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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 GPUonCLOUD?

Previously, completing tasks like deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take days or even weeks. However, with GPUonCLOUD's specialized GPU servers, these tasks can now be finished in just a few hours. Users have the option to select from a variety of pre-configured systems or ready-to-use instances that come equipped with GPUs compatible with popular deep learning frameworks such as TensorFlow, PyTorch, MXNet, and TensorRT, as well as libraries like OpenCV for real-time computer vision, all of which enhance the AI/ML model-building process. Among the broad range of GPUs offered, some servers excel particularly in handling graphics-intensive applications and multiplayer gaming experiences. Moreover, the introduction of instant jumpstart frameworks significantly accelerates the AI/ML environment's speed and adaptability while ensuring comprehensive management of the entire lifecycle. This remarkable progression not only enhances workflow efficiency but also allows users to push the boundaries of innovation more rapidly than ever before. As a result, both beginners and seasoned professionals can harness the power of advanced technology to achieve their goals with remarkable ease.

Media

Media

Integrations Supported

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

Integrations Supported

MXNet
PyTorch
TensorFlow

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

$1 per hour

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / 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

GPUonCLOUD

Company Location

India

Company Website

gpuoncloud.com/gpu-as-a-service/

Categories and Features

AI/ML Model Training

Not specified

Deep Learning

Not specified

Categories and Features

AI Cloud Providers

Not specified

AI Infrastructure

Not specified

Cloud GPU

Not specified

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