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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Originally created by Uber, Horovod aims to simplify and accelerate the process of distributed deep learning, significantly reducing model training durations from several days or weeks to mere hours or even minutes. By utilizing Horovod, users can effortlessly scale their existing training scripts to leverage the power of hundreds of GPUs with just a few lines of Python code. It offers flexibility for deployment, as it can be installed on local servers or seamlessly operated in various cloud environments such as AWS, Azure, and Databricks. In addition, Horovod is compatible with Apache Spark, allowing a cohesive integration of data processing and model training into one streamlined pipeline. Once set up, the infrastructure provided by Horovod supports model training across any framework, facilitating easy transitions between TensorFlow, PyTorch, MXNet, and potential future frameworks as the landscape of machine learning technologies continues to progress. This adaptability ensures that users can keep pace with the rapid advancements in the field without being locked into a single technology.

Description

Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
Flyte Yes 
Microsoft Azure Yes 
Alibaba Cloud No 
Amazon SageMaker Model Training No 
BHK Cloud No 
Bayesforge No 
Huawei Cloud ModelArts No 
Intel Open Edge Platform No 
JFrog ML No 
Lightning AI No 
LiteRT No 
MLReef No 
MegaETH No 
NVIDIA PhysicsNeMo No 
PyTorch Yes 
Qualcomm Cloud AI SDK No 
Segments.ai No 
TensorWave No 

Integrations

Amazon Web Services (AWS) Yes 
Flyte Yes 
Microsoft Azure Yes 
Alibaba Cloud Yes 
Amazon SageMaker Model Training Yes 
BHK Cloud Yes 
Bayesforge Yes 
Huawei Cloud ModelArts Yes 
Intel Open Edge Platform Yes 
JFrog ML Yes 
Lightning AI Yes 
LiteRT Yes 
MLReef Yes 
MegaETH Yes 
NVIDIA PhysicsNeMo Yes 
PyTorch No 
Qualcomm Cloud AI SDK Yes 
Segments.ai Yes 
TensorWave Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Horovod

Website

horovod.ai/

Vendor Details

Company Name

PyTorch

Founded

2016

Website

pytorch.org

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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