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
No
API Access
Has API
No
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
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