Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
API Access
Has API
Integrations
Python
Amazon Web Services (AWS)
Azure Databricks
Flyte
Keras
MXNet
Microsoft Azure
PyTorch
TensorFlow
Integrations
Python
Amazon Web Services (AWS)
Azure Databricks
Flyte
Keras
MXNet
Microsoft Azure
PyTorch
TensorFlow
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
Giotto
Website
giotto.readthedocs.io/en/latest/
Vendor Details
Company Name
Horovod
Website
horovod.ai/
Product Features
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization