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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

Keras is an API tailored for human users rather than machines. It adheres to optimal practices for alleviating cognitive strain by providing consistent and straightforward APIs, reducing the number of necessary actions for typical tasks, and delivering clear and actionable error messages. Additionally, it boasts comprehensive documentation alongside developer guides. Keras is recognized as the most utilized deep learning framework among the top five winning teams on Kaggle, showcasing its popularity and effectiveness. By simplifying the process of conducting new experiments, Keras enables users to implement more innovative ideas at a quicker pace than their competitors, which is a crucial advantage for success. Built upon TensorFlow 2.0, Keras serves as a robust framework capable of scaling across large GPU clusters or entire TPU pods with ease. Utilizing the full deployment potential of the TensorFlow platform is not just feasible; it is remarkably straightforward. You have the ability to export Keras models to JavaScript for direct browser execution, transform them to TF Lite for use on iOS, Android, and embedded devices, and seamlessly serve Keras models through a web API. This versatility makes Keras an invaluable tool for developers looking to maximize their machine learning capabilities.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Activeeon ProActive
TensorFlow
Amazon Web Services (AWS)
BentoML
Cleanlab
Flyte
Gemma 3n
Gradient
Graphcore
Horovod
Intel Tiber AI Studio
Keras
ModelOp
Radicalbit
Spell
Superwise
Weights & Biases
Zepl
teX.ai

Integrations

Activeeon ProActive
TensorFlow
Amazon Web Services (AWS)
BentoML
Cleanlab
Flyte
Gemma 3n
Gradient
Graphcore
Horovod
Intel Tiber AI Studio
Keras
ModelOp
Radicalbit
Spell
Superwise
Weights & Biases
Zepl
teX.ai

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Horovod

Website

horovod.ai/

Vendor Details

Company Name

Keras

Country

United States

Website

keras.io

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Alternatives

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