for offloading. This efficient method offers a clear path towards efficiently scaling on multiple GPUs by working with ZeRO-powered data parallelism.
How ZeRO-Offload Works
ZeRO-Offload maintains a single copy of the optimizer states on the CPU memory regardless of the data paral
ZeRO: A Sharded Data Parallel Method for Distributed Training
What is ZeRO?
ZeRO (Zero Redundancy Optimizer) is a novel method for distributed deep learning training. It is designed to reduce memory consumption in distributed deep learning operations, which are crucial, especially for large-scale processing of deep neural networks. With ZeRO, researchers and practitioners can partition the model states instead of replicating them, thus reducing memory redundancy across data-parallel processes