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Average Ratings 0 Ratings

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features
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support

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Description

The framework operates on the principles of Model, View, and Controller architecture. It prioritizes a structured approach that leads to code that remains easy to manage over time. In contrast, many widely-used web frameworks focus on rapid launch capabilities, often resulting in code that may deploy swiftly but becomes increasingly complicated after numerous updates. For instance, processes like Apache and Gunicorn serve as examples of controller operations. When initiated, a controller process receives a manifest, which acts as a roadmap. All requests directed towards the controller process are then navigated to a specific program outlined in the manifest. Essentially, a manifest is a compilation of various programs that can be executed. Users can interact with the controller process through web requests, command line inputs, or other actions, showcasing its versatile handling capabilities. This system underscores the importance of a well-organized structure in software development.

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.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Python Yes 
Amazon Web Services (AWS) No 
Azure Databricks No 
Flyte No 
Keras No 
MXNet No 
Microsoft Azure No 
PyTorch No 
TensorFlow No 

Integrations

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

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

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

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

Types of Training

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

Vendor Details

Company Name

Giotto

Website

giotto.readthedocs.io/en/latest/

Vendor Details

Company Name

Horovod

Website

horovod.ai/

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 

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