Overview of KungFu
KungFu is a powerful machine learning library that is designed to work with TensorFlow. It allows users to create adaptive training models that can adjust in real-time based on various input metrics.
What is KungFu used for?
KungFu is primarily used to create distributed machine learning models that can be trained across multiple machines simultaneously. This makes it ideal for larger datasets that would take a long time to train on a single machine.
One of the key benefi
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 Mesh-TensorFlow
Mesh-TensorFlow is a programming language used to distribute tensor computations. Like data-parallelism that splits tensors and operations along the "batch" dimension, Mesh-TensorFlow can split any dimensions of a multi-dimensional mesh of processors. This allows users to specify the exact dimensions to be split across any dimensions of the mesh of processors.
What is Tensor Computation?
Tensor computation is a concept in which matrices and higher-dimensional arra
What is Parallax?
Parallax is a method used to train large neural networks. It is a hybrid parallel framework that optimizes data parallel training with the use of sparsity. By combining both the Parameter Server and AllReduce architectures, Parallax improves the amount of data transferred and maximizes parallelism while minimizing computation and communication overhead.
How does Parallax work?
Parallax combines the Parameter Server and AllReduce architectures for handling sparse and dense v
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
What is PipeTransformer?
PipeTransformer is a novel method for training artificial intelligence models, specifically Transformer models, in a distributed and efficient manner. The ultimate goal of PipeTransformer is to speed up the time it takes to train these models, which can be used for a variety of tasks, such as natural language processing and image recognition.
How Does PipeTransformer Work?
One of the key features of PipeTransformer is its use of an adaptive on-the-fly freeze algorith
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
Introducing SEED RL: Revolutionizing Reinforcement Learning
SEED (Scalable, Efficient, Deep-RL) is a powerful reinforcement learning agent that is optimized for scalability, efficiency, and deep learning. It utilizes an innovative architecture that features centralized inference and an optimized communication layer. By harnessing two state-of-the-art distributed algorithms, IMPALA and V-trace (policy gradients), and R2D2 (Q-learning), SEED RL is at the forefront of advanced machine learning and
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
Overview of Tofu
Tofu is a system designed to partition large deep neural network (DNN) models across multiple GPU devices, reducing the memory footprint for each GPU. The system is specially designed to partition a dataflow graph used by platforms like TensorFlow and MXNet, which are frameworks used for building and training DNN models.
Tofu makes use of a recursive search algorithm to partition different operators in a dataflow graph in a way that minimizes the total communication cost. This
TorchBeast is an open-source platform that focuses on reinforcement learning research in PyTorch, a popular machine learning framework. It utilizes an implementation of the IMPALA algorithm that enables fast and asynchronous parallel training of RL agents.
What is Reinforcement Learning?
Reinforcement Learning, commonly abbreviated as RL, is a technique used in machine learning where an agent learns to interact with an environment by performing certain actions to get rewards. The goal of an R
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