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What is Horovod?

Horovod, initially developed by Uber, is designed to make distributed deep learning more straightforward and faster, transforming model training times from several days or even weeks into just hours or sometimes minutes. With Horovod, users can easily enhance their existing training scripts to utilize the capabilities of numerous GPUs by writing only a few lines of Python code. The tool provides deployment flexibility, as it can be installed on local servers or efficiently run in various cloud platforms like AWS, Azure, and Databricks. Furthermore, it integrates well with Apache Spark, enabling a unified approach to data processing and model training in a single, efficient pipeline. Once implemented, Horovod's infrastructure accommodates model training across a variety of frameworks, making transitions between TensorFlow, PyTorch, MXNet, and emerging technologies seamless. This versatility empowers users to adapt to the swift developments in machine learning, ensuring they are not confined to a single technology. As new frameworks continue to emerge, Horovod's design allows for ongoing compatibility, promoting sustained innovation and efficiency in deep learning projects.

What is AWS Neuron?

The system facilitates high-performance training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances, which utilize AWS Trainium technology. For model deployment, it provides efficient and low-latency inference on Amazon EC2 Inf1 instances that leverage AWS Inferentia, as well as Inf2 instances which are based on AWS Inferentia2. Through the Neuron software development kit, users can effectively use well-known machine learning frameworks such as TensorFlow and PyTorch, which allows them to optimally train and deploy their machine learning models on EC2 instances without the need for extensive code alterations or reliance on specific vendor solutions. The AWS Neuron SDK, tailored for both Inferentia and Trainium accelerators, integrates seamlessly with PyTorch and TensorFlow, enabling users to preserve their existing workflows with minimal changes. Moreover, for collaborative model training, the Neuron SDK is compatible with libraries like Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), which boosts its adaptability and efficiency across various machine learning projects. This extensive support framework simplifies the management of machine learning tasks for developers, allowing for a more streamlined and productive development process overall.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Flyte
MXNet
Microsoft Azure
PyTorch
Python
TensorFlow

Integrations Supported

Amazon Web Services (AWS)
AWS Deep Learning AMIs
AWS Deep Learning Containers
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon EKS Anywhere
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Horovod

Company Website

horovod.ai/

Company Facts

Organization Name

Amazon Web Services

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/machine-learning/neuron/

Categories and Features

AI/ML Model Training

Not specified

Deep Learning

Not specified

Categories and Features

AI Inference

Not specified

AI Infrastructure

Not specified

AI/ML Model Training

Not specified

Deep Learning

Not specified

Machine Learning

Not specified

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