Distributed training refers to multi-node machine learning algorithms and systems designed to increase performance, accuracy, and scalability with larger input data sizes.

"Machine intelligence is the last invention that humanity will ever need to make." - Nick Bostrom.

The machine learning algorithms can extract meaningful patterns and derive correlations between input and output. However, it is also true that the development and training of these complex algorithms can take days or even weeks. 

This challenge requires a fast and effective model design and development method. These models cannot be trained on a single GPU due to the resulting information bottleneck. We need multi-core GPUs to avoid the data bottlenecks that plague single-core GPUs. Distributed training is a solution to this problem.

Distributed training typically relies on scalability, which refers to the ML algorithm's ability to learn or cope with any quantity of data. 

• The ML model's size and complexity,
• The quantity of training data, and
• The infrastructure includes hardware such as GPUs and storage units and the smooth integration of these devices.

Distributed training meets all the above three requirements. It handles model size and complexity, manages training input in batches, and divides and distributes the training process across several processors known as nodes. More importantly, it significantly reduces training time, resulting in shorter iteration times and, thus, faster experiments and deployment.

There are two forms of distributed training:

• Data-parallel training
• Model-parallel training

The following are the best frameworks for distributed training in 2023:

DeepSpeed

PyTorch distributed training is a data parallelism expert. DeepSpeed, built on PyTorch, focuses on additional elements, such as model parallelism. DeepSpeed is a Microsoft project that attempts to provide distributed training for large-scale models. 

When training models with trillions of parameters, DeepSpeed can efficiently handle memory challenges. As a result, it keeps computing and communication efficient while reducing memory footprint. Interestingly, DeepSpeed supports 3D parallelism, allowing you to share data, models, and pipelines. It means that you can now train a large model that consumes a vast amount of data, such as a GPT-3 or a Turing NLG.  

TensorFlowOnSpark

TensorFlowOnSpark is a framework that allows you to run TensorFlow on Apache Spark. Apache Spark Clusters enable distributed training and inference. It is provided by Yahoo and is intended to function in tandem with SparkSQL, MLlib, and other Spark libraries in a single pipeline.

It supports all TensorFlow programmes and allows synchronous and asynchronous training and inference. On Spark clusters, it supports model and data parallelism and TensorFlow tools like TensorBoard. It will enable TensorFlow processes-worker and parameter servers to communicate directly. TensorFlowOnSpark can readily scale by adding computers thanks to process-to-process connectivity.

Elephas

Elephas is an extension of Keras that enables distributed deep learning models to be executed at scale with Spark. Elephas aims to maintain Keras's simplicity and high usability, enabling rapid prototyping of distributed models that can be performed on vast data sets.

Horovod

Horovod is a TensorFlow, Keras, PyTorch, and Apache MXNet-based distributed deep learning training framework. Horovod's mission is to make distributed deep learning quick and straightforward. Horovod training scripts can run on one GPU, several GPUs, or multiple hosts without code changes.

Mesh TensorFlow

Mesh TensorFlow (mtf) is a distributed deep-learning language that may express a wide range of distributed tensor computations. Mesh TensorFlow's objective is to formalise and implement distribution techniques for your computation graph across your hardware/processors. For example, "split the batch over rows of processors and split the units in the hidden layer across columns of processors." Mesh TensorFlow is built as a layer on top of TensorFlow.

BigDL

BigDL is an Apache Spark distributed deep learning framework that lets users write Spark programmes that run directly on Spark or Hadoop clusters. In addition, a high-level Analytics Zoo for end-to-end analytics + AI pipelines is provided to make it easier to create Spark and BigDL applications.

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